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Amfiteatru Economic Journal The Bucharest University of Economic Studies

Faculty of Business and Turism Volume XIX ● February 2017 ● No. 44

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• Issue no. 45/2016 – Competition's policy – a tool to protect consumer's rights and interests

• Issue no. 45/2017 – International migration – economic implications

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Editorial Board

Editor-in-Chief Vasile Dinu, Bucharest University of Economic Studies, Romania

Associate Editors

Dan-Cristian Dabija, Babeş-Bolyai University, Cluj-Napoca, Romania Cristian-Mihai Dragoş, Babeş-Bolyai University, Cluj-Napoca, Romania Irina Drăgulănescu, University of Studies of Messina, Messina, Italy Madălina Dumitru, Bucharest University of Economic Studies, Romania Valentin Hapenciuc, University „Ştefan cel Mare”, Suceava, Romania Dan-Tudor Lazăr, Babeş-Bolyai University, Cluj-Napoca, Romania Nicolae Lupu, Bucharest University of Economic Studies, Romania Čudanov Mladen, University of Belgrade, Belgrade, Serbia Paulo Macas Nunes, University of Beira Interior, Covilha, Portugal Alexandru Nedelea, University „Ştefan cel Mare”, Suceava, Romania Marieta Olaru, Bucharest University of Economic Studies, Romania Gheorghe Săvoiu, University of Pitesti, Romania Cătălina Soriana Sitnikov, University of Craiova, Romania Laurenţiu Tăchiciu, Bucharest University of Economic Studies, Romania Horst Todt, University of Hamburg, Hamburg, Germany George-Sorin Toma, University Bucharest, Romania Aharon Tziner, The Academic College of Netanya, Netanya, Israel Cristinel Vasiliu, Bucharest University of Economic Studies, Romania Călin Valsan, Bishop’s University, Sherbrooke, Québec, Canada Milena-Rodica Zaharia, Bucharest University of Economic Studies, Romania

Editorial Advisory Board

Dan-Laurenţiu Anghel, Bucharest University of Economic Studies, Romania Andrej Bertoncelj, University of Primorska, Koper, Slovenia Slobodan Cerovic, University Singidunum, Belgrad, Serbia Emilian Dobrescu, Romanian Academy, Bucharest, Romania Romualdas Ginevicius, Vilnius Gediminas Technical University, Vilnius, Lithuani Daniel Glaser-Segura, College of Business, Texas A&M University - San Antonio, USA Petkovska Tatjana Mirchevska, University St. Cyril and Methodius, Skopje, Macedonia Puiu Nistoreanu, Bucharest University of Economic Studies, Romania Rodolphe Ocler, ESC Chambery, Chambery-Savoie, Franta. Bogdan Onete, Bucharest University of Economic Studies, Romania Giovanni Palmeiro, Lumsa Univiersita, Roma, Italia Rodica Pamfilie, Bucharest University of Economic Studies, Romania Marinko Škare, Juraj Dobrila University of Pula, Paula, Croatia Ion Stancu, Bucharest University of Economic Studies, Romania Daniel Stavarek, Silesian Univestity, Karvina, Czech Republic Nicola Yankov, D. Tsenov Academy of Economics, Svishtov, Bulgaria

Founders

Vasile Dinu, Bucharest University of Economic Studies, Romania Sandu Costache, Bucharest University of Economic Studies, Romania

Editorial Office

Valentin Dumitru, Bucharest University of Economic Studies, Romania Raluca Mariana Petrescu (Assistant Editor), Bucharest University of Economic Studies, Romania Irina Albăstroiu, Bucharest University of Economic Studies, Romania Mihaela Dobrea, Bucharest University of Economic Studies, Romania Violeta Rogojan, Publishing House, Bucharest University of Economic Studies, Romania Silvia Răcaru, Publishing House, Bucharest University of Economic Studies, Romania

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Contents Quality Management and Business Excellence .................................................................. 5 Vasile Dinu Quality Management and Business Excellence MBNQA Approach in Quality Management Supporting Sustainable Business Performance in Agribusiness ............................................................................................ 10 Radovan Savov, Juraj Chebeň, Drahoslav Lančarič and Roman Serenčéš Correlations within the EFQM Business Excellence Model by Applying a Factor Analysis ................................................................................................................ 28 Nicolae Al. Pop and Corina Pelau Assessment of the State of Implementation of Excellence Model Common Assessment Framework (CAF) 2013 by the National Institutes of Research – Development – Innovation in Romania ........ 41 Cristina Raluca Popescu, Gheorghe N. Popescu and Veronica Adriana Popescu The Three-Dimensional Approach of Total Quality Management, an Essential Strategic Option for Business Excellence .................................................... 61 Armenia Androniceanu Towards Sustainability: Effective Operations Strategies, Quality Management and Operational Excellence in Banking ...................................... 79 Vesna Tornjanski, Sanja Marinković and Željka Jančić Study on the Development of Quality Measurements Models for Steering Business Services in Relation to Customer Satisfaction ............................ 95 Katrin Marquardt, Marieta Olaru and Ioana Ceausu Measuring Quality in Automobile Aftersales: AutoSERVQUAL Scale ...................... 110 Yasin Galip Gencer and Ulas Akkucuk Total Quality Management Implementation and Guest Satisfaction in Hospitality .. 124 Miroslav Knežević, Slobodan Čerović, Vladimir Džamić and Tijana Radojević A Cross-Country Empirical Study Towards the Impact of Following ISO Management System Standards on Euro-Area Economic Confidence ....................... 144 Ştefan Daniel Armeanu, Georgeta Vintilă and Ştefan Cristian Gherghina Exploring the Impact of ISO 9001, ISO 14001 and OHSAS 18001 Certification on Financial Performance: The Case of Companies listed on the Bucharest Stock Exchange ................................................................................... 166 Mihaela Ionașcu, Ion Ionașcu, Marian Săcărin and Mihaela Minu

4 Amfiteatru Economic

Cost Analysis of Poor Quality Using a Software Simulation ........................................ 181 Jana Fabianová, Jaroslava Janeková and Daniela Onofrejová Achieving Business Excellence by Optimizing Corporate Forensic Readiness ........... 197 Gojko Grubor, Ivan Barać, Nataša Simeunović and Nenad Ristić Quality Management in Project Management Consulting. A Case Study in an International Consulting Company ............................................... 215 Eduard-Gabriel Ceptureanu, Sebastian-Ion Ceptureanu, Cristian-Eugen Luchian and Iuliana Luchian Economic Interferences Timing of Proactive Organizational Consulting: Difference between Organizational Perception and Behaviour ...................................................... 232 Ichak Adizes, Mladen Cudanov and Dusanka Rodic Estimation of Relationship between Inflation and Relative Price Variability: Granger Causality and ARDL Modelling Approach .................................................... 249 Saghir Pervaiz Ghauri, Rizwan Raheem Ahmed, Jolita Vveinhardt and Dalia Streimikiene Determinants of Foreign Direct Investment in Romania: a Quantitative Approach ................................................................................................. 275 Calcedonia Enache and Fernando Merino Amfiteatru Economic recommends An Assessment of the First Round Impact of Innovation Industries on Europe’s Regional Economies .................................................................................... 289 Adrian Oţoiu, Ramona Bere and Cătălin Silvestru Development and Operationalization of a Model of Innovation Management System as Part of an Integrated Quality-Environment-Safety System ....................... 302 Dorin Maier, Irmer Sven-Joachim, Astrid Fortmüller and Andreea Maier Best Practices for Integrating the Romanian Small Farmers into the Agri-Food Chain ................................................................................................. 315 Dan Boboc, Gabriel Popescu, Mirela Stoian, Georgiana-Raluca Lădaru and Dan Cosmin Petrache The Market for Ideas. For Common Sense Elites ......................................................... 327 Octavian-Dragomir Jora and Alexandru Georgescu

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Quality Management and Business Excellence

Please cite this article as: Dinu, V., 2017. Quality Management and Business Excellence. Amfiteatru Economic, 19(44), pp. 5-7

An excellent organization involves much more than the implementation and the certification of one or more models of management systems. It means developing techniques and tools of busin excellence which lead the organization to outstanding performance on quality, costs and deadlines in order to meet the expectations of all their stakeholders. Such an approach is needed especially in the context of an economy marked by globalization, extremely complex and dynamic that causes spectacular changes in the business environment by integrating quality management principles on purpose to develop sustainable excellence.

Not coincidentally, the new edition of the European excellence model EFQM integrates for the first time the principle "managing with agility“ with the principles: “developing organizational capability”, “harnessing creativity & innovation”, “adding value to the customer”, “sustaining outstanding results” for the organization and “creating a sustainable future”. Also, the new model for quality management system defined by the edition from 2015 of ISO 9000 standards , promotes the process-based approach, incorporating the cycle "Plan - Do − Check − Act" (PDCA) and the risk-based thinking, focusing on organizational change and innovation, in order to ensure a sustainable performance in business. Noteworthy is the endeavor for the development of a high-level structure for all international standards for management systems, aiming to harmonize these standards to facilitate the implementation of integrated management systems (quality − environment − security − social responsibility).

In these circumstances, the importance of organizational agility development and of the capability to control the inherent processes in relation to all stakeholders is extensively acknowledged, taking into account the principles of quality management and business excellence, in connection with developing the capacity for innovation so as to minimize the growing risks in business development and sustainability.

By taking into consideration these evolutions, the articles comprised in the current issue of the journal bring to the fore a myriad of aspect referring to the quality management and to its more refined version, that is, Total Quality Management (TQM) and business excellence which stands for better quality of products and services, competitive costs and satisfied clients. The prizes for quality/excellence established at the national and international levels offer relevant criteria according to which companies are able to assess to what extent they conform to the standards for implementing TQM and for achieving business excellence. In this vein, the articles “MBNQA approach in quality management supporting sustainable business performance in agribusiness” and “Correlations within the EFQM business excellence model by applying a factor analysis” draw upon the two most important prizes in the field, namely the American one - Malcolm Baldrige National Quality Award and the European one - European Foundation of Quality Management. The objectives of the paper “Assessment of the State of Implementation of Excellence Model Common Assessment


Framework (CAF) 2013 by the National Institutes of Research – Development – Innovation in Romania” concern with the evaluation of the current state of implementation of the Excellence Model Common Assessment Framework (CAF) 2013 (a developed instrument of total quality management, inspired by the excellence model of the European Foundation of Quality Management - EFQM) by the National Institutes of Research – Development – Innovation in Romania, as well as the determination of the implementation impact of one or multiple management systems on achieving excellence in research – innovation activities. In the same context, the article “The three-dimensional approach of total quality management, an essential strategic option for business excellence” advances a three-dimensional perspective on total quality management, providing several specific actions by means of which Romanian organizations - implementing total or partial integrated systems of quality management - are able to engender significant competitive advantages. Similarly, the article “Towards sustainability: effective operations strategies, quality management and operational excellence in banking” intends to go deeper into the understanding of the ways to economic durability by means of efficient operations strategies, of quality management and operational excellence in the banking sector. In this study, new practices of quality management are depicted, all of them having a high influential potential on excellence in banking affairs.

The growing competition on the market, the higher exigencies of the customers and globalization push service suppliers towards increasing the quality of their outputs. At present, it falls short for a supplier to provide an adequate quality, it is more important to gratify the customer by offering him more than he would expect. Moreover, well-known research companies forecast that customer satisfaction will become the main competitive edge in the next few years. In this framework, the paper “Study and development of quality measurements models for steering business services in relation to customer satisfaction” elaborates on the relationship between customer satisfaction and service quality, highlights the most influential factors of the customer satisfaction and, in the end, accounts for the new six-step model which was developed to establish an efficient method for measuring service quality. The article “Measuring quality in automobile aftersales: AutoSERVQUAL scale” underscores the growing importance of the precise measurement of the quality level in the services field, especially in the automobile aftersales services. This study, based on experts’ opinions and statistical analyses, aims at developing a questionnaire for the measurement of automobile aftersales services, called AutoSERVQUAL, comprising a 28-item scale considered to support a proper appraisal of the service quality of the highly-reliable automobile. The instrument may be extended to cover the aftersales services in general, for any type of products. In its turn, the paper “Total quality management implementation and guest satisfaction in hospitality” is focused on measuring the customer/guest expectation and satisfaction regarding the quality of the hospitality product, by investigating several cases and finally positing that the main difference in customer satisfaction towards service quality in the studied hotels is correlated with the personnel and cost efficiency.

The group of quality management standards (ISO 9000), as well as the ones referring to food safety (ISO 22000), environment (ISO 14001), information security (ISO 27001) and occupational safety (OHSAS 18001) reflect an international consensus on the mandatory managerial best practices able to ensure the organizational capability to satisfy – timely and consistently – the customer’s demands towards product and service quality, along with the regulatory exigencies of increasing customer satisfaction and of continuously enhancing self-performance. In this respect, two articles are of the essence. “A cross-country

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empirical study towards the impact of following ISO management system standards on euro-area economic confidence” aims to examine the impact of the adoption of international ISO management system standards on the economic sentiment indicator (ESI) comprising the organizations’ standpoints on the euro-area economy. In the empirical research, there were included ISO quality management system standards (ISO 9001, ISO 13485, ISO 16949), food safety (ISO 22000), environment (ISO 14001), information security (ISO 27001). Additionally, the article “Exploring the impact of ISO 9001, ISO 14001 and OHSAS 18001 certification on financial performance: the case of companies listed on the Bucharest stock exchange” discusses the impact of standards certification on financial performance in the case of Romanian companies listed on the Bucharest stock exchange.

The aspects related to quality, costs of poor quality and to the factors which influence quality are essential for preserving competitiveness in business activities. The usage of software applications and simulations facilitate a more efficient quality management. The simulation instruments provide the inclusion of multiple variables in experimental designs and the assessment of their aggregate impact on the final outcome. The article “Cost analysis of poor quality using a software simulation” introduces a case study referring to the usage of Monte Carlo simulations in the quality management field.

In the contemporary period, fraud is often committed by means of computers and it may be discovered by a forensic examiner acting in the digital environment. As a consequence, with a view to enhance business excellence, all the organizations, irrespective of their size (small, medium or large) should address the risk of fraud. In the paper “Achieving business excellence by optimizing corporate forensic readiness”, an optimal model of investigating computer incidents in the corporate field is advanced, the result of the proposed model entailing the dramatic reduction of the organization’s potential losses in the future and the improvement of the business quality.

The paper “Quality management in project management consulting. A case study in an international consulting company” addresses quality management from the specific perspective of organizations providing consulting services for large infrastructure project management. The article analyzes the case of a project management consulting firm which is focused on achieving different goals in large infrastructure projects in Romania, Bulgaria, Republic of Moldova, Ukraine and Serbia. The results of the study empirically underline the main success factors for project management consulting services and are credited to be useful for improving the firm and project performance, contributing to the attainment of business excellence.

In the current economic framework, challenged by globalization, it is highly acknowledged that each business process contributes to the accomplishment of organizational objectives and influences its ability to ensure a high level of customer and stakeholder satisfaction. This is why, over the past years, the emphasis is laid on the integration of quality management in business analyses.

Editor in Chief,

Vasile Dinu


Contents Quality Management and Business Excellence MBNQA Approach in Quality Management Supporting Sustainable Business Performance in Agribusiness ............................................................................................ 10 Radovan Savov, Juraj Chebeň, Drahoslav Lančarič and Roman Serenčéš Correlations within the EFQM Business Excellence Model by Applying a Factor Analysis ................................................................................................................ 28 Nicolae Al. Pop and Corina Pelau Assessment of the State of Implementation of Excellence Model Common Assessment Framework (CAF) 2013 by the National Institutes of Research – Development – Innovation in Romania ........ 41 Cristina Raluca Popescu, Gheorghe N. Popescu and Veronica Adriana Popescu The Three-Dimensional Approach of Total Quality Management, an Essential Strategic Option for Business Excellence .................................................... 61 Armenia Androniceanu Towards Sustainability: Effective Operations Strategies, Quality Management and Operational Excellence in Banking ...................................... 79 Vesna Tornjanski, Sanja Marinković and Željka Jančić Study on the Development of Quality Measurements Models for Steering Business Services in Relation to Customer Satisfaction ............................ 95 Katrin Marquardt, Marieta Olaru and Ioana Ceausu Measuring Quality in Automobile Aftersales: AutoSERVQUAL Scale ...................... 110 Yasin Galip Gencer and Ulas Akkucuk Total Quality Management Implementation and Guest Satisfaction in Hospitality .. 124 Miroslav Knežević, Slobodan Čerović, Vladimir Džamić and Tijana Radojević A Cross-Country Empirical Study Towards the Impact of Following ISO Management System Standards on Euro-Area Economic Confidence ....................... 144 Ştefan Daniel Armeanu, Georgeta Vintilă and Ştefan Cristian Gherghina Exploring the Impact of ISO 9001, ISO 14001 and OHSAS 18001 Certification on Financial Performance: The Case of Companies listed on the Bucharest Stock Exchange ................................................................................... 166 Mihaela Ionașcu, Ion Ionașcu, Marian Săcărin and Mihaela Minu

Vol. 19 • No. 44 • February 2017 9

Cost Analysis of Poor Quality Using a Software Simulation ........................................ 181 Jana Fabianová, Jaroslava Janeková and Daniela Onofrejová Achieving Business Excellence by Optimizing Corporate Forensic Readiness ........... 197 Gojko Grubor, Ivan Barać, Nataša Simeunović and Nenad Ristić Quality Management in Project Management Consulting. A Case Study in an International Consulting Company ............................................... 215 Eduard-Gabriel Ceptureanu, Sebastian-Ion Ceptureanu, Cristian-Eugen Luchian and Iuliana Luchian

AE MBNQA Approach in Quality Management Supporting Sustainable Business Performance in Agribusiness


MBNQA APPROACH IN QUALITY MANAGEMENT SUPPORTING SUSTAINABLE BUSINESS PERFORMANCE IN AGRIBUSINESS

Radovan Savov1, Juraj Chebeň∗2, Drahoslav Lančarič3 and Roman Serenčéš4

1)3)4) Slovak University of Agriculture in Nitra, Slovakia 2) Academy of Media Studies, Bratislava, Slovakia

Please cite this article as: Savov, R., Chebeň, J., Lančarič, D. and Serenčéš, R., 2017. MBNQA Approach in Quality Management Supporting Sustainable Business Performance in Agribusiness. Amfiteatru Economic, 19(44), pp. 10-27

Article History Received: 30 September 2016 Revised: 2 December 2016 Accepted: 18 December 2016

Abstract Despite attention being paid to quality management in the literature, little empirical research has been conducted on developing the link between adoption of quality management approach and business performance in agricultural enterprises, and moreover, only a few empirical studies have investigated this issue in Central and Eastern Europe. The conducted empirical survey examines the relationship between adopting the quality management approach and business performance from the perspective of agricultural enterprises in Slovakia. The empirical findings are based on 70 responses from agribusinesses in Slovak Republic. To measure the adopting of quality management approach the MBNQA model was used. The authors have used linear regression as an evaluation method. Based on the results it can be concluded the adopting of quality management approach is determined by ownership. The enterprises owned by the owner from abroad adopt the quality management approach more readily than the domestic ones. The size of the enterprise and type of production do not determine the adopting of quality management approach in the agricultural enterprises in the Slovak Republic. This study contributes to the European research that studies the relation between quality management and business performance of agribusinesses by means of an empirical investigation in agricultural organizations in a transition economy such as Slovakia. Keywords: MBNQA model, quality management, business performance, agribusiness JEL Classification: Q13, M11, O13

∗ Corresponding author, Juraj Chebeň – [email protected]

Quality Management and Business Excellence AE

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Introduction

Since 1989 majority of Central and Eastern European countries have gone through many democratic reforms which stimulated economic liberalization in the agricultural sector (Deininger, 2003; Swinnen and Rozelle, 2009). Another historical milestone was joining the European Union in 2004. Since becoming the EU member state, the agribusinesses in Slovakia are challenged by cheaper products and products of superior quality from other member states. Some agribusinesses have responded to this challenge by embracing a broader view of quality. The firms began to stress the management of quality in all phases and aspects of the business. Some of the major concepts that are introduced in the strategic quality management approach are: reduction of variation, continuous improvement of products and services to meet customers’ needs, costs of ensuring quality and costs emerged because of poor quality, cross functional management systematic approach to quality, competitiveness, profitability, quality planning and organization wide commitment. Successful implementation of strategic quality management is not an easy task and changes are required.However, the implementation process in some companies is more difficult than in others. We presume that in agriculture it can be determined bya) thesize of the farm (measured by the utilized area), b) the type of production (the specialization towards live or crop production) and c) the ownership structure of a farm (foreign capital). The role of quality management in the company is to improve the business performance. The importance of using quality management standards such as ISO 9000, therefore, cannot be ignored (Najmi and Kehoe, 2001; Gotzamani et al., 2007; Magd, 2008).

The agriculture in Slovakia is specific in many regards (Ciaian et al., 2009; Pokrivčák and Rajčániová, 2014). In Western Europe, the USA and other developed countries the agricultural sector is usually dominated by relatively small family farms. The situation is different in Central and Eastern European countries (CEEC) where a dual structure of farms exists and there are large corporate farms and relatively small family farms (Ciaian et al., 2009). Agriculture in Central and Eastern European countries is also a much more important component of the economy than in industrialized countries. It traditionally accounted for 15-20% of GDP and total employment, compared to only 2–3% in the EU (Klomp, 2014).

The quality in agriculture is usually dealt with from the point of view of water and soil quality (da Silva et al., 2015) and food quality and safety (Carvalho, 2006; Kafetzopoulos and Gotzamani, 2014). The literature exploring the quality management approach in agriculture is lacking (especially in CEEC). This paper extends the existing research on quality management in agriculture by examining the impact of selected factors on adopting the quality management approach in agricultural companies and examining the link between adoption of MBNQA quality management model approach and business performance in agricultural enterprises.According to our observations we can state that there are only few agribusiness companies in Slovakia with implemented TQM model with mostly positive managerial experience. TQM implemented in these companies brought them more transparency, better processes, quality and efficiency improvements; this is visiblemainly in the area of planning, logistics and sales. The only negative experience is connected with longer TQM implementation time.

The rest of the paperis structured as follows: section 2 reviews the empirical literature on management of quality, section 3 presents the data and the methodology, in section 4, empirical results are summarized and implications are discussed, section 5 concludes the paper.



1. Literature Review

With increased access to information, with a strong desire for knowledge, and with increased standards, both companies and consumers pay an important attention to quality and information technologies, which are, on the one hand, a sure path to success for an entrepreneur and, on the other hand, the confirmation of a correct choice for a consumer (Dinu, 2011, p.667; Dabija and Băbuţ, 2014). Due to the growing demands of customers and several food crises, quality assurance schemes have become increasingly popular in agribusiness. Whereas in the beginning, certification systems emphasized pure product control, these days such systems seek to establish comprehensive quality management (Schulze et al., 2008). Although some previous research has focused on the use of quality management systems in agribusiness, we can hardly find a research which would analyse the effectiveness and efficiency of MBNQA within a performance framework in agribusiness.It specifically deals with application of MBNQA model in agribusinesses in a transition economy and this kind of research was not conducted in the sector of agriculture.

1.1 Quality management

Total quality management (TQM), as a tool created to manage and to control quality, is an approach to management embracing both social and technical dimensions aimed at achieving excellent results effectively and efficiently, which needs to be put into practice through a specific framework. Most TQM definitions emphasize the concepts such as continuous improvement, customer focus, human resource management and process management. Some authors also include soft and hard TQM elements, organization performance and benchmarking as well (Talib et al., 2012, p.264).According to Rungtusanatham et al. (2005) when applying TQM model cultural differences should not be neglected.

TQM seeks to integrate all organizational functions to focus on meeting and surpassing customers’ requirements and organizational objectives. TQM empowers every member of the organization and offers the opportunity to participate, contribute and develop a sense of ownership. It is intended to promote continuous, sustained and long-term improvement in quality and productivity, and eliminate employees’ fear of change (Talib et al., 2012, p.260).There are two broad dimensions of TQM: social (soft) and technical (hard) (Lewis et al., 2006; Fotopoulos and Psomas, 2009; Psomas et al., 2014). Social part of TQM deals with human resources management and includes leadership, teamwork, employee support, employee engagement, employee empowerment, top management commitment, strategic quality planning, employee involvement, supplier involvement. Technical part is focused on improving production methods, benchmarking, control charts, procedures to make improvement of goods and services towards customers. According to Zairi and Thiagarajan (1997) the “social” TQM elements are long-standing concerns and cannot be exploited after an organization’s TQM implementation and they should be reinforced by the “technical” elements of TQM (Zairi and Thiagarajan, 1997). The “soft” TQM elements have a significant direct impact on quality improvement, employee benefits and customer satisfaction. However, the impact of the “hard” TQM elements on the above quality management benefits is not direct but indirect, through their significant correlation with the “soft” TQM elements. Therefore, food companies should accept the leading role of the “soft” aspect of TQM and the supporting role of the “hard” aspect in maximizing the


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quality management benefits. Quality improvement is also a significant factor that directly influences employee benefits, customer satisfaction and business performance (Psomas et al., 2014, p. 431).

A quality improvement and the consolidation of the company’s market position are influenced mainly by adopting “soft” TQM elements and secondarily “hard” TQM elements. Quality tools usage alone cannot lead a company to continuous process improvement, customer satisfaction and consolidation of its market position, without the proper guidance by top management and employee and supplier support (Fotopoulos and Psomas, 2009). TQM can also facilitate decision-making process and simplify problem solving processes because it serves as standardized guideline for managers. An exhaustive list of benefits of TQM (e.g. improved financial performance and improved process management, increased social responsibility and ethics) as reported in literature can be found in Talib et al. (2012, p. 265). Živanović et al. (2015) have researched that in the area of agriculture the great success achieved in outcomes in Serbia was based on the application of techniques of TQM. They also state that although there are many followers who suggest using TQM, there are obstacles to the implementation of TQM, as a result of increased costs.

There are no direct financial supports for adopting TQM in the organizationson EU`s policy part in the agricultural sector. Nevertheless, there are some minor nonproduction subventions which can be used for quality improvement. Common Agricultural Policy (CAP) has changed support based on product to subventions based on producerthrough main policy objectives such a viable food production, sustainable management of natural resources and climate action, and balanced territorial development. The main task of these objectives is to face enhanced competition, improved sustainability, and greater effectiveness (European Commission, 2013). Based on this logic we could say that the influence of EU decisions in area of CAP is much more significant than the implementation of TQM for the company’s performance.

1.2 Drivers of TQM and evaluation quality systems

Economical and organizational factors are the most significant incentives required to motivate the food industry to adopt ISO 14001 (Massoud et al., 2010). Managerial requirements in order to ensure the quality of production are published in international standard ISO 9000:2015 that describes requirements on quality management systems and ISO 9004:2009 which focuses on how to make a quality management system more efficient and effective along with managing sustainable success of an organization ISO (2016).

The main driver to Quality management system implementation is effectiveness of all operations within the organization as well as productivity enhancement. The increase of productivity and profitability is linked to savings that support a competitive advantage of a company. Prajogo and Sohal (2003) recognize TQM is known worldwide as a tool that provides a company with a competitive advantage. To achieve a sustainable competitive advantage a TQM culture is primarily required Fotopoulos and Psomas (2009).Quality management systems aim to ensure highly competitive and consistently high quality products or services and a better corporate image that subsequently could lead to market share increase or market expansion. These quality management models are increasingly



more important in period of globalization where competitors can easily emerge from a country all over the world.

Foreign capital that exceeds 51% in the company also contributes to eagerness to implement TQM system. According to Wali (2010), firms which implemented TQM are characterised by a participating leadership, have a good experience in quality, have good partnership relations and mainly belong to a multinational group will display a more open attitude towards change and innovative organizational practices.Adoption of Quality assurance systems by small food enterprises can be influenced by perception of internal and external benefits, by type (kind) of production, costs of TQM implementation, organizational quality culture, reduction of controls and inspections, reduction of non-conformities, cohesion inside organization, customers` satisfaction, corporate image, the positioning in the market place compared to the competition, impact of a poor quality on company`s performance, quality goals in company, and supply chain partners (Karipidis et al., 2008).

Size of a company plays also a significant role in Quality assurance system implementation. Small food companies are keener on implementing Quality assurance systems than large companies or public organizations which are mainly food distributors, retailers, manufacturers, hospitals or restaurants (Rodrìnges-Escobar et al., 2006). Implementation of quality assurance models can be also based on company`s managerial strategy that could focus on high-end quality products for premium prices, customers` trust increase with highly reliable products, reputation increase promoted by exceptional quality of products (Hooker and Caswell, 1999). Strategic orientation of an organization (cost leadership, market differentiation and focus strategy) is a moderating factor influencing the relationship between registration to a quality model system such as the ISO 9000, and the organization`s financial performance (Dimara et al., 2004).

Following international food sector trend, improving environmental performance and enhancing company image are the most salient drivers to adopt ISO (Massoud et al., 2010)

Implementation of quality models based on ISO norms, national agrifood certification systems, GLOBALGAP (Global standard that assures food safety and sustainability), BRC Global Standard (Safety and quality certification programme especially for retailers), TQM system, European Quality Award (European Foundation for Quality Management – EFQM) model or Malcolm Baldrige National Quality Award (MBNQA) model has also been driven by stakeholders` preferences, especially by customer requirements. In some cases these models help to overcome import barriers or regulations created by governments in order to protect their national markets. According to Massoud et al. (2010) the lack of government support and stakeholder demand as well as the fact that ISO 14001 is not a legal requirement constitute the most salient factors hindering the adoption of the standard.

Last, but not the least important drivers to widely implement a quality model in the company can emerge from innovation performance, supply chain partners, and international sector trend. According to Prajogo and Sohal (2003, p.914) TQM significantly and positively contributes to innovation performance, in terms of both product and process. Its contribution to innovation performance, however, seems to be inferior to that of quality performance. The achievement of quality performance as a result of the implementation of TQM practices does, to certain degree, lead to realization of innovation performance.


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The continuous improvement tools that are typically presented in studies associated with the supply chain and operations, such as that conducted by Foster et al. (2011), include the following: Six Sigma, FMEA (Failure Mode Effect and Analysis), PDCA (Plan-Do-Check-Analysis), lean tools (Kaizen), control charts and SPC (Statistical Process Control), 5S, DOE (Design of Experiment), QFD (Quality Function Deployment), and QC (Quality Control) Story (Mayer et al., 2016).ISO and TQM have inspired quality award models that present powerful tools for effective management of product or service quality in modern organizations. The most known and basic quality models (systems) that outline requirements for organizations in quality management are ISO, TQM, EFQM in Europe, MBQNA in USA, Deming prize (PDCA) in Japan. The exhausting list of different quality assurance systems has been presented in Theuvsen and Spiller (2007, pp.14-15). Their categorization is according to:degree of harmonization, geographic focus, number of participating firms, and area of application: local, national, international, and other criteria presented in table no. 1.

Service quality models are reviewed in the work by Tachiciu and Dinu (2010). Dudin et al. (2014) have revealed that further stimulation of sustainable development of the world's agribusiness can be performed through total quality management as well. For agricultural companies, quality management should be based on the Deming Cycle concept.

Table no. 1: Examples of different quality assurance systems criteria Categories Examples of quality systems focus • product characteristics and

documentation standards • process characteristics • system characteristics

Qualität und Sicherheit in Germany, British assured farm standards, Protected Designation of Origin {PDO}, Protected Geographical Indication {PGI}

environment-friendly, welfare standards IFS

target group

• consumer-oriented schemes • business-to-business standards

organic farming, Fairtrade EurepGAP, IFS, GLOBALGAP, BRC Global

Standard, ISO goal • guarantee of legal minimum

requirements in a mass market • product differentiation

IFS, IKB in the Netherlands, QS in Germany PDO, PGI, TSG, organic farming schemes

content • product quality • process quality • product safety

PDO schemes organic farming standards IKB

standard owner

• state-run systems • international standard owner

standardization organisations • stakeholder approaches • retailer driven schemes

organic farming in Denmark ISO 9001 and ISO 22000 Fairtrade BRC Global Standard and IFS

Source: own adaptation after Theuvsen and Spiller, 2007, pp.14-15

In order to enhance the supply chain performance and to ensure continuous improvement of an organization, Lin et al. (2013) propose to use quality management models, such as ISO 9000, EFQM Excellence model, and MBNQA model. Some quality award models are used as a guide to TQM implementation by a large number of organizations (Bou-Llusar et al., 2009). Variety of researchers has considered quality models as operational frameworks for TQM and for higher performance (Bohoris, 1995; Van der Wiele et al., 2000; Yong and Wilkinson, 2001; Lee et al., 2003).



1.3 Malcolm Baldrige National Quality Award

The quality in the company should be evaluated by the more models, such as the Deming Prize, EFQM Excellence Model, and the MBNQA. The main idea of these models is to create competitive advantage based on improvement of all activities in small, medium, and large enterprises (Rehor et al., 2014). Quality management models are used as a guide to TQM implementation by a large number of organizations. Findings of Bou-Llusar (2009) support the EFQM Excellence Model as an operational framework for TQM, and also reinforce the results obtained in previous studies for the MBNQA, suggesting that quality award models really are TQM frameworks.

Malcolm Baldrige National Quality Award was created in 1987 by the U.S. Commerce Department and established by U.S. Congress in the same year in order to raise awareness of quality management and to encourage the American businesses and all other organizations to practice an efficient control of quality for products and services (Bujna et al., 2012; Korenko et al., 2013).The model was created to offer an excellence quality standard and also to help companies to achieve a high level of performance (Garvin, 1991). The Malcolm Baldrige National Quality Award (MBNQA) has evolved from a mean of recognizing the best quality management practices to a comprehensive framework for world class performance, where it is widely used as a model for process improvement (Flynn and Saladin, 2001).

Winn and Cameron (1998) examine the validity of the proposed relationships among the MBNQA dimensions. Research of Curkovic et al. (2000) shows that MBNQA and its criteria do capture TQM core concepts. Wilson and Collier (2000) empirically test the relationships between the Baldrige Award constructs. Goldstein and Schweikhart (2002) investigate the extent to which the improvement of Baldrige criteria leads to improved results. Lee et al. (2003) and Sun (2011) test the link between MBNQA criteria and performance. Flynn and Saladin (2006) analyze the relevance of MBNQA model application framework across national cultures and Kull and Wacker (2010) inspect the impact of national culture on quality management effectiveness.

MBNQA is a model of excellence to recognize best practices in management of the companies. Application of this model is based on seven criteria: leadership, strategic planning, customer focus, measurement, analysis and knowledge management, human resources focus and performance results. Garvin (1991) described it as “The most important catalyst for transforming American Business.” Results of MBNQA criteria can enhance decision making about resource allocations because such measures allow evaluation of processes and a better understanding of the integration among these processes (Prybutok, Zhang and Peak, 2011).The Baldrige award has arguably become one of the most influential vehicles for creating quality awareness and a widely accepted model of performance excellence. It was built upon a set of interrelated core values and concepts that exemplify beliefs and behaviors found in high performing organizations (Flynn and Saladin, 2006).

2. Methodology

The purpose of the study is to evaluate the link between selected drivers (utilized area, type of production, foreign capital) and quality management in agribusinesses in Slovakia. For the purpose of the study the level of adoption of quality management approach is measured


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using MBNQA model. The authors assume that MBNQA score indicates the economic performance of the farm. The descriptive (initial mapping of the field) and exploratory (determining the significance of impact of selected drivers and examining the link between MBNQA score and economic performance of the farm) research approaches have been chosen. (Figure no. 1)

Figure no. 1: Adopting the quality management approach in agribusinesses in Slovakia

We set up following hypotheses:

H1A: The adopting of concept of quality management in agribusinesses in Slovakia is affected by utilized area.

H1B: The adopting of concept of quality management in agribusinesses in Slovakia is affected by their type of production.

H1C: The adopting of concept of quality management in agribusinesses in Slovakia is affected by the foreign capital.

H2: The achieved MBNQA scoresufficiently indicates the economic performance of agribusinesses in Slovakia.

To evaluate the influence of selected drivers (independent variables) the authors used linear regression. The dependent variables are MBNQA scores in the individual areas (leadership; strategic planning; customer and market focus; measurement, analysis and knowledge management; human resource focus; process management; business performance results) and the overall MBNQA score. Independent variables are: foreign capital (FOR_C); type of production (TYP_P); certificated system of quality (QUA_S); utilized area (UT_A). The functional form of regression is as follows:

𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑟𝑟𝑟𝑟ℎ𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝐹𝐹𝐹𝐹𝐹𝐹_𝐶𝐶 + 𝛽𝛽2𝑇𝑇𝑇𝑇𝑇𝑇_𝑇𝑇 + 𝛽𝛽3𝑄𝑄𝑄𝑄𝑄𝑄_𝑆𝑆 + 𝛽𝛽4𝑄𝑄𝑇𝑇_𝑄𝑄 + 𝜀𝜀 (1)

𝑟𝑟𝑠𝑠𝑟𝑟𝑙𝑙𝑠𝑠𝑙𝑙𝑠𝑠𝑖𝑖𝑠𝑠 𝑖𝑖𝑙𝑙𝑙𝑙𝑝𝑝𝑝𝑝𝑖𝑖𝑝𝑝𝑠𝑠 = 𝛽𝛽0 + 𝛽𝛽1𝐹𝐹𝐹𝐹𝐹𝐹_𝐶𝐶 + 𝛽𝛽2𝑇𝑇𝑇𝑇𝑇𝑇_𝑇𝑇 + 𝛽𝛽3𝑄𝑄𝑄𝑄𝑄𝑄_𝑆𝑆 + 𝛽𝛽4𝑄𝑄𝑇𝑇_𝑄𝑄 + 𝜀𝜀 (2)

𝑠𝑠𝑐𝑐𝑟𝑟𝑠𝑠𝑐𝑐𝑐𝑐𝑙𝑙𝑟𝑟 𝑙𝑙𝑝𝑝𝑙𝑙 𝑐𝑐𝑙𝑙𝑟𝑟𝑚𝑚𝑙𝑙𝑠𝑠 𝑓𝑓𝑐𝑐𝑠𝑠𝑐𝑐𝑟𝑟 = 𝛽𝛽0 + 𝛽𝛽1𝐹𝐹𝐹𝐹𝐹𝐹_𝐶𝐶 + 𝛽𝛽2𝑇𝑇𝑇𝑇𝑇𝑇_𝑇𝑇 + 𝛽𝛽3𝑄𝑄𝑄𝑄𝑄𝑄_𝑆𝑆 + 𝛽𝛽4𝑄𝑄𝑇𝑇_𝑄𝑄 + 𝜀𝜀 (3)

𝑐𝑐𝑙𝑙𝑙𝑙𝑟𝑟𝑐𝑐𝑟𝑟𝑙𝑙𝑐𝑐𝑙𝑙𝑝𝑝𝑠𝑠, 𝑙𝑙𝑝𝑝𝑙𝑙𝑙𝑙𝑎𝑎𝑟𝑟𝑖𝑖𝑟𝑟 𝑙𝑙𝑝𝑝𝑙𝑙 𝑚𝑚𝑝𝑝𝑐𝑐𝑘𝑘𝑙𝑙𝑙𝑙𝑙𝑙𝑠𝑠𝑙𝑙 𝑐𝑐𝑙𝑙𝑝𝑝𝑙𝑙𝑠𝑠𝑙𝑙𝑐𝑐𝑙𝑙𝑝𝑝𝑠𝑠 = 𝛽𝛽0 + 𝛽𝛽1𝐹𝐹𝐹𝐹𝐹𝐹_𝐶𝐶 + 𝛽𝛽2𝑇𝑇𝑇𝑇𝑇𝑇_𝑇𝑇 + 𝛽𝛽3𝑄𝑄𝑄𝑄𝑄𝑄_𝑆𝑆 + 𝛽𝛽4𝑄𝑄𝑇𝑇_𝑄𝑄 + 𝜀𝜀 (4)

ℎ𝑐𝑐𝑐𝑐𝑙𝑙𝑝𝑝 𝑟𝑟𝑙𝑙𝑟𝑟𝑐𝑐𝑐𝑐𝑟𝑟𝑠𝑠𝑙𝑙 𝑓𝑓𝑐𝑐𝑠𝑠𝑐𝑐𝑟𝑟 𝑓𝑓𝑐𝑐𝑠𝑠𝑐𝑐𝑟𝑟 = 𝛽𝛽0 + 𝛽𝛽1𝐹𝐹𝐹𝐹𝐹𝐹_𝐶𝐶 + 𝛽𝛽2𝑇𝑇𝑇𝑇𝑇𝑇_𝑇𝑇 + 𝛽𝛽3𝑄𝑄𝑄𝑄𝑄𝑄_𝑆𝑆 + 𝛽𝛽4𝑄𝑄𝑇𝑇_𝑄𝑄 + 𝜀𝜀 (5)



𝑖𝑖𝑟𝑟𝑐𝑐𝑠𝑠𝑙𝑙𝑟𝑟𝑟𝑟 𝑐𝑐𝑙𝑙𝑝𝑝𝑙𝑙𝑠𝑠𝑙𝑙𝑐𝑐𝑙𝑙𝑝𝑝𝑠𝑠 = 𝛽𝛽0 + 𝛽𝛽1𝐹𝐹𝐹𝐹𝐹𝐹_𝐶𝐶 + 𝛽𝛽2𝑇𝑇𝑇𝑇𝑇𝑇_𝑇𝑇 + 𝛽𝛽3𝑄𝑄𝑄𝑄𝑄𝑄_𝑆𝑆 + 𝛽𝛽4𝑄𝑄𝑇𝑇_𝑄𝑄 + 𝜀𝜀 (6)

𝑏𝑏𝑐𝑐𝑟𝑟𝑖𝑖𝑝𝑝𝑙𝑙𝑟𝑟𝑟𝑟 𝑖𝑖𝑙𝑙𝑟𝑟𝑓𝑓𝑐𝑐𝑟𝑟𝑐𝑐𝑙𝑙𝑝𝑝𝑠𝑠𝑙𝑙 𝑟𝑟𝑙𝑙𝑟𝑟𝑐𝑐𝑙𝑙𝑠𝑠𝑟𝑟 = 𝛽𝛽0 + 𝛽𝛽1𝐹𝐹𝐹𝐹𝐹𝐹_𝐶𝐶 + 𝛽𝛽2𝑇𝑇𝑇𝑇𝑇𝑇_𝑇𝑇 + 𝛽𝛽3𝑄𝑄𝑄𝑄𝑄𝑄_𝑆𝑆 + 𝛽𝛽4𝑄𝑄𝑇𝑇_𝑄𝑄 + 𝜀𝜀 (7)

𝑠𝑠𝑐𝑐𝑠𝑠𝑙𝑙𝑙𝑙 = 𝛽𝛽0 + 𝛽𝛽1𝐹𝐹𝐹𝐹𝐹𝐹_𝐶𝐶 + 𝛽𝛽2𝑇𝑇𝑇𝑇𝑇𝑇_𝑇𝑇 + 𝛽𝛽3𝑄𝑄𝑄𝑄𝑄𝑄_𝑆𝑆 + 𝛽𝛽4𝑄𝑄𝑇𝑇_𝑄𝑄 + 𝜀𝜀 (8)

To examine the link between MBNQA score and economic performance the authors used Kruskal-Wallis ANOVA.

2.1 Operationalization of the variables

The operationalization of variables is shown in table no. 2. The questionnaire for data obtaining was based on MBNQA model and included 104 items. These items were divided into 7 categories: leadership; strategic planning; customer and market focus; measurement, analyses and knowledge management; human resource focus; process management; business and organisational performance results (Garvin, 1991).

Table no. 2: The operationalization of the variables Variable Measurement Utilized Area 1=less than 100ha; 2=101ha-1000ha; 3=1001ha-2000ha; 4=over 2000ha Production Type 1=crop production; 2=animal production; 3=mixed production Foreign Capital 1=no; 2=yes Certificated Quality Management System (ISO) 1=no; 2=yes

MBNQA Score Likert scale; 1=totally disagree, 4=neutral attitude, 7=totally agree Performance 1=loss; 2=profit

Note: ha=hectares

2.2 Data obtaining

The survey was performed from January 2016 to June 2016. There were 70 valid questionnaires from randomly selected agribusinesses.

The majority of questionnaire items were scaled from 1 to 7 (the Likert-type scale). Value 1 corresponded to the absolute disagreement of the respondent with the statement in the wording of the item (formulated as a positive statement), whilst value 7 corresponded to the absolute agreement of the respondent. The rest of the questionnaire items were categorical (table no. 2).

The reliability of the questionnaire was verified by means of Cronbach´s alpha With regard to the achieved results it is therefore possible to regard the reliability of the measurement instrument as sufficient (table no. 3). The construct validity was verified by means of factor analysis using the varimax rotation of factors. It is the most commonly used method for rotation procedure. Varimax rotation of factors is an orthogonal method of rotation that minimises the number of variables with high loadings on a factor, thereby enhancing the interpretability of the factors (Malhotra and Birks, 2006). The suitability of applying the factor analysis was verified on the basis of Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO test) and Bartlett´s test of sphericity. The results of both testing statistical methods were satisfactory (Bartlett´s test: p <0.05; KMO test >0.7). The assumptions of applying the factor analysis were met. The factor analysis identified background factors


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which altogether explained almost 70% of the total variance. Based on the fact the identified factors fully correspond with questionnaire’s content, we regard the construction validity of the measurement instrument satisfactory (table no. 3).

Table no. 3: Data reliability and validityevaluation

No. of items α1> 0.7 KMO2> 0.7 χ2; df; p3 Nr. of

factors Factors

cumulative % Leadership 13 0.889 0.801 564.9; 78; 0.000 3 69.935

Strategic Planning 16 0.939 0.845 795.2; 120; 0.000 4 75.621 Customer and Market Focus 13 0.886 0.830 465.6; 78; 0.000 3 67.242

Measurement, Analysis, and Knowledge Management

15 0.900 0.839 641.1; 105; 0.000 3 65.438

Human Resources Focus 17 0.891 0.822 629.2; 136; 0.000 5 71.364

Process Management 17 0.877 0.788 542.1; 136; 0.000 4 65.982 Business/Organizational Performance Results 13 0.881 0.798 439.7; 78; 0.000 3 63.139

Note: 1 – Cronbach Alpha coefficient; 2 – Kaiser-Meyer-Ohlin coefficient; 3 – p value

2.3 Characteristics of the sample

The certified quality system (ISO) is implemented in 10 agribusinesses (14.5%). Out of total number of 70, the majority of the farms are involved in livestock as well as crop production (65.7%). There are only 2 specialized livestock producers. Only 11.9% of farms utilizes an area smaller than 100 ha. There are 16 farms utilizing area larger than 2000 ha. There are 8 farms owned by owner from abroad. The sample characteristics are summarised in table no. 4.

Table no. 4: Cross-tab: Certified System of Quality, Type of Production, Foreign Capital and Utilized Area

QUA_S Total yes no TYP_P crop 2.90% 27.54% 30.43%

livestock 0.00% 2.90% 2.90%

mixed 11.59% 55.07% 66.67%

Total 14.49% 85.51% 100.00%

FOR_C yes 0.00% 11.59% 11.59%

no 14.49% 73.91% 88.41%

Total 14.49% 85.51% 100.00%

UT_A less than 100 1.52% 9.09% 10.61%

101ha-1000ha 3.03% 24.24% 27.27%

1001ha-2000ha 4.55% 33.33% 37.88%

over 2001ha 6.06% 18.18% 24.24%

Total 15.15% 84.85% 100.00%



3. Results and Discussion

Authors measured the MNBQA score in the set of agriculture enterprises in Slovakia. In theory, the maximum MBNQA score is 1 (100%), the minimum score is 0 (0%). The higher the score, the more positive self-evaluation of the company is. The calculated MBNQA scores were divided into enterprise categories with respect to surveyed drivers (foreign capital (FOR_C); type of production (TYP_P); certificated system of quality (QUA_S); utilized area (UT_A)). Authors calculated the overall MBNQA score as well as MBNQA score for every category (leadership; strategic planning; customer and market focus; measurement, analysis and knowledge management; human resource focus; process management; business performance results) separately.

Our research calculations show that the most obvious differences in average MBNQA score are based on the foreign ownership of the company. Type of production, certificated system of quality and utilized area do not account for obvious differences in MBNQA score. For evaluation of the statistical significance of the differences simple linear regression was used.

Based on results of tests of robustness it can be concluded the assumption of equal variances was not violated. The authors have applied linear model in order to determine the relations between the total MBNQA score, MBNQA scores in individual categories and selected drivers. The results are given in table 5.The independent variables explain the variance in the range of 15.8 % to 34.8%. These results suggest there are important independent variables which were not included in the model.

There was significant association between FOR_C and the overall MBNQA score (p = 0.003). This association was confirmed in each individual MBNQA category. The hypothesis H1C was confirmed. We conclude the foreign ownership is a driver of adopting quality management approach in agribusinesses in Slovakia.

The evaluation of the other variables (type of production, certificated system of quality and utilized area) yield mixed results. There is no statistically significant association between type of production (TYP_P), utilized area (UT_A) and overall MBNQA score. The hypotheses H1B and H1A were not confirmed. However, the results of regression showed statistically significant associations between production (TYP_P), utilized area (UT_A) and MBNQA score in some individual categories. The type of production statistically significantly associated with categories “process management” and “business/organizational performance results”. Utilized area statistically significantly associated with category “leadership”.

When certificated quality system (QUA_S) concerned, the regression confirmed its significant association with categories “measurement, analysis, and knowledge management” and “process management”. The statistically significant association with overall MBNQA score was not confirmed. (Table no. 5)

To be able to evaluate the link between MBNQA score and economic performance of the agriculture companies the variable MBNQA score was transformed into categorical variable Using quartiles the variable was transcoded 1-4 where 1=the best 25% and 4=the worst 25%.


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Table no. 5: Regression of foreign capital, production, certificated system of quality, and utilized area results

(Constant) FOR_C TYP_P QUA_S UT_A R2

leadership B 0.605 0.160 -0.002 -0.003 -0.037

0.295 p level 0.000 0.001*** 0.888 0.947 0.030**

strategic planning B 0.094 0.322 -0.015 0.008 -0.002

0.348 p level 0.622 0.000**** 0.486 0.873 0.909

customer and market focus

B 0.226 0.238 -0.034 -0.016 0.006 0.223

p level 0.248 0.000**** 0.126 0.761 0.796 measurement, analysis, and knowledge management

B 0.548 0.181 -0.019 -0.135 0.008 0.220

p level 0.008 0.007*** 0.386 0.017** 0.737

human resources focus B 0.347 0.227 -0.032 -0.061 0.014

0.270 p level 0.044 0.000**** 0.098 0.198 0.484

process management B 0.591 0.163 -0.043 -0.109 0.007

0.265 p level 0.001 0.003*** 0.022** 0.017** 0.708

business/organizational performance results

B 0.504 0.166 -0.038 -0.021 0.015 0.158

p level 0.005 0.005*** 0.044** 0.666 0.448

total B 0.457 0.190 -0.029 -0.037 0.005

0.252 p level 0.003 0.002*** 0.084 0.370 0.774

Note: ****p < 0.001; *** p < 0.01; ** p < 0.05; * p < 0.1; FOR_C=foreign capital; TYP_P=type of production; QUA_S=certificated quality system; UT_A=utilized area; R2=% of variance explained by the model

The Kruskal-Wallis ANOVA confirmed the existence of statistically significant differences in the given economic performance based on the achieved MBNQA score (p=0.018). The companies with higher MBNQA score stated their economic performance as profit more often when compared to companies with lower MBNQA score. The Hypothesis H2 was confirmed. We conclude the MBNQA score indicates the economic performance of agricultural company in Slovakia.

Adopting quality management (QM) approach could be dependent on the number of different factors (number of markets, intensity of quality management implementation). Since differences do exist among firms, it can be expected the impact of QM is different across different firms.

A number of studies examine the effect of QM on economic and financial performance of a firm (Samson and Terziovski, 1999; Hendricks and Singhal, 2001; Douma et al., 2002; Kannan and Tan, 2005). Many studies are dealing with the effect of QM on performance from the perspective of company characteristics. One of the commonly used is its size. The size of a company is usually measured using the number of employees. In agriculture the size of the company is measured by the utilized area (MacDonald et al., 2013). Another broadly used characteristics is the company`s ownership (Javorcik, 2004; Cerrato and Piva, 2010). Therefore, we inquired about the ownership of the agricultural enterprises. Another relevant characteristic of an agricultural firm is the type of production (indicating its level of specialization). This characteristics is also often examined (Lemaire et al., 2014).



Even though agriculture has many specifics (Macours and Swinnen, 2000; Latruffe et al., 2012) some results of our study are in line with previously conducted surveys. Similarly to other studies (Girma, 2005; Todo, 2006) we conclude there is aspillover of firm`s foreign ownership. It takes a form of higher MBNQA score indicating better adoption of quality management approach in enterprises owned by the foreign owner. There is evidence the foreign ownership benefits the domestic firms in terms of higher productivity (Javorcik, 2004) and more effective R&D (Chuang and Lin, 1999). The foreign ownership also influences the attitude towards quality culture (Sun, 2010; Goedhuys and Sleuwaegen, 2013). Usually, the foreign owned companies tend to adopt the concept of quality management more voluntarily compared to domestic ones.

Conclusions

This study examined the influence of selected factors on adopting the quality management approach in agricultural enterprises and the link between adoption of quality management approach and the business performance. To measure the adoption of quality management approach the MBNQA model was used.

Research results proved that the foreign capital can be considered a significant determinant of adopting the quality management approach in the agricultural enterprises. Regression showed us differences in companies with foreign capital in all components of MBNQA. This shows that there is smarter focus on quality in companies where foreign capital is allocated. These companies apply their foreign know-how from abroad. Managers of these companies are strongly focused to meet objectives and reach better economic performance in harmony with high quality processes inside the company. Usually they adopt quality management based on ISO standards which can help to build and improve competitive advantage on the market. Implementing the ISO standards in agribusiness companies in Slovakia should bring companies more opportunities to find business partners abroad and applying MBNQA model will provide companies better financial performance.

The type of production and the utilized area have no significant effect on adoption of quality management approach. We selected these drivers because Slovak agriculture is very specific compared to other countries. Livestock production has decreased and in Slovakia there are farms with a large area that is utilized. We hypothesised that decreasing the livestock production (due to a bad economic performance) should have effect on quality management approach because companies with only crop production have much better economic results. We also presumed that in companies with a large utilized area the quality management approach is more needed than in smaller farms, but these assumptions were not confirmed in our research. The regression analysis showed us that the difference in leadership occurs in companies with a larger utilized area. It confirms the general managerial rule about the type of leadership in small and large companies. Companies with larger area need more workers and they must be leaddistinctively. Numbers in research point out that similar evaluation could be adopted in other industries. MBNQA criteria could be used in all kinds of business entities but also in non-profit organizations to improve the quality, regardless of the size of the business.

While this study provides useful findings about adopting the quality management approach by agricultural enterprises in Slovakia, it also has limitations that call for further research. First, the results of regression suggest that there are some factors not included into model.


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The number of markets where the enterprise operates could be one of these factors as well as the age of the company. Second, while the MBNQA score indicates the economic performance of agribusinesses, the link between TQM and performance could be strongly mediated by legislation and cultural values in countries where awareness of quality management has been almost non-existent (e.g., former communist countries or certain Asian countries as stated in Kull and Wacker (2010)). Another restriction of fully applying our results is a context limitation and therefore perfect fit of our research results would be within economies in transition where foreign capital enters into market. The number of evaluated companies is not particularly high; therefore results could be slightly different if we researched more companies.

Acknowledgement

This work was supported by the Slovak Research and Development Agency under the contract no. APVV-15-0552. The authors acknowledgethe helpful comments from peer reviewers.

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AE Correlations within the EFQM Business Excellence Model by Applying a Factor Analysis


CORRELATIONS WITHIN THE EFQM BUSINESS EXCELLENCE MODEL BY APPLYING A FACTOR ANALYSIS

Nicolae Al. Pop1 and Corina Pelau2∗

1), 2) The Bucharest University of Economic Studies, Romania

Please cite this article as: Pop, N.A. and Pelau, C., 2017. Correlations within the EFQM Business Excellence Model by Applying a Factor Analysis. Amfiteatru Economic, 19(44), pp. 28-40

Article History Received: 30 September 2016 Revised: 29 November 2016 Accepted: 23 December 2016

Abstract Specialists in the field of business administration have tried over time to identify those elements or combination of elements which lead to a higher performance and excellence of companies. One of these models was created and it is applied by the European Foundation of Quality Management. This model defines nine elements, divided into enablers and results. Although this model is widely used in the evaluation of businesses, empirically there are still concerns regarding the characterization and correlations between the elements of the model and the connection with the performance of a company. This article aims to determine the correlations between the elements of the EFQM model based on the results of a research conducted in a clothing company in Romania. For this, the factor analysis was applied in order to determine both the correlations between the elements of the model and in order to group them in several strategic directions. Based on the results, there were determined three strategic directions. Out of these directions, one was oriented towards the external environment of the company, one was oriented towards the internal environment and the third one towards the "brain" of the company, who has the vision and the strategic thinking. Keywords: business excellence model, EFQM model, factor analysis, performance, correlations, total quality management JEL Classification: M10, M11, M31

Introduction

In an increasingly competitive world, characterized by globalization and hypercompetition, the economic agents are forced to differentiate their products and services, in order to survive and grow in the arena of economic exchanges. Competitive advantage is no longer an element of a favorable situation, but the result of a practical transposition of a strategic ∗ Corresponding author, Pelau Corina – [email protected]


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vision, being able to differentiate the economic agent from its competitors through the excellence performance achieved by them.

Starting from the importance of the value for the customer (Hollensen, 2010), in the literature there have been developed a variety of business models (Zikmund and d`Amico 2001), having effects not only in the economic and business world, but also with certain connotations of social responsibility (Kotler and Keller, 2009). Those who have understood to run a business in a holistic manner (Kotler et al., 2009), by involving all stakeholders for achieving the company's performance, constitutes the genuine champions of marketing (Young et al., 2006). Behind them, there are standing different driving structures and actions, which took the form of excellence models. Based on a meta-analysis done by Krasnikov and Jayachandran in the existing literature, the role of the marketing competences on the companies’ performance are identified, by determining its profit (Krasnikov and Jayachandran, 2008). The focus emphasized by the author is put on the organizational structure of the company, by stimulating its human resources towards a learning process for an increased adaptation to a multitude of factors, with which the company interacts on the market.

The practical implementation of these efforts of focusing more on the operationalization of indicators for the performance (values) of the organization (Homburg et al., 2012) points out the need for the modelling of the processes of interaction of the company with its markets, in order to obtain business excellence.

In this article, there are presented the results of a research, which has as objective to determine the interdependences of the elements of the EFQM excellence model. In a first step with the help of the factor analysis, the ideal number of factors is determined and also the belonging of the items to these factors. In a second step, the three factors are characterized.


One of the main concerns of specialists in the field of business administration is to determine the elements that ensure the performance and excellence of businesses. Along time, there have been several models of business excellence which tried to provide a framework for analysis in the total quality management (Bou-Llusar and Escrig-Tena, 2009). They rely on a set of criteria for assessing the performance of a company (Cătuneanu and Drăgulănescu, 2001), with the purpose of differentiating the organization from its competitors by a high level of performance. Moorman and Day, based on literature review of the most important journals such as Marketing Science and Journal of Marketing Research along 25 years, develop a model of the marketing excellence of a company (Moorman and Day, 2016). Although the excellence of the entire company cannot be equaled with the excellence of the marketing activity, this latter remains from an organizational point of view the key element of a business. According to the quoted authors, the organization needs leaders, who have the experience and are able to develop four essential elements for the excellence of a company: the human capital, the organizational configuration of the company based on its activities, the values and the motivation system, the competences and the organizational culture. All these issues are integrated in the companies’ activities and its focus towards excellence. These activities are oriented towards the contributions for the following: the anticipation of market changes, the



capacity of the company to adapt towards these changes, the adaptation of the processes, structures and employees to these changes, the level of the activation of the organizational behavior, responsibility of decision makers towards performance and the attraction of human, material and financial resources (Moorman and Day, 2016).

A predecessor of the excellence models are the total quality management systems (Bou-Llusar and Escrig-Tena, 2009). Analyzing these models of total quality management, it can be observed that most of them contain elements linked to the orientation, retention and satisfaction of customers and elements of leadership. Other elements which appear in most of these models are process management and continuous improvement (Andersen et al., 1994; Powell, 1995; Thummim and Tang, 1996; Silas Ebrahimpour, 2002; Bou-Llusar and Escrig-Tena, 2009). The orientation towards the employees appears in various forms in these models such as training (Silas Ebrahimpour, 2002; Powell, 1995), participation and partnerships with employees (Thummim and Tang, 1996) and employee satisfaction (Andersen et al., 1994; Powell, 1995). Other items are the strategic planning (Thummim and Tang, 1996), internal and external cooperation, particularly with suppliers and various elements of controlling and performance management.

Based on these initial models, there were developed in different linguistic areas, three main models of business excellence. In Japan and the Asian region there was developed the Deming Price (DP Model), in the US, the Malcolm Baldrige National Quality Award (MBNQA) and in Europe there was developed the European Foundation of Quality Management (EFQM) (Bou-Llusar and Escrig-Tena, 2009; Jankalova, 2012).

The EFQM model was developed in 1991, based on the previously described elements in the literature review (Escrig and Menezes, 2015), in order to represent an evaluation guideline for the Excellence Awards granted by the European Foundation for Quality Management (Bou-Llusar and Escrig-Tena, 2009). In figure no. 1, there can be observed the main elements of the model, divided into two categories: enablers and results. In the category of enablers, there are included the leadership, the people, the strategy, the partnerships-resources and the processes including products and services. The category results included in the EFQM model refer to people, customers, society and business performance as a whole. According to the European Foundation for Quality Management, a company is successful if it has leaders to think about its future, a stakeholder oriented strategy and if it knows how to value its people and to optimize its processes. These actions will be reflected in achieving and exceeding the expectations of the customers, of the employees and of the entire society and therefore it will lead to exceptional results (European Foundation for Quality Management, 2013). In an extended model, the elements shown in Figure no. 1 are subdivided into 32 items, by which the criteria of excellence are checked (Escrig and Menezes, 2015).

Like any other performance measurement tool, this model can be considered both a way of verifying the fulfillment of the excellence standards and also as a guide for the strategic direction of a company (Escrig and Menezes, 2015).

In Europe the EFQM model has been accepted by specialists and has been applied in many situations. From an empirical point of view, there are still concerns regarding the interpretation of its elements and the connection to the company's performance. Several researches have attempted to determine the interdependences between the elements of the EFQM model (Eskildsen and Kristensen, 2002) or to differentiate the elements in technical


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and social factors (Suareza et al., 2016) or to analyze the relationship between the elements of the EFQM model and the company’s performance (Calvo et al., 2014; Bou-Llusar and Escrig-Tena, 2009). This article will focus on the European EFQM model and the subsequent analysis will be based on the European model.

Figure nr. 1: EFQM – The European Excellence Model Source: European Foundation for Quality Management, 2012

2. Coordinates and methodology of the research

The objective of the research presented in this paper is to determine the correlations between the elements of the EFQM model in a garment factory in Romania, in order to indicate the strategic directions of the company. There are also analyzed the differences of perception between the top management, the middle management and the employees with execution positions regarding the implementation of the EFQM elements in the same garment company, in an optic of a total quality management system. In order to obtain the necessary information, a quantitative selective research was done, using the survey method, the interview technique and a self-administered semi-structured questionnaire as an instrument (Smith and Albaum, 2005; Plaias et.al., 2008; Wilson, 2012). Following the collection of data, the main decision-makers of the company have been interviewed. The questionnaire contains questions regarding the key elements of the excellence model, having answers in form of a Likert scale, from 1 to 5, where 5 represents strong agreement and 1 represents total disagreement. It is important to mention that the analyzed garment factory is located in the Western part of Romania, it has predominantly female staff (185 of the 210 employees are women) and it is in its 57th year of existence (founded in 1959). For the survey, there were obtained 120 questionnaires completed by company employees from different departments and different levels of management.

In order to determine the correlations between the elements of the EFQM model, the factor analysis was applied with the help of the SPSS program for the data obtained in the survey. In a first phase, in order to determine the optimum number of factors, the factor analysis was run having as criterion eigenvalues greater than one (Backhaus et al., 2000). The cumulative total variance and the Elbow-criterion in the Scree plot (see Figure no. 2) indicate an optimum number of three factors. Thus, the factor analysis was applied for a



second time for a fixed number of three factors. The second run of the factor analysis was done by the authors as a logical consequence of the results of the first modelling of the factor analysis; Backhaus et al. (2000) point out the fact that the users shoud decide in a subjective way which factor analysis method should be applied; therefore the first factor analysis has been done in order to determine the optimum number of factors, while the second one has been done in order to establish the inclusion of items in the three determined factors.

1110987654321

Component Number

2.5

2.0

1.5

1.0

0.5

0.0

Eigen

value

Scree Plot

Figure no. 2: Elbow criterion for the determination of an optimum number of factors

In order to determine the adequacy of the data for the factor analysis, there was used the Kaiser-Meyer-Olkin criterion. The value of 0.606 indicates an average adequacy of the results for a factor analysis (Backhaus et al., 2000). This suggests that although there is a tendency to associate the elements of a model of excellence, they still have their own influences in the model. Analyzing the cumulative variance, it can be observed that the grouped elements have 50.4% of the total influence in the excellence model, as it can be observed in annex 1. In the following, there will be analyzed the influence and characteristics of these strategic directions, based on the associations resulted from the excellence model, without neglecting the own influences of each item. The Cronbach-Alpha value of 0.603 confirms this result, also showing an average adequacy of the interdependence.

For measuring the adequacy of each of the item used in the factor analysis, there were analyzed the values of the main diagonal in the anti-image correlation matrix. It can be observed that the lowest adequacy for this analysis have the employees (MSA = 0.365) and the society (MSA = 0.494). A factor analysis run without these elements indicate a cumulative total variance of 58.8%. However, for the completeness of the model we go on with the factor analysis model with all the EFQM elements. Lower MSA values have also the vision (0.542) and the strategy (0591). All other remaining elements have an adequacy for the sample, with values higher than 0.6.

Regarding the significance of the dependence relations, they can be seen in the correlation matrix in Table no. 1.


Vol. 19 • No. 44 • February 2017 33

Table no. 1: Correlation Matrix

Sig. (1-tailed) vi-

sion

em-plo-yees

stra-tegy

re-sour-ces

part-ner-ships

pro-ducts

pro-duc-tion

cus-to-

mers So-

ciety

in-vol-ve-

ment

ex-cel-

lence Vision Employees .246 Strategy .001 .261 Resources .392 .129 .032 Partnerships .284 .490 .012 .000 Products .301 .006 .183 .000 .000 Production .005 .211 .462 .028 .206 .002 Customers .351 .396 .146 .017 .037 .008 .333 Society .125 .327 .139 .001 .431 .005 .018 .286 Involvement .000 .240 .068 .087 .246 .324 .101 .012 .379 . Excellence .002 .007 .085 .000 .002 .129 .319 .136 .222 .000

Analyzing the significance indicator in the correlation matrix, it can be observed that there are elements with a probability of error of the relationship of 0.0001%, but also relations with a probability of error as high as 46.2%. Among the relations with a low probability of error (almost equal to 0) can be observed the relationship between resources-partnerships, resources-products, products-partnerships, involvement-vision. The result excellence is best correlated with involvement (sig = 0.000), resources (sig = 0.000) followed by vision (sig = 0.002) and partnerships (sig = 0.002). The highest probability of error exists for the correlation between excellence and production (sig = 0.319) followed by the relationship with the society (sig = 0.222). The weakest correlation among all, are the relations between strategy-production (sig = 0.462) and society-partnerships (sig = 0.431).

3. Strategic directions within a company based on the results of the factor analysis

Based on the correlations between the elements, we have the following factor loadings (see Table no. 2). Based on this information the three factors counting for the three strategic directions of the company, will be delimited. The value of the loadings of each of the items will determine the belonging and the influence of the resulted factors. Each of the factors will be named based on its characteristics.

The component matrix in the factor analysis represents the position of each of the items related to the resulted factors. The loadings are calculated as the cosinus value of the angle between each item’s vector and the factor’s vector calculated as a resultant. It depends on the subjective interpretation of the user to determine the assignment of the items into each factor. Indeed, it is recommended to take into consideration loadings with values higher than 0.50 (angles smaller than 60 degrees) in order to have a definite assignment of an item. In spite of these, we consider that it is also important to analyze the positioning of items in-between factors, as long as they have been considered in the analysis.



Table no. 2: Component Matrix

Component

1 2 3 Vision .023 .819 -.082

Employees .262 -.069 .503 Strategy .300 .364 -.610

Resources .812 .018 -.084 Partnerships .665 .038 -.355

Products .656 .174 .325 Production .293 .442 .490 Customers .406 -.068 -.088

Society .347 .266 .239 Involvement -.258 .761 -.076 Excellence -.561 .465 .117

Note: Extraction Method: Principal Component Analysis.

Analysing the loadings of the items for each of the three factors, as it can be observed in table nr. 2, these can be named as follows:

• Resources and market orientation

• Vision and strategic orientation

• Employees and production

Analyzing in detail the three strategic directions, one can observe that in the first component the items with highest loadings are the resources (0.812), third parties partnerships (0.665) and the products (0.656). Taking into consideration the fact that this factor contains items, which have an external orientation focusing on elements like marketing, relations with third parties (partnerships) and resources, it was named “resources and market orientation”. The second component has been named “vision and strategic orientation”, because it deals most with the "brain" of the company. In this factor the items with the highest loadings are the vision (0.819), the implication (0.503) and the production (0.490). The third component contains the items employees (0.503) and production (0.490) and was named “employees and production”.

In the following, these three strategic directions will be analyzed, focusing on the differences in perception among the employees depending on the hierarchical position in the company. An analysis of the results indicates that there is not an obvious differentiation between the main categories of professions present in the factory (engineers, economists or tailors). Given that the company's staff is predominantly female with an over average age (as it is in the entire garment industry Romania), an analysis by gender or by age is not presented in this study.

Strategic direction: resources and market orientation

As it can be observed in table no. 2, in this strategic direction there are grouped the resources, the partnerships, the products, the customers and the society. A closer look to the items included in this factor, one can say that they represent the external component of the


Vol. 19 • No. 44 • February 2017 35

model. On one hand, there are the customers who represent a key element of the marketing strategy and an essential resource for a company's success. Moreover, the current trend in businesses is to develop long-term relationships with its customers. For this reason, the customers play an important role for the success of a company. Another important element of the marketing mix is the design and creation of the product, which currently must be modeled to the customer preferences and needs. Therefore, the item product belongs to this factor, having a good correlation with the customers (sig = 0.008). In a larger vision of relationship marketing, there are included not only the relationships with the customers but the ones with all strategic partners (stake-holders), as it is confirmed in this research. Therefore, the partnerships are also included in this factor, having a significant correlation with both the products (sig = 0.000) and the customers (sig = 0.037). Resources are an essential element for a company's success, demonstrated also in this research. The resources are linked with all elements of this strategic directions, having sig <0.017 for all correlations. The society representing the external environment is the element with the lowest loading for this factor (0347). It is well correlated with the resources (sig = 0.001) and the products (sig = 0.005), but it is weakly correlated (with a high probability of error) with the partnerships (sig = 0.431) and the resources (sig = 0.286). Moreover, the society is one of the elements in the factor analysis model, which had one of the lowest adequacies.

In figure no. 3, there can be observed the perception of the employees regarding the elements of the strategic direction resources and market orientation, based on their hierarchical position in the company.

4.15 4.07 4.19 4 43.41 3.07

3.93 3.56 3.673.32 3.324.02 3.88 3.95

0

1

2

3

4

5

Resources Partnerships Products Customers Society

Top Management Execution position Middle Management

Figure no. 3: Evaluation of EFQM elements depending on the hierarchical position

for the strategic direction Resources and market orientation

Note: There are presented the mean values of the perception; 5 represents a total agreement, while 1 represents a total disagreement

Analyzing the results in figure no. 3 regarding the mean values of the perception, it can be observed that the top management gives a higher importance to all elements of this strategic direction. As it can be observed in figure no. 3, the difference in perception between the top management and the rest of the employees is higher for the resources and the partnerships. The resources and the products are perceived in a similar way by the middle management and the employees with execution functions, having lower values than the top management. One explanation for this difference may be the fact that assuring the necessary resources and establishing partnerships are the responsibility of the top management, other employees having less contact with these elements.



Strategic Direction: Vision and strategic orientation

The second strategic direction resulted from the factor analysis, includes the vision, the strategy and the commitment. The relationship between the three items can be easily explained by the fact that in order to fulfill a vision, there is a need for a strategic thinking and commitment. Analyzing the component matrix, it can be seen that the vision has the highest loading for this factor (0.819). Looking at the correlation matrix it can be observed that the vision has a significant relationship with both the strategy (sig = 0.001) and with the involvement (sig = 0.000). Besides this, the relationship between the strategy and the involvement is relevant, with a probability of error of 6.8% (sig = 0.068).

Compared to the first strategic direction, where the top management attributed greater importance to all items, within this strategic direction there are differences of perception among the different levels of management, as shown in figure no. 4.

4.7 4.52 4.264.523.89 4.3

4.824.27 4.58

0123456

Vision Strategy Involvement

Top Management Execution position Middle Management


for the strategic direction Vision and strategic orientation


Analyzing the results in figure no. 4, it can be seen that the strategy is the only element of this component, which has the highest evaluation from the top management (4.52). This rating is followed by the middle management (4.27) and the rest of the employees (3.89). Despite these, it is also the lowest evaluation given by the execution positions for this strategic direction. For the involvement, the highest evaluation is given by the middle management (4.58), followed by the execution employees’ functions (4.30) and the top management (4.26). All categories of employees agree that the vision is the most important one, having an average evaluation higher than 4.50. Moreover, in this case, the middle management makes the highest rating (4.82).

It is interesting to observe, that despite the fact that the top management is most involved in this strategic direction, they don’t give the highest evaluations for these items. One explanation may be the fact, that they are the ones who have an overview of the company and the direction it is heading to and for this reason they don’t evaluate it with such a high importance. By the way, the discussions with the managers of the company reveal that these are some natural elements in their everyday thinking without having a special delimitation.


Vol. 19 • No. 44 • February 2017 37

Strategic direction: Employees and production

The last strategic direction resulted from the factor analysis includes the employees and the production process. More precisely this component refers to some internal elements of the company. Despite the fact that none of the components has a very high loading in absolute value, it is the component where both the employees (0.503) and the production (0.490) have the highest loading. By the way, the correlation between the two elements production and employees is not an ideal one (sig = 0.211), fact that influences the loadings of the two items for this factor. In spite of these relatively weak correlations, both elements are best grouped together and not in other factors. Looking at the overall model, it is important to keep in mind the fact that the element employees had one of the lowest adequations for this analysis. One explanation for this result is the fact that in the analysis the employees are taken as a whole, without differentiating them according to their positions in the company, which could lead to different perceptions. Besides this, the correlation to the production can be sustained by the fact that most of the questioned employees have responsibilities directly related to the production.

In figure no. 5 there can be observed the differences of perception between the different levels of management for the elements of the strategic direction employees and production. As expected the persons responsible for execution tasks give the highest evaluation for the employees (4.67). This assessment is followed by the one of the top management (4.56) and the one of the middle management (4.33). Regarding the element production, the top management is giving the highest evaluation to this element (4.26), while the employees give the lowest evaluation (4.11). The middle management has an average production rating (4.18). One explanation for this result may be again the fact that the employees directly involved in the production, do not rate so high a process they know and they do every day.

4.56

4.26

4.67

4.11

4.334.18

3.84

4.24.44.64.8

Employees Production

Top management Execution position Middle Management


for the strategic direction Employees and production


One of the most interesting loading is the one of the result excellence. As it can be observed from the component matrix, it has the highest loading in the strategic direction vision and strategic orientation. Besides, the correlation with all the elements of this strategic direction is significant (sig < 0.085). The absolute value of the loading is the highest for the first strategic direction (0.561). However, in this case the excellence has a significant correlation with the resources (0.000) and the partnerships (0.002), but the correlation is weaker for the other



elements (sig > 0.316). For the last strategic directions, there is a relevant correlation for the employees (sig = 0.007), but a high probability of error for the production (sig = 0.319).

Conclusions

The research results reveal the fact that the elements of the EFQM model are interdependent and that they can be grouped in the following three strategic directions: resources and market orientation, vision and strategic orientation and employees and production. Depending on the hierarchic position, these elements are perceived and evaluated in a different way by the employees of the company. The top management rate the resources and the market orientation with the highest importance, while the middle management perceives the vision and strategic orientation as the most important one. This perception can be determined by the fact that relation with the customers and the resource procurement represent the highest challenge for the top management and the success of the company. The middle management considers the items from the direction vision and strategic orientation as the most important one, while the personnel with execution functions rate the employees as the most important one.

In the model there are elements that relate well to each other and which can be grouped, but there are elements, such as the employees or the society, which influence more than one factor. For this reason, it is difficult to categorize them. As shown in this research, the grouping of the elements is done based on the environment in which they interact. There are elements grouped in a strategic direction related to the external environment, to the internal environment and one with the "brain" of the company, who has the vision and the strategic thinking. Incidentally this is also the novelty brought by this research. Most analysis done for the development of a company, are mainly focused on the internal or external factors of a company, few of them being connected with the decision makers in the company, who play an important role in its development.

Another important aspect that could influence the results of such a research, is the type of business. The analyzed company is a manufacturing company, where most of the employees are directly involved in the production process. This fact can influence the outcome in the sense that all decisions are taken from the perspective of the production process, rating it with a higher importance. There is the possibility that a similar study conducted in a service oriented company or having another department as preponderant, could lead to slightly different results. This hypothesis can be tested in future studies. One potential source of error is the subjectivity of the respondents, which otherwise is difficult to remove from any survey. However, the employees are the ones who know the company best and can most accurately evaluate its priorities and the way it operates.

In conclusion, we consider that the existence of such a model is important both for the evaluation of a company and for developing its strategy. The results of this research show that the decisions taken in the company should not be taken alone, based on a single element of the excellence model, but they require an overall perspective, especially considering the correlations between them. Besides this, the presented model points out the way in which decisions are taken based on the key elements of a company and the interdependences among them. Not less important is the emphasis of the importance given by all management levels to some decisions. Based on the results of the research, there can


Vol. 19 • No. 44 • February 2017 39

be observed an orientation of the management of a company towards resources and customers, pointing out the optic of the company according to the actual tendencies.

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Sila, I. and Ebrahimpour, M., 2002. An Investigation of the Total Quality Management Survey Based Research published between 1989 and 2000. A Literature Review. International Journal of Quality & Reliability Management, 19(7), pp. 902-970.

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Annex no. 1: Total Variance Explained

Component Initial Eigenvalues

Extraction Sums of Squared Loadings

Total % of

Variance Cumulativ

e % Total % of

Variance Cumulat

ive % 1 2.444 22.217 22.217 2.444 22.217 22.217 2 1.906 17.329 39.546 1.906 17.329 39.546 3 1.195 10.865 50.411 1.195 10.865 50.411 4 1.059 9.625 60.036 5 .973 8.848 68.884 6 .896 8.146 77.030 7 .717 6.518 83.548 8 .622 5.659 89.207 9 .457 4.156 93.363 10 .369 3.351 96.714 11 .361 3.286 100.000

Extraction Method: Principal Component Analysis.


Vol. 19 • No. 44 • February 2017 41

ASSESSMENT OF THE STATE OF IMPLEMENTATION OF EXCELLENCE MODEL COMMON ASSESSMENT FRAMEWORK (CAF) 2013

BY THE NATIONAL INSTITUTES OF RESEARCH – DEVELOPMENT – INNOVATION IN ROMANIA

Cristina Raluca Popescu1, Gheorghe N. Popescu2∗

and Veronica Adriana Popescu3

1) University of Bucharest and The Bucharest University of Economic Studies, Romania

2)3) The Bucharest University of Economic Studies, Romania

Please cite this article as: Popescu, C.R., Popescu, G.N. and Popescu, V.A., 2017. Assessment of the State of Implementation of Excellence Model Common Assessment Framework (CAF) 2013 by the National Institutes of Research – Development – Innovation in Romania. Amfiteatru Economic, 19(44), pp. 41-60


Abstract The paper highlights the part of the results of a research based on a questionnaire conducted at the National Institutes for Research, Development and Innovation (NIRDI) of Romania, under the coordination, under the authority or the subordination of the National Agency for Scientific Research and Innovation (NASRI), goals being pursued: Assess the current status of implementation of the Common model of excellence Self-Assessment framework (CAF) in 2013 by NIRDI in Romania; Determining the impact that the implementation of one or more management systems has on achieving excellence in research and innovation; Capacity assessment CAF 2013 model of excellence to provide enlightening information on the most important risks and challenges facing the entity being (self-) evaluation. The research was conducted in July 2016 to a representative sample of 51 of the 53 INCDI, under the coordination, under the authority or subordinate NASRI. It appears that although there is a high degree of fulfillment of the 9 criteria of the excellence model CAF 2013 NIRDI have not assimilated the spirit and the purpose of the excellence model induced by the principles of total quality management by importance (percentage) given to each criterion and sub-criterion. It appears real progress criteria "results on customers" and "processes, products and services". At the same time there are serious shortcomings in the criteria "results on the organization" and "results on the staff." On the one hand, based on the results analyzed, it was determined that to achieve excellence in research is not sufficient to implement two or more management systems, on the other hand it was confirmed that the results of (self-) evaluation stage of implementation of the model of

∗ Corresponding author, Popescu Gheorghe – [email protected]

mailto:[email protected]

AE Assessment of the State of Implementation of Excellence Model Common Assessment Framework (CAF) 2013

by the National Institutes of Research – Development – Innovation in Romania


excellence CAF 2013 provides significant and enlightening information to identify the principal risks and deficiencies faced by the entity (self) assessed. Keywords: model of excellence, research-development-innovation, quality, certification, performance criteria. JEL Classification: A10, D02, F63, I23, M21, M41, M42, P47 Introduction

The long period of transition, without a strategy and a consistent plan, experienced by the Romanian economy has affected all areas of activity inevitably including the research and development and innovation. A proof of this is given by the unflattering last place occupied by Romania in the European statistics in terms of innovation (European Commission, 2015).

The incoherence and the lack of perspective which we had to face in the most part of the transition period were manifested in all areas: political, legislative, economic, managerial. With certaininty we can not even talk about strategy in the research activities in this period.

Although these issues are generally known, and the researchers have felt them in their own activities, we will present some examples with a few legislative inconsistencies. Initially treated as autonomous bodies, the former research centers have been through two or three restructuring stages until the current form of national institute. The evaluation systems have been treated with the same lack of consistency in order to receive the status of a national institute of research and development. The system of evaluation and promotion of researchers, found even now in an extremely difficult period, experienced the same inconsistency. It was expected that these multiple inconsistencies in organizing and funding the research to put its fingerprint including on the work of the managers of NIRDI.

The researchers had no problems in communicating in foreign languages, neither difficulties to analyze and to interpret the requirements or specifications, sometimes very demanding, of various research projects. They were faced with other problems such as, for example, the bureaucracy and the hidden discrimination.

Thus, in order to be eligible in a European project, an entity, as a representative of the East, had to be certified, usually by someone in the West. The same applied in the case of receiving a note of good behavior quality, environmental protection, social protection, security, and so on. It is believed, perhaps rightly, that obtaining this certification represents a guarantee of survival, growth and competitiveness, and why not, a manner to achieve excellence in research activities. The complex interdependencies between quality, certification and excellence are identified and explained in the literature. From our own research we have not found any relevant results on excellence in the research institutes.

Through our research we aim to receive a more nuanced answer to this issue, on the importance and the role of these certifications, searching answeres to the following questions: "Did the certifications achieve their goal?", "Are these certifications sufficient in order to achieve excellence?".


Vol. 19 • No. 44 • February 2017 43

In these circumstances, the purpose of this research is to evaluate the current state of implementation of the Model of Excellence CAF 2013 by the National Institute of Research Development and Innovation (NIRDI) from Romania. To achieve these objectives of our study we presented: the main results reflected in the literature on the subjects of research, quality and business excellence; the methodology, goals and assumptions of research and then the research results.

1. Literature review The research, development and innovation are the "main engines for progress" (Kao, 2007) at the micro and macroeconomic level. The research and development activity includes scientific research, experimental development and innovation based on scientific research and experimental development. Some authors define innovation as "a complex process that enables the communication between the scientific community, the market and the technology" (Brad et al., 2006). The innovation processes together with the creative activity are the main engines of the economic growth and development (Dinu, Grosu and Săseanu, 2015). Innovation is a key element in achieving sustainable competitive advantages for the success of organizations (Petrariu, Bumbac and Ciobanu, 2013). Finally, the initiative, the creativity, the innovativeness, the development of organizational capacity and the ability to take risks are all attributes of a progress capable generating national wealth. (Popescu, Garcia-Sanchez, Nicolae and State, 2015).

The quality concept was linked from the beginning with the procurement and the sales of goods constituting a guarantee, an additional insurance for the user. This concept has evolved together with the development of the society. The modern history of the concept of "quality" starts with the twentieth century when the principles, methods, techniques and tools for the quality assurance and the improvement processes, products and services were discovered and developed and applied. During this century, the approach of the concept of quality has seen successive four stages (Drăgulănescu, 1996), each of these steps being remarked by improving the principles and practices of the previous stage, which was additionally enhanced inevitably by supplementing it with its own principles and practices.

Starting from the "Deming" program, which the Japanese have accepted, assimilated and promoted since the '50s (Vokurka, Stading and Brazeal, 2000), perfecting it and taking advantage of it to the "Japanese miracle" at the early '80s in the US, Australia, Canada and the '90s in the European Union, several programs have been developed for the assessment and the recognition practices in the field of the total quality high performance level organizations. These programs have become models of reference for the organizations concerned with the problem of implementing the TQM practices (total quality management) in their business processes. The studies and especially the practical experience has shown that no two organizations have the same implementation of the TQM (Issac, Rajendran and Anantharaman, 2004; Sila and Ebrahimpour, 2003). This largely explains the rich literature dedicated to the total quality management such as: Azhashemi and Ho, 1999; Bollapragada and Sadeh, 2004; Dahlgaard and Dahlgaard, 2002; Kanji and Wallace, 2000; Kara, 2005; Parameshwaran și Srinivasan, 2008; Roman, Roman și Jaba, 2009; Androniceanu, 2012; and so on.

Despite the diversity of the TQM models implemented, yet today there are several reference TQM models supported at a regional scale as models of organizational




excellence, such as the EFQM European model, the American MBNQA model, the CAE Canadian model and the ABEF Australian model.

The EFQM model is promoted by the European Foundation for Quality Management, established in 1989. It was developed in 1991 and was later improved in several stages. The EFQM European model is subject to several studies: Blessing and Parker, 2000; Hall, 2000; Hammond, 2000; Hughes and Halsall, 2002; London, 2002; Marra, 2002; Androniceanu, 2014 and so on.

In the current time period, the quality has become the most important competitive advantage. But in order to achieve the expected results, the organizations will have to place "quality" on a special position in their strategy.

The European Union promotes a quality policy. The elaboration of the White Paper on the subject of the European Single Market in 1994 represented an important contribution to the development of the EU policy on promoting quality ("Growth, competitiveness, employment – challenges and ways for the 21st Century"). Later the document called "European policy to promote the quality and the excellence path to Europe" was elaborated. Here it is stated that the European policy includes "a strategic vision on quality" and "a European program for promoting quality" (Olaru et al., 2011).

For increasing the quality and the competitiveness of the public institutions and the public interest, the representatives of the European Union have asked the experts to develop an appropriate tool in order to assist the public institutions in their journey towards continuous improvement, towards excellence. Defined through its characteristics of organization and implementation of the law (Sararu, 2016), the public administration plays an important part in reaching this objective.

The Common Assessment Framework (CAF) is a total quality management tool developed by the public sector for the public sector, inspired by the Excellence Model of the European Foundation for Quality Management (EFQM). CAF is a free tool, easy to use, representing the support for the public interest entities, using the quality management techniques in order to improve performance. CAF has been revised three times, namely in 2002, 2006 and 2013. As the EFQM Excellence Model, CAF 2013, figure no. 1, uses nine criteria.

The criteria from 1 to 5 take into account the management practices of the organization (the factors). They reflect what the organization does and how to achieve its objectives. The criteria 6-9 (the results) reflect the results quantified by measuring the perception and the performance relating to citizens / customers, personnel, social responsibility and key performance. The 9 criteria contain 28 sub-criteria reflecting the main issues that need to be considered when making of the organization. The sub-criteria are explained with the help of numerous examples, suggestions and best practices to be better understood and properly evaluated. Integrating the conclusions of the (self-) evaluation in managerial practices, it is in fact the actually learning cycle and continuous innovation on the way to excellence.

The recommendations for use of the CAF model 2013 grant a freedom quite large to interested parties but, however, both the structure with the 9 criteria and 28 sub-criteria and the use of the tables of scoring, are the bases from which deviations are not permitted.


Vol. 19 • No. 44 • February 2017 45

Figure no. 1: The CAF 2013 excellence model Source: EIPA and EUPAN, 2013

"CAF aims to be a catalyst for a full improvement process and has five main goals: • To introduce public administrations in the culture of excellence and the TQM

principles; • To progressively guide them to a PDCA cycle (planning, implementation,

verification, action); • To facilitate self-assessment of the public sector organizations in order to obtain a

diagnostic analysis and a definition of the improvement actions; • To act as a bridge between different models used in quality management, both in the

public and private sectors; • To facilitate the bench learning between different public sector institutions." (EIPA

and EUPAN, 2013)

To achieve its goals the 2013 CAF model promotes the eight principles of total quality management (Olaru et al., 2013), and for promoting the spirit and the purpose of the excellence model, it gives different levels of interest (share in percent) to the criteria and the sub criteria of the model that can change with time. 2. Research methodology In order to achieve the objectives of this research a study was conducted based on a questionnaire. The questionnaire was offered for completing to the 53 representatives belonging to the top management of the 49 National Institutes of Research Development (NIRD) coordinated by NASR, to the two Institutions and Units for Scientific Research, Technological Development and Innovation subordinated to NASR and the two Institutions and Units of Scientific Research, Technological Development and Innovation, which operate under the authority of NASR, during a work meeting held in July 2016. Out of the 53 questionnaires set up for completing, 51 questionnaires received were correctly filled in.




The questionnaire, as an “instrument and research technic in social and human sciences” (Chelcea, 2001), was used in two key areas of the research, as follows.

In the first instance, the objective was the analysis of the perception of decision makers from the National Institutes of Research – Development and Innovation of Romania on the most important issues that a research institute is facing. The questionnaire contained an open question (question B: Please indicate at least five of the most important issues that your institute is facing).

In the second phase of the research, the questionnaire was used to allow the assessment of the current state of the implementation of the excellence model after the model CAF 2013 in the National Institutes of Research – Development and Innovation in Romania.

The questionnaire used in this research is divided into the categories of the process of the CAF model (Common Assessment Framework), see figure no. 1, namely: Leadership; Strategy; Human Resources; Partnerships and Resources; Processes, Products and Services; Staff’s satisfaction; Customer’s satisfaction; Society’ Satisfaction; Key Business Performance.

The questionnaire on the factors of the CAF model 2013, including the criteria from 1 to 5, see Table no. 5 the lines having the codes from 1 to 5, was preceded by question A, considered “closed qualifying question” (question A: Please specify how many management systems are certified in your institute: In our institute are certified: 0 management systems; ISO 9001; ISO 9001 plus 1 management systems; ISO 9001plus 2 management systems; ISO 9001 plus more than 2 management systems.).

The questions regarding the results of the CAF model 2013, criteria from 6 to 9, see Table no. 5 the lines having the codes from 6 to 9, have been separated from the questions concerning the factors, the criteria from 1 to 5, by question B, an open question, previously presented.

The questionnaire was accompanied by an explanatory letter about its usefulness and necessity and by ensuring the confidentiality of the responses, and so on. Also, the assessment criteria were presented for each of the criteria and sub-criteria of factors or results, according to the CAF 2013 methodology, which can be found in Table no. 1. – Rules regarding the evaluation of factors (criteria 1-5) and respectively Table no. 2. – Rules regarding the evaluation of results (criteria 6-9).

Table no. 1: Rules regarding the evaluation of factors (criteria 1-5)

We do not perform activities in this field; we have no information or there is insufficient information held upon this topic. 0-10

We plan to achieve this. 11-30 We implement this. 31-50 We are checking to see if the right steps were correctly followed. 51-70 Based on the analysis, we make the necessary changes. 71-90 Everything we do we plan, implement and adjust regularly and we continue the learning process from others. We are in a cycle of continuous improvement on the subject.

91-100


Vol. 19 • No. 44 • February 2017 47

Table no. 2: Rules regarding the evaluation of results (criteria 6-9)

No results measured and / or no information available 0-10 The results are measured and show a negative trend and / or results do not meet relevant targets 11-30

The results show a trend of stagnation and / or some relevant targets are met 31-50 The results show improving trends and / or most relevant targets are met 51-70 The results indicate a major breakthrough and / or all the relevant targets are met 71-90 Excellent results were achieved. All relevant objectives were met. Positive comparisons were achieved for key results with relevant entities 91-100

The research objectives are:

• The analysis of the perception of the decision makers in the Research Development and Innovation National Institutes of Romania on the most important issues that the research institutes are facing.

• The assessment of the current status of implementation of the CAF 2013 Model of Excellence by the National Institute of Research Development and Innovation in Romania by determining the degree of fulfillment by the Romania INCDIE of the 9 criteria and the 28 sub-criteria of the model by:

- The analysis of the achievement of the excellence criteria and sub-criteria of the CAF 2013 model based on the self-assessments made by the respondents;

- The verification of the criteria and the sub-criteria of the excellence model in order to see if they have similarities in culture.

In our study we will also check the following assumptions:

• Verifying the hypothesis: if an excellent organization requires much more than the implementation and the certification of one or more models of management systems;

• Verifying the hypothesis: if based on the assessment of the current state of the 9 criteria implementation used by the CAF model 2013 can be identified key weaknesses and risks.

3. Research results

The results of the questionnaire are summarized in Tables no. 3 – 5.

At question A) of qualification, requesting information on the status of the implementation of the quality management and of other management systems in the institute, the answers received were presented in Table no. 3 – Management Systems Certified in NIRDI.




Table no. 3: Management Systems Certified in NIRDI

In our institute are certified: Total: 0 management systems 0 ISO 9001 2 ISO 9001plus 1 management systems 19 ISO 9001plus 2 management systems 23 ISO 9001plus more than 2 management systems 7

At question B), open, which require the submission of at least 5 of the most important problems the institute is facing, the following statement need to be taken into consideration: the problems were summarized, analysed and systematized into different categories. Thus, on the basis of the 357 problems mentioned by the 51 respondents to the questionnaires were identified 12 groups of main issues (Table no. 4). The presentation was made in the order of their occurrence while processing the questionnaires, except that in the case of similar types of problems, a more comprehensive reformulation of the entire set of problems was performed.

Table no. 4: Categories of problems identified in NIRDI

No.: The main group of problems: No. of times when encountered:

Frequency (%):

1. Reluctance and insufficient involvement of the employees 31 60,78

2. Lack of team spirit and initiative 23 45,10

3. No set of indicators to measure employee performance was implemented and the motivation tools are limited

33 64,71

4. Old technical equipment and inadequate spaces 28 54,90

5. Lack of reasearch atractivity for exceptional young individuals 31 60,78

6. Lack of syncronization between the costs and the profit 50 98,04

7. Lack of impact assessments of NIRDI on society’s activity 8 15,69

8. Lack of employee satisfaction assessments 19 37,25 9. Lack of customer satisfaction ratings 33 64,71 10. Difficulties in using research results 43 84,31

11. The long period between the submission and project funding 48 94,12

12. Lack of integrated computer systems 10 19,61 TOTAL PROBLEMS 357 X

The responses to the questionnaire regarding the assessment of the current state of implementation of the CAF 2013 Excellence Model by the NIRDI in Romania are summarized in Table no. 5.


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Table no. 5: The Implementation stage of the CAF model 2013 in NIRDI

Code: Criterion / sub-criterion / question Average score:

CAF importance:

1. With regard to the institute’s management team the following statements can be made: 88,67 0,1

1.1. Does the managerial team ensure the institute’s direction for further evolution, by developing its mission, its vision and its values?

90,24 0,25

1.2. Does the managerial team administrate the institute, its performances and its continuous development? 88,20 0,25

1.3. Does the managerial team motivate and support the institute’s personnel and acts in terms of a behavioural model for the personnel?

88,51 0,25

1.4. Does the leadership team manage effective relationships with political authorities and other stakeholders?

87,73 0,25

2. With regard to the institute’s policy and strategy team the following statements can be made: 80,17 0,08

2.1. The policy and the strategy are based on the current and future shareholders? 84,69 0,25

2.2.

The policy and the strategy are based on the assessments, data and indicators from both the institute’s internal environment and the external environment?

88,25 0,25

2.3. The institute’s policy and strategy are reviewed and adjusted periodically? 76,00 0,25

2.4. The policy and the strategy are communicated and implemented in all the organizational structures of the institute?

71,73 0,25

3. With regard to the institute’s employees the following statements can be made: 73,15 0,09

3.1. The need for human resources is identified and planned in a transparent manner, consistent with the institute’s strategy and policy?

75,88 0,33

3.2. Are the skills of the employees’ identify, develop and harnessed at the institute’s level aligning to both the individual and organizational goals?

75,76 0,33

3.3. Is the staff are involved in achieving the institute’s by promoting an open dialogue about its rights and responsibilities?

67,80 0,33

4. With regard to the institute’s partnerships and resources the following statements can be made: 85,24 0,09

4.1. Does the institute develop and manage mutually beneficial partnerships with relevant organizations? 83,73 0,16

4.2. Does the institute develop and implement long-term partnerships with customers and suppliers? 73,27 0,16




Code: Criterion / sub-criterion / question Average score:

CAF importance:

4.3. Does the institute manage the financial resources based on budgets in order to achieve its targets? 85,59 0,16

4.4. Does the institute manage the information and knowledge through quick access to both information and knowledge?

91,76 0,16

4.5. Does the institute manage the appropriate technologies? 88,00 0,16

4.6. Does the institute value the strengths and opportunities? 89,08 0,16

5. With regard to the processes occurring in the institute the following statements can be made: 79,95 0,14

5.1. Are the processes identified, described, used and updated on an on-going basis, involving the stakeholders?

77,80 0,33

5.2. Are the services and products developed and implemented taking into account the customers’ requirements?

81,76 0,33

5.3. Is the coordination processes within the institute correlated with the requirements of other relevant organizations?

80,29 0,33

6. According to your opinion regarding the results oriented towards the citizens / the customers: 86,92 0,2

6.1. Are there indicators that measure customers’ satisfaction? 84,24 0,25

6.2. Are the customers’ satisfied with the services provided by the institute? 89,61 0,75

7. According to your opinion regarding the results concerning the employees: 68,27 0,09

7.1. Are there indicators that measure employees’ satisfaction? 64,31 0, 25

7.2. Are the employees’ satisfied? 72,24 0,75

8. According to your opinion regarding the social responsibility: 73,83 0,06

8.1. Are there indicators for measuring and tracking the environmental and social involvement? 75,24 0, 25

8.2. Is the institute known and appreciated by the individuals? 72,43 0,75

9. According to your opinion concerning key results regarding performance: 86,69 0,15

9.1. Are there indicators for measuring and tracking the institute’s results? 87,65 0,50

9.2. Are the results obtained from the measurements excellent? 85,73 0,50


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The correlation of the results of this questioner allows us to give an answer based on the objectives and the assumptions of our study.

3.1. The assessment of the current stage of implementation of the CAF 2013 Model of Excellence by the National Institutes for Research, Development and Innovation in Romania

Based on the data summarized in the Table no. 4: Categories of problems identified in response to question B, after grouping them in an ascending order based on their frequency, the frequency can represent the major obstacles as shown in Figure no. 2: Frequency by the categories of problems that INCDIE is facing.

Figure no. 2: Frequency by the categories of problems

From the analysis of the main problems the National Institutes of Research, Development and Innovation in Romania are facing, we discovered that the most delicate ones, the ones with the highest frequency are based on external causes (The lack of the costs and the incomes synchronization and The length of time between the submission and the financing of the projects), the following problem, as frequency, is based on both an external business environment cause and an internal cause due to the lack of entrepreneurship of the managers (Difficulties in using research results). The problems with a lower degree of generality, in the perception of the decision makers, concern the Lack of the impact assessments of the INCDIE activity on society and the Lack of the integrated computer systems.




3.2. The assessment of the current status of the CAF 2013 Model of Excellence implementation by the National Institute of Research Development and Innovation in Romania

To assess the current state of implementation of the excellence model CAF 2013 by the National Institutes of Research, Development and Innovation in Romania: we will analyse the criteria and sub-criteria model of excellence fulfillment and we will also assess whether the criteria and sub-criteria of the model of excellence CAF 2013 are fulfilled taken into consideration its spirit and purpose.

• The degree of fulfillment of criteria and sub-criteria of the model of excellence

On the basis of processing the results from table no. 5, in figure no. 3 the chart radar represents the degree of fulfillment of the nine criteria of the CAF 2013 excellence model. The degree of compliance was calculated for each criterion based on the averages obtained from the calculation of the sub-criteria reported. By analyzing the chart we can notice a high level of the degree of fulfillment of its nine criteria.

Figure no. 3. The radar graph of the degree of fulfillment of those 9 criteria of the excellence model CAF 2013 without taking into account the importance

(expressed in percentage) given by the model to its criteria and sub-criteria

The results obtained in each criterion are presented below.

The degree of fulfillment of criterion 1: "Leadership"

According to the research, the National Institutes of the Research and Development and Innovation (NIRDI) achieved an average score of 88.67%, which corresponds to the stage of "leadership based on analyzes propose the necessary amendments". This criterion values ranged from 46.25%, which is "implementing a management system” to 92.5%, that "everything is done planning, implementing and adjusted regularly. Learn from others".


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The degree of fulfillment of criterion 2: "Strategy"

According to the research, NIRDI achieved an average score of 80.17%, which corresponds to the stage "are based on analyzes necessary changes". This criterion values ranged from 52.25%, respectively "checks are being conducted to see if they followed the right steps" to 92.25%, respectively "continuous improvement cycle strategy".

The degree of fulfillment of criterion 3: "Human Resources"

According to the research, NIRDI achieved an average score of 73.15%, which corresponds to the stage "based on the analysis the necessary adjustments are made". The values for this criterion ranged from 50.00%, which represents that it "is being implemented" to 71.66%, which is "based on the analysis necessary adjustments are made".

The degree of fulfillment of criterion 4: "Partnerships and resources"

According to the research, NIRDI achieved an average score of 85.24%, which corresponds to the stage "based on the analysis necessary adjustments are made". This criterion values ranged from 70.16%, which is "based on the analysis necessary adjustments are made" to 94.5%, meaning "cycle of continuous improvement".

The degree of fulfillment of criterion 5: "Processes, products and services"

According to the research, NIRDI achieved an average score of 79.95%, which corresponds to the stage "based on the analysis necessary adjustments are made". The values of this criterion ranged from 54.66%, respectively "checks are being conducted to see if they followed the right steps" to 94.33%, respectively "continuous improvement cycle".

The degree of fulfillment of criterion 6: "Results on customers"

According to the research, NIRDI achieved an average score of 86.92%, which corresponds to the stage "results indicate a major breakthrough". This criterion values ranged from 74.00%, respectively "the results indicate a major breakthrough" to 96.50%, respectively "excellent results were achieved".

The degree of fulfillment of criterion 7: "Results about staff"

According to the research, NIRDI achieved an average score of 68.27%, which corresponds to the stage "results show improving trends". This criterion values ranged from 50.50%, "the results show a trend of stagnation" to 90.50%, "excellent results were achieved".

The degree of fulfillment of criterion 8: "Results on the organization"

According to the research, NIRDI achieved an average score of 73.83%, which corresponds to the stage "results show improving trends". This criterion values ranged from 50.00%, "the results show a trend of stagnation" to 92.50%, "excellent results were achieved".

The degree of fulfillment of criterion 9: "Key Performance"

According to the research, NIRDI achieved an average score of 86.69%, which corresponds to the stage "results indicate a major progress". This criterion values ranged from 68.00%, "the results show a trend of improvement" to 93.00%, "excellent results were achieved".

• The evaluation of whether the spirit and purpose of the excellence model CAF 2013 were assimilated




To assess whether the spirit and the purpose of the excellence model CAF 2013 were assimilated into the culture of the NIRDI we can use several methods (Lefter et al., 2000). Our option was the evaluation based on results. Specifically, we assessed the degree of fulfillment of the criteria of excellence model induced by CAF 2013 in the spirit of that model, based on the importance assigned to each criterion and sub-criterion within the model.

As it results from the analysis and conclusions presented in the section 3.3, the NIRDI of Romania has not yet assimilated the spirit, nor purpose of the CAF model 2013.

In conclusion, the evaluation of the present stage of implementation of the CAF 2013 Model of Excellence by the National Institute of Research Development and Innovation in Romania reveals a poor level of implementation. It can be noted, however, the real progress in the following criteria: the "results concerning the customers" and the "processes, products and services". In the same time, we note the existence of serious shortcomings in the criteria "results concerning the organization" and "results concerning the staff."

3.3. Testing the hypothesis of whether the implementation of one or more management systems is sufficient to achieve excellence

To substantiate the answer to the question “Is the implementation of one or more management systems sufficient to achieve excellence?”, it will be analyse to what extent the research and innovation institutes implemented or not one or more management systems and it will be assess to whether the criteria and sub-criteria of the CAF 2013 model of excellence are met in its spirit and purpose (weights).

According to the data analysis it can be noticed that, on the one hand, according to the results summarized in Table no. 3 all the NIRDI analysed have implemented at least one management system (namely ISO 9001) and, on the other hand, based on the results reflected in the synthesis image provided by Figure no. 3, our hypothesis is verified (more precisely: it is enough to implement one or more management systems in order to achieve excellence).

Instead, if it is analysed whether the criteria and sub-criteria of the CAF 2013 model of excellence are met in its spirit and purpose, a different conclusion is reached. More exactly:

• when the radar chart represented by the degree of fulfilment of the 9 criteria of the CAF 2013 model based on the importance (weights) assigned to each criteria of the model, as shown in Figure no. 4, an imbalance and a decrease in the degree of fulfilment for more criteria is noticed.


Vol. 19 • No. 44 • February 2017 55

Figure no. 4: The radar graph of the degree of fulfilment of the 9 criteria of the

excellence model CAF 2013 taking into account the importance (weight) given model only to the criteria.

• moreover, the radar chart in Figure no. 5 presents the degree of fulfilment for the 9 criteria of the CAF 2013excellence model, taking into account the importance (weight) given by the model to its criteria and sub-criteria.

Figure no. 5: The radar graph of the degree of fulfilment of the 9 criteria of the

excellence model CAF 2013 taking into account the importance (weight) given by the model to the criteria and sub-criteria.

Figure no. 5 represents the closest reality representation of the achievement of the 9 criteria and sub-criteria of the excellence model CAF 2013 and, as stated, it is much different than the one shown in Figure no. 3. In the Figure no. 3 the CAF 2013 excellence model was




formal, without having implemented the basic principles of the CAF model, otherwise similar to those of the EFQM model. It is noted therefore that the eight principles of the model are reflected by the importance given to different criteria and sub-criteria.

For example: The focus on the customer’s satisfaction is reflected in the model by the weight of 20% given to evaluate the criteria and 75% in terms of regarding the customer’s perception towards this satisfaction.

We can conclude that: excellence cannot be achieved simply by the implementation and certification of two or more management systems. It is imperative that the 8 principles of total quality philosophy should be assimilated and implemented into the organizational culture in their true essence (spirit) of which, incidentally, is based the road to excellence.

Assimilating the spirit of the 8 principles of total quality philosophy underpinning the sustainable approach to excellence, clearly offers a new perspective in terms of capitalizing the results of (self-) evaluation in terms of the criteria of the achievement of the CAF model. Thus the management’s principle oriented towards the processes and the facts correlated with the focus on customer satisfaction, transforms the objectives into the outputs of the processes. “The customer’s satisfaction is given by successful processes whose outputs represent its needs and requirements. Once defined the processes outputs (approved by the administrator and the customers), regardless of whose needs and requirements are met (namely an internal or external customer), they become objectives” (Zybaczynski and Manole., 2005, p. 29).

In the spirit of this approach, if our goal is to meet the 9 criteria of the excellence model CAF 2013, any cause that stops us to achieve our objective, is certainly a risk that must be avoided. Analysing the root causes of risk of default objective of meeting the 9 criteria of the excellence model CAF 2013 and proposing solutions to eliminate or reduce the risk outcomes, the (self) assessment of the achievement of criteria CAF model of excellence, may underlie the rationale of the strategy and the organization’s policies, contrary to what some authors may claim that they do not offer the possibility of developing future actions to improve (Andersen, Lawrie și Shulver (2000)). Instead, we can agree with them on the fact that the models of excellence do not formulate strategies, do not evaluate strategies, but rather evaluate the process of the strategies’ creation.

3.4. Testing the hypothesis whether based on results of the (self-) evaluation of the achievement of the criteria of the CAF 2013 excellence model there could be identified some weaknesses and risks

To substantiate the answer to this issue it will examined whether the top managers’ perceptions about the problems they are facing is consistent or not with the results provided by the CAF 2013 model.

Based on the frequency analysis of the categories of problems, as shown in Figure no. 2, a higher frequency is noticed concerning the following aspects: the performance (the problems no. 6, 11, 10); the employees (the problems no. 3, 1, 6, 4); the processes (the problems no. 4, 12). These problems identified by the top management consistent, in most cases, with the failures reflected in the chart radar the degree of fulfilment of the 9 criteria of the excellence of the CAF 2013 excellence model in Figure no. 5, but in a different


Vol. 19 • No. 44 • February 2017 57

order: the satisfaction, the society, the employees, the processes, and the performance. It is noted that some differences appear concerning the problem no. 9, evidenced by the frequency of the categories of problems regarding the evaluation of customer satisfaction, which is not consistent with the information shown in chart radar. In this case, it is believed that the issue of the customer satisfaction assessment was not properly understood. The fact that the institutes did not evaluate the customers’ satisfaction through a market study does not mean that there are unsatisfied customers or that there are no other criteria to assess their dissatisfaction (such as, for example, complaints, returns, trials, etc.).

In conclusion, based on the analyses conducted, it can be stated that this hypothesis is verified. More exactly, based on the assessment of the current state of the CAF 2013 – model of excellence, there can be identified key shortcomings and risks.

Conclusions

The research conducted allowed the determination of the implementation stage of the 2013 CAF excellence model for the first time in Romania. It appears that although there is a high degree of fulfillment of the 9 criteria of the excellence model CAF 2013 NIRDI have not assimilated the spirit and the purpose of the excellence model induced by the principles of total quality management by importance (percentage) given to each criterion and sub-criterion. It appears real progress criteria "results concerning the customers" and "processes, products and services". At the same time there are serious shortcomings in the criteria "results concerning the organization" and "results concerning the staff".

On the one hand, it has been found that excellence can not be obtained simply by the implementation and the certification of two or more management systems. It is mandatory that the organizational culture is assimilated and applied in the spirit of the eight principles of total quality philosophy underlying the approach to excellence. They are induced in the model of excellence by the importance (share in percent) given to each criterion and sub-criterion.

On the other hand, based on assessment of the current state of the implementation of the CAF 2013 excellence model the main weaknesses and risks can be identified. Even more, the assimilation of the eight principles of the total quality management philosophy underlying the approach to sustainable excellence in practice and in the organizational culture forces us to value the results of the (self-) evaluation.

Taking into account the research results, we can conclude that the (self-) evaluation stage of the implementation of the excellence model CAF 2013 can be a beneficial alternative that does not require additional expenses to increase the quality and the competitiveness of the research and innovation activities and of achieving the excellence in business. The study results may constitute a starting point for future research aiming to identify the best avenues to finding and resolving risks and deficiencies.

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THE THREE-DIMENSIONAL APPROACH OF TOTAL QUALITY MANAGEMENT,

AN ESSENTIAL STRATEGIC OPTION FOR BUSINESS EXCELLENCE

Armenia Androniceanu1* 1) The Bucharest University of Economic Studies, Romania

Please cite this article as: Androniceanu, A., 2017. The Three-Dimensional Approach of Total Quality Management, an Essential Strategic Option for Business Excellence. Amfiteatru Economic, 19(44), pp 61-78

Article History Received: 29 July 2016 Revised: 8 November 2016 Accepted: 4 December 2016

Abstract In a complex and dynamic business environment, managers widely appeal to modern methods and techniques that would help them cope with the competition and offer their customers new, attractive, good quality products and services and at competitive prices. In this context, total quality management is a viable and sustainable option that can systematically contribute to the consolidation of the capacity of organizations. The aim of this paper was to put forth a three-dimensional approach of total quality management and provide some concrete action ways through which organizations in Romania that implement total or partial quality management integrated systems would produce significant competitive advantages. The main research methods used were: the questionnaire, document analysis, applications offered by Word and Excel and the Statistical Package for Social Sciences, one of the most complete software packages with which we calculated means and standard deviations and determined and analysed correlations between variables and various quality parameters. In this research, have been identified the main key success factors, the vulnerabilities and weaknesses of the systems, their causes and the necessary changes through which the three-dimensional approach of total quality management could become a preferred strategic option with a major positive impact upon business excellence. The most important results obtained are a three-dimensional approach of the Total Quality Management and a substantial number of certain proposals for the Romanian firms in order to achieve business excellence. Keywords: total quality management, business excellence, quality, customer, competitiveness, standards, three-dimensional approach JEL Classification: L15; M11

* Corresponding author, Armenia Androniceanu – [email protected]


AE The Three-Dimensional Approach of Total Quality Management, an Essential Strategic Option for Business Excellence


Introduction

Globalization has significantly influenced the behaviour of the companies on the market and the profile of goods and services consumers (Zeithaml, Rust and Lemon, 2001). Most customers have increasingly higher demands regarding the quality and prices of goods and services (Kantardjieva, 2015; Leonard, 2015; Schor, 2016).

Recent studies (Dixon, 2016; Svizzero, and Tisdell, 2016) have shown that 80% of the consumers of products and services on international markets consider quality to be at least as important as the price when making the decision to purchase (Pellettieria, 2015; Pollifroni, 2014).

As the strategic management system and competitive advantage are based on quality, every member of the organization should have a clear picture of the definition and measurement of quality (Nica, Manole and Potcovaru, 2016), depending on their position and the work they perform (Morselli, 2015).

The intensified competition under the globalization (Androniceanu and Drăgulănescu, 2012) determined most high performing companies to develop new quality management integrated systems based on information and communication technologies that consistently generate business excellence (Foster, 2015). This explains the fact that the managers of the successful companies are constantly preoccupied with developing comprehensive strategies based on total quality management (TQM).

This paper is based on recent research on TQM developed by various authors (Akyuz, 2015; Psomas, 2016), but is different through the contribution to the knowledge development and the applied research carried out on a representative and selective sample of Romanian organizations.

The scope of the research has two dimensions, one is theoretical and the other is practical. From the theoretical perspective, I aimed to offer a new three-dimensional approach of TQM. From practical perspective, I was focused on discovering the main features of TQM that are implemented in the Romanian organizations and the possible main ways in which the three-dimensional approach can be implemented in order to achieve business excellence.

The research methods used were: the questionnaire, Excel and SPSS, with the help of which the creation of the database was possible, the results were analyzed, the research hypotheses were checked and the research objectives, presented in the second section of the paper, were achieved.

Competition increase at national and international level, companies' strategy orientation towards quality, perceived as a major competitive advantage, goods and services consumers' increased exigency for quality are only a few reasons that determined me to undertake research on this topic in Romania and to present in this paper interesting and relevant aspects regarding total quality management as an essential strategic option that generates business excellence.


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1. State of art regarding quality and total quality management

Quality has gained increased importance and is more and more distinguished as a determinant factor of competitiveness. Whereas today it is easy to compare prices, it is more difficult to compare quality levels. According to the standards ISO 9001 in 2015, "The quality is a set of properties and features that give the product or service the property to meet specific needs" (Romanian Society for Quality Assurance, 2016).

In Europe, there was established the European Foundation for Quality Management, which approaches quality based on a widely recognized model called EFQM Excellence Model. This model is implemented in more than 30,000 companies in Europe with very good economic results. The model explains best how quality can contribute to development and business excellence. An interesting study on this subject was drawn up by a group of Romanian specialists, who managed to provide a relevant picture on how the total quality excellence model is implemented in companies in Romania. (Olaru, et al., 2010). Often, quality is defined or perceived differently in the same company, depending on the stage of the life cycle of a product (Hackman and Wageman, 1995).

In the literature, there can be identified some of the most important and relevant views on quality and quality management, whose main founders were: Shewhart, Deming, Juran, Crosby, and the Japanese: Ishikawa, Taguchi, Massaki Imai.

It is well known the case of the Bell System Company from Western Electric, where the great American physicist Walter Andrew Shewhart, employed by this company, designed and implemented a system of quality statistical control for the production of phones aimed at "zero defects". In his famous book "Economic Control of Quality of Product Manufacturing", Shewhart (1931) explained the concept of quality control, which was later taken up and developed by Juran, Deming, Feigenbaum and Crosby.

According to J. M. Juran, an American expert of Romanian origin, quality means "suitable for use" (Juran and Gryn, 1973). He also defines quality as "the property of a product to be suitable for consumption in terms of design, conformity to the specifications, reliability, market availability and scope of use". Juran expressed his approach to quality management as "The Quality Trilogy", considering that quality management involves three managerial processes: quality planning; quality control; quality improvement.

Armand Feigenbaum had an outstanding contribution to the development of the concept of quality and to its implementation in organizations from the United States of America. He argued that it was more efficient to follow and ensure quality throughout the manufacturing process than to control quality at the end of this process (Ionescu, 2016).

Philip Crosby successfully promoted his ideas through the book "Quality is free". According to Crosby (1979), quality is "the conformity to the requirements". He argues that poor quality costs approximately 20% of the revenues of an average company, costs that can largely be avoided by improving quality.

Edwards Deming (1986) explained that "good quality means a predictable degree of uniformity and durability, with quality appropriate for the market". Deming brought a new perspective on the concept of quality, that of predictability in relation to the market. Deming is the first specialist who focuses on the human resources that contribute to obtaining quality products, repeatedly stating that it is people who design and manufacture quality products. Therefore, he said, they must be motivated and encouraged to understand and correctly exercise their role in relation to the quality.



In Japan, the idea of total quality control was materialized by Kaoru Ishikawa (1985), who supported "quality control throughout the company." Kaoru Ishikawa is the one who proposed for the first time voluntary organization of employees in quality circles to identify punctual solutions to specific problems (Line, 2006; Laratta and Nakagawa, 2016).

In 1988, David Garvin, professor at Harvard University, published his famous work "Managing Quality: The Strategic and Competitive Edge", in which he demonstrated the multidimensionality of the concept of quality, namely: transcendent, product based, user or consumer based, manufacturing and value based. Thus, the concept of total quality emerged, which comprises all activities undertaken by an organization.

Another interesting approach of total quality belongs to Kelada (1990), who explained this concept in his work "La gestion integrale de la qualite. Pour une qualite totale" as "a process of satisfying customers' needs regarding product or service quality, delivering the required amount at the required time and locations, at a cost as low as possible for the customer (Sigauke, 2016), in terms of pleasant and effective relations with them and of a flawless administrative system, starting from the order fulfilment to the payment of the bill" (Leonard, 2013).

Total quality management (TQM) consists in the integration of all functions and processes within an organization in order to achieve continuous improvement of the quality of goods and services, that is to achieve customer satisfaction (Dean and Bowen, 1994). Research (Popescu et al., 2014; Popescu, 2016) shows that most managers of competitive companies consider that total quality fundamentally contributes to business success (Leonard and McAdam, 2003).

According to the general theory of TQM, a product, service or process can be improved, and the company can be successful only if they find and consciously capitalize on the possibilities to improve quality at all levels in order to satisfy customers (Levine and Toffel, 2010).

Total quality management is a modern management system that integrates not only the basic principles formulated by Deming, Juran, Philip Crosby, Armand Feigenbaum and Kaoru Ishikawa, but also the Japanese values referring to quality and continuous improvement (Ashok et al., 1996). One of the definitions of TQM that emphasizes the management system was given by Hellsten and Klefsjö (2000) who define TQM as "a management system with continuous development, consisting of values, methodologies and tools, which aim to increase external and internal customers' satisfaction, with a reduced amount of resources". Karl J. Koller (2006), the founder of the Institute for Total Quality Management in Zurich, has a different opinion. He defines TQM as: "a systematic way for managing an organization".

Professor Marieta Olaru (1999) is an expert recognized in Romania for the publications and research in the field of quality management. In her opinion, total quality is first of all: "a model of company culture, which aims to orient all its activities and processes towards the customer and to optimize them so as to bring long-term benefits". In Romania, the Foundation of the Romanian Quality Award J.M. Juran proposed "the Romanian Model of Business Excellence" (the Foundation of the Romanian Quality Award J.M. Juran, 2016).

According to the author of this paper, TQM is both an instrument and a management philosophy that should be approached three-dimensionally (Figure no.1). The three closely


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interdependent dimensions are: the technical, social and economic dimension, among which inter-conditioning relationships exist.

Each dimension integrates the quality of the products and services as the primary and fundamental parameter, to which two specific elements grouped into three subsystems (technical, economic and social) are being added. (Figure no.1)

The technical subsystem is represented in the triangle of excellence and has the following three elements: quality, standards, technical characteristics.

The economic subsystem is represented in the triangle of efficiency and has the following three elements: quality, product/service parameters, costs.

The social subsystem is represented in the triangle of products and services customer consisting of the following three elements: quality, price, terms. According to the author of this work, professional implementation of TQM can naturally lead to business excellence.

Figure no.1. The three-dimensional approach of Total Quality Management

Customer's triangle

Triangle of Excellence

Triangle of Efficiency

THE THREE-DIMENSIONAL APPROACH OF TOTAL QUALITY

MANAGEMENT

TE

CH

NIC

AL

: Standards, C

haracteristics

EC

ON

OM

IC:

Parameters, C

osts

SOC

IAL

: Price, Term

s

Quality



Through business excellence, it is understood constant economic performance achieved by an organization as a result of the efficient and progressive implementation of total quality management based on modern information and communication technologies integrated into processes and activities and highly qualified human resources motivated to offer customers excellent quality products and services, at a competitive and accessible price and within the planned schedule. In my opinion, investing in TQM is a viable and sustainable option that I have studied within an extensive research process whose main results are presented in the next section of this paper.

2. Research regarding total quality management in companies in Romania

As the literature has shown (Dinu, 2014; Popescu and Popescu, 2015), there is a variety of ways of development that companies have at their disposal, but most of them require investments in modern management systems that include quality as a fundamental objective for business excellence.

2.1 Research methodology

The research process was conducted during October 2015 and April 2016 and is representative for the segment of organizations in Romania that started to implement total quality management at least five years ago, have a turnover of less than 50 million euro and less than 250 employees.

The undertaken scientific research had a specific methodology based on the online questionnaire as the main tool for data collection, which was processed using specific applications available in Word, Excel and SPSS. The questionnaire is one of the most effective tools in building a representative, accurate and complete database, which facilitates testing and verification of the research hypotheses. The questionnaire had 21 questions and was filled in by 280 managers from 56 companies included in the target group, selected to participate in this research, which ensures the representativeness of the results of this research for the targeted segment. Data systematization was performed in an Excel file in order to obtain a configuration that permits their statistical processing. For an analysis as correct and consistent as possible, the collected data was grouped according to certain research variables: turnover, duration since total quality management has been implemented; areas of employment of the respondents and the management positions they hold; quality dimensions and key success factors. Based on the data obtained using the questionnaire and on the statistical data picked out from the websites of the National Registry of Commerce and the National Institute of Statistics as well, we conducted a series of correlations in SPSS using the Pearson coefficient, which helped us understand the connections between the research variables and how they influenced the implementation of total quality management in the organizations selected for this research. For certain questions in the questionnaire, a simple classification was performed, that is by a single variable, while for others, where two or more variables reflected a clearer situation, a combined classification was used. Most of the data was introduced in SPSS and processed.


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2.1.1 Research aim and objectives

The aim of this research was to identify relevant ways of action for the organizations in Romania through which TQM and its three-dimensional approach can contribute to attaining business excellence.

The specific objectives of the research were:

• To know the components and dimensions of TQM that had a major impact on business in Romania in managers' opinion;

• To identify the main methods used by Romanian managers in TQM; • To identify the key success factors that contributed to TQM; • To know the main vulnerabilities of TQM; • To know to what extent Romanian managers consider that, on the medium and long

term, the three-dimensional approach of TQM is an essential strategic option for achieving excellence in their businesses on the Romanian, European and international market.

2.1.2 Research hypotheses

The main hypotheses were as follows:

• Hypothesis 1: The organisations that implement TQM improve their capacity to cope with competition and register turnover and profit increases.

• Hypothesis 2: The improvement of the TQM components, the three-dimensional approach of TQM and of the quality dimensions can contribute to business excellence.

• Hypothesis 3: The reduction of TQM vulnerabilities influences managers' decision for this preferred strategic option.

2.1.3 Sample dimension and characteristics

The procedure used to establish the selection basis of the organizations involved in this research was a ordered and systematic one and was based on statistical data published on the website and the National Registry of Commerce and on the official website of the National Institute of Statistics.

The size of the sample (N) was determined using the formula below, adapted from Virgil Balaure (2003):

𝑛𝑛 =NZ² x 0,25

[d² x (N − 1)] + (z² x 0,25)

Where:

n – the size of the sample; N – the total size of the research basis (in the study, it is the number of registered organizations that have implemented the quality management system); d – the level of precision (in the study, it is 3%, 0.03 in absolute value); Z – 1.96 corresponding to a 95% confidence level.

(1)



Following the calculations, has been determined the sample size to be of 56 organizations that implemented total quality management out of the 343 that represented the selection basis. The 56 selected organizations have less than 250 employees and recorded a turnover of less than 50 million euro per year.

For the research to be representative for this segment, I involved in the research 5 managers from each organization, namely: the general manager; the technical director for production; the quality director; the commercial director and the financial manager. Therefore, the number of respondents was of 280 subjects with managing positions from the determined sample.

2.2 Research results and analysis

In general, the organizations included in the sample registered favourable economic and financial developments in the last five years, as shown below (Figure no. 2). The results show that more than half (52,38%) of the surveyed companies recorded increases in their turnover and profit. To these, another 32,44% are added consisting in companies that maintained their turnover and profit at a relatively constant level. Only (11,31%) of the companies included in the sample registered fluctuations or decreases of the business indicators (3,87%).

From data analysis, it resulted that TQM contributes to strengthening the capacity of the organizations to cope with the competition, while the impact on turnover and profit is positive in most companies. Most managers added that, following the implementation of the TQM, the companies have experienced sustainable development and strengthened their capacity to face competition on the Romanian and European market (Nica, 2016). Based on data entered into SPSS, we determined and analysed several relevant correlations for this research point using the Pearson coefficient (Table no.1)

0102030405060

Increase Maintenance Fluctuation Decrease

Figure no.2: Identified trends in the analysed organizations in the past five years


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Table no. 1: The correlation between total quality management and the evolution of the turnover in recent years (2012-2015) based on the Pearson coefficient

How did the turnover and profit of your company evolve in the last 5 years?

Pearson Correlation 1 ,255**

Sig. (2-tailed) ,000 N 280 280

The intensification of the competition prompted you to take action for increasing competitiveness ?

Pearson Correlation ,255** 1 Sig. (2-tailed) ,000 N 280 280

The results show that the competitive pressure positively influences the business of the surveyed companies, these being largely helped by the fact that they have implemented the total quality management system. As can be noted in the figure above, the resulting correlation is perfect, direct and positive. These results confirm the first research hypothesis according to which the organizations that implement total quality management gradually and significantly improve their capacity to face competition, increasing turnover and profit.

Another objective of this research was to identify the key success factors, which resulted from the implementation of total quality management. The undertaken research revealed that the majority of the subjects (83%) consider quality-price report to be a key success factor in the business they conduct in Romania (Popescu, Comănescu and Sabie, 2016). Also, more than half of the subjects consider that business excellence will be conditioned by their capacity to maintain a quality-price ratio favourable for their customers and profitable for their businesses (Friedman, Friedman and Leverton, 2016). They pointed out that this performance can be maintained only if they continue to invest in total quality management improvement.

The second key success factor highlighted by the managers of the surveyed companies was the integrated approach of total quality management, which included all areas, processes and core activities (. More than half of the respondents (64%) consider that the implementation of total quality management system has significantly contributed to systemic and balanced development of the organization, determining them to efficiently cooperate in key areas: research and development; production; marketing; human resources and economic-financial.

The third key factor, which according to the respondents is crucial in the total quality management system, is the relationship of the organization with the customers. The data and information obtained were immediately capitalized on in the internal research in order to develop products with a structure appropriate to customers' expectations. More than half of the respondents (51%) considered that this key success factor will continue to be in their attention for a good relationship with traditional as well as with new customers (Popescu and Popescu, 2015). Through the research, the author aimed to identify the components of the total quality management system that had a major impact on business in the last five years (Figure no. 3).

The components we considered in this research are the following: the methods used; the quality standards; internal leadership; marketing strategies; "flawless" production and quality circles.



As we can see in the figure no. 3, the components of the quality management system produced different influences. It can also be noted that, according to the managers, there are components of quality management system in the surveyed organizations that should be improved. It is worth noting that all components positively influenced the business of the organizations.

It was found that less attention is paid by the subjects to the influence produce by marketing strategies, methods and leadership (Nica, 2015). Significant variations in the points of view were found with regard to the quality circles and the "flawless" production, while the quality standards are considered by most subjects as having a relevant and determining role in business development.

Figure no. 3: The components of TQM and their influence on business development

Another investigation parameter in this research was represented by the dimensions of total quality. To know the opinion of the subjects regarding these dimensions the author applied Garvin's approach (1988). Garvin identified the following dimensions of quality that I took into consideration in this research: economic performance; reliability; compliance; sustainability; utility (service-ability); aesthetics; perceived quality.

The research revealed subjects' different opinion that were justified by the influence of the following variables: the area of activity in which managers performed, management level and seniority (Machan, 2016) within the organization.

32

40

23 2126

7

28

52

34 36

47

21

29

43

27

1924

5

21

54

62

21

83

1717

34

47

26

84

29

-10

0

10

20

30

40

50

60

70

80

90Methods

Quality standards

Leadership

Marketing strategies

"Flawless" production

Quality circles

Poly. (Leadership)

Expon. (Marketingstrategies)

Power ("Flawless"production)

Poly. (Quality circles)


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As shown in the Figure no. 4, the managers with basic training in the technical area (70%) considered the reliability and compliance of products to be dimensions with a major impact on the quality, while 65% of the managers with basic economic training and average seniority within the organization considered performance, sustainability and quality to be the dimensions with the highest relevance for the products and services offered by the organization (Lazaroiu, 2015).

A special category was formed by the respondents specialized in the fields of standardization and quality from the specialty departments. 73% of them considered compliance to be a defining parameter, followed by quality perceived as relevant by 21% of managers, while the remaining parameters such as performance, aesthetics and reliability were perceived in relatively small proportions.

Figure no. 4: The main dimensions of quality and choices of managers in various fields

These results support the second research hypothesis according to which if companies improved the dimensions of the quality of products and services and the TQM components, the economic indicators would significantly improve and business excellence would be reached.

70

0

0

0

0

0

30

20

31

20

10

12

4

4

5

5

5

65

15

0

5

7

10

3

62

10

5

3

1

0

1

2

21

2

73

0 20 40 60 80

Reliability

Sustainability

Aesthetics

Performance

Perceivedquality

Utility

Compliance

Standardization and quality

Research

Economic

Commercial

Production



Through this research the author aimed to know the methods and instruments used by the managers of the organizations in the implementation of total quality management (Figure no. 5).

Thus, the following methods were submitted to subjects' attention: management by objectives; management by projects; quality by objectives and value added productivity; quality circles, Deming cycle; Kanban, Total Productive Maintenance and Kaizen.

The results showed that, in the analysed companies, the total quality management systems integrate in different manners the methods submitted to their attention.

Figure no. 5: Main methods used by managers

Thus, 83 of the 280 managers included in this research use management by objectives and another 52 managers turn to management by projects. Of the total number of managers, 71 frequently appeal to the quality by objectives and value added productivity, while only 13 managers organize quality circles. It is interesting to note that total productive maintenance is integrated by 37 managers from the surveyed companies, proving their concern for the excellent functioning of the production systems (Madanhire and Mbohwa, 2016). Other methods used by a relatively small number of managers are: The Deming cycle (7), Kanban (10), Kaizen (7).

Research has shown that TQM principles are well known and assumed by most of the questioned managers (68%). The author also discovered a major responsiveness (73%) to the three-dimensional approach of TQM. The elements considered in this research were grouped into the three subsystems of the three-dimensional approach, as follows: (1) the social subsystem: customer focus; employee involvement; mutually beneficial relationships with the suppliers; (2) the technical subsystem: process-based approach; system-based approach; continuous improvement; (3) the economic subsystem: leadership; decision making approach based on indicators.

Managers' opinions were differentiated according to the level of their position in the organizational hierarchy. The results show that the majority of the senior managers (75%)

83

71

13

37

7 10 7

0

10

20

30

40

50

60

70

80

90Management by objectives

Management by projects

Quality by objectives

Quality circles

Productive maintenance

Deming cycle

Kanban

Kaizen

52


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stated that the principles of leadership (Laudan, Nica and Lazaroiu, 2016), systemic approach and decisions improvement have a major role in their work to develop quality, while medium level managers (32%) considered the process-based approach, employee involvement and mutually beneficial relationships with the suppliers (Popescu and Predescu, 2016) to be essential in their work for continuous quality improvement.

Low level managers, 85% of them, mentioned customer relationships, decision making based on indicators and employee involvement as essential for quality improvement in their work (Figure no.6).

Figure no. 6: The correlation between the principles and three-dimensional approach of TQM

Overall, the three-dimensional approach of TQM is supported by managers at all organizational levels, which determines us to support and propose it as a viable alternative for the development of TQM integrated systems in organizations from Romania and elsewhere. These results confirm the second research hypothesis according to which the improvement of the TQM components, the three-dimensional approach of TQM and of the quality dimensions can contribute to business excellence.

In order to identify respondents' opinions regarding the vulnerabilities of total quality management system, we included two questions in the questionnaire (What are in your opinion the main vulnerabilities derived from implementing TQM?; What is in your opinion the level of risk derived from each of them?). As can be noted from Table no. 2, the vulnerabilities considered the managers were grouped by them into three levels of risk: major, medium, minor.

85

20

313723

14

22

58

Leadership Systemic approach

Decisions improvement Relationships with the suppliers

Employee involvement Process-based approach

Continuous improvement Customer orientation



Table no. 2: Main vulnerabilities and their positioning depending on the levels of risk

No. Vulnerabilities Level of risk perceived by managers

Number of manager who assessed the level of risk

Percentage of managers' answers

1. Complexity of the procedures and standards

Major Medium Minor

115 84 81

0.41 0.30 0.29

2. Overload of the information systems

Major Medium Minor

256 15 9

0.91 0.05 0.03

3. Implementation and monitoring costs

Major Medium Minor

142 88 50

0.50 0.31 0.17

4. The value of the quality certificates

Major Medium Minor

128 72 80

0.45 0,25 0.28

5. Human resources professionalism

Major Medium Minor

34 103 143

0.12 0.36 0.51

These results confirm the third research hypothesis that if the vulnerabilities of total quality management with high levels of risk were reduced within the organizations, total quality management could become a preferred strategic option with a major impact on business excellence.

The main ways of action relevant for organizations in Romania through which TQM and its three-dimensional approach can contribute to the attainment of business excellence proposed by the managers included in this research were: improved monitoring of the operational processes and quality (56%); integration of the quality function together with the other five functions within the organization (62%); motivation of human resources depending on the quality (83%); diversification of the specific methods integrated in the total quality management (52%); refinement of the methods of production statistical control (43%); introduction of new methods to prevent and avoid errors (73%); integrated development of the TQM based on the three-dimensional approach (68%).

Conclusions

The research revealed the fact that the quality dimensions (Nihal and Sevil, 2015), the key success factors and the components of TQM as well as its vulnerabilities need to be addressed with utmost attention by the managers of Romanian companies in order to effectively contribute to the development of some essential skills that generate strategic advantages and increase of the market segments (Mohammadi and Najafi, 2016).

The performed research showed the need to renew the tools, methods and techniques integrated by the total quality management. Thus, there were expressed options for new methods (Burlacu and Jiroveanu, 2012), such as Kaizen; Quality circles; HACCP; PokaYoke; Lean management and Six Sigma (Dragulanescu and Popescu, 2015). The


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research facilitated the identification of the key success factors and of the main vulnerabilities of TQM.

In the end, we can state that the research this paper was based on demonstrated that TQM, in general, and its three-dimensional approach, in particular, represent an essential, feasible, viable and sustainable option for companies in Romania, whose managers continue to be concerned with the development and modernization of management instruments in order to reach higher levels of quality and business excellence.

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TOWARDS SUSTAINABILITY: EFFECTIVE OPERATIONS STRATEGIES, QUALITY MANAGEMENT AND OPERATIONAL EXCELLENCE IN BANKING

Vesna Tornjanski1*, Sanja Marinković2 and Željka Jančić3

1),2) University of Belgrade, Serbia 3)Eurobank a.d. Belgrade, Serbia

Please cite this article as: Tornjanski, V., Marinković, S. and Jančić, Z., 2017. Towards Sustainability: Effective Operations Strategies, Quality Management and Operational Excellence in Banking. Amfiteatru Economic, 19(44), pp. 79-94

Article History Received: 19 July 2016 Revised: 15 November 2016 Accepted: 21 December 2016

Abstract This paper sets out to extend and deepen the understanding the ways toward economic sustainability through efficient and effective growth operations strategies, quality management and operational excellence in banking. In this study we define new quality management practices based on developed conceptual architecture of digital platform for operations function in banking. Additionally, we employ decision making framework consisted of two parts: introduction of new operations services using Total Unduplicated Reach and Frequency (TURF) statistical analysis and segregation of core from actual and augmented operations services utilizing Analytic Network Process (ANP) method based on BOCR model. Proposed quality management practices were used for the first time in this paper for particular purposes and have the high potential to impact the excellence in banking business. The study can contribute to operations management, quality management, innovation management, IT management, business process management and decision making in service organizations. Keywords: Economic sustainability, operations strategies, quality management, operational excellence, banking industry, TURF statistical analysis, ANP-BOCR model JEL Classification: C25, C44, C83, D02, G21, L15, O31

Introduction

The concept of sustainable development has been extensively studied from various aspects in the theoretical literature over the time. The analyses of hundreds definitions of sustainable development concept depicts that there is no unique and comprehensive formulation which provides clear understanding of the phenomenon due to its complex and multi-dimensional issue, which indicates that sustainable development should be * Corresponding author, Vesna Tornjanski – [email protected].

AE Towards Sustainability: Effective Operations Strategies, Quality Management and Operational Excellence in Banking


considered as an integrated system of economic, ecological, social and institutional perspectives (Ciegis, Ramanauskiene and Martinkus, 2009).

In today’s intense competition, comprehensive changes in technology, customers’ demands, regulatory requirements and demographic changes into the volatile business environment, strategic managers worldwide understand sustainable business development not only as an option, but as a fully integrated paradigm into the strategy and operations of organizations. Businesses worldwide are rapidly digitizing, breaking down industry boundaries, building new opportunities and at the same time harming long-successful business models (Weill and Woerner, 2015; Tornjanski et al., 2015). These underlying influences create big pressure on banks to continuously evolve, by changing its strategic context and competitive dynamics (Propa, Banwet and Goswami, 2015; Weill and Woerner, 2015). As a result, banking sector is undergoing significant transformation towards structure that follows principles of flexibility, openness and customer-centricity (Fasnacht, 2009; Huo and Hong, 2013; Tornjanski et al., 2015a) founded on quality management and business excellence frameworks with the aim not just to meet, but to exceed a variety of stakeholders’ expectations of whom customers are the most prominent to banks (Talib, Rahman and Qureshi, 2012).

Having that in mind, operations as a function, operations management, its effective strategies and dynamic capabilities have to be acknowledged as a strategic element in banking industry in order to be able to continuously create superior value to stakeholders through quality management and operational excellence, thus to contribute to the overall organizational performances and sustainability (Brown, Bessant and Lamming, 2013; Gupta, Czernik and Sharma, 2001; Hietschold, Reinhardt and Gurtner, 2014; Metaxas and Koulouriotis, 2014; Slack, Lewis and Bates, 2004; Zangiski, de Lima and da Costa, 2013; Slack, 2015; Sprogies and Schmidt, 2015). On the other hand, sustainability from operations management perspective may be achieved if operations managers in banking institutions understand what they have to provide and how to leverage resources and capabilities as an integrated system (Bridoux, 2004). More precisely, operations managers in banking industry have to develop the ability to tailor appropriate strategies with clear long-term vision and strong short-term implementation capacities to effectively boost transformation of operating model from traditional "back-office" role towards "strategic" role with the possibility not only to support business strategies, but also to shape new ones for well-being of an organization on the long run (Henderson and Venkatraman, 1999).Such initiative implies appropriate management of quality, operational excellence and the ambidextrous approach to operations management.

Quality management supports all functions of an organization to flourish throughout continuous development, improvement and organizational change (Asif and de Vries, 2015; Kaynak and Hartley, 2005; Kim, Kumar and Kumar, 2012). Current literature advocates a number of quality management practices that contribute to the quality management system and organizational performances, accordingly. Talib, Rahman and Qureshi (2011) stated that most organizations apply statistical process control, Six Sigma or the ISO 9000 standards. However, total quality management (TQM) has recently received particular attention from scholars and practitioners worldwide (Durmaz, Düşün and Demir, 2015; Hietschold, Reinhardt and Gurtner, 2014; Sila and Ebrahimpour, 2003; Wiengarten et al., 2013). Yet, Asift and de Vries (2015) summarized quality management practices into the following categories: customer satisfaction management, process management, supplier


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management, data and information analysis, employee training and development, and employee empowerment. Durmaz, Düşün and Demir (2015) found three core dimensions to the quality in banking referring to technical quality of the service, functional quality processes and general image results. Despite a wide spectrum of views on quality management, a common denominator to all of them represents a need for technology based services, methodologies, techniques and tools which will effectively carry out underlying quality management practices (Asift and de Vries, 2015; Talib, Rahman and Qureshi, 2012) for achieving business excellence and sustaining preferable levels of organizational performances (European Foundation for Quality Management, 2010).

The paper is organised as follows. The literature review section unfolds the conceptual foundations by defining recently advocated paradigms that will significantly reshape operations’ management in banking industry from which effective operations’ strategies derive. Also, it exhibits background on quality management and operational excellence in banking. The research methodology is then introduced with a detailed explanation of the steps, measures and sample. Afterwards, the results and discussion of the study are presented. Finally, the paper concludes with the implications and recommendations to managers in banking, along with the suggestions for future research.


The field of operations management in service organizations has evolved tremendously over the years, mainly due to external factors that forced service operations managers to face the transition with the fundamental requirement to disclose effective operations strategies, to use adequate quality management methodologies, techniques and tools, and to focus on operational excellence in order to sustain outstanding organizational performance (Gunasekaran and Ngai, 2012; Kriščiūnas and Daugėlienė, 2006; Vinodh, Sarangan and Vinoth, 2014). As a result of years of the evolution, service operations management has entered into the “mass customization-lean-agile-fit” era (Zhao and Lee, 2009; Gunasekaran and Ngai, 2012; Brown, Bessant and Lamming, 2013; Tseng and Hu, 2014; Vinodh, Sarangan and Vinoth, 2014). In this paper we propose mass customized-lean-agile-fit operations strategies for banking industry that originated from conceptual foundation of mass customization, lean, agile and fit paradigms, that are described hereinafter.

Mass customization has emerged as an important paradigm due to the heterogeneity of individual needs, competitive intensity and shorten product life cycles (Huang, Kristal and Schroeder, 2008; Khalili-Araghi and Kolarevic, 2016). Mass customization is a “paradigm that seeks, as its goal, to combine the value-added effectiveness associated with product customization with the cost-efficiency closely related to mass production” (Huang, Kristal and Schroeder, 2008). To effectively support mass customization strategy, organizations should reconsider the entire value chain from front to end, by efficiently adjusting economy of scale, variety and time to market (Tseng and Hu, 2014) and effectively managing the dynamics and trade-offs among product design, system design and supply chain design (Hoekstra and Nahmens, 2005). Service organizations are increasingly inspired by lean management nowadays. Among all, financial services are pressed to improve operational efficiency due to strict governmental regulations and intense competition determined by non-financial companies and changes in customers' behaviour (Staikouras and Koutsomanoli‐Fillipaki, 2006; Leyer and Moormann, 2014). Despite increased interest in the application of this paradigm, financial services often experience difficulties in



successful adoption of lean. According to De Koning and De Mast (2006), lean represents an integrated system of principles, tools, techniques and practices aimed at waste reduction, synchronization and management of workflows in organization. Rother (2009) recognizes that employees' capability to think and act towards lean, i.e. to be focused on efficiency and value-creating processes in day-to-day business activities is equally important element in lean management. A common denominator in all lean aspects is fundamental idea of optimization and continual improvement of operations processes (De Koning, Does and Bisgaard, 2008). Agile service operations’ management requires quick respond to changes in demand in terms of both volume and diversity (Christopher, 2000; Power, Sohal and Rahman, 2001). Naylor, Naim and Berry (1999) define agility as a usage of market knowledge and a virtual organization to bring about profitable solutions and alternatives in a volatile marketplace. A holistic approach that integrates mass customization, lean and agility, i.e. fit framework is a new model for economically sustainable organization (Pham and Thomas, 2011; Vinodh, Sarangan and Vinoth, 2014). Prior theories strengthen fit framework which combines these concepts and allow organizations to tune its operational and technological capabilities (Pham and Thomas, 2005).

The shift of operations’ strategies in banking further implies alignment of new strategies with corresponding quality management practices. Extensive literature review that focuses on quality management in banking industry shows that majority of research was focused on quality services and total quality management in banking, aiming at achieving higher financial performances, competitiveness and sustainable business excellence, while satisfying customers’ needs (e.g. Agrawal, Tripathi and Seth, 2014; Ayo et al., 2016; Durmaz, Düşün and Demir, 2015; Hossain and Dwivedi, 2015; Liang and Pei-Ching, 2015; Metaxas and Koulouriotis, 2014;P. Afthonidis and D. Tsiotras, 2014; Talib, Rahman and Qureshi, 2012). Despite extensive research on service quality and total quality management in banking, little is known about quality management and business excellence, viewed from operations perspective in banking. With regard to operational excellence in banking, the Boston Consulting Group (BCG, 2015) has recognized that efficient and effective processes, streamlined organization, customer excellence and strong underlying capabilities represent four levers of operational excellence. On the other hand, EY (2013) noted that achieving excellence in operations implies changes in current operating models, streamlining processes by particular focus on control improvements and employment of the latest technologies. Besides, EY (2013) pointed out that alignment of operating model with the overall organizational strategy, usage of appropriate technologies and tools to automate and manage operational capacities, consideration of cultural and employees’ change aspect, application of business process management tools to establish effective end-to-end processes and adjustment of IT capability with business changes, represent key focus area when finding a way to achieve operational excellence in banking today.

2. Research Methodology

For the purpose of this paper, the method of case study has been recognized as the most suitable research method to deeply understand issues of operations management in banking, thus to ensure a holistic view on the researched phenomena in real life (Săvoiu, 2014). Within the case study, we have incorporated a multi-method approach to strengthen the results on a given research problem. To understand a specific problem, in-depth interviews were conducted with the senior managers in operations area of the bank under study as a


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targeted sample of respondents for ensuring that the participants meet the requirements to be included in the study. As a response to the given problem, we have developed a conceptual architecture of digital platform for operations’ function to improve service quality, achieve operational excellence and to satisfy end-users of the bank. Further, we propose a two-phase approach for service selection process to facilitate effective decision making.

Phase I

First phase encompasses the selection of new services. According to the market requirements, five new services were recognized as significant contributors to the sustainability and thus proposed by the authors of the paper to be further evaluated. The beginning of process is based on the principles of mass customization paradigm, which implies the inclusion of end users in the evaluation process. A survey method was carried out among experts of the bank under the study by conducting a web-based questionnaire as a survey instrument. Experts of the bank were asked to select the combination of proposed services with the aim to ensure that the possible introduction of new services would reach its maximum usage according to the business needs. In other words, yi={1,end user i will use service; 0,end user i will not use service}. In this phase, 39 experts of the bank took participation in the evaluation process. The results were derived using a statistical analysis employed to estimate potentials of new services' usage (Săvoiu, 2012; Săvoiu, 2014; Săvoiu, 2015). For that purpose, Total Unduplicated Reach and Frequency (TURF) analysis is applied, as the most suitable method to acquire the optimal solution to the given problem. The results were substantiated by the equations (1) and (2) (Serra, 2013). Equation 1 was used to reach maximum penetration of final combination of services, i.e. number of end users that are subjected to a limited number of varieties.

max𝑍𝑍 = ∑ 𝑦𝑦𝑖𝑖𝑚𝑚𝑖𝑖=1 (1)

Equation 2 was used to reach maximum frequency of varieties, i.e. the highest number of selected services by each user.

max𝑍𝑍 = ∑ 𝑓𝑓𝑖𝑖𝑦𝑦𝑖𝑖𝑚𝑚𝑖𝑖=1 (2)

The results of TURF were analysed in Excel spreadsheet with the advanced option for solving mathematical programming problems.

Phase II

Second phase implies final selection and segregation of core from actual and augmented mass customized operations services. In this phase, we have recognized that the most suitable method for solving a certain real problem is the Analytic Network Process (ANP) multi-criteria method. ANP is applied in modelling and quantitative analysis of service lines and their relative weights in regard to benefits, opportunities, costs and risks (BOCR) (Saaty and Vargas, 2001; Saaty and Ozdemir, 2005; Saaty, 2009; Tornjanski, Marinković and Lalić, 2014) to estimate all the effects when making decision. To create a decision model, a problem is decomposed into components of a controlled hierarchical structure. At the top of the hierarchy is the main objective, followed by four criteria at the second level, which reflect both positive and negative impacts on the final goal. Each criterion incorporates a set of sub-criteria derived from the broader literature and therefore suggested by the authors of the paper to be included in the model (Tornjanski, Marinković and Lalić, 2014). Pair-comparisons were carried out by decision maker in operations area of the bank



under the study using a nine-point Saaty’s scale. According to the pair-comparison matrix, local priority vector was obtained by solving (3) (Liang and Li, 2008):

𝐴𝐴𝐴𝐴 = λ 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 (3)

Where:

A – the pair-comparison matrix

Λmax – its largest eigenvalue

W – the local priority vector

Next, unweighted supermatrix, weighted supermatrix, cluster matrix and limit supermatrix were constructed. Final results were obtained by synthesizing the whole model.To calculate the final rank of alternatives, Saaty proposes two ways of calculation. In this paper, the subtractive method is applied using the formula (4) (Saaty and Ozdemir, 2005; Saaty, 2008; Liang and Li, 2008; Tornjanski, Marinković and Lalić, 2014):

𝑏𝑏𝑏𝑏 + 𝑜𝑜𝑜𝑜 − 𝑐𝑐𝑐𝑐 − 𝑟𝑟𝑟𝑟 (4)

Where:

b, o, c, r – the priorities for BOCR merits in regard to strategic criteria (Liang and Li, 2008).

The results were derived using Super Decisions software package for decision making.

3. Results and discussion

The bank under this study (hereafter: the bank) is a European bank which established operations in Serbia in 2003. Today it counts more than 900.000 clients with 1.500 employees. With total market share of 4.61% it is ranked among the top 10 banks according to balance sheet assets in the banking sector of Serbia (NBS, 2015).

Decision maker from the operations area of the bank made the introduction to the interview highlighting the significance of the operations function for overall organizational performances: “...Operations function in our bank represents an engine of the organization which simultaneously provides services, information and knowledge to numerous internal and external stakeholders that have different and often confronted requirements (Figure no.1). Nevertheless, during the years of successful business in Serbia, operations function has significantly contributed to the results of the bank, mainly due to established strategic alignment with business strategies and proper management of operations. These and many other efforts invested by the management and experts’ team from the operations function have resulted in an overall improvements of the bank’s performances…”. Decision maker emphasized that: “Operations function should continue to evolve to ensure economic sustainability for the bank. To this end, we have recognized that it is of paramount significance to develop a model which will visualize operations activities, reduce complexity and time to market with the possibility to effectively support mass customized services and lean operations strategies into everyday business".


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Figure no. 1: General overview of key focus area of operations’ services

and stakeholders

Further, one of the senior managers of the bank has explained: “Operations management faces multidimensional complexity when looking for ways in supporting all functions of the bank. Regardless of the organizational model followed, operations’ activities are placed at the core of the bank that puts great challenge on operations’ managers to determine the right mix of product and resources. To this direction the first aspect which needs to be put in place is segregation between core and less core services which operations’ manager needs to support. It is clearly dependent as priority by regulatory requirements as for such activities there is no alternative, but going further from those the variety of indicators coming from market needs (e.g. digitalization), top management strategic focuses (e.g. corporate vs. retail banking), external opportunities for collaboration (e.g. outsourcing) needs to be continuously evaluated in order to provide balanced operational support. Common denominator which always needs to be considered is available resources and how to keep them as flexible as the demand. This is where appropriate models will play a key role for operations’ managers. Key challenge is how to filter variety of data in order to identify key indicators which ought to shape operations manager strategy and how to monitor and have early alert on changes of those indicators in a way to efficiently adjust and prioritize operations services and resources”.

By summing up the results from in-depth interviews, two key questions were recognized:

• In which way to effectively support mass customized-lean-agile-fit operations’ strategy to satisfy customers, to improve quality of delivered services from operations’ function and to achieve operational excellence with the particular focus on establishing and managing complex and non-linear processes, yet to have established adequate control process in real time?

• In which way to introduce new services, yet to be maximally in use, and how to segregate basic from less core services provided by operations’ function?

As a response to the first question, we have developed a conceptual architecture of digital platform for operations function to reduce complexity, time to market, and at the same time



to visualize operations’ activities. The purpose of the platform is to effectively support mass customized-lean-agile-fit operations’ strategy, improve quality, achieve operational excellence and increase customers’ satisfaction accordingly. A conceptual architecture of digital platform for operations’ function aims to link value chain from front to end in order to provide mass customized services, information and knowledge. Our intention is to enable continuous optimization, improvement of operations’ processes by eliminating waste and by allowing quick respond to changes in demand in terms of both, volume and diversity through digitized end-to-end process. The platform consists of three layers. First layer is depicted in the Figure no. 2, and represents operations’ interactive product catalogue which allows users self-service by selecting multiple available services, in any combination, provided by operations function.

Figure no. 2: First layer of the conceptual architecture of digital platform –

Operations interactive product catalogue

Each service has a detailed specification to enable stakeholders sheer understanding of the offer in real time, and at the same time enables interactions with experts from the organizational part which delivers service by using:

• on-line chat for instant support and • video call for deeper understanding of addressed issue.

Besides, end users at this level have the possibility to evaluate quality of delivered services by particular organizational part of operations’ function. Second layer of the architecture represents operational layer with the purpose of collecting, storing and sorting data of requested activity, thus allows adequate allocation of selected task to particular organizational part that should process it. Furthermore, it enables feedback to the requestor and storage data after completing the process for further analysis and control. Conceptual architecture of the second layer is shown in Figure no.3.

Figure no. 3: Second layer of the conceptual architecture of digital platform –

Operational layer

Third layer (Figure no. 4) of the architecture represents a strategic layer which aims to yield an insight into the quality level of delivered services, the results of organizational performances, as well as to ensure effective support in decision making using data from previously derived qualitative and quantitative analysis.


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Figure no. 4: Third layer of the conceptual architecture

of digital platform – Strategic layer

With the holistic and systematic design of processes that are digitized, we enabled streamlined operations’ function with clear and simplified non-linear processes, strong focus on control point and strategic tools for effective decision making, aiming at improving overall operations’ service quality and achieving operational excellence. Also, we see this solution as an integral part of operations’ strategies that can effectively support mass customized services and lean strategies. Finally, the platform allows extensive digital capabilities for further business optimization, and can be used not only for operations function in banking, but for the entire organization in service industry.

Further, based on the in-depth interviews we have understood that services operations’ managers face a challenge of product offerings in today's dynamic and unpredictable business environment. To this end, we provide a comprehensive framework to facilitate strategic decision making and to improve quality of decisions. The framework consists of two parts. First part refers to the introduction of new services for operations function. Based on the business needs, we have acquired the following results each time with the reach and the frequency obtained. The results from TURF analysis are presented in Table no. 1.

Table no. 1: TURF analysis results Service

line Proposed new services Reach* (%)

Frequency** (%)

Line 1

Business consulting services 76.92 76.92 Reporting services 12.82 74.36 Change management services 5.13 35.90 Total reach / Average frequency per line 94.87 62.39

Line 2

Business consulting services 76.92 76.923 Reporting services 12.82 74.359 Innovation and development management services 0.00 12.821 Total reach / Average frequency per line 89.74 54.701

Line 3

Business consulting services 76.92 76.923 Reporting services 12.82 74.359 Strategic and operational planning services 0.00 5.128 Total reach / Average frequency per line 89.74 52.14

Line 4

Business consulting services 76.92 76.923 Change management services 10.26 35.90 Innovation and development management services 0.00 12.821 Total reach / Average frequency per line 87.18 41.88



Service line Proposed new services Reach*

(%) Frequency**

(%)

Line 5

Business consulting services 76.92 76.92 Change management services 10.26 35.90 Strategic and operational planning services 0.00 5.128 Total reach / Average frequency per line 87.18 39.32

* Reach is the number of end users who intend to use at least one service.

** Frequency represents the number of times a service received a maximum score.

According to the results, service Line 1, i.e. business consulting, reporting and change management have been recognized by the experts’ group as the most beneficial services that satisfy actual business needs. Therefore, services in Line 1 represent candidates to be included in the second part of the decision making process. Second part refers to the segregation of core from actual and augmented operations’ services. In this phase, the authors of the paper have selected eight existing and three new services to be evaluated through BOCR decision model that is shown in Figure no. 5.

Figure no. 5: BOCR decision model

Segregation of core from actual and augmented operations services was carried out by a decision maker from the operations area of the bank. The acquired results using ANP-BOCR method are shown in Table no. 2.


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Table no. 2: Results from ANP-BOCR model Existing and new services with the highest score Ideals Normals Raw Segregation Domestic and international payment transactions services

1.000 0.128 1.000 Core

Reporting services 1.000 0.128 1.000 Core Business consulting services 0.800 0.103 0.800 Core Centralized cheques clearing services 0.800 0.103 0.800 Core Retail and corporate loan services 0.800 0.103 0.800 Core Premises maintenance 0.800 0.103 0.800 Core Trade finance services 0.800 0.103 0.800 Core Business analysis services 0.600 0.077 0.600 Actual Procurement services 0.600 0.077 0.600 Actual Change management services 0.400 0.051 0.400 Augmented Treasury settlement services 0.200 0.026 0.200 Augmented

According to the results, 64% of the selected services are categorized as core, followed by 18% of actual, while 18% of services are recognized as augmented. Besides, of the three new services from Line 1, two have been identified as core by a decision maker of the bank.

Presented decision making framework employs two different methods that were combined for the first time in this paper for different purposes. Total Unduplicated Reach and Frequency (TURF) statistical analysis is first time used for introduction of service lines in the operations’ area and Analytic Network Process based on the BOCR model is first time used for segregation purposes. Accordingly, we define new quality management practices in banking based on:

• Conceptual architecture of digital platform for operations’ function that can be designed at the organizational level and used not only in banking, but in service industry in general;

• Comprehensive framework for effective decision making that consists of:

- Total Unduplicated Reach and Frequency (TURF) statistical analysis for introduction of new operations services and

- Analytic Network Process (ANP) method based on BOCR model for segregation of core from actual and augmented operations services.

Finally, we have recognized that presented solutions have the high potential to improve the excellence in business.

Conclusions

Today’s continuously evolving business environment demands by operations’ management in banking to be agile and lean with the capacities and capabilities to effectively support mass customized product and services in order to meet a variety of stakeholders’ expectations, emphasizing the creation of superior value and customers' satisfaction. To achieve that, previous literature suggests set of underlying principles that should be taken



into account. One of the fundamental requirements lies in acknowledgment of operations as a function and its effective strategies to be viewed as a strategic element in banking. Further, it is necessary to understand what operations management needs to provide and how to leverage resources and capabilities as an integrated system. Finally, operations’ managers in banking have to develop the ability to tailor appropriate strategies with clear long-term vision and strong short-term implementation capacities to effectively shift operating model from traditional "back-office" to "strategic" one with the particular focus on quality management, operational excellence and the ambidextrous approach of operations. Existing literature suggests numerous quality management practices acting in the capacity of overall quality management ecosystem. A common denominator to all of them is a need for adoption of adequate methodologies, techniques and tools to effectively carry out quality management practices and to achieve excellence in business operations, thus to sustain preferable levels of organizational performances.

In our paper we define new quality management practices based on developed conceptual architecture of digital platform for operations service and introduction of techniques for effective decisions making, which have the high potential to improve the business excellence in banking. The purpose of the digital platform is to enable streamlined operations function with clear and simplified non-linear processes, strong focus on control point and strategic tools for effective decision making, aiming at improving overall the operations’ service quality and achieving operational excellence. We propose this solution to be integral part of operations’ strategies that can effectively support mass customized services and lean strategy. This solution allows extensive digital capabilities for further business optimization, and can be used not only for operations’ function in banking, but for the entire organization in services’ industry. A conceptual architecture of digital platform for operations’ services with all presented features and capabilities is first time developed for operations’ function in banking. Proposed framework for effective decisions aims to increase overall quality in decision making process. For the purpose of this work, we employed two different methods that were combined for the first time in this paper for different purposes. Total Unduplicated Reach and Frequency (TURF) statistical analysis is first time used for introduction of service lines in the operations area, and Analytic Network Process based on the BOCR model is first time used for segregation purposes.

Based on the reviewed literature and the results obtained from the study, this paper draws some recommendations for managers in financial services: (a) Incorporation of integrated mass customization, lean and agile paradigms into the operations strategies, (b) Development of innovative and flexible solutions with established control point, continuous monitoring and measuring of operations performances, (c) Development of smart processes focused simultaneously on the efficiency and effectiveness of banking operations, (d) Adoption of comprehensive, quantitative, and objective approach to decision-making with the development of a systematic, logical and ambidextrous view on the problem, and (e) The use of appropriate tools and decision support software solutions.

Future research should focus on testing the proposed quality management practices in banking. The study can contribute to operations management, quality management, innovation management, IT management, business process management and decision making in service organizations.


Vol. 19 • No. 44 • February 2017 91

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STUDY ON THE DEVELOPMENT OF QUALITY MEASUREMENTS MODELS FOR STEERING BUSINESS SERVICES

IN RELATION TO CUSTOMER SATISFACTION

Katrin Marquardt1*, Marieta Olaru2 and Ioana Ceausu3 1),2),3) The Bucharest University of Economic Studies, Romania

Please cite this article as: Marquardt, K., Olaru, M. and Ceausu, I., 2017. Study on the Development of Quality Measurements Models for Steering Business Services in Relation to Customer Satisfaction. Amfiteatru Economic, 19(44), pp. 95-109

Article History Received: 30 September 2016 Revised: 20 November 2016 Accepted:16 December 2016

Abstract The growing competition in the market, higher customer demands and globalisation forces the business service providers to improve their services much faster. Nowadays, it is not enough to provide good quality it is more important to delight the customers and to deliver more than they expect. Likewise, renowned research companies predicted that customer satisfaction will become the competitive differentiator within the next years. Thus, the main reasons of the present study are at first, establishing a common understanding on the term “quality” and presenting the relationship between customer satisfaction and service quality. Secondly, the study summarizes the identified factors, which mostly influence the customer satisfaction, as well as the common methods used to measure service quality in relation to these factors. Thirdly, the authors introduce and explain the newly developed six step model for establishing an effective measurement method for service quality and the proposed three level service quality model with the related measurements and outcomes. Both models will assist business service providers to protect and improve their service quality and with that their customer satisfaction. The methodology used for this research is a systematic literature review focused on subjects of quality, customer satisfaction and best-practice metrics for service quality. In addition, surveys and studies from well-known research companies were evaluated. The outcome of the study is always focused on the business service area. Keywords: service quality, customer satisfaction, business services, service quality measurement models, performance indicators JEL Classification: M55, L84, L86, L14, O14 * Corresponding author, Katrin Marquardt – [email protected]

AE Study on the Development of Quality Measurements Models for Steering Business Services in Relation to Customer Satisfaction


Introduction

To ensure own economic survival and sustainability, the business-service providers must compete worldwide on market share, revenue and customer retention. Hence the vital importance of service quality and its constant improvement as well as customer satisfaction has grown in the last decades in all areas of business.

Gartner (2015) and Forrester (2016) predicted in their forecasts for 2016 that customer experience and the quality of customer service will be the competitive differentiator within the upcoming years. Especially in the field of business services, one of the key priorities is to continuously improve the quality of service to ensure a high customer satisfaction and a positive customer experiences. And as said by Kaplan and Norton: „Measurement matters: If you can’t measure it, you can’t manage it” (Kaplan and Norton, 1996). Most of the customer surveys conducted on a regular basis cannot give a full picture about the performance of a company. Thus, one important step for quality improvement is to identify a right set of measurements. Those measurements should be a mixture of early and subsequent indicators and a combination of qualitative and quantitative KPI´s (Key Performance Indicators). Also, it is crucial to have the right balance between the effort to evaluate the measurements and their added value (Ehrmann, 2003).

Based on that thought the main research objectives for this study are:

• Provide further clarification regarding quality and customer satisfaction and the correlation between them;

• Evaluate current status of known performance measurement systems;

• Develop a performance metric model for correlating customer satisfaction criteria and operational indicators.

The paper is structured in three parts. The theory behind the main terms quality and customer satisfaction and their correlations will be explained in the first part. The second part contains the results of the conducted online research about measurement methods for service quality and used KPI´s. Furthermore, this part also presents the six step model developed by the authors for establishing an effective measurement method for service quality in a structured approach. The third part will conclude this study by presenting a theoretical metrics model designed by the authors for service quality aligned with the three criteria from the Kano model (Coleman, 2014) and in agreement with the authors experiences. The results of the research will be a source for managers of business service operations to rethink their way of implementing and measuring the influencing factors for quality and help to secure and improve their customer satisfaction.

1. Literature review

1.1 Quality

Noting that everyone knows what quality means for oneself, quality is an indistinct and elusive construct therefore it is necessary to gain a common understanding about the term “quality”. The most relevant statements from philosophical and economical points of view where extracted, analysed regarding their equality and summarized in table no. 1:


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Table no. 1: Definitions of the term quality in literature

Author Source Statement Pierer, Heinrich August

Universal Encyclopaedia

Quality (lat. Qualitas), 1) nature, what is something or what its purpose is. The qualities of the thing are therefore their properties (Pierer, 1861, vol. 13).

Eisler, Rudolf

Dictionary of the Philosophic Terms

Quality (qualitas, poiotês): nature, it is one of the basic forms of the thinking of objects (Eisler, 1904, vol. 2).

Friedrich Kirchner, Carl Michaëlis

Dictionary of the Philosophic Fundamental Terms

Quality (lat. Qualitas from qualis = as textured gr. Poiotês), i.e. nature is attributed to things, definitions and judgments. The qualities of a thing are the properties detected by the senses (Kirchner and Michaëlis, 1907).

Meyers Big conversation encyclopaedia

Quality (lat.), nature, characteristic of the essence (Meyers, 1908, vol. 16)

Brockhaus Picture-conversation encyclopaedia

Quality of a thing means its nature or all its properties (Brockhaus, 1837, vol. 3).

UTB Online dictionary philosophy

From Latin qualitas, > nature <: When asking about the quality of something, then about properties that something has compared with other (similar) objects or with themselves (as it was at an earlier time), about differences it has (or had) (UTB, 2003).

Gabler economy encyclopaedia

Compliance of services with demands. Demands come from customers, users (consumer / producer), distributors and manufacturers (Springer Gabler, 2016).

Kenline Projects Quality management

There are different definitions for the term “quality” in the literature. Widely used is that, quality is the degree of correspondence between demands and expectations (target) of a product and its characteristics (actual) (Kenline Projects, 2009).

ISO standard 9000:2015 “An organization focused on quality promotes a culture that results in the behaviour, attitudes, activities and processes that deliver value through fulfilling the needs and expectations of customers and other relevant interested parties” (ISO, 2015).

As a result, the definition of quality can be summarized by using these three characteristics:

• Quality has an appropriation; • Quality is the sum of properties of a product or service; • Quality is related to certain demands at a specific time.

In the case of business services, the appropriation will be represented by the customer demands and the quality indicates how the provided services met or exceed the customer requirements and expectations.

1.2 Customer Satisfaction

In the ISO standard 1004 for “Quality management ‒ Customer satisfaction ‒ Guidelines for monitoring and measuring” it is illustrated clearly and plainly how the delivery (provider) and the customer side interact in terms of quality and satisfaction.



Figure no. 1: Customer satisfaction conceptual model

Source: Adapted after ISO standard 1004:2010, 2010, p. 18

The gap between the customer expectations and the service delivered by the provider is defined as customer satisfaction (ISO, 2010). In general, customer satisfaction can be either positive or negative and is a subjective perception influenced by previous experiences and expectations (Raab and Werner, 2009).

Another model, often-cited in literature, which explains the customer satisfaction is the Confirmation-Disconfirmation-Paradigm. It illustrates the comparison of the customer expectation with the delivered service and the resulting customer satisfaction or dissatisfaction (Homburg et al., 2013).

Figure no 2: Confirmation-Disconfirmation-Model

Source: Adapted after Homburg et al., 2013, p.105


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Based on the model, customer satisfaction is seen as a two-part system of opposites: satisfaction and dissatisfaction. In this context, satisfaction contains two types, the simple fulfilment and the overachievement in the way of excellence.

In the 1980s, Professor Noriaki Kano developed the so called Kano model, a theoretical model relating product development and customer satisfaction to each other (Coleman, 2014). This model has change since its first publication from the original five criteria’s to the now most common used three quality drivers:

• Basic needs or Expected quality;

• Performance needs or Normal quality;

• Delighters or Exciting quality.

In comparison with the Confirmation-Disconfirmation model, the Kano model is better at showing the enthusiasm level during the clustering of customer satisfaction. The model illustrates for each of the three categories in which direction they develop if the requirements are fulfilled or not fulfilled. The fulfilment of the basic needs does not result in satisfaction and the not fulfilment of delighters does not result in dissatisfaction. Based on their practical experiences, the authors state that a sustainable customer satisfaction can only be achieved, if first the basic needs are fulfilled, then the performance needs and, as last, the delighters. It is important to note, that the delighters of today can be the performance needs of tomorrow and the basic needs of the day after tomorrow, because the customer get used to a certain level of satisfaction after some provision time (Königskonzept, 2016).

Figure no. 3: Kano − Model for customer satisfaction Source: Adapted after Coleman, 2014, p.3 and Königskonzept, 2016



The influence of customer satisfaction on a company is pictured precisely by the success chain of quality management developed by Manfred Bruhn (2008) (see Figure no. 4).

The starting point or input for all outputs is formed by the service quality. As the first output customer satisfaction is defined and as the second, customer loyalty. Both of them can result in the economic success of the company, which is illustrated as the third output. For better understanding of the interactions there is an example: If the service quality is negative, the customer will be dissatisfied and will possibly quit the contract. The result will be that the company will experience a negative effect on its income, at an internal level, and its reputation, at market level.

Based on Giese and Cote (2002) and on the results from the literature review customer satisfaction can be summarized by the following general points:

• Customer satisfaction is a response: it can be cognitive or emotional;

• The response is related to a certain focus and occurs at a specific time.

The customer satisfaction is highly influenced by the quality of the service or product and it has impact on the sustainability of a company and its position on the market. Therefore, the importance of quality is unequivocal for companies and consumers.

2. Research methodology

In order to gain an understanding about the topic and to identify the best approach and way to develop a measurement for service quality the authors conducted a systematic examination of primary and secondary literature. This methodology of a systematic literature review is a replicable, scientific and transparent process which allows the assessment of scientific literature and leads to a synthesis of findings (Tranfield, Denyer, and Smart, 2003). It consists out of three elements:

• The general literature search;

• The selection of the relevant publications;

• The provisioning of the selected literature.

In addition, this methodology helps to secure results in an argumentative way and to arrive at the own theses on the basis of the material reviewed (Universität Bielefeld, 2016). The research was done online by using the following main search engines and databases:

Figure no. 4: Success chain of quality management Source: Adapted after Bruhn, 2008, p.9


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Emerald Management Journals, Scopus, Elsevier, Springer and Google Scholar. It was conducted with a combination of the keywords “quality” and “customer satisfaction” and outcome was always related to the business services area. In addition to that, surveys and studies from well-known research companies about influencing factors of customer satisfaction and quality were evaluated. This research has been realized between August and September 2016.

Likewise, where used the personal practical experience of one of the authors as manager in an international customer service department of an ICT (Information and Communications Technology) company for 10 years was incorporated in the development of the six step model and the theoretical metrics model. Especially the outcomes and lessons learned from quality improvement projects for several customers she has managed and executed between 2007 and 2014 were reviewed and included.

These established the foundation for recommendations for best practices in this study.

3. Measurement methods and models for service quality and customer satisfaction

Measurement of the product quality is simple, as it is based on pre-defined parameters, which the product should possess to serve its intended purpose (use). Measurement of the service quality is more complex, because of its intangibility and subjectivity, its strong dependency on the time when it was experienced and the individuals involved. In literature different measurement approaches are discussed but they have one common classification, they can be either subjective or objective measurement.

One of the most popular subjective service quality measurement models selected from the literature is SERVQUAL (Acronym from SERVice and QUALity). This model was developed by Parasuraman, Zeithaml and Berry (1985, 1991) and has in its prototype ten evaluation criteria, from which the following five main dimensions of service quality emerged. (Figure no. 5)

There are partially significant differences between the importance of these factors as evaluated by Arlen (2008). In this evaluation the most important dimension is reliability with 32% and the less important is tangibles with 11%. But from the authors’ point of view the importance of the dimensions can vary, because they depend on the type of provided

Figure no 5: Prototype and revision of the model for quality dimensions Source: Consolidated after Parasuraman, Zeithaml and Berry (1985; 1991)



service and the type of customer. SERVQUAL is used mostly to structure customer surveys, where the users are invited to rate the delivered service compared to their expectations. Such surveys contain highly subjective and time depending results. That’s why they should be only used as indicators for the areas with dissatisfaction and can give directions for improvement.

Nevertheless, in addition to results of customer surveys or feedback rankings, it is important to define objective measurements for service quality to have a more comprehensive overview. In practice, those metrics are often extracted from existing financial and operational reports. Figures like the following are often used to judge the quality of service: Volume per entry channel; Time to answer; Handling time; First contact resolution; Meantime to repair; Number of rerouting; SLA (Service Level Agreement – description of the ordered services to their full extent) compliance (red ratio); Number of complains; Agent productivity; Costs per ticket.

In 2012, Forrester Consulting made a comprehensive study to identify the current trends in customer service and one of the outcomes was the following diagram with the main parameters of success for customer service operations based on the answers of almost 200 US customer service decision makers (Forrester, 2013).

Figure no. 6: Companies rely on customer feedback and operational parameters

to measure successful customer service Source: Forrester Consulting, 2013, p.11

The result confirms the authors’ hypotheses, that a measurement of the service quality via customer survey is the most common method, in addition to the list of objective measurable KPI´s. Those figures are evaluated by the companies generally in different reports and could be reused theoretically with the objective to measure the quality of service. One common mistake in practice is, that the figures are taken as they are and as the single source of truth. This leads to missing pieces of information and does not show the full possibilities to identify weak points and start proactive actions before the customers recognize them and become dissatisfied.


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To establish an efficient metrics for service quality with a real sustainability it is recommended to work off the following six steps from the model designed by the authors:

The following thoughts were leading the authors to the development of the six step model. Managers and researchers confirm that service quality involves an analogy between expectations and performed service, that’s why the first step must be the identification and understanding of the customer requirements. As the next step, it is necessary to map those needs with corresponding and measureable quantities. Before starting the regular measuring, it is mandatory to evaluate them once, check the effort needed, identify the baseline and define the actions which must be taken at risk or when the thresholds are broken. If this is left out, the whole measurement can end up in a useless reporting without any added value as often seen in practice and experienced by the authors. By performing the suggested activities in this order it can be ensured that the right figures are measured with economically justifiable effort. Missing data will be eliminated and the service quality will improve continuously.


4.1 Result 1 – Summary of the results to the main research points

The examination of the main research objectives in the systematic literature review and the collected survey data added up to the following key findings:

• The quality of a subject is represented by its properties and is related to certain demands at a specific time;

• Customer satisfaction is a response related to a certain focus at a specific time;

Figure no 7: Six step model for establishing an effective measurement method for service quality



• The properties of quality have a direct impact on the customer satisfaction; • According to Kano the satisfaction can be clustered in three levels based on their

expectation and influence as it is illustrated in figure no. 3; • The evaluation of service quality and customer satisfaction can be done by subjective

(surveys) and objective (KPI´s) metrics, a combination of both gives the most comprehensive result.

4.2 Result 2 – Three level service quality model

Based on the research conclusions, the Kano model and in alignment with the authors’ experience of defining operational reports, a three level service quality model was developed. The model opposes the quality drivers on customer side to the performance requirements on delivery side. (Figure no. 8)

The fundamental (basic) level in each service operation is the safeguarding of the daily operation, therefore it is mandatory to have a functioning environment and the right amount of people with the right capabilities at the right time. If this can be ensured the customer will not be dissatisfied. As performance measurements for that level, KPI´s from operational and HR (Human Resources) reporting can be used, such as fluctuation statistic and number of outages.

The next (performance) level is the compliance to the contracted SLA´s. At that level the operation needs to perform compliant to the official contracted requirements of the customer. Therefore, existing data from the contract reporting like pick-up rate of phone calls can be compiled and completed with data from the operational reporting like misrouting and agent productivity.

The third (delighter) level represents the evolution from supplier service to a partnership and innovation approach. This can be only reached by proactive actions, seniority and experience of the operation. Metrics for that level can come from operational reporting and quality improvement cycles like agent retention rate and number of innovations.

The figure no. 9 presents the developed three level service quality model by including a possible set of measurements for each level. As seen in the illustration, the levels are based on each other and have clear outcomes.

Figure no. 8: Three level service quality model − Quality driver vs. performance requirements


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The set of measurements for each level was selected based on their significance for the related level. For example, language and knowledge capabilities are considered as basic requirements for providing a customer service and will result in dissatisfaction when they are not fulfilled, but do not imply a satisfaction when they are ensured. Also, missing resources and technical issues can lead to dissatisfaction and result mostly in customer escalations as often experienced in the practice by one of the authors. The measures for the performance level are a subset from measurements contracted in standard service level agreements. By ensuring the compliance the customer will be generally satisfied. In the last level mainly business related soft facts are evaluated. They will show, on one side, the loyalty of the customer and the employee and, on the other side, the results of the cooperation with the customer. The backward arrows on the left side of the model illustrate the inverse feedback when failing one of the levels.

In general, it can be stated that valid measures are essential, but equally important is the interpretation of their value; these always have to be analysed in the relevant context. As mapped in the model the failing of the basic level has the most impact on dissatisfaction and the highest potential for escalation. Failures in the performance level will mostly influence the satisfaction and the cash flow, while the delighter level gives the upmost positive advantages for market competition and ensures sustainability.

4.3 Result 3 – Metrics set for service quality in the area of business service

When recapping the newly developed six step model for establishing a proficient measurement method for service quality, the next major step after the definition of the measurements and base line is the specification of actions which should be taken when the thresholds are breached

Figure no. 9: Three level service quality model with the related measurements and outcomes



or at risk. Therefore, the defined three level service quality model was further broken down into three tables with the related KPI´s, their evaluation basis, the effect they are showing and the corresponding actions which needs to be done when the threshold is breached or at risk. The content in the tables no. 2, 3 and 4 are related to the area of business service.

Table no. 2: Safeguarding of basic operation level − KPI set

Basic Needs / Expected Quality ‒ Safeguarding of basic operation KPI Formula Effect Action Language Level

Language qualification based on Berlitz

Agent and customer can communicate in one common language

Job based language training

Process Awareness

Quality check on soft skills and process adherence

Agent knows basic phone etiquette & the general handling process

Refresher training & coaching

Service Knowledge

Result of basic & customer specific education

Agent has proper knowledge about the main topics he deals with

Update training & coaching

Fluctuation / Newcomer

Number of leavers / newcomer

Identification of resource & service issues

Overstaffing & recruiting pipeline

Availability / Absenteeism

How many agents are available for contact based on schedule

Enough agents for support are available

Support from other resources

Outage Number of technical outages

Identification of technical service issues

Emergency plan

Table no. 3: Compliance to contracted SLA´s level ‒ KPI set

Performance Needs / Normal Quality ‒ Compliance to contracted SLA´s

KPI Formula Effect Action First Contact Resolution

Number of cases solved in the first contact

Customer requests will be directly answered

Update training & coaching

MTTR (mean-time-to-repair)

Time between case recorded and case resolved

Customer request will be solved as soon as possible (asap)

Dedicated case management

Productivity Number of active employees compared with handled issues

Employees will have a balanced workload

Review skills based on routing

Reachability Time between ring and call acceptance

Service will be delivered asap

Overstaffing

Misroutes Number of cases rerouted more than x times

Process & knowledge gaps will be eliminated

Update of routing matrix

Red Ration Number of cases which failed the SLAs

Compliance to contracted SLA´s will be ensured

Dedicated case management


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Table no. 4: Partnership & Innovation Level ‒ KPI set Delighters / Exited Quality ‒ Partnership & Innovation KPI Formula Effect Action Customer Improvement

Number of suggested improvements

Growing customer loyalty Improvement initiative

Customer Retention

Number of contract renewals

Higher customer knowledge & experiences

Customer survey

Customer Promotion

Number of active recommendations by customers

Growing customer base & raising synergies

Customer loyalty program

Employee Retention

Aging of employees Higher seniority & experiences

Employee survey & loyalty program

Top Customer Score

Customers with the highest number of interactions

Proactive engagement for improvement

n/a

Lessons Learned

Using customer feedback and best practices for improvement

Identification for improvements

n/a

Corresponding actions can be, for example, proactive information towards the customer and service management, internal escalation for process improvements and stability, setup of knowledge refreshment sessions and taking out resources. These actions added to the three tables are only examples, due to their high dependence on the type of provided business service, the setup of the operation, the structure and size of the company.

It is important to know and essential that these actions are defined upfront, that they are documented and aligned with the customer to ensure customer acceptance and to avoid escalations. In practice, often such actions are taken on the fly, which result in dissatisfaction on customer´s and employee´s side because of lacking information and guidance.

Conclusions

Based on the analysis of the referenced data sources, a vitally important co-dependency exists between service quality and customer satisfaction, as well as a big difference between the perception of quality from the point of view of the user and the provider. It is not enough to establish a quality measurement system; it is essential to do this in a structured way as defined in the six step model.

Already now, and also in the upcoming years, the customer experience and satisfaction will be of more importance for business service companies on the highly competitive market.

The research pinpoints the necessity of a dedicated measurement model for service quality with reused existing values from other reports, summarized and interpreted in the context of changing service quality and improvement of customer satisfaction. By using the developed three level service quality model in combination with defined actions, it can be ensured that the customer satisfaction will improve continuously. The effort to initially setup the metrics



will be lesser than the effort needed to bring back a solid level of quality and ensure the satisfaction and loyalty of the customers.

Nowadays, no company can survive without appropriate customer satisfaction. The important insights gained through the research are compiled within the developed three level measurement model for service quality, which will, hopefully, produce an interest in further empirical and practical researches in this area.

The major results of the research are based on an exploratory literature review, synthesised information from other researchers and the practice oriented experiences of the authors. An opportunity to prove the applicability of the model in practice is needed. Therefore, the next steps might be the verification of the findings through an expert survey and the contact with business service providers to validate the developed measurement model by practical implementation. The results of this proof of concept will then be published in further research papers.

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AE Measuring Quality in Automobile Aftersales: AutoSERVQUAL Scale


MEASURING QUALITY IN AUTOMOBILE AFTERSALES: AutoSERVQUAL SCALE

Yasin Galip Gencer1* and Ulas Akkucuk2

1) University of Yalova, Yalova, Turkey 2) Bogazici University, Istanbul, Turkey

Please cite this article as: Gencer, Y. G. and Akkucuk, U., 2017. Measuring Quality in Automobile Aftersales: AutoSERVQUAL Scale. Amfiteatru Economic, 19(44), pp. 110-123


Abstract It is becoming more important to accurately measure the level of quality in services and especially automobile related services such as after sales service. This study aims to generate a survey to measure the service quality in automobile aftersales services, eventually to be called AutoSERVQUAL. The roadmap for this survey generation process starts by examining the service quality measurement scales in literature first and then continues by the adaptation process of SERVQUAL over automobile aftersales customers. Each and every step in survey generation processes is explained starting from expert interviews and finally statistical analyses. Starting with 45 candidate questions, the study offers a 28 item scale that can measure service quality in after sales car services with high reliability. The scale can be extended to cover aftersales services in general for any type of product and also could be used as input to other models such as QFD and MADM. The scale should also be empirically tested in other countries with specific requirements for after sales services. Keywords: AutoSERVQUAL, SERVQUAL, automobiles, after sales, services JEL Classification: M11, M31

Introduction

There are many sector adapted service quality scales but there is not a density in terms of scale generation on automobile aftersales found in the literature, thus this study will be a milestone for the area of service quality in automobile aftersales services. The survey generated from this study will be for use in the automobile aftersales sector. SERVQUAL is the most popular and is intensively used in many service quality measurement problems in the literature (Parasuraman, Zeithaml and Berry, 1988; 1994). The SERVQUAL scale consist of 22 items which is used by many academics to measure the service quality in a variety of industries, such as acute care hospital, tire retailing, business school placement * Corresponding author, Yasin Galip Gencer – [email protected].


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center, dental services (Carman, 1990), car services (Bouman and van der Wiele, 1992), restaurants (Lee and Hing, 1995), health sector (Youssef, Nel and Bovaird, 1995), holiday resorts (Tribe and Snaith, 1998), internet web-sites (van Iwaarden et al., 2003), university computer labs (Hughey, Chawla and Khan, 2003), banking (Cui, Lewis and Park, 2003), and airline sectors (Gilbert and Wong, 2003). SERVQUAL has been used alongside other important service measurement surveys including ServPerf, The B&W (Bouman and van der Wiele) Model.

Companies continuously need to monitor the needs and satisfaction levels of the customers in order to make informed decisions about their operations at the level of manufacturing facility, distribution network or service network. Measuring satisfaction for durable products like cars or electronics requires detailed questionnaires. For services the detailed questionnaires have long been used with the pioneering work resulting in the SERVQUAL scale. In this study, as Cronin and Taylor (1992) stated, perception side of SERVQUAL is examined and modified by various steps to reach an AutoSERVQUAL scale. In the subsequent sections we explain the steps including Focus Group Interviews, Item Generation Process, Content and Reliability Analysis. Next section continues with the literature review of service quality measurement scales.

1. Literature review: service quality measurement

SERVQUAL is a name which is originated from the combination of the first letters of Service Quality. In this scale, service quality is determined by the discrepancy between a customer's expectations of a service offered and the perceptions of the same customer’s about the service he/she received, by asking the customer questions about both his/her perceptions and expectations (Parasuraman, Zeithaml and Berry, 1988).

SERVQUAL measure is differentiated from satisfaction because it uses the term perceived in place of the actual service received. SERVQUAL is a valid, reliable and applicable survey for the services sector and it is ready for the use of the companies and organizations that work in the services sector. Measuring quality in services is more difficult than production thus the importance of the SERVQUAL is mainly a result of its capability to measure the quality of the services provided. Results are determined by customer perceptions, so the customers and providers of a service may differ in terms of their perceived level about the services’ quality. SERVQUAL is the most popular survey for measuring the service quality in the literature. There are other alternatives; nevertheless, neither of them is used as much as SERVQUAL. The scale has 22 questions on five dimensions; the full questionnaire in two versions as expectation and perception formats is reported by Parasuraman, Zeithaml and Berry (1988).

Literature has many examples of developed versions of the SERVQUAL applied in various industries. One such sector is online services where different scales have been generated to measure service quality in electronic services, a very recent example being the E-S-Qual scale extended to online banking services (Akkucuk and Teuman, 2016). In the area of airline transportation, SERVQUAL has been extended to measure customer satisfaction from e-ticketing on airlines’ websites (Elkhani, Soltani and Jamshidi, 2014). In a recent review article Pizam, Shapoval and Ellis (2016) state that “SERVQUAL has been used in hundreds of studies, including numerous studies in the hospitality and tourism industries”. Galeeva (2016) has extended the model to measure quality in higher education settings and also offered a



solution for the main criticism of SERVQUAL in terms of the use of difference scores. In some studies, the exact SERVQUAL items are used but in some the elements are changed, Blesic, Cerovic and Dragicevic use 15 original questions and 9 new questions in their study on service quality of hotels (2011). While it is true that recent studies using SERVQUAL focus on hospitality sector (Rauch et al., 2015; Min, 2016) there is one recent study by Cho et al. (2016) that applies a SERVQUAL like questionnaire in order to build a House of Quality for the electronic services sector. The number of questions used is equal to the SERVQUAL questionnaire (22) although they are adapted to electronic after sales services (actually the adaptation could be valid for any after sales service).

Weitz and Wensley (2002) inform about another advantage of SERVQUAL. They state that this method signals the companies the perceptions and needs of the customers apart from measuring the quality. Pikkemaat and Peters (2006) evaluate SERVQUAL as having the purpose of identifying the gap between customer perceptions and expectations. Lastly, Fedoroff and Courage (2009) explain the importance of employee opinions with respect to customer expectations. The most popular service quality measurement scale after SERVQUAL in the literature is ServPerf. Cronin and Taylor (1992) introduced the ServPerf scale as an opposition to the researchers who were studying on the SERVQUAL scale. The conceptual basis of the scale was questioned and they stated that SERVQUAL was confusing directly with the concept of service satisfaction. Then, the expectation (E) component of SERVQUAL is not used in this new scale and the performance (P) component is used alone. This new scale is named as ServPerf scale in the literature and the assessment procedure was adapted to the case of travel agencies (Moisescu and Gica, 2014) and also a fuzzy ServPerf model combined with ELECTRE III was used to comparatively evaluate service quality of international airports in Sicily (Lupo, 2015).

On the other hand, the creators of the ServPerf provided empirical evidence across four industries besides the theoretical arguments. They used the data of fast food, dry cleaning, pest control and banks to prove the ability of their new performance-only instrument. ServPerf comes from the performance component of original SERVQUAL scale, thus it includes 22 items. This single item scale explained better variance (Cronin and Taylor, 1992). Another advantage of this new scale is that it decreases the number of questions and the time for filling the questionnaire. This signals the popularity of this scale in the literature across several authors (Mazis, Antola and Klippel, 1975; Churchill and Surprenant, 1982; Woodruff, Cadotte and Jenkins, 1983; Bolton and Drew, 1991; Boulding et al., 1993; Gotlieb, Grewal and Brown, 1994; Hartline and Ferrell, 1996). There has also been criticism of the scale in terms of unidimensionality, use of the difference scores for the gap model and the negative item wording used for some questions (Babakus and Boller, 1992).

Moreover, the important issue here is the ideas of founders of SERVQUAL about this popular scale. Even, Zeithaml observed in a study that their results were directly influenced only by perceptions (Boulding et al., 1993), which is regarded as a positive criticism about the ServPerf. The ServPerf versus SERVQUAL debate is ongoing in the literature. Recently, there are many supporters of both groups which consider one method to be more superior over the other. Another frequently used measure in various sectors, including automobile services, is the Customer Satisfaction Index (CSI). Each and every automotive brand has its own Customer Satisfaction Index to measure the satisfaction level of their brands’ customer. Customer satisfaction index is an instrument which examines a number of items related to the service


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provided by the organization; the customers are required to rate each of the items according to their encounter with the services provided (perceptions only). The number and the type of questions may vary between organizations. Each of them is pre-weighted by the organization instead of the customers. Customer satisfaction has an important effect on the repurchase in the new car retail market heavily. The overall performance of the dealers is examined by these scores and many criteria are determined by the manufacturers regarding the quotas and incentives for their dealers according to these results.

2. Research design: item generation

Quality and Efficiency are the two bases for being competitive in terms of automobile aftersales services. For the measurement of aftersales services quality SERVQUAL is selected by the authors as the starting point in survey generation which, in addition to showing the level of service quality, also signals firms the critical ways to better trace the customer perceptions and expectations over time (Weitz and Wensley, 2002). In automobile aftersales sector, besides measuring the service quality, to understand the customer expectations and perceptions is also very important.

The advantages/disadvantages and literature examples of each of the other important alternative is discussed in previous sections. As seen, in literature SERVQUAL is adopted by many sectors, and there are many studies about this adoption process. The scale is modified in nearly all studies accordingly with the sector and country studied. Thus, in our study to analyze the quality of automobile aftersales services in Turkey, the same processes will be performed. This section includes the academic steps to reach an AutoSERVQUAL which will be ready for Turkish automobile aftersales customers. Furthermore, the scale developed in this research will provide information to the automobile aftersales service providers concerning their customers’ perceptions and expectations to a new examination system. The implementation of the new system aims to increase the level of service quality as perceived by customers. Based on the literature, the dimensions that can be included in our study are the same as the five dimensions above. These assumptions are coming from the literature review about the service quality and automotive industry, but may be changed after the research is completed. Our study aims to understand the details of the dimensions from the results of studies including focus groups, expert opinions and surveys. Ultimately, we hope to see the modified version of SERVQUAL, which will be applicable for Turkey. This part will explain the academic steps about how the AutoSERVQUAL is developed.

For the purpose of our study we chose a qualitative research design. By exploratory research, we will try to calibrate the SERVQUAL scale to understand the dimensions of service quality in automobile aftersales services in Turkey and with future research this scale may also be valid for the international after sales automotive sector or the aftersales sector in general. In exploratory studies; besides the focus group interviews, there are different study methods as case study method, verbal protocol analysis, cognitive mapping, Critical Incident Technique (CIT), participant observation and individual depth interviews alternatives. In focus groups, group interaction and synergy makes the sum of knowledge greater than separate individual opinions. The distinguishing feature of focus groups is the explicit use of group interaction to produce data and insight that would be less accessible without the interaction found in a group (Morgan, 1997).



Focus groups generate greater depth of information on an issue than a general count of single opinions gleaned from a survey (Carson et al., 2001). As a result, Focus Group study has been our selection as a starting point. In our study, in accordance with the purposes of the research, three focus groups were used in order to generate ideas about the construct. Carson et al. (2001) state that most focus group research seeks some element of homogeneity so that opinions stem from a factor of commonality among participants. This can be of age group, lifestyle, consumption patterns, expertise or experience. According to Morgan (1997), a range of six to ten is the rule of thumb for group size even though these are no strict lower and upper boundaries. Carson et al. (2001) affirm that the contemporary view of an appropriate number of respondents in each group is five to eight. Saturation is the criterion in determining the number of groups. Saturation is the point at which additional data collection no longer generates new understanding.

We formed three focus groups, consisting of six, six and seven people. The first six people were automotive service business professionals still working on the leading automotive brands in Turkey and ranged between the ages of 33 and 45. They are working in the service departments of different automotive distributors and their main business is to check the processes of their service dealers all over Turkey. Moreover, they all own a car so they were both the business professionals and aftersales customers in automobile services sector. The second focus group of six people was the automobile aftersales customers in a car service waiting for their cars to be repaired and were aged between 22 and 54, and. The advantage here is that each respondent, even each person in this group was an aftersales service customer and had fresh ideas about the service given to their cars. The third focus group consisted of automobile service station employees and managers who work in the field of aftersales. This last group consisted of seven people, aged between 29 and 42. The first group consisted of five males and one female; the second group consisted of three males and three females, whereas the third group consisted of three males and four females.

As a moderator, following advice in the literature, the researcher controlled both the dynamics of the group discussion and the topics that were discussed. He was careful about not biasing the participants’ responses. The researcher tried not to involve too much with group discussions and did not impose his own sense of what is important and let the members speak their opinions freely about automobile services and the service quality dimensions. Focus Group Interviews are done to generate knowledge from multiple perspectives, so to get the advantage of this method, the meeting was recorded. These records will be used for both content analyses and scale development. Despite the same purpose of the questions, they are a bit reworded in other questions to pick the issue up again. Following open-ended questions were the focal points during the focus group interviews:

Q1: What are your most important expectations of the car service that you bring your car? (Try to describe your preferences)

Q2: Think about the service station that you bring your car, and talk about the conditions which are done by them, to fail to meet your expectations?

Q3: What are your most essential expectations when you consider the service given to your car in aftersales service stations?

Q4: Can you please share any other aftersales service-related issues that you consider important about car service business? (Any problems you faced or suggestions/comments)


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In the development of the scale that seeks to measure aftersales service quality through the five aforementioned dimensions, we followed the recommendations of Robinson, Shaver, and Wrightsman (1991) about scale construction and evaluation. They state that the first step for scale builders, and the first dimension on which their work can be evaluated, is writing or locating items to include in a scale. They also assume the scale builder to know the research field for constructing an instrument which would cover the main theoretical constructs good enough which will also be useful for other studies. They state minimal considerations for constructing a scale as:

• Proper Sampling of Content: Sampling of items is an important process and needs a careful study about the details of the content. Here, the experience of the researcher is very important. In our study, one of the researchers has experience of more than ten years in automobile aftersales business which is an important advantage, but the item generation process is done by the help of business professionals and the second author of the paper.

• Simplicity of Item Wording: Wording of the items is another important area which determines the success of the scale generated. We observed in the literature that less complex and less “stuffy” items bring better results. Converse and Presser (1986) and Sudman and Bradburn (1982) have such recommendations.

• Item Analysis: This last process is a very broad process which requires an understanding of the researcher by the respondents. Talking with the same language in terms of the researched subject is very important.

The data coming from Focus Groups have been collected by recording of the answers, and decoded by writing each opinion to a piece of paper. The variables that are produced by group members are used to develop a questionnaire about the components of service quality in automobile aftersales services, and to generate a scale for Turkish automobile aftersales customers, which is called AutoSERVQUAL and will be tested quantitatively in the course of the research. To make use of the ideas generated from both focus groups, a detailed understanding of the variables is needed. According to Morgan (1997), neither the group nor the individual constitutes a separable unit of analysis. Instead, our analytical efforts must seek a balance that acknowledges the interplay between these two levels of analysis. Three common ways of coding focus group transcripts are available in the literature by noting: (a) all mentions of a given code, (b) whether each group’s discussion contained a given code, or (c) whether each individual participant mentioned a given code.

Descriptive counting is especially useful in research projects that compare distinctively different groups to determine how often various topics are mentioned at different times. The ideas coming from focus groups are analyzed and the results are interpreted in light of the research construct. There are at least three overlapping sorts of activities that go into data analysis: data reduction, data display and the drawing and verifying of conclusions (Griggs, 1987). Data reduction is a method of organizing data to make them more meaningful. This step is one of the most important ones and is not ignoring data, or throwing it away. Here, the aim is to bring the data to a manageable position. The focus group results are examined with data reduction purposes. To generate the items, expert opinions from sector professionals and service managers are used to categorize items.

The focus group results are organized into different topics to make the data more understandable. In other words, we grouped the statements that have a similar pattern of meaning together. This is needed, because, according to Griggs (1987), showing the data and analysis in one place make it easier to compare different sets of data and to see where further analyses are called for. To determine a base for item categorization is not organized



by only the authors’ capability, here the professionals from automobile aftersales sector and opinions of the experts were important decision variables for item categorization. Reliability and validity analysis can be conducted, only after items that share similar patterns are grouped, so the data display was needed before the verification. The steps of content analysis on the research construct are carried out based on Golden and Zimmer’s (1988) article. After careful analysis of the results of open ended questions we end up with the 45 questions which will be used for the initial questionnaire that will be administered to a group of 250 customers. The questions and the codes that will be used for the remaining sections of the paper are provided in the next section. 3. Sampling process and item deletion

Item generation is explained above and the dimension distribution of the items is also done by expert opinion of a group which consisted of three people, two business professionals and one academician except the author. Expanded SERVQUAL, in other words pilot study of AutoSERVQUAL included 45 questions from five same dimensions as given in Table no. 1. The list includes the original 22 SERVQUAL items and “extra” items that result from the focus groups which are marked with the prefix “ek”.

Table no. 1: Item pool of 45 questions for the AutoSERVQUAL survey

1) Tangibles (8 items) Up-to-Date Equipment (TAN1) Quality of Physical Facilities (TAN2) Neat Dressed Staff (TAN3) Compatible Physical Materials (TAN4) Parking Convenience (ekTAN1) Take Advantage of Technology (ekTAN2) Certificate of Quality Standards (ekTAN3) Comfortable Waiting Lounge (ekTAN4)

2) Assurance (9 items) Trustable Staff (ASS1) Feel Safe in Transactions (ASS2) Politeness of the Staff (ASS3) Adequate Support from the Producer (ASS4) Knowledgeable Staff (ekASS1) Trained Staff (ekASS2) Informative Explanations (ekASS3) Reasonable Pricing (ekASS4) Guaranteed Maintenance (ekASS5)

3) Responsiveness (10 items) Telling Customers Exactly the time of Service (RES1) Quick Solution to Problems (Prompt Service) (RES2) Concerned Staff (Willing to help customers) (RES3) Not Being Too Busy to Respond to Customers (RES4) Compatible Staff (ekRES1) Adequate Transportation Facilities (ekRES2) Qualified Labor Force (ekRES3) Replacement Vehicle during Repair (ekRES4) Equal Treatment (ekRES5) Attentive Service (ekRES6)

4) Reliability (10 items) Delivery of the Service at the Time Promised (REL1) Sincerity of the Staff (Sympathetic and Reassuring) (REL2) Trustworthiness (REL3) Keeping Promises (REL4) Keep Accurate Records (REL5) Accurate Detection of the Failure (ekREL1) Use of Original Products (ekREL2) Repairing According to Procedures (ekREL3) Reliable Repairing (ekREL4) Personal Information Security (ekREL5)

5) Empathy (8 items) Individual Attention (EMP1) Employees’ Personal Attention (EMP2) Understanding the Needs of the Customers (EMP3)

Focus on Customer Expectations (EMP4) Convenient Operating Hours (EMP5) Payment Options (ekEMP1) Offering Gifts and Promotions (ekEMP2) Delivery Quality (ekEMP3)


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To reflect the Turkish aftersales customer population better, stratified random sampling method is selected. A professional agency was contracted under Turkish Council of Higher Education research budget and after agreeing on the final form of the survey the agency employees carried out the data collection using the personal interview method. AutoSERVQUAL scale has to reflect the ideas of consumers all over Turkey. The numbers of respondents from each of the 12 regions of Turkey are chosen according to the share of the region’s population to the Turkish population as shown in Table no. 2. Number of respondents from all regions of Turkey is included in this study. According to Youthmedia (the company that undertook the data collection) 611 consumers were approached, 416 accepted to answer the questionnaire and after the screening question finally we were left with 250 usable questionnaires.

Table no. 2: Number of Respondents in the Pilot Study

District Population Ratio Number of Respondents

İstanbul 14,377,018 18.50% 46

Western Marmara 3,351,582 4.31% 11

Aegean 10,023,549 12.90% 32

Eastern Marmara 7,332,137 9.44% 24

Western Anatolia 7,499,242 9.65% 24

Mediterranean 9,906,771 12.75% 32

Middle Anatolia 3,886,251 5.00% 13

Western Black Sea 4,493,559 5.78% 14

Eastern Black Sea 2,566,840 3.30% 8

Northeast Anatolia 2,206,326 2.84% 7

Middle East Anatolia 3,801,911 4.89% 12

Southeast Anatolia 8,250,718 10.62% 27

Total 77,695,904 250

According to the information gathered from our Research Company, in-home and mall interviews were the most expensive alternatives, and our study is conducted in these two categories. As Trivedi and Malhotra (1993) state, face-to-face interviews are high in response rates and are more convenient for high number of questions. These advantages determined our survey method selection for the pilot study of AutoSERVQUAL.

Researchers need not go so far as to factor-analyze their data to select items to be included or discarded, but an item inter correlation matrix (on perhaps a small subsample or pretest sample) can be a simple and convenient surrogate for determining which items to include, particularly when using most of the statistical packages available for personal computers (Robinson et al., 1991). A four stage elimination procedure was applied in order to reduce the number of variables. First, we eliminated the items highly correlated items, next comes



overall factor and reliability analysis, then factor and reliability analysis for five separate dimensions is applied, and finally overall factor and reliability analysis with the remaining variables determines the scale. In the first step, Inter-Item Correlation Matrix of all items is used. Thus, highly correlated items are assumed to measure similar concepts, so the elimination is started by these items. Highly correlated items are mainly from the same dimensions, so this is a result of questions with excess detail. By the expert opinions of a group consisting of four people, including the authors and two aftersales business professionals the eliminated questions because of their high correlation scores are: ASS1, ASS2, EMP2, EMP4, ekASS2, ekASS4, ekASS5. Table 2 provides Item Total statistics after this elimination. Using the item-total correlations in this table TAN1, TAN2, TAN3, TAN4 are also eliminated.

Table no. 3: Item Total Statistics of the 38 items after elimination in first stage

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item Deleted

TAN1 168.78 126.172 0.248 0.928 TAN2 168.91 125.851 0.202 0.929 TAN3 169.04 125.384 0.171 0.931 TAN4 169.41 128.789 -0.027 0.94 REL1 168.45 123.02 0.541 0.925 REL2 168.49 122.725 0.492 0.926 REL3 168.4 123.534 0.629 0.925 REL4 168.38 123.056 0.673 0.924 REL5 168.42 122.502 0.685 0.924 RES1 168.42 122.309 0.677 0.924 RES2 168.47 121.68 0.622 0.924 RES3 168.43 122.005 0.732 0.924 RES4 168.49 120.998 0.71 0.923 ASS2 168.82 126.073 0.306 0.927 ASS3 168.83 125.899 0.324 0.927 EMP1 168.62 120.374 0.611 0.924 EMP3 168.46 122.073 0.608 0.924 EMP5 168.46 121.647 0.701 0.924

ekTAN1 168.93 124.682 0.402 0.926 ekTAN2 168.97 123.393 0.434 0.926 ekTAN3 168.94 124.145 0.347 0.927 ekTAN4 168.92 124.789 0.449 0.926 ekREL1 168.54 121.446 0.524 0.925 ekREL2 168.46 121.326 0.755 0.923 ekREL3 168.53 121.584 0.57 0.925 ekREL4 168.46 121.712 0.717 0.924 ekREL5 168.47 121.808 0.659 0.924 ekRES1 168.43 122.455 0.683 0.924 ekRES2 168.46 121.519 0.711 0.924 ekRES3 168.42 122.181 0.704 0.924


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Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item Deleted

ekRES5 168.48 121.455 0.709 0.924 ekRES6 168.46 121.952 0.71 0.924 ekASS1 168.9 124.487 0.466 0.926 ekASS3 168.93 124.316 0.369 0.927 ekASS4 168.9 124.407 0.383 0.927 ekEMP1 168.45 122.296 0.571 0.925 ekEMP2 168.46 121.952 0.653 0.924 ekEMP3 168.41 122.62 0.648 0.924

In the first stage of the scale purification process coefficient alpha is calculated for the entire 45-item instrument (Cronbach, 1951), according to Churchill’s (1979) recommendations. In the first step, the multi-item correlation scores decreased the number of items to 38. By these results of the first phase of our reliability analysis, now second step is overall factor and reliability analysis. Berthon, Ewing and Hah (2005) describe the elimination of all items whose corrected item-to-total correlation is less than 0.40 in their study, in our study the consensus is eliminating the items that have the value less than 0.30, resulting in the removal of four items. Thus, the second phase of our scale purification includes the elimination of the items whose corrected item total correlation is less than 0.30, which results by the exclusion of a further four items. This process left a total of 34 items by the end of the second step.

In the second step, four items are deleted according to their Corrected Item-Total Correlation value (TAN1, TAN2, TAN3, TAN4). Third step is the factor and reliability analysis for five separate dimensions (the results are not shown here to save space). Here, the only elimination comes from Reliability Dimension because of the Rotated Matrix of Reliability Dimension. All other dimension has no problem in Rotated Component Matrix figures except Reliability, which shows no problem after the elimination of REL1, REL2 and REL3. Finally, overall factor and reliability analysis by the remaining items is processed by SPSS and the results made three more dimensions to be eliminated, which make the number of remaining dimensions reach the number of 28. Pilot study of AutoSERVQUAL had 45 questions from five dimensions whereas after the factor and reliability analysis, this number is now 28, including all five dimensions.

Conclusions

Companies need to increase their sales together with increasing profits. Aftersales services are centrally important for both increasing sales and increasing profits. Monitoring the satisfaction level of customers is the main issue in terms of successful aftersales services. Durable products including cars need detailed questionnaires like SERVQUAL; however, there was not a scale in the literature that directly targets automobile aftersales services. Thus, AutoSERVQUAL serves the purpose of measuring automobile aftersales services and is a pioneer for automobile aftersales area in literature. Car dealers require high investments and high investments require high profits. The best way to create a sustainable profit making business system comes with successful sales and aftersales relationship. It is more difficult to measure and understand the customer in terms of the quality of the service provided. So, AutoSERVQUAL is adopted academically for this purpose to measure and monitor the satisfaction of automobile aftersales customers.



There are many versions of SERVQUAL in the literature adopted for different business sectors but this study is the first one which adapts to automobile aftersales industry. This adoption process took more than a year to collect the relevant data and process it by the opinions of academic and business professionals. The results will also be used by some of the automotive firms in our country including the top five firms in their further surveys. Also, AutoSERVQUAL scale is being used in continuous funded academic research to classify specific brands in the Turkish automotive firms in terms of quality they present to their customers. AutoSERVQUAL has five dimension same as the original SERVQUAL scale, in processed version Tangibles dimension includes four modified variables, Reliability dimension includes, one modified and one original SERVQUAL items and four added items. Responsiveness dimension consist of three original SERVQUAL items, one of them is modified and five added items. Assurance and Empathy dimensions accommodate two original SERVQUAL items with three recently added items. Following these changes, updated AutoSERVQUAL for Turkish automobile aftersales customers is as given in Table no. 4.

Table no. 4: 28 questions for the AutoSERVQUAL survey

1) Tangibles (4 items) Entry Exit and Parking Convenience (ekTAN1) Take Advantage of Technology (ekTAN2) Certificate of Quality Standards (ekTAN3) Visually Appealing Waiting Lounge (ekTAN4)

2) Assurance (5 items) Feel Safe in Transactions (ASS2) Politeness of the Staff (ASS3) Knowledgeable Staff (ekASS1) Informative Explanations (ekASS3) Reasonable Pricing (ekASS4)

3) Responsiveness (8 items) Telling Customers Exactly the time of Service (RES1) Concerned Staff (Willing to help customers) (RES3) Not being too Busy to Respond to Customers (RES4) Compatible Staff (ekRES1) Adequate Transportation Facilities (ekRES2) Qualified Labor Force (ekRES3) Equal Treatment (ekRES5) Attentive Service (ekRES6)

4) Reliability (6 items) Delivery of the Service at the time Promised (REL1) Keep Accurate Records (REL5) Accurate detection of the Failure (ekREL1) Use of Original Products (ekREL2) Reliable Repairing (ekREL4) Personal Information Security (ekREL5)

5) Empathy (5 items) Individual Attention (EMP1) Delivery Quality (ekEMP3)

Payment Options (ekEMP1) Offering Gifts and Promotions (ekEMP2) Understanding the Needs of the Customers (EMP3)

As it can be observed the questions may also be extended to after sales of other industries such as electronics. The Cho et al. (2016) study includes exactly the same number of questions as the SERVQUAL but some questions are repeated in a similar fashion (such as “A variety of materials and equipment are visually appealing” and “facilities are visually appealing”). Also there is no mention of transportation and parking as in that study. The question in our survey about “Adequate Transportation Facilities” can also be adapted to other after sales services. In auto after sales it is very important because the consumer would need an alternative car, a taxi cab or other means of transportation for a while during the repair. Any other electronics device would also require “a replacement” during the course of the repair (a smart phone for example). This item can be generalized to any after sales service by simply involving a phrase “providing replacement device during repair” which was eliminated in our study. Gifts and promotions are also important nowadays since


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the customer experience journey can be made more exciting with these items. Quality standards of the service station (such as visibly placed ISO certifications) can also play a role in a customer’s satisfaction with the service experience and this is one of our novel introductions to the SERVQUAL model.

We should also talk about some drawbacks of our study. The criticism on the unidimensionality of the scale may still be valid. It is suggested by some scientists that if alpha is too high it may suggest that some items are redundantly testing the same question (Tavakol and Dennick, 2011). Some authors have suggested a maximum alpha level of 0.90 (Streiner, 2003). In future studies on automobile aftersales or general after sales for electronics or other appliances (with a minor change about the transportation question) we could observe different empirical results. Use of different brand names in the future studies and larger consumer groups, perhaps with a focused data collection in service centers, could better confirm the dimensions proposed.

Uses of the AutoSERVQUAL scale in Quality Function Deployment (QFD) studies, or incorporating fuzzy approaches in combination with Multi Attribute Decision Making methods can also be potential application areas of the scale and deserve future attention. Companies can use satisfaction and quality assessments in ranking or awarding their service stations and AutoSERVQUAL scale can be used as input to further MADM applications.

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AE Total Quality Management Implementation and Guest Satisfaction in Hospitality


TOTAL QUALITY MANAGEMENT IMPLEMENTATION AND GUEST SATISFACTION IN HOSPITALITY

Miroslav Knežević1*, Slobodan Čerović2, Vladimir Džamić3

and Tijana Radojević4 1) 2) 3)4) Singidunum University, Faculty of Tourism

and Hospitality Management Belgrade, Serbia

Please cite this article as: Knežević, M., Čerović, S., Džamić, V. and Radojević, T., 2017. Total Quality Management Implementation and Guest Satisfaction in Hospitality. Amfiteatru Economic, 19(44), 124-143


Abstract Total quality management (TQM) has become a modern system of constant improvement of the quality of all company activities. The purpose of this study is to measure the expectations and satisfaction of the guests concerning the attribute quality of the hotel product. Furthermore obtained results were compared in such a way as to analyse particularly the reviews of hotels which have implemented TQM and have the ISO 9001 certificates with reviews from hotels which have not implemented TQM and do not have the ISO 9001 certificates. The conducted analysis included 55 hotels in Serbia belonging to the 4- and 5-star categories, i.e. 1308 guests who have stayed in them. The results show that between the observed groups of guests there are fewer differences in expectations than in perception, and that generally speaking guests who have stayed in the hotels that have implemented TQM are more satisfied. The biggest difference concerning the guest satisfaction with the quality of service in the observed hotels is noticeable in relation to the employees and the value-for-money. Keywords: total quality management, ISO 9001 certificate, Guest satisfaction, Hospitality JEL Classification: L83, C12, Z33 Introduction

Defining the quality of services is much more complex than defining product quality, in accordance with numerous particularities of the service process (services are heterogeneous, intangible, they cannot be possessed and stored, etc.). Because of all these qualities and the diversity of services in relation to the products, their quality can neither be tested nor evaluated before use. Kotler believes that the quality of services is a set of service characteristics which arise from their ability to meet the expectations and satisfy the * Corresponding author: Miroslav Knežević – [email protected]


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customers’ needs (Kotler, Bowen and Makens, 2010). Avelini defines quality as the degree of meeting the expectations and demands of a customer, i.e. a compliance with the expectations of ever more demanding consumers on the market (Avelini – Holjevac, 2002). The quality of service is very important in the hotel business and, consequently, is the research topic of many scientific papers (Salazar, Costa and Rita, 2010; Crick and Spencer, 2011; Renganathan, 2011). Čačić (2013) defines the quality of hotel services as a set of its intangible and tangible properties on the basis of which interpersonal relationships with guests are established and, accordingly, their expectations and needs are met. In his definition of the quality of hotel services, Barjaktarović (2014) points out that what is essential are the characteristics of services that enable meeting the guests’ needs, and stresses that it is the user (guest) who provides the final evaluation (judgment) of the level of the achieved satisfaction with the provided service. In accordance with this, the service quality must meet or exceed the guests’ expectations. In this way, the defined quality includes all the processes in the business and the relationships which they establish with the guests and with one another. The quality of hotel services is often defined as an attitude of the difference between what the expectation was, and what the provided service (Parasuraman, Zeithaml and Berry, 1985; Seth, Deshmukh and Vrat, 2005; Jemmasi, Strong and Taylor, 2011). Thus, many authors believe that the quality of service is the result of the assessment process in which the service users compare their expectations with the provided service they used (Pyo, 2000; Grönroos, 2001; Kang and James, 2004).

Powers also stresses the significance of quality in the hotel industry and notes that the quality management refers to designing a long-term program and is based on the following (Radosavljević, 2007):

• identification of the key quality determinants in relation to the demands of the target customers groups;

• ensuring customer satisfaction in relation to services; • achieving quality in all (both tangible and intangible) elements of the service; • the development of a process of informing users; • the development of a culture of service quality in the hotel; • technological quality improvement on the basis of the introduction of automation

and modern technologies; • improving service quality continuously.

Due to the fact that quality is emphasized as a key factor in long-term competitiveness, Total Quality Management (TQM) has become a modern system of constant improvement of the quality of all the company activities. A large number of managers have come to recognize the potential of TQM as a factor in the small and medium-sized enterprises (SMEs) survival on the market, especially in times of an economic crisis. Small and medium-sized enterprises (SMEs) are the engines of growth and job creation in most economies around the world (Mencinger, Aristovnik and Verbič, 2014).

TQM approach allows a reduction of quality costs relating to the modification, reparation, junk and control, while improving the processes that enhance productivity and the quality of goods. A special place is reserved for the intangible and difficult-to-measure elements of TQM, such as fostering good business relationships with customers, suppliers, employees, owners, the general public, but also the care for the environment. Some authors conclude



that very important aspect of excellence in any organisation involves the employees, i.e. human resources management (Tomaževič, Seljak and Aristovnik, 2014). Although it is difficult to accurately measure the effects of TQM pertaining to these particular elements, many authors confirm a positive impact on the image of the company and the trust of customers, staff motivation, more advanced cooperation with suppliers, and innovation, which is eventually reflected mostly on the financial performance as well. Due to ever more demanding expectations of the guests, the hotel companies see Total Quality Management as a key component of competitiveness (Milovanović, 2014).

The goal of this study is to measure the expectations and satisfaction of the guests concerning the attribute quality of the hotel product. Furthermore obtained results were compared in such a way as to analyze particularly the reviews of hotels which have implemented TQM and have the ISO 9001 certificates with reviews from hotels which have not implemented TQM and do not have the ISO 9001 certificates. We analysed 55 hotels in Serbia as 4 and 5 * and 1308 guests who have stayed in them.

1. Standardization as a basis to create high-quality services in hospitality

Standardization is nowadays often defined as a certain effort of a company to offer a joint product by using a common marketing approach and without altering its business activities, in certain markets (Yalcin, 2009). A key role in standardization is reserved for standards that include permanent management decisions such as procedures, guidelines, requirements, characteristics, and instructions, which are aimed at defining actions that are carried out continuously in repeating situations (Lončarević, Mašić and Đorđević-Boljanović, 2007). Access to standardization depends primarily on the size of the hotel, but also on the business orientation of the hotel company. However, access to the standardization of business functions is found more often in a hotel where a single independent object is managed, while access to the standardization of business processes is more frequent in companies which have directed their business focus towards internationalization and globalization. Regardless of the approach which a company chooses, the most frequent final product of standardization are written instructions, i.e. guidelines for a proper performance of duties, which are known as standard operating procedures. Consequently, the largest number of procedures in hotels are written for work tasks which are critical for running the essential business processes in the hotel (these are processes that are aimed at achieving customer satisfaction) (Harrow, 2006). In fact, these are processes which take place in order to provide guests with basic services in the hotel (booking, checking-in and boarding, preparing and serving food and drinks, room cleaning, checking out) (Mohapatra, 2009). The key features of the standard operating procedures are the following: the procedures are accurate, clear, complete and concise, realistic, written for those who know how to use them, are corrected without delay, define the authorizations, include unplanned emergency situations, determine whom the user should turn to for help, define the minimum performance standards, and are routinely used during the course of training (Hill, 2012). Also, Hill (2012) believes that the implementation of these procedures achieves the following objectives: documenting the right processes, control of the company, control of the work processes, providing training facilities, facilitating innovation and achievement of results which are to be constant.

From the standpoint of the hotel company, standards can be internal (developed within the company), external (developed by other stakeholders). This group of external standards


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also includes international standards prescribed by the International Organization for Standardization (ISO). Its role is reflected primarily in the fact that, on the basis of the best of experiences in the world, it is to develop standards that can be used in different fields and industries. Some of the goals of the International Organization for Standardization are (ISO 2013): establishment, maintenance and continuous improvement of product quality and services in order to meet the needs of users; improving the health and safety of all stakeholders; global protection of the interests and rights of consumers; the promotion of international business management at the global level, etc.

As the key international standards focused on securing quality in the hotel industry, Barjaktarović (2014) singles out the following series:

• ISO 9000 (a group of standards in the field of quality management) • ISO 14001 (a group of standards in the field of environmental management) • OHSAS 18001 (a group of standards in the field of health protection and safety at

work) • ISO 26000 (a group of standards in the field of corporate responsibility) • ISO 50001 (a group of standards in the field of energy management) • ISO 10018 (standards of consumer protection) • ISO 18513 (standards related to the terminology used in tourism) • ISO 27001 (standards concerning the security of information property) • A group of standards for food safety (HACCP, ISO 22000, HALAL).

Those standards are the basis for establishing a quality management system and are used as a framework to guide companies in improving the impact (performance) and results in business management.

2. Measuring the quality of hotel services

Measuring the quality of hotel services is much more demanding and difficult than measuring the quality of physically tangible products because, in addition to material components in the hotel, a great importance is attached to the process of service delivery in which employees play a crucial role. In order to properly determine the quality of services in the hotel industry, it is necessary to measure the level of the hotel guests’ satisfaction with the provided service. It should be noted that in assessing the quality the guests are active participants, in the creation and delivery as much as in the final assessment of the overall quality of service (Barjaktarović, 2014).

There are a number of determinants of the quality of tourism and hotel services. Through a multi-dimensional approach, the users experience and use them as important criteria in the decision-making process pertaining to the use of certain services. From the standpoint of customers, the key determinants of the quality of hotel services include (Ćosić, 2007):

• palpability • reliability • responsiveness



• competences • availability • security • communication • empathy, etc.

All the listed determinants are very important and some of them are the bases of various models by which the quality of hotel services is defined and measured.

Živković (2011) believes that the main objectives in all measurements of the customers’ satisfaction with the provided services or products are:

• monitoring the guests’ attitudes and perceptions concerning the expected quality of products and services

• choosing a good feedback system related to the experiences of consumers.

Determining the hotel guests’ satisfaction with the provided services is a crucial activity for the hotel management so that they could objectively measure the quality of the service process. Customers’ satisfaction can be determined by means of direct and indirect measurements methods.

Direct methods include (Živković, 2011; Barjaktarović, 2014):

• direct measurement of customer satisfaction (conducted with the help of questionnaires and other techniques used for gathering information on the satisfaction)

• method of critical events (it is based on the assumption that customer satisfaction is the result of critical events concerning the relationship of staff with the service users)

• system of problem detection (the goal is to detect problems that users encounter while using the services)

• review of customer satisfaction (it is based on periodic measurements of the level of customer satisfaction and a comparison of indicators over time)

• Kano model (it is aimed at determining the preferences of guests through three different types of satisfaction in relation to the needs). The most used indirect methods (Živković, 2011; Barjaktarović, 2014) are:

• monitoring of sales revenue (it can be divided according to different criteria) • analysis of the complaints by the guests (the method is based on the analysis of

customer complaints about the used services) • collecting the guests’ reviews (enables detection of certain defects that need to be

eliminated) • index of retained guests (shows the number of guests who stay loyal at the end of the

year as compared to those from the year’s beginning as well as new guests) • analysis of lost guests (it is used to determine the causes for which consumers stop

using a product or service) • engagement of false buyers (who present themselves as interested and then submit

reports on the impressions related to the elements they observed).


Vol. 19 • No. 44 • February 2017 129

The aim of the presented method is to identify the attitudes of the guests and to measure their satisfaction with the used products or provided services. By obtaining their ratings it is possible to identify the shortcomings whose removal means gaining a competitive advantage in the market.

3. Review of the scientific literature

Certain studies show that the quality of service is an important prerequisite for the profitability of business management (Chang and Chen, 1998; Chiou and Chang, 2009; Chang, Chen and Chiou, 2015). Tomaževič, Seljak and Aristovnik (2016) state that it is very important to measure the performance and highlight their importance in the public sector. They also listed several different models such as the European Foundation for Quality Management model, Common Assessment Framework (CAF), Business Scorecard, etc. from which CAF is very often used. Aristovnik and Obadić (2015) presented the impact and efficiency of bureaucracy on the development of small and medium-sized enterprises (SMEs) in the European Union (EU) using data envelopment analysis (DEA).

The results of most studies on this topic have contributed to the emergence of a large number of conceptual models and new measuring instruments related to quality. In a series of papers between 1985 and 1994, Parasuraman et al. developed the so-called GAP model that measures the gap between the expectations before the service process and the perception of service quality after the completion of the service process. The same authors developed, in the context of the GAP model, a model for the assessment of service quality -- SERVQUAL, which consists of five determinants of quality (tangibility, reliability, responsibility, security, and compassion) (Parasuraman, Zeithaml and Berry, 1985; Parasuraman, Zeithaml and Berry, 1988). This model has been employed in a number of studies related to measuring the quality of services in various sectors (Chou et al., 2011; Blešić, Čerović and Dragićević, 2011; Wu Tao and Yang, 2012; Debasish and Dey, 2015). Despite having become a model with the widest application, SERVQUAL, as a representative of a disconfirmational method of measuring the quality of service, is also facing numerous criticisms, especially from the conceptual and methodological aspects (Buttle, 1996; Coulthard, 2004; Ladhari, 2009). Consequently, many authors modify SERVQUAL according to areas and specific characteristics they explore: the measurement of quality and customer satisfaction in Islamic banks in Pakistan (Ali and Raza, 2015), in higher education (Gale, 2016), in health care (Altuntas and Yener, 2012), public sector (Regber, 2016), etc. In the case of tourism and hotel industry, most authors modify the SERVQUAL model and adapt it according to the characteristics of services in these sectors. In their research of the hotel industry, and as opposed to the SERVQUAL questionnaire, Saleh and Ryan include 33 questions and five different determinants: sociability, tangible elements, trust, avoiding of sarcasm, and empathy (Saleh and Ryan, 1991). A group of authors has tested the SERVQUAL model in a research conducted in the coastal resorts of Turkey and presented a model based on tangible and intangible quality determinants (Ekinici, Riley and Fife-Schawe, 1998). Based on the research of the quality of services in the hotel business in Turkey, Akbaba created a questionnaire containing 29 questions, also based on the SERVQUAL model (Akbaba, 2006), while Carrasco and others adapted SERVQUAL according to the perceptions of electronic services in the hotel industry, through 20 questions (Carrasco Sánchez-Fernández et al., 2015).



So, when it comes to research on the quality of services in the hotel business, the disconfirmational method and the modified SERVQUAL model has been used by a large number of authors (Blešić, Stefanović and Kicošev, 2010; Markovic and RASPOR, 2010; Mok, Sparks and Kadampully, 2013; Madar, 2014), but this model has rarely been used to compare the quality of services in hotels which have implemented TQM with those which have not. The research on the use of standards from the series ISO 9000 and TQM implementation is mainly focused on their impact on the company performance (Tara Molina-Azorín and Heras, 2012), while for the research of the satisfaction of customers who have stayed in these hotels, other methods have been generally used (Heras -Saizarbitoria, Arana and Boiral, 2015).

The research on the impact of the total quality management implementation on the performance of hotel companies in Serbia has been very scarce and mostly directed to:

• CSR as an element of TQM and the benefits gained in this way (the satisfaction of internal and external customers, public opinion, revenue increase, cost savings, etc.). (Blešić, Čerović and Dragicević, 2011; Milovanović, 2014 )

• the impact of the application of TQM on the financial indicators of the hotel companies (Milovanović, 2014)

The obtained results have shown that the level of the implementation of total quality management in Serbian hotels is relatively high, especially in relation to the key factors such as customer orientation and teamwork. However, the analysis has shown that a higher level of the total quality management implementation does not affect the financial performance of the hotel (Milovanović, 2014).

Since there has not been any adequate research on measuring the quality of services in hotels that have implemented TQM in Serbia, we have decided to focus our research in this direction and to measure the quality of services in the above mentioned hotels by using the disconfirmational model, with certain modifications in relation to the observed hotel product attributes.


As we have previously stated, the goal of our research is to measure the expectations and satisfaction of the guests concerning the attribute quality of the hotel product. Furthermore obtained results we compared in such a way as to analyse particularly the reviews of hotels which have implemented TQM and have the ISO 9001 certificates with hotels which don’t have. This was done in order to identify whether the implementation of TQM and ISO 9001 results with a higher guests satisfaction. Research on attitudes and reviews of the guests took place between May 1st and June 30th 2015. The study covered a sample of 55 hotels, i.e. 76%† of hotels of the 4- and 5-star categories in Serbia (35 hotels which have not yet implemented TQM and do not have the ISO 9001 certificates and 20 hotels which have them), i.e. 1500 guests who have stayed in the observed hotels.

† According to the Ministry of Trade, Tourism and Telecommunications, in 2014 Serbia had 63 4-star as well as 9 5-star hotels.


Vol. 19 • No. 44 • February 2017 131

To determine the guests' satisfaction with the provided services in 4- and 5-star hotels in Serbia, we used a direct method -- a questionnaire. All the information was provided strictly anonymously and voluntarily, with prior consent of the respondents in line with high ethical standards. In this way, the risk of giving socially acceptable answers was reduced as well. The guests are interviewed in person, and out of 1500 guests, the questionnaire was filled out properly by 1308 of them, which is 87.2% of respondents. The guests graded the expectations and perceptions (experience) of the quality of the hotel product with marks on the scale of 1-5 (Likert scale) for 5 attributes in five dimensions (25 questions):

• Cleanliness: Cleanliness; Cleanliness in the corridors; Cleanliness of the kitchen and the restaurant; Cleanliness of the hall; The hotel surroundings

• Comfort: Comfortable beds; Comfort in the rooms; Comfortable chairs in the restaurant; Pleasant atmosphere; Eliminating noise

• Location: Proximity of the bus station; Proximity of the airport; Proximity of the city center; Proximity of cultural and historical attractions; Proximity of the competition

• Facilities: Wellness facilities; Spa facilities; Opportunities for sports and recreation; Entertainment facilities; Possibilities for organizing various meetings

• Staff: Kindness of the staff; Professionalism of staff; Communicative staff; Employees’ foreign language skills; Employees’ ability to cope with unexpected situations

Finally, we asked the guests to assess the expectations and perception when in relation to: free use of WiFi and the value-for money. We selected these attributes because they are evaluated by guests on the most visited Internet distribution system -- Booking.com. The website Booking.com™ has a large market share, especially in Europe, operating on a commission-based model and allowing its registered users to carry out a complete booking procedure online quickly and securely. Shortly after a stay, a user is routinely invited via email to fill out a guest review form. The first part of the form allows users to evaluate the property they stayed in, using a standardised set of criteria – specifically: cleanliness, comfort, location, facilities, staff, and value for money – while the second part of the form gives users the option to write additional comments (Radojevic, Stanisic & Stanic, 2015).

Many authors have analysed data generated from online reviews (for a comprehensive review, see Serra Cantallops and Salvi, 2014), but we have decided not to use a data from online reviews because internet distribution systems (IDS) enable quality assessment only to users who make a reservation of hotel services over them.

In order to collect the more objective results we used a direct method -- a questionnaire. On that way we gave the possibility for all guests to rate the quality of those dimensions, no matter how they have reserved the hotel services.

We calculated the guests’ satisfaction with the analyzed attributes by using the disconfirmational model according to which the service quality is a function of expectations and perception, which can be represented as (Nitin, Deshmukh and Perm, 2005):

(1)



where:

SQ – The overall service quality; k – the number of attributes Pij (perception) – perception of the incentive performance i under the influence of the attribute j Eij (expectation) – expected quality of service for the attribute j in comparison to the set stimulant i

In accordance with the objectives of the study, we have set the following hypotheses:

H1: There is no statistical difference between the expectations of the guests who have stayed in hotels that have implemented TQM in comparison to those guests who have stayed in hotels which have not implemented TQM H2: There are significant differences between the perceptions of the guests who have stayed in hotels that have implemented TQM in comparison to the guests who have stayed in hotels that have not implemented TQM H3: The guests who have stayed in hotels that have implemented TQM are as satisfied as those in hotels that have not implemented TQM

The collected data were analyzed with the help of the statistical software IBM SPSS Statistics 22, and descriptive statistics and T test were used in the analysis (Independent Samples Test and Paired Differences).

5. Results and Discussion

The obtained results (table no. 1) show that the largest number of guests who participated in the research possesses university education, that most of them stayed in 4-star hotels, and that they were between 35 and 44 years of age.

Table no. 1: Descriptive statistics – hosts

Variables Frequency Valid Percent

Gender Male 721 55.1

Female 587 44.9

Age

-34 457 34.9 35-44 481 36.8 45 and over 370 28.3

Education

Elementary school 3 0.2

High school 137 10.5

Junior college 385 29.4

Bachelor’s degree 684 52.3

Master’s degree 98 7.5

Doctorate degree 1 0.1

Hotel Category 4 * 1.094 83.6 5 * 214 16.3


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To test the first hypothesis we used the Independent Samples Test. The results (table no. 2) show that there are statistically significant differences in the estimations related to the guests’ expectation about the hotel product quality attribute when in the cases of: Cleanliness (t = -21.338; df = 1306, p <0.001), Comfort (t = -17.458; df = 1306, p <0.001), Location (t = -4.051, df = 1306, p <0.001) Facilities (t = 14.481, df = 1303, p <0.001) and Staff (t = 6.151, df = 1306; p <0.001), whereas in the case of Free WiFi (t = 0.755, df = 1.306, p = 0.451) and Value for money (t = 1.783, df = 1.275, p = 0.075) there were no statistically significant differences, i.e. the guests who have stayed in hotels that have implemented TQM and those who have stayed in hotels that have not implemented TQM, have the same expectations as far as these attributes are concerned. With the help of a more detailed score analysis (table no. 3), we have concluded that in terms of the differences in the evaluations of the five aforementioned attributes (Cleanliness, Comfort, Location, Facilities, and Staff), the guests who have stayed in hotels that have implemented TQM have higher expectations in relation to the first four attributes, while those who have stayed in hotels that have not implemented TQM have higher expectations only with regard to Staff. Since the results show that there are differences between the guests' expectations in relation to the greatest number of the observed attributes, we must discard the H1.

Table no. 2: Differences in the expectations of guests Independent Samples Test

Attributes

Levene's Test for Equality of Variances

t-test for Equality of Means

F Sig.

t df

Sig.

(2

-taile

d)

Mea

n D

iffer

ence

Std.

Err

or

Diff

eren

ce 95%

Confidence Interval of the

Difference Lower Upper

Cleanliness

Equal variances assumed

0.275 0.6 -21.338 1306 0 -0.402 0.019 -0.439 -0.365

Equal variances not assumed

-21.739 964 0 -0.402 0.018 -0.438 -0.366

Comfort


0.234 0.629 -17.45 1306 0 -0.358 0.021 -0.398 -0.318


-17.425 911 0 -0.358 0.021 -0.398 -0.318



Attributes



F Sig.

t df

Sig.

(2

-taile

d)

Mea

n D

iffer

ence

Std.

Err

or

Diff

eren

ce 95%

Confidence Interval of the

Difference Lower Upper

Location


0.055 0.815 -4.051 1306 0 -0.103 0.025 -0.153 -0.053


-4.192 1006 0 -0.103 0.025 -0.151 -0.055

Facilities


213.624 0 -11.986 1306 0 -0.407 0.034 -0.473 -0.340


-14.481 1303 0 -0.407 0.028 -0.462 -0.352

Staff


0.037 0.847 6.151 1306 0 0.107 0.017 0.073 0.141


6.527 1077 0 0.107 0.016 0.075 0.139

Free WiFi


1.657 0.198 0.755 1306 0.451 0.014 0.018 -0.022 0.050


0.781 1008 0.435 0.014 0.018 -0.021 0.048

Value for money


12.565 0 1.777 1306 0.076 0.031 0.017 -0.003 0.065


1.783 1275 0.075 0.031 0.017 -0.003 0.065


Vol. 19 • No. 44 • February 2017 135

Table no. 3: Differences in assessments expectations of guests

Attributes Mean Std. Deviation

(Eij1-Eij)

Expectation (other guests)

Expectation (guests TQM)

Expectation (other guests)

Expectation (guests TQM)

Cleanliness 4.11 4.51 0.33 0.31 -0.40 Comfort 4.15 4.50 0.31 0.35 -0.35 Location 3.58 3.69 0.45 0.41 -0.11 Facilities 3.36 3.77 0.68 0.34 -0.41 Staff 4.79 4.68 0.32 0.26 0.11 Free WiFi 4.92 4.77 0.35 0.42 0.15 Value for money 4.92 4.91 0.33 0.29 0.01

To test the second hypothesis, we also used the Independent Samples Test. Based on the obtained results (table no. 4), we conclude that there are statistically significant differences in evaluations concerning the experience (perception) of the guests in relation to the quality of all hotel product attributes: Cleanliness (t = -18.161; df = 1193, p <0.001) Comfort (t = -19.507; df = 1302, p <0.001), Location (t = 4.948, df = 1147, p <0.001), Facilities (t = -22.325; df = 1296, p <0.001), Staff (t = -13.054; df = 995; p <0.001), Free WiFi (t = -2.459, df = 1.306, p = 0.014) and Value for money (t = -7.198, df = 929, p <0.001). With the help of a more detailed score analysis (table no. 5), we have concluded that the guests who have stayed in hotels that have implemented TQM are more satisfied with all the monitored attributes (Cleanliness, Comfort, Facilities, Staff, Free WiFi, and Value for money) except with Location. In accordance with the obtained results, which confirm that there are differences when in relation to the guests’ perception of all the monitored attributes, we confirm the H2.

Table no. 4: Differences in the perceptions of guests Independent Samples Test

Attributes



F Sig.

df

Sig.

(2

-taile

d)

Mea

n D

iffer

ence

Std.

Err

or

Diff

eren

ce 95% Confidence

Interval of the Difference

Lower Upper

Cleanliness


52.665 0 -16.401 1306 0 -0.435 0.027 -0.487 -0.383


-18.161 1193 0 -0.435 0.024 -0.482 -0.388



Comfort


85.387 0 -16.543 1306 0 -0.475 0.029 -0.531 -0.418


-19.507 1302 0 -0.475 0.024 -0.522 -0.427

Location


25.904 0 4.547 1306 0 0.112 0.025 0.064 0.161


4.948 1147 0 0.112 0.023 0.068 0.157

Facilities


201.621 0 -18.318 1306 0 -0.722 0.039 -0.799 -0.645


-22.325 1296 0 -0.722 0.032 -0.786 -0.659

Staff


13.687 0 -12.668 1306 0 -0.398 0.031 -0.459 -0.336


-13.054 995 0 -0.398 0.03 -0.457 -0.338

Free WiFi


1.128 0.288 -2.459 1306 0.014 -0.174 0.071 -0.313 -0.035


-2.474 931 0.014 -0.174 0.07 -0.312 -0.036

Value for money


27.926 0 -7.158 1306 0 -0.272 0.038 -0.347 -0.198


-7.198 929 0 -0.272 0.038 -0.346 -0.198


Vol. 19 • No. 44 • February 2017 137

Table no. 5: Differences in assessments perceptions of guests

Attributes Mean Std. Deviation (Pij1-Pij)

Perception (other guests)

Perception (guests TQM)

Perception (other guests)

Perception (guests TQM)

Cleanliness 3.96 4.40 0.50 0.36 -0.44

Comfort 3.87 4.35 0.57 0.31 -0.48

Location 3.61 3.49 0.46 0.35 0.12

Facilities 3.30 4.03 0.79 0.38 -0.73

Staff 3.99 4.39 0.56 0.50 -0.40

Free WiFi 4.27 4.44 1.26 1.20 -0.17 Value for money 4.03 4.31 0.66 0.65 -0.28

According to the mentioned disconfirmational model, the guests’ satisfaction with the quality represents the difference between perception and expectations. Accordingly, by using the T test, we have compared the perceptions of both groups of guests with the expectations they had. Based on the analysis of the assessments provided by the guests who have stayed in hotels that have not implemented TQM (table no. 6), we can conclude that there are statistically significant differences between the experience (perception) and expectations in terms of Cleanliness (t = -7.717, df = 856; p <0.001), Comfort (t = -12.269; df = 856; p <0.001), Staff (t = -35.395; df = 856; p <0.001), Free Wi Fi (t = -15.182; df = 856; p <0.001) and Value for Money (t = -36.800; df = 856; p <0.001). It is notable that for each of the listed attributes, the results are negative, i.e. that the guests’ experience is below their expectations. There are no statistically significant differences in terms of Location (t = 0.987, df = 856; p = 0.324) and Facilities (t = -1.920, df = 856; p = 0.055), which means that the guests’ experience in relation to these attributes is on par with their expectations.

Table no. 6: The satisfaction of guests who have stayed in hotels that have implemented TQM

Perception: Expectation (other guests)

Paired Differences t df Sig.

(2-tailed) Mean Std. Dev.

Cleanliness -0.15 0.555 -7.717 856 0.000 Comfort -0.27 0.650 -12.269 856 0.000 Location 0.02 0.671 0.987 856 0.324 Facilities -0.06 0.922 -1.920 856 0.055 Staff -0.79 0.655 -35.395 856 0.000 Free WiFi -0.65 1.262 -15.182 856 0.000 Value for money -0.89 0.705 -36.800 856 0.000

Based on the obtained results in relation to the reviews from guests who have stayed in hotels that have implemented TQM (table no. 7), we can conclude that there are statistically



significant differences between the experience (perception) and expectations with regard to: Cleanliness (t = -5.950 ; df = 450; p <0.001), Comfort (t = -7.931, df = 450, p <0.001), Location (t = -7.738, df = 450, p <0.001), Facilities (t = 11.580, df = 450; p <0.001) Staff (t = -11.909; df = 450; p <0.001), Free Wi Fi (t = -5.530, df = 450, p <0.001), and Value for Money (t = -16.913; df = 450; p <0.001). It is notable that for each of these attributes, except for Facilities, the results are negative, i.e. that the guests’ experience is below their expectations.

Table no. 7: The satisfaction of guests who have stayed in hotels that have implemented TQM

Perception: Expectation (tqm

guests)

Paired Differences t df Sig. (2-

tailed) Mean Std. Dev.

Cleanliness -0.11 0.404 -5.950 450 0.000 Comfort -0.16 0.418 -7.931 450 0.000 Location -0.19 0.528 -7.738 450 0.000 Facilities 0.25 0.468 11.580 450 0.000 Staff -0.29 0.513 -11.909 450 0.000 Free WiFi -0.33 1.252 -5.530 450 0.000 Value for money -0.60 0.754 -16.913 450 0.000

We tested the third hypothesis by the T Test for independent samples, and for each indicator of satisfaction we have tested the zero hypotheses, which is, that there is no difference in satisfaction with the quality between the two groups of guests, i.e.

H0: μother guests = μtqm guests (2)

Based on the conducted tests (table no. 8), it can be concluded that by all the indicators, except for Cleanliness, there is a statistically significant difference in satisfaction between the guests who have stayed in hotels that have not implemented TQM (the Other Guests column), and those who have stayed in hotels which have implement TQM (the TQM guests column). The test results show that in terms of location there is greater satisfaction in the guests staying in hotels which have not implemented TQM, while in terms of all other indicators in which a statistically significant difference has been observed, satisfaction is greater among the guests who have stayed in hotels that have implemented TQM, which confirms the H3.

Table no. 8: The differences in satisfaction ratings

Attributes Other guests TQM guests 95% Confidence

Interval of the Difference t df p

Mean St. dev Mean St.dev lower upper

Cleanliness -0.15 0.555 -0.11 0.404 -0.9813 0.0181 -1.35 1304 0.1766

Comfort -0.28 0.65 -0.15 0.418 -0.1964 -0.0636 -3.84 1304 < 0.0001

Location 0.03 0.671 -0.2 0.528 0.1585 0.3015 6.31 1304 < 0.0001


Vol. 19 • No. 44 • February 2017 139

Attributes Other guests TQM guests 95% Confidence

Interval of the Difference t df p

Mean St. dev Mean St.dev lower upper

Facilities -0.06 0.922 0.26 0.468 -0.411 -0.229 -6.91 1304 < 0.0001

Staff -0.8 0.655 -0.29 0.513 -0.5798 -0.4402 -14.35 1304 < 0.0001

Free WiFi -0.65 1.262 -0.33 1.252 -0.464 -0.176 -4.37 1304 < 0.0001

Value for money -0.89 0.705 -0.6 0.754 -0.3726 -0.2074 -6.89 1304 < 0.0001

Conclusions

On the basis of the conducted research and the analysis of the obtained results, we conclude that the guests who stayed in hotels that had implemented TQM had expectations similar to those of the guests who stayed in hotels that had not implemented TQM, but their perceptions were completely different. When the guests’ satisfaction, as a difference between perception and expectations, is compared to the quality of the observed attributes, it is noticeable that the perception is generally below the expectations, but the guests who stay in hotels that have implemented TQM are more satisfied with Comfort, Facilities, Staff, Free WiFi, and Value for money. The biggest difference, in comparison to the satisfaction of the guests from hotels which have not implemented TQM, lies in the assessments of the qualities of the employees. Accordingly, we can say that the implementation of TQM in the observed hotels in Serbia has had the greatest impact on the behaviour of employees which, in turn, due to the high degree of interpersonal relations, had a direct impact on the guests’ satisfaction. In this way, there is another confirmation of the importance and significance of the employees, but also of the importance of applying the ISO 9001 standards which directly affect the quality improvement, with the full involvement of all employees implied.

The limitations of this study could be that it was conducted on a national level. However, it should be noted that hotels of the best categories were analysed and some of them representing international corporative hotel chains. Thus, the results presented in this study could be considered and generalised in a broader context, which could be the subject of further research in this area.

In addition, further research on this topic could relate to a comparative analysis of hotels which have implemented TQM and strengths and weaknesses of that implementation as well.

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AE A Cross-Country Empirical Study Towards the Impact of Following ISO Management System Standards on Euro-Area Economic Confidence


A CROSS-COUNTRY EMPIRICAL STUDY TOWARDS THE IMPACT OF FOLLOWING ISO MANAGEMENT SYSTEM STANDARDS

ON EURO-AREA ECONOMIC CONFIDENCE

Ştefan Daniel Armeanu1*, Georgeta Vintilă2 and Ştefan Cristian Gherghina3 1) 2) 3) Bucharest University of Economic Studies, Romania

Please cite this article as: Armeanu, S.D., Vintilă, G. and Gherghina, S.C., 2017. A Cross-Country Empirical Study Towards the Impact of Following ISO Management System Standards on Euro-Area Economic Confidence. Amfiteatru Economic, 19(44), pp. 144-165

Article History: Received: 30 September 2016 Revised: 23 November 2016 Accepted: 21 December 2016

Abstract This study aims to examine the impact of following ISO management system standards on economic sentiment indicator (ESI) as proxy of economic agents’ general view concerning economic activity, for 21 European Union member states over 2005-2014. The empirical research comprises ISO standards with reference to management systems towards quality (ISO 9001, ISO 13485, ISO 16949), food safety (ISO 22000), environment (ISO 14001), and information security (ISO 27001). Panel data fixed effects regression models provide support for a positive impact of the quality management systems related to automotive industry, as well as information security management systems, on the ESI. Further, dynamic panel data approach by way of two-step system generalized method of moments emphasizes a positive influence of quality management systems standard for the medical device industry on Euro-area economic confidence, but a negative effect of food safety management systems. Also, ISO 9001, ISO 22000, and ISO 14001 Granger cause ESI. Keywords: ISO management systems standards, EU economic sentiment indicator, panel data fixed effects regression model, generalized method of moments, Granger causality JEL Classification: C33, L15

Introduction

The globalization phenomenon of world economy alongside the extent of international trade has driven to the quick processes of quality internationalization, as a fundamental part of companies’ competitiveness (Ruževičius, 2008). Likewise, the development of technology augmented the diversity of the products and services existing on the market (Başaran, 2016). Therefore, the worldwide setting constrains companies towards being more responsive to customers and intensify requirements for higher quality (İşeri-Say, * Corresponding author, Ştefan Daniel Armeanu – [email protected]



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Toker and Kantur, 2008). The focus should be on innovation so as to satisfy consumers and to overcome competitors in a competitive circumstance (Martínez-Costa and Martínez-Lorente, 2008). The aim of the International Organization for Standardization (hereinafter ‘ISO’) is to develop standards on a wide-reaching foundation to let businesses to go beyond national borders without creating trade barriers (Prajogo, Tang and Lai, 2012). Quality Management Systems (hereinafter ‘QMS’), Environmental Management Systems (hereinafter ‘EMS’), alongside Occupational Health and Safety Management Systems (hereinafter ‘OHSMS’) are imperative as clients impose high standards of quality, commitment to better environmental practices, as well as tasks that care for workers commencing illegal or unsafe practices (Oliveira, 2013). Moreover, information security is a major concern for businesses, public bodies, their customers, and the public (Gillies, 2011). Thereby, ISO 27000 was established on behalf of protecting organizations’ information assets, the ‘life-blood’ of all companies (Humphreys, 2006) and carries a management approach to information security, supporting companies to set, uphold, and constantly develop an Information Security Management System (hereinafter ‘ISMS’).

Nevertheless, the market shows information asymmetry since customers frequently do not have broad understanding about features of the products and its suppliers. Standardization can relatively solve the issue of information asymmetry through product quality standards which may allow consumers to differentiate high quality products from low quality ones. Thus, Nicolau and Sellers (2002) provided evidence that the stock market reacts positively to ISO 9000 following, proving that quality certification can be regarded as a useful tool for lessening the information asymmetry between buyers and sellers. Also, Terlaak and King (2006) suggested that certification may give a way of communicating about unobservable firm attributes, so creating a growth effect for certified organizations. Further, quality management standards support to distinguish firms that guarantee a steady product quality from those that might be less trustworthy (Manders, Vries and Blind, 2016). Accordingly, King, Lenox and Terlaak (2005) noticed that companies certify with ISO 14001 to lessen information asymmetries with supply chain partners.

Large-scale organizations require suitably designed formalized procedures and hierarchical structure to circumvent disorder and assure efficiency, quality, and timeliness (Adler, 1999). Gotzamani and Tsiotras (2002) highlighted the positive or optimistic view which claim that standards’ implementation may help meliorate internal structure, external and internal connection by means of obvious fixed duties and responsibilities, employees’ awareness towards quality, and customer’s trust through improved products’ compliance. On the other side, the negative or pessimistic view pretends the occurrence of a static quality system, which augment bureaucracy and cut flexibility abreast innovation.

Standards are intended to increase efficiencies in a company and so improve performance (Ochieng, Muturi and Njihia, 2015). There are two approaches which stress how quality management practices influence business performance (Lafuente, Bayo-Moriones and García-Cestona, 2010). According to operational view, the companies that follow quality management schemes enhance their performance due to managing production process failures, guiding workers to discover potential sources of quality gains, and engaging to customer contentment. Further, the strategic approach suggests that the costs caused by quality melioration should be viewed as investments, whilst the related benefits are reflected in firm performance.

This research contributes to the literature by empirically exploring the impact of following ISO management system standards on economic agents’ overall view concerning economic



activity as proxied by economic sentiment indicator (hereinafter ‘ESI) for the European Union (hereinafter ‘EU’) member states. The economic sentiment indicator aims to pursue the whole economic activity, being a composite measure made up of five sectoral confidence indicators with different weights, namely Industrial confidence indicator (40%), Services confidence indicator (30%), Consumer confidence indicator (20%), Retail trade confidence indicator (5%), and Construction confidence indicator (5%). Besides, the weights are set according to the representativeness of each considered sector, as well as the tracking performance regarding the gross domestic product growth. ESI, abreast confidence indicators for industry, construction, retail trade, services, consumers, and financial services, are computed by Commission’s Directorate General for Economic and Financial Affairs (hereinafter ‘DG ECFIN’) based on survey-data. According to OECD (2001), the harmonised business survey in industry took place initially in 1962, the harmonised business survey in the construction sector was introduced in 1996, the harmonised survey in retail trade was applied in 1984, the harmonised consumer survey was employed in 1972, whereas the harmonised business survey in the service sector was started in 1996. The surveys undertaken by DG ECFIN allow comparisons regarding the business cycles amongst EU Member States, therewith emphasizing an essential monitoring tool towards the economic evolution of the Eurozone, also examining the progress achieved by the applicant countries. Therefore, the main advantage of these indicators is their ability to properly point out the forthcoming economic change. Likewise, confidence indicators can provide essential facts regarding optimist or pessimist beliefs which are significant drivers of the business cycle. In this context, the financial crisis abreast the sovereign debt period highlighted an overall climate of extremely low confidence

The paper proceeds as follows: previous related studies are discussed in the next section. Data and empirical specifications are presented in Section 2. Empirical results are showed in Section 3. Last section concludes the study.


ISO 9001 refers to an international standard that establishes requirements for the management of quality (Elmuti and Kathawala, 1997). Based on customer focus principle, firms hinge on their customers and should recognize and meet the needs of current and future consumers and further should attempt to go beyond their expectations, whereas the leadership principle set a clear image of the company future (Manders, Vries and Blind, 2016). As such, Rao, Ragu-Nathan and Solis (1997) examined the association between ISO 9000 and the level of quality management practices and quality results within firms from China, India, Mexico, and the United States and emphasised that ISO 9000 certification had a significant impact on leadership, strategic quality planning, good supplier relationships, and customer satisfaction. Clougherty and Grajek (2008) analysed the influence of ISO implementation on economic affairs between countries, based on panel data disclosed by OECD states over 1995-2002 and find no effect of ISO diffusion in developed nations, but a positive impact on international trades and foreign direct investments in developing nations. Tzelepis et al. (2006) studied a sample of 1,572 firms from three Greek manufacturing industries and notice that ISO 9001 is a managerial item that lessen productive inefficiency. Ullah, Wei and Xie (2014) investigated a sample of companies from 31 Latin American and Caribbean countries and provide evidence that ISO-certified firms show significantly higher labour productivity and lower cost of sales than non-certified firms. Koc (2007) found that ISO 9000 implementation engenders a noteworthy difference on firm performance between certified and non-certified firms. Therewith, Aba,


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Badar and Hayden (2016) surveyed the operating performance for a sample of 397 firms that had received ISO 9001 certification over 1991-2002 and reveal that certified firms performed better than those non-certified. Contrariwise, Quazi, Hong and Meng (2002) contradicted Rao, Ragu-Nathan and Solis (1997) and provided evidence for Singaporean firms that ISO 9001 has no influence on quality management practices and quality results. Also, Lima, Resende and Hasenclever (2000) compared the performance of Brazilian firms holding ISO 9000 certificates with a control group of companies without certification and document no performance gap across the two sets. İlkay and Aslan (2012) revealed no statistically significant difference between certified and non-certified companies in terms of performance for 255 small to medium-sized enterprises in Turkey.

Likewise, customer care, healthy and safe food, in conjunction with ecological standards embody merely some of the requisites that modern business call for from manufacturers of food products (Djordjevic, Cockalo and Bogetic, 2011). The food industry is not only accountable for producing safe food, but also for proving in a clear way how food safety has been planned and assured (Fotopoulos, Kafetzopoulos, and Psomas, 2009). Thus, Baş, Yüksel and Çavuşoğlu (2007) seen better customer self-reliance as a benefit of applying a food safety management system (hereinafter ‘FSMS’). Beyond greater consumer confidence, Qijun and Batt (2016) mentioned as well the benefits most often associated with the implementation of a third party certified FSMS: ‘improved product quality and safety, reduced costs, access to new markets, increased market share, fewer customer complaints, improved productivity, improved profitability, an improved company image or reputation, the need to comply with legislation’.

Certifying to the ISO 14001 standard is an internationally recognized signal that a firm has applied a system to alleviate and constantly improve its environmental impact (McGuire, 2014). The central foundation for the creation of ISO 14001 was that its universal approval should ease international trade by harmonizing other environmental management standards and by giving a transnationally recognized blueprint for sustainable development, pollution prevention, and compliance assurance (Delmas, 2002). Hence, based on a panel study of 108 countries over seven years, Prakash and Potoski (2006) stated that trade relations boost ISO 14001 taking on if states’ key export markets have applied this voluntary regulation. Besides, ISO 9001 is customer-driven, whilst ISO 14001 is more driven by stakeholders, the community or regulators (Zeng et al., 2005). Consequently, ISO 14001 may be noticed by the market as a reactive rather than a proactive investment (Cañón-de-Francia and Garcés-Ayerbe, 2009). For a panel data of Japanese manufacturing firms over 1996-2007, Nishitani (2011) documented that EMS implementation increases firm value added via demand increase and productivity improvement. On the contrary, Watson et al. (2004) did not establish any significant difference in financial performance between EMS adopters and non-EMS adopters. Also, He et al. (2015) pointed out that embracing ISO 14001 has insignificant effects on financial performance of Chinese firms. However, Yang, Hong and Modi (2011) exposed positive and significant effect of environmental management on firm performance in developed countries, but such links did not occur in developing states.

Further, since security issues emerge as a vital part of everyday life, organizations should guarantee that they are adequately secured (Saint-Germain, 2005). Thus, Filipek (2007) conveyed that information security come to be a business priority. ISO 27000 certification confirm that the company own the right people, processes, and technology in place, whilst the senior management is devoted to the firm security comprising the protection of private customer facts (Benner, 2007).




2.1 Data and variables

The initial sample comprised all EU 28 countries except Ireland (IE), which reported only the consumer confidence indicator. Subsequently, we remarked missing data for Denmark (DK), Estonia (EE), Latvia (LV), Lithuania (LT), Finland (FI), and Sweden (SE). Hence, the estimations will be performed for 21 EU countries for the period of time spanning from 2005 to 2014, as follows: Austria (AT), Belgium (BE), Bulgaria (BG), Croatia (HR), Cyprus (CY), Czech Republic (CZ), France (FR), Germany (DE), Greece (EL), Hungary (HU), Italy (IT), Luxembourg (LU), Malta (MT), Netherlands (NL), Poland (PL), Portugal (PT), Romania (RO), Slovak Republic (SK), Slovenia (SI), Spain (ES), and United Kingdom (UK).

The variables employed within empirical investigation, as well as period availability and data sources are presented in Table no. 1.

Table no. 1: Description of the variables

Variables Description Period Source Dependent variables

(1) ESI The economic sentiment indicator, computed for every year as mean of monthly reported values. 1993-2014

DG ECFIN ‒ Business and

Consumer Surveys

Independent variables Variables related to ISO management system standards

• Quality

(2) ISO9001 The number of certifications regarding the implementation of standards for quality management systems.

1993-2014 ISO Survey

(3) ISO13485 The number of certifications regarding the implementation of quality management system standards for the medical device industry.


(4) ISO16949 The number of certifications regarding the implementation of standards for quality management systems related to automotive industry.


• Safety and security

(5) ISO22000 The number of certifications regarding the implementation of standards for food safety management systems.


• Environment

(6) ISO14001 The number of certifications regarding the implementation of standards for environmental management systems.


• Information security

(7) ISO27001 The number of certifications regarding the implementation of standards for information security management systems.



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Variables Description Period Source Country-level control variables

(8) Growth Real gross domestic product growth. 1993-2014 Euromonitor International

(9) DCPS Domestic credit to private sector (% of GDP), namely financial resources provided to the private sector by financial corporations.

1993-2014 World Bank

(10) RDE Research and development expenditure (% of GDP), covering basic research, applied research, and experimental development.

1996-2014 World Bank

(11) CBB Commercial bank branches (per 100,000 adults). 2001-2014 World Bank (12) NBR The number of new businesses registered. 2004-2014 World Bank

(13) PRB The number start-up procedures to register a business. 2003-2014 World Bank

(14) TSB The time required to start a business (days). 2003-2014 World Bank (15) LDC The number of listed domestic companies. 1993-2014 World Bank

(16) MCLDC Market capitalization of listed domestic companies (% of GDP). 1993-2014 World Bank

(17) ST Stocks traded, total value (% of GDP). 1993-2014 World Bank

(18) BEDI Business extent of disclosure index (0 = less disclosure to 10 = more disclosure). 2005-2014 World Bank

(19) EMAS The number of organizations and sites with eco-management and audit scheme registration. 2005-2014

European Commission ‒ Directorate general for

environment

(20) REGQ

Regulatory quality which reflects perceptions of the ability of the government to formulate and apply sound policies and regulations that allow and support private sector development (ranges from approximately ‒2.5 (weak) to 2.5 (strong) governance performance).

1996-2014 World Bank ‒ Worldwide Governance Indicators

Therefore, we examine ISO standards concerning quality (ISO 9001, ISO 13485, ISO 16949), food safety (ISO 22000), environment (ISO 14001), and information security (ISO 27001). Moreover, since the effectiveness of ISO certification may also be determined by the country’s whole institutional setting and economic progress (Ullah, Wei and Xie, 2014), several country-level control variables were covered within research. For instance, Djankov et al. (2002) explored the regulation of entry by start-up firms in 85 countries in 1999 and reveal that difficult law of access is related with greater corruption and a larger unofficial economy, but not with better quality of private or public goods. Using panel data for 130 countries from 1995 to 2001, Neumayer and Perkins (2005) stressed that a low regulatory burden, great manufacturing operations, large rates of secondary school enrolment, and weak productivity are positively linked with the number of ISO 9000 certificates. Contrariwise, based on firm-level data for 48 developing and emerging states, Paunov (2016) revealed that corruption negatively impacts firms’ possession of quality certificates.



2.2 Quantitative approach

Our baseline model explores the impact of implementing ISO management system standards at country-level, both individual, as well as integrated, on ESI, by means of panel data fixed effects regressions, as below:

= + + + (1)

where:

i – EU countries: 1-21

t – period of time: 2005-2014

ESI – economic sentiment indicator (dependent variable)

ISO – variables regarding ISO management system standards (explanatory variables)

Controls – country-level control variables

– constant term

, , …, , , , …, – regression coefficients

– the error term.

Besides, the explanatory variables could be correlated with the error term, issue known as endogeneity. Onward, in order to address the potential endogeneity issue and to examine the robustness of empirical results, the basic model is estimated through a dynamic panel data (hereinafter ‘DPD’) framework by means of two-step system generalized method of moments (hereinafter ‘GMM’), as follows:

= + + + + + (2)

Furthermore, we will employ the Granger approach in order to check the causal relationship between ISO management system standards and ESI. Accordingly, ISO certifications Granger-cause ESI if predictions of the value of ESI based on its own past values and on the past values of ISO certifications are better than predictions of ESI based only on its own past values. The aforementioned statement implies the following general system which comprises two sets of equations (Kónya, 2006):

(3)

(4)

where:

i – EU countries: 1-21

t – period of time: 2005-2014

l – lag.


Vol. 19 • No. 44 • February 2017 151

In addition, and are supposed to be white-noise errors that may be correlated for a given country, but not across countries, whereas ESI and ISO are presumed stationary.

3. Empirical findings

3.1 Summary statistics, correlation analysis, and stationarity examination

Descriptive statistics for the selected variables are revealed in Table no. 2, whereas Figure no. 1 and Annex no. 1 provide the evolution of ESI, respectively ISO management system standards, over the examined period. The highest mean values of ESI are registered by FI (102.03), SE (101.90), and DK (101.64), but the lowest mean values are observed in PT (99.07), DE (99.04), and HR (98.16). In respect of the ISO management system standards, we notice that ISO 9001 standard for quality management systems and ISO 14001 standard for environmental management systems show the highest mean rate of adoption, whilst ISO 27001 standard for information security management systems and ISO 22000 standard for food safety management systems reveal the lowest mean rate of implementation.

Table no. 2: Descriptive statistics

Variables Obs. Mean Std. Dev. Min. Max. (1) ESI 555 100.22 8.86 74.76 117.44 (2) ISO9001 584 10,357.84 21,715.27 1.00 168,960.00 (3) ISO13485 282 282.66 651.30 0.00 4,140.00 (4) ISO16949 280 342.17 614.97 1.00 3,356.00 (5) ISO22000 213 204.74 291.35 1.00 1,720.00 (6) ISO14001 428 2,203.91 4,011.57 1.00 27,178.00 (7) ISO27001 234 164.07 313.10 1.00 2,261.00 (8) Growth 594 2.32 3.55 -16.20 11.60 (9) DCPS 552 77.19 51.31 1.13 311.98 (10) RDE 459 1.38 0.87 0.20 3.91 (11) CBB 291 39.33 22.92 11.29 110.98 (12) NBR 251 45,663.05 76,473.12 1,641.00 53,7658.00 (13) PRB 308 6.47 2.74 2.00 15.00 (14) TSB 308 20.76 18.60 2.50 138.00 (15) LDC 514 365.66 658.51 0.00 3,538.00 (16) MCLDC 407 56.15 49.10 0.06 330.02 (17) ST 491 27.43 37.65 0.00 265.96 (18) BEDI 260 5.73 2.50 1.00 10.00 (19) EMAS 259 151.95 350.83 0.00 1,619.00 (20) REGQ 432 1.16 0.45 -0.16 2.08

Notes: Variables’ description is provided in Table no. 1.



60

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BE

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BG

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CZ

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DK

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FR

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IT

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LV

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HU

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FI

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UK

Figure no. 1: Panel-data line plots for ESI (EU 28, except IE) Notes: Variables’ description is provided in Table no. 1.

The highest versus the lowest mean values of ISO certifications by EU countries are pointed out in Table no. 3.

Table no. 3: Highest vs. lowest mean values of ISO certifications

ISO Highest mean values Lowest mean values

ISO9001 IT (75,903.09), UK (47,851.45), DE (34,259.18) CY (338.41), MT (240.14), LU (130)

ISO13485 DE (2,476.55), IT (1,424.60), UK (1,250.82) LV (3.27), MT (1.83), CY (1.57)

ISO16949 DE (2,870.36), FR (970.18), IT (948.45) LT (4.70), MT (4.10), EL (3.91) ISO22000 EL (1,158.38), RO (720.38), PL (510.13) EE (13.75), LU (3.17), MT (2.86) ISO14001 IT (10,898.88), ES (10,527.69), UK (8,763.56) CY (54.69), LU (35.31), MT (11.40) ISO27001 UK (1,243.89), ES (495.56), IT (446.44) LU (4.33), MT (3.25), EE (1.50)



Vol. 19 • No. 44 • February 2017 153

We notice that Italy registers almost the highest mean values for all ISO standards, except ISO 22000, whereas Malta shows nearly the lowest mean values for all ISO standards.

The correlations between variables are exhibited in Table no. 4. We remark strong uphill (positive) linear relationships between: ESI and Growth (0.71), ISO9001 and ISO14001 (0.83), ISO9001 and EMAS (0.71), ISO131485 and ISO16949 (0.85), ISO16949 and EMAS (0.82), ISO27001 and NBR (0.72). Thereby, in order to get rid of the occurrence of multicollinearity statistical phenomenon, we will include the variables previously stated in separate regression equations.

Table no. 4: Correlation matrix

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (1) ESI 1

(2) ISO9001 -0.05 1 (3) ISO13485 -0.03 0.63 1

(4) ISO16949 -0.03 0.58 0.85 1 (5) ISO22000 -0.19 0.28 0.14 0.12 1

(6) ISO14001 -0.16 0.83 0.57 0.52 0.31 1 (7) ISO27001 -0.06 0.49 0.51 0.33 0.24 0.75 1

(8) Growth 0.71 -0.17 -0.1 -0.07 -0.15 -0.2 -0.04 1 (9) DCPS -0.12 0.21 0.12 0.1 -0.02 0.23 0.16 -0.29 1

(10) RDE -0.01 0.1 0.27 0.24 -0.26 0.13 -0.04 -0.17 0.23 1 (11) CBB -0.05 0.24 -0.12 -0.06 0.1 0.22 0 -0.03 0.27 -0.31 (12) NBR -0.04 0.44 0.4 0.3 0.05 0.56 0.72 -0.05 0.26 0.06 (13) PRB 0.05 0.15 0.09 0.21 0.26 0.08 0.1 0.11 -0.08 -0.42 (14) TSB 0.18 -0.03 -0.17 -0.03 -0.01 -0.03 -0.05 0.26 -0.15 -0.26 (15) LDC 0.01 0.48 0.23 0.31 0.06 0.57 0.52 -0.06 0.36 0.15 (16) MCLDC 0.25 0.07 -0.05 -0.04 -0.15 0.01 -0.04 0.21 0.22 0.29 (17) ST 0.18 0.44 0.37 0.39 -0.05 0.37 0.4 -0.02 0.33 0.4 (18) BEDI 0.06 0.16 0.17 0.05 0.07 0.24 0.27 0.02 0.12 0.11 (19) EMAS -0.07 0.71 0.67 0.82 0.18 0.64 0.31 -0.12 0.12 0.21 (20) REGQ 0.03 0.04 0.18 0.13 -0.41 0.04 0.05 -0.04 0.4 0.65

Variables (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (11) CBB 1 (12) NBR 0.01 1 (13) PRB 0.14 -0.02 1 (14) TSB 0.26 -0.09 0.52 1 (15) LDC 0.24 0.66 0.15 0.2 1 (16) MCLDC 0.32 0.2 0 0.1 0.3 1 (17) ST 0.04 0.64 0.01 -0.04 0.58 0.43 1 (18) BEDI 0.23 0.45 -0.42 -0.23 0.26 0.07 0.2 1 (19) EMAS 0.15 0.15 0.32 0.14 0.43 -0.02 0.39 -0.05 1 (20) REGQ -0.18 0.18 -0.24 -0.11 0.22 0.55 0.44 0.12 0.04 1

Notes: Bold facts indicate high correlations. Variables’ description is provided in Table no. 1.

Further, the results of panel unit root tests are displayed in Table no. 5. The outcomes provide support for data stationarity for the first generation, except ISO13485, ISO14001, ISO27001, PRB, BEDI, and REGQ. However, even though non-stationarity in panels may result in spurious regressions, Kao (1999) proved that the fixed effects estimator is consistent.



Table no. 5: Panel unit root tests

Variables

Method Null: Unit root (assumes

common unit root process) Null: Unit root

(assumes individual unit root process) Levin, Lin & Chu t*

(statistic) Im, Pesaran and Shin

W-stat (statistic) ADF ‒ Fisher Chi-square (statistic)

PP ‒ Fisher Chi-square (statistic)

(1) ESI -7.74318*** -7.65341*** 166.282*** 226.456*** (2) ISO9001 -0.95007 1.42815 52.7039 69.7778† (3) ISO13485 0.32100 3.02772 35.6778 39.7858 (4) ISO16949 -12.5151*** -6.42945*** 144.328*** 263.231*** (5) ISO22000 -4.39855*** 1.81592 45.3321 88.7089** (6) ISO14001 3.27271 6.81171 25.3602 28.5966 (7) ISO27001 3.13455 5.86611 11.1873 28.1555 (8) Growth -6.83729*** -6.43212*** 136.214*** 198.049*** (9) DCPS -4.61434*** -0.44645 55.1170 55.5625 (10) RDE -1.68481* 2.63611 35.2616 34.9380 (11) CBB -3.29354*** 1.20308 60.1126 80.5880* (12) NBR -7.97692*** -1.68620* 74.5213** 33.8277 (13) PRB -1.16400 0.26476 40.5824 36.5631 (14) TSB -19.4807*** -6.58508*** 116.610*** 146.645*** (15) LDC -3.08679** -0.93660 59.8568 53.0682 (16) MCLDC -5.64707*** -2.59260** 67.8331* 74.1638** (17) ST -3.87557*** -2.90752** 79.6549** 83.3967** (18) BEDI -0.10783 1.44694 4.04213 11.2690 (19) EMAS -5.30540*** 1.05262 51.8122 63.0977 (20) REGQ -0.52312 0.94301 49.5265 56.9581 Notes: ***, **, *, † indicates statistical significance at the 0.1%, 1%, 5% and 10% significance level, respectively. Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution.

All other tests assume asymptotic normality. Variables’ description is provided in Table no. 1.

3.2 Estimation results

The output of panel data fixed effects regressions concerning the influence of adopting ISO standards, independently considered and country-level controls on ESI is provided in Table no. 6. The results reveal that ISO 16949 standard for quality management systems related to automotive industry positively influences ESI (Eq3), but for the rest of ISO examined standards the associations are not statistically significant. The worldwide automotive industry runs under great pressures of cost and time and call for world class levels of product quality, productivity and competitiveness, alongside sustained progress. However, ISO 16949 underlines the development of a process adapted to quality management system that affords constant improvement, defect prevention, and decrease of variation and waste in the supply chain. The standard also supports manufacturers from emerging economies set dealings with producers in developing countries since this certificate serves as proof that they are skilled towards offering goods in line with the best industry practices. Also, several manufacturers cooperate only with enterprises which own such certification since they claim that suppliers should follow the rigorous technical provisions expose in the standard.

As regards the effect of control variables, we notice the positive impact on ESI by the Growth (Eq1-Eq6), MCLDC (Eq1-Eq3, Eq5, Eq6), as well as ST (Eq1-Eq6), but a negative influence as of LDC (Eq4) and BEDI (Eq1, Eq2, Eq4, Eq5). Based on the values related to R-sq within, the percent of the variation that can be explained by the regression equations varies between 83% and 86%.


Vol. 19 • No. 44 • February 2017 155

Table no. 6: Panel data fixed effects regression models on the impact of adopting individual ISO management system standards and country-level controls on ESI

Variables Eq1 Eq2 Eq3 Eq4 Eq5 Eq6

ISO9001 -0.00 (-0.24)

ISO13485 0.00 (1.38)

ISO16949 0.0026† (2.09)

ISO22000 -0.00 (-0.14)

ISO14001 0.00 (0.44)

ISO27001 0.01 (1.68)

Growth 2.44*** (11.48)

2.44*** (10.89)

2.44*** (10.98)

2.43*** (8.62)

2.43*** (11.15)

2.44*** (11.22)

DCPS 0.01 (0.23)

0.03 (0.62)

0.03 (0.52)

-0.00 (-0.03)

0.01 (0.15)

0.02 (0.31)

RDE 1.13 (0.33)

-0.74 (-0.22)

0.76 (0.22)

3.76 (1.04)

0.14 (0.04)

-2.94 (-0.75)

CBB 0.04 (0.59)

0.05 (0.48)

0.02 (0.31)

0.11 (1.70)

0.04 (0.47)

0.12 (1.62)

NBR 0.00 (0.41)

0.00 (0.03)

0.00 (0.29)

0.00 (0.86)

0.00 (0.44)

PRB 0.74 (0.93)

0.67 (0.81)

0.82 (1.07)

1.26 (1.15)

0.73 (0.94)

0.58 (0.86)

TSB -0.09 (-1.24)

-0.10 (-1.60)

-0.09 (-1.31)

-0.07 (-0.56)

-0.10 (-1.46)

-0.10 (-1.27)

LDC -0.00 (-0.30)

-0.00 (-0.23)

-0.00 (-0.28)

-0.03* (-2.72)

-0.00 (-0.14)

-0.01 (-1.54)

MCLDC 0.03† (2.03)

0.04* (2.11)

0.03† (2.02)

0.03 (1.17)

0.04* (2.10)

0.05† (1.88)

ST 0.08* (2.64)

0.08* (2.53)

0.08* (2.39)

0.09* (2.50)

0.07* (2.58)

0.09** (2.93)

BEDI -1.47*** (-4.05)

-1.12* (-2.62)

-1.24 (-1.53)

-1.13* (-2.14)

-1.41** (-3.56)

0.53 (0.83)

EMAS -0.00 (-0.60)

0.00 (0.30)

-0.00 (-0.44)

-0.01† (-1.84)

REGQ 5.53 (0.91)

5.00 (0.96)

6.04 (1.00)

10.86 (1.71)

4.85 (0.95)

-0.01 (-0.00)

_cons 85.90*** (4.88)

85.28*** (5.76)

82.10*** (4.73)

79.54*** (4.31)

87.03*** (6.17)

86.35*** (5.25)

F stat 392.59*** 666.29*** 541.66*** 1374.24*** 554.20*** 189.90*** R-sq within 0.84 0.84 0.84 0.86 0.84 0.83 Period 2005-2014 2005-2014 2005-2014 2007-2014 2005-2014 2006-2014 # Obs. 135 127 130 98 130 135 # Countries 21 20 20 20 20 20

Notes: ***, **, *, † indicates statistical significance at the 0.1%, 1%, 5% and 10% significance level, respectively. The t-statistics are given in parentheses. Clustered robust standard errors at country

level were considered. Eqs. 2, 3, 5, and 6 does not comprise Croatia. Eq. 4 does not comprise Cyprus. Variables’ description is provided in Table no. 1.



Onward, the impact of implementing ISO standards, simultaneously employed and country-level controls on ESI is provided in Table no. 7. We notice that ISO 27001 standard for information security management systems positively influences ESI (Eq1, Eq2), whereas the other ISO standards show no influence. Within current era of amplified information security breaches, ISO 27001 reveals to customers the pledge to high levels of information security. Thus, the clients may be confident that their information is set aside private, its integrity is preserved, being reachable only by right persons. In addition, Growth (Eq1-Eq4), CBB (Eq1-Eq3), MCLDC (Eq1-Eq2), and ST (Eq1-Eq4) exert a positive influence on ESI, whilst LDC (Eq1-Eq4) and BEDI (Eq4) negatively influence ESI. Almost as in Table no. 6, the explanatory power of the estimated models varies between 84% and 86%.

Table no. 7: Panel data fixed effects regression models on the impact of implementing integrated ISO management system standards and country-level controls ESI

Variables Eq1 Eq2 Eq3 Eq4

ISO9001 -0.00 (-1.51)

-0.00 (-1.45)

ISO13485 0.00 (0.77)

0.00 (0.38)

ISO16949 -0.01 (-0.44)

-0.00 (-0.03)

ISO22000 -0.00 (-0.19)

-0.00 (-0.26)

-0.00 (-0.41)

-0.00 (-0.27)

ISO14001 0.00 (0.23)

0.00 (0.48)

ISO27001 0.0078† (1.82)

0.0099* (2.31)

Growth 2.52*** (10.73)

2.55*** (10.47)

2.45*** (8.78)

2.44*** (8.77)

DCPS 0.02 (0.47)

0.03 (0.45)

0.02 (0.29)

0.02 (0.28)

RDE -0.98 (-0.29)

-0.27 (-0.08)

3.10 (0.85)

4.79 (1.46)

CBB 0.19† (1.91)

0.17** (2.92)

0.17† (1.79)

0.09 (1.20)

NBR 0.00 (0.61)

0.00 (0.88)

PRB 0.33 (0.47)

0.41 (0.53)

1.33 (1.13)

1.43 (1.27)

TSB -0.08 (-0.91)

-0.06 (-0.67)

-0.12 (-1.00)

-0.08 (-0.60)

LDC -0.01* (-2.57)

-0.01† (-2.09)

-0.03* (-2.33)

-0.03* (-2.10)

MCLDC 0.04* (2.14)

0.04† (2.08)

0.03 (1.16)

0.03 (1.19)

ST 0.11* (2.58)

0.10* (2.50)

0.09* (2.42)

0.09* (2.57)

BEDI 0.25 (0.43)

0.52 (0.62)

-0.80 (-1.18)

-1.81† (-2.06)

EMAS 0.00 (0.21)

REGQ -0.44 (-0.08)

0.92 (0.15)

10.98 (1.65)

12.01 (1.72)

_cons 87.40*** (5.95)

86.38*** (5.25)

72.01** (3.45)

76.95** (3.19)

F stat 145.97*** 260.50*** 4040.27*** 490.53***


Vol. 19 • No. 44 • February 2017 157

Variables Eq1 Eq2 Eq3 Eq4 R-sq within 0.84 0.84 0.86 0.86 Period 2007-2014 2007-2014 2007-2014 2007-2014 # Obs. 120 117 95 97 # Countries 21 20 20 20

Notes: ***, **, *, † indicates statistical significance at the 0.1%, 1%, 5% and 10% significance level, respectively. The t-statistics are given in parentheses. Clustered robust standard errors at country

level were considered. Eq. 2 and Eq. 4 does not comprise Cyprus. Eq. 3 does not comprise Croatia. Variables’ description is provided in Table no. 1.

3.3 Robustness checks

Two-step system GMM estimations’ results on the influence of adopting ISO standards, independently considered and country-level controls on ESI is provided in Table no. 8. The results show the lack of any significant association between ISO standards and ESI. Concerning the control variables, the coefficients of Growth (Eq1-Eq5), PRB (Eq1-Eq3, Eq5), MCLDC (Eq5), and ST (Eq1-Eq3, Eq5) are positive and statistically significant, whilst TSB negatively influences ESI (Eq1-Eq5). Besides, the results of Sargan and Hansen test support the validity of the over-identifying restrictions. Also, Arellano-Bond test for AR(1) and AR(2) documents the absence of second order serial correlation.

Table no. 8: DPD estimations (two-step system GMM) on the impact of adopting individual ISO management system standards and country-level controls on ESI

Variables Eq1 Eq2 Eq3 Eq4 Eq5 Eq6

L.ESI -0.46 (-1.43)

-0.17 (-0.40)

-0.30 (-0.77)

-0.17 (-0.72)

-0.29† (-1.93)

0.04 (0.11)

L2.ESI 0.67* (2.19)

0.11 (0.13)

0.25 (0.71)

0.26 (0.36)

0.16 (0.45)

-0.44 (-0.47)

ISO9001 -0.00 (-0.85)

ISO13485 -0.00 (-0.33)

ISO16949 -0.00 (-0.38)

ISO22000 -0.00 (-0.10)

ISO14001 0.00 (0.17)

ISO27001 0.01 (0.31)

Growth 3.07*** (5.63)

2.39** (3.02)

2.50** (3.79)

3.14** (3.00)

2.55*** (4.59)

1.91 (1.11)

DCPS 0.00 (0.01)

-0.03 (-0.35)

-0.09 (-0.47)

-0.02 (-0.19)

-0.03 (-0.37)

-0.04 (-0.40)

RDE 3.41 (0.37)

1.86 (0.30)

5.97 (1.24)

5.01 (0.77)

-2.95 (-0.99)

-2.42 (-0.32)

CBB -0.00 (-0.01)

-0.02 (-0.09)

0.08 (0.37)

0.22 (0.86)

0.03 (0.19)

0.16 (0.56)

NBR -0.00 (-0.31)

-0.00 (-0.37)

-0.00 (-0.15)

-0.00 (-0.11)

0.00 (0.03)

PRB 2.95** (3.05)

2.32* (2.12)

2.67* (2.60)

2.42 (0.83)

3.22*** (6.10)

0.65 (0.22)

TSB -0.32** (-2.92)

-0.29* (-2.54)

-0.25* (-2.25)

-0.47† (-1.74)

-0.42*** (-6.61)

-0.02 (-0.04)

LDC -0.00 -0.00 -0.00 -0.00 0.00 -0.01



Variables Eq1 Eq2 Eq3 Eq4 Eq5 Eq6 (-0.21) (-0.19) (-0.26) (-0.33) (0.14) (-0.77)

MCLDC -0.01 (-0.59)

0.05 (1.68)

-0.00 (-0.09)

0.03 (0.51)

0.06* (2.55)

0.08 (1.45)

ST 0.13† (2.01)

0.11* (2.58)

0.16** (3.29)

0.09 (1.19)

0.07* (2.41)

0.16 (1.24)

BEDI 1.88 (1.26)

0.32 (0.13)

0.37 (0.25)

-0.44 (-0.25)

0.06 (0.05)

-1.68 (-0.31)

EMAS -0.00 (-1.05)

-0.00 (-0.14)

-0.01 (.)

0.00 (0.05)

REGQ -3.47 (-0.23)

1.39 (0.06)

-3.99 (-0.24)

5.35 (0.29)

7.12 (0.39)

12.08 (0.41)

_cons 48.31 (0.96)

85.61† (1.88)

81.53† (1.82)

55.57 (0.98)

88.06*** (5.29)

122.59 (1.69)

F stat 30.58*** 127.81*** 77.12*** 15.97*** 10.33*** 173.40*** AR(1) (p-value) 0.021 0.196 0.096 0.297 0.110 0.298 AR(2) (p-value) 0.229 0.421 0.194 0.632 0.084 0.881 Sargan test (p-value)

0.626 0.219 0.415 0.204 0.259 0.000

Hansen test (p-value)

0.682 0.957 0.856 0.553 0.996 0.549

# Instruments 26 28 26 28 28 26 Period 2005-2014 2005-2014 2005-2014 2007-2014 2005-2014 2006-2014 # Obs. 133 127 128 98 130 135 # Countries 21 20 20 20 20 20

Notes: ***, **, *, † indicates statistical significance at the 0.1%, 1%, 5% and 10% significance level, respectively. The t-statistics are given in parentheses. Robust standard errors were considered. Eqs. 2, 3, 5, and 6 does not comprise Croatia. Eq. 4 does not comprise Cyprus. Variables’ description is

provided in Table no. 1.

Forwards, the impact of implementing ISO standards, simultaneously employed and country-level controls on ESI assessed via two-step system GMM is showed in Table no. 9.

Table no. 9: DPD estimations (two-step system GMM) on the impact of implementing integrated ISO management system standards and country-level controls on ESI

Variables Eq1 Eq2 Eq3 Eq4

L.ESI 0.40 (0.66)

0.52 (0.62)

0.66 (1.44)

0.34 (1.02)

L2.ESI -0.17 (-0.23)

-0.43 (-0.40)

-0.42 (-0.81)

-0.34 (-1.31)

ISO9001 -0.00 (-0.44)

-0.00 (-0.51)

ISO13485 0.01† (1.77)

0.01 (1.52)

ISO16949 0.02 (1.24)

-0.01 (-0.86)

ISO22000 0.00 (0.10)

0.00 (0.25)

-0.00 (-0.64)

-0.01* (-2.13)

ISO14001 -0.00 (-1.69)

-0.00 (-0.56)

ISO27001 -0.02 (-1.22)

-0.00 (-0.00)

Growth 3.68*** (4.87)

3.34* (2.54)

0.97 (0.67)

1.35 (1.32)

DCPS 0.19 0.30 -0.08 -0.32


Vol. 19 • No. 44 • February 2017 159

Variables Eq1 Eq2 Eq3 Eq4 (1.29) (1.00) (-0.59) (-1.62)

RDE -3.76 (-0.58)

-3.95 (-0.47)

-26.60† (-1.94)

-1.16 (-0.16)

CBB 0.22 (0.90)

0.22 (0.93)

-0.08 (-0.37)

-0.21 (-0.48)

NBR 0.00 (0.04)

-0.00 (-0.37)

PRB -0.80 (-0.50)

-0.32 (-0.12)

-1.58 (-0.76)

1.51 (1.07)

TSB -0.44* (-2.27)

-0.29 (-1.30)

-0.15 (-0.62)

0.01 (0.03)

LDC 0.01 (1.17)

-0.00 (-0.23)

0.00 (0.23)

0.01 (0.81)

MCLDC 0.06† (1.86)

0.05 (1.09)

0.16* (2.13)

0.14† (1.79)

ST 0.04 (0.68)

0.06 (0.86)

-0.05 (-1.00)

0.01 (0.19)

BEDI -1.69 (-0.63)

-1.61 (-0.51)

5.14 (1.62)

4.38 (0.72)

EMAS 0.00 (0.44)

REGQ -39.66† (-1.93)

-27.03 (-0.62)

8.33 (0.72)

9.84 (0.71)

_cons 101.34*** (5.79)

86.91* (2.78)

97.07* (2.32)

93.16** (2.94)

F stat 54.79*** 444.34*** 97.86*** 37.61*** AR(1) (p-value) 0.064 0.414 0.312 0.191 AR(2) (p-value) 0.749 0.559 0.545 0.855 Sargan test (p-value) 0.019 0.054 0.352 0.045 Hansen test (p-value) 1.000 0.987 1.000 1.000 # Instruments 30 30 32 42 Period 2007-2014 2007-2014 2007-2014 2007-2014 # Obs. 118 115 95 95 # Countries 21 20 20 20

Notes: ***, **, *, † indicates statistical significance at the 0.1%, 1%, 5% and 10% significance level, respectively. The t-statistics are given in parentheses. Robust standard errors were considered. Eq. 2

and Eq. 4 does not comprise Cyprus. Eq. 3 does not comprise Croatia. Variables’ description is provided in Table no. 1.

We notice a positive influence on ESI by ISO 13485 standard of quality management systems for the medical device industry (Eq1) due to giving potentially entrance into new marketplaces since this standard fulfils the demands of legislation in every corner of the international trade. However, even if food safety has become an overwhelming challenge, the reduced mean number of ISO 22000 certifications (Table no. 2) caused a negative impact on ESI (Eq4).

In addition, the coefficients of Growth (Eq1, Eq2) and MCLDC (Eq1, Eq3, Eq4) are positive and statistically significant, but RDE (Eq3), TSB (Eq1), and REGQ (Eq1) negatively influence ESI. The Sargan and Hansen test indicate the validity of the instrumental variables, whilst Arellano-Bond test for AR(1) and AR(2) shows the lack of second-order serial correlation.



3.4 Causal relationships examination

The results of Granger causality analysis are revealed in Table no. 10. Therefore, we notice that ISO 9001 Granger causes ESI (for all the three lags) and ESI Granger causes ISO 9001 (for the first lag). Also, ISO 22000 (for the second and third lag) and ISO 14001 (for the first lag) Granger cause ESI. Moreover, ESI Granger causes ISO 16949 (for the third lag), ISO 14001 (for the second and third lag), and ISO 27001 (for the second and third lag).

Table no. 10: Pairwise Granger Causality Tests

Null Hypothesis

Lags

1 2 3

# Obs.

F-Stat (Prob)

# Obs.

F-Stat (Prob)

# Obs.

F-Stat (Prob)

ISO9001 does not Granger Cause ESI 519

3.19008† (0.0747) 492

2.47103† (0.0856) 465

3.47129* (0.0161)

ESI does not Granger Cause ISO9001 3.19415† (0.0745)

0.94754 (0.3884)

0.67828 (0.5657)


0.01823 (0.8927) 218

0.01149 (0.9886) 190

1.84723 (0.1402)

ESI does not Granger Cause ISO13485 0.07253 (0.7879)

0.02309 (0.9772)

0.61076 (0.6088)

ISO16949 does not Granger Cause ESI 250 0.07582 (0.7833) 224 1.97638

(0.1410) 198 0.32088 (0.8103)

ESI does not Granger Cause ISO16949 2.22892 (0.1367) 0.68176

(0.5068) 3.58529* (0.0148)

ISO22000 does not Granger Cause ESI 184 1.96094 (0.1631) 156 5.60956**

(0.0045) 128 4.11530** (0.0081)

ESI does not Granger Cause ISO22000 0.81197 (0.3687) 0.50941

(0.6019) 0.04039 (0.9891)


3.68305† (0.0557) 361

2.05204 (0.1300) 334

1.42095 (0.2365)


5.14232** (0.0063)

2.95759* (0.0325)


0.99843 (0.3189) 178

0.28774 (0.7503) 151

1.61627 (0.1882)


3.51970* (0.0317)

2.36593† (0.0734)

Notes: ***, **, *, † indicates statistical significance at the 0.1%, 1%, 5% and 10% significance level, respectively. Variables’ description is provided in Table no. 1.

Concluding remarks

Quality management systems are tremendously imperative for producers aiming to achieve better global competitiveness. However, customers are more selective in the lookout for only those products that are of high quality. Besides, an essential way of reaching high product quality is the attainment of ISO certification. Thus, implementing international standards within market emphasizes an essential approach in the course of improving competitiveness. Current research aimed to empirically investigate the impact of following ISO management system standards on economic sentiment indicator for European Union member states.


Vol. 19 • No. 44 • February 2017 161

This study documented a positive impact of ISO 16949 standard for quality management systems related to automotive industry on ESI since innovation, first-class quality, and the highest standards across the whole supply chain are vital for success in the global automotive industry. The augmented worldwide competition within automotive industry, along with customer pressure determined manufacturers seek other ways aiming to become superior than the opponent, and provide higher quality products. Therefore, a certified management system in compliance with ISO 16949 is a pass to novel markets and clients.

As well, breaks in information security can let key data to be retrieved, stolen, degraded, or misplaced. Inasmuch as information is a precious resource which requests to be suitably sheltered, we find that ISO 27001 standard for information security management systems positively influences ESI. Besides, ISO 27001 aid to an enhanced oversight of information assets and apply controls to uphold defend for an information security breach.

Furthermore, supervisory authorities in major markets request manufacturers selling medical products in their countries to have a third-party audited and certified quality management system ready. Thus, ISO 13485 set requisites for organisations asked to exhibit their skill to offer medical devices that constantly meet client and regulatory requirements. Our results provide support for a positive influence of ISO 13485 standard of quality management systems for the medical device industry on ESI.

Due to globalization of production and procurement, food chains are extended and more complex, the safety of food being affected. Therefore, ISO 22000 is planned to afford safety by certifying that there are no fragile portions in the food supply chain. Unfortunately, we find a reduced mean number of such certifications and a negative impact on ESI. As for ISO 9001 standard for quality management systems and ISO 14001 standard for environmental management systems, we document the lack of statistically significant associations with ESI. Likewise, ISO 9001, ISO 22000, and ISO 14001 Granger cause ESI. Also, future research should be conducted by accounting for developed and developing nations.

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Vol. 19 • No. 44 • February 2017 165

Annex no. 1: Panel-data line plots for ISO management system standards (EU 28, except IE)

0

50,000

100,000

150,000

200,000IS

O9001

1995 2000 2005 2010 2015Year

AT/LT BE/LU BG/LV CY/MTCZ/NL DE/PL DK/PT EE/ROEL/SE ES/SI FI/SK FR/UKHR HU IT

0

1,000

2,000

3,000

4,000

ISO

13485

1995 2000 2005 2010 2015Year


0

1,000

2,000

3,000

4,000

ISO

1694

9

1995 2000 2005 2010 2015Year


0

500

1,000

1,500

2,000IS

O22

000

1995 2000 2005 2010 2015Year


0

10,000

20,000

30,000

ISO

1400

1

1995 2000 2005 2010 2015Year


0

500

1,000

1,500

2,000

2,500

ISO

2700

1

1995 2000 2005 2010 2015Year



AE Exploring the Impact of ISO 9001, ISO 14001 and OHSAS 18001 Certification on Financial Performance: The Case of Companies listed

on the Bucharest Stock Exchange


EXPLORING THE IMPACT OF ISO 9001, ISO 14001 AND OHSAS 18001 CERTIFICATION ON FINANCIAL PERFORMANCE: THE CASE

OF COMPANIES LISTED ON THE BUCHAREST STOCK EXCHANGE

Mihaela Ionașcu1∗, Ion Ionașcu2, Marian Săcărin3 and Mihaela Minu4 1), 2), 3), 4) The Bucharest Academy of Economic Studies, Romania

Please cite this article as: Ionașcu, M., Ionașcu, I., Săcărin, M. and Minu, M., 2017. Exploring the impact of ISO 9001, ISO 14001 and OHSAS 18001 certification on financial performance: the case of companies listed on the Bucharest Stock Exchange. Amfiteatru Economic, 19(44), pp. 166-180


Abstract The purpose of this paper is to investigate the impact of ISO 9001, ISO 14001 and OHSAS 18001 certification on the financial performance of Romanian companies listed on the Bucharest Stock Exchange (BSE). Based on audited financial statements of a population of 67 non-financial companies listed during 2013-2015 and using regression models, this paper shows, for the first time in Romania, that companies listed on BSE, implementing complex management systems (quality – environmental and/or occupational health and safety) perform better, and that their performance is directly linked to the complexity of the management systems implemented. Keywords: ISO 9001, ISO 14001, OHSAS 18001, quality management, environmental management, occupational health and safety management, financial performance JEL Classification: D29, L25, M19, M41

Introduction

To meet the quality needs of customers, as well as various environmental and labour safety requirements, in recent years, the implementation and certification of Quality Management Systems (QMS), Environmental Management Systems (EMS) and Occupational Health and Safety Management Systems: OHSMS have become a priority for organizations. Nevertheless, the continuous growth of these management systems certification in Romania suggests companies’ expectations regarding business excellence and achieving benefits that enable sustainable development.

∗ Corresponding author, Ionașcu Mihaela – [email protected]


Vol. 19 • No. 44 • February 2017 167

Implementing and certifying quality management systems and, successively, improving them by adding environmental and occupational safety components is presumed to allow for risks minimization and benefits increase, enhancing the effectiveness of processes and resource efficiency, which ultimately lead to an increase in financial performance. For the Romanian environment, the potential relationship between management systems certification and corporate performance has not yet been investigated. Most of the studies in the field focus on increasing competitiveness and customer satisfaction, and mainly target small and medium-sized Romanian enterprises. This paper aims to field the void in the literature by analysing the impact of ISO 9001, ISO 14001 and OHSAS 18001 certification on the financial performance of companies listed on the Bucharest Stock Exchange (BSE).

A quality management system is a means to define how an organization can meet the requirements of its customers and other stakeholders of its activities (IOS, 2015a). Although there are different ways to improve a quality management system, organizations increasingly resort to quality certifications based on ISO 9001 "Quality Management Systems".

First published in 1987, ISO 9001, with the last version issued in 2015, has become a standard for certifying quality management systems recognized throughout the world (Corbett, 2006). Thus, by the end of 2015, there were 1,033,936 ISO 9001 certifications issued worldwide (IOS, 2015b).

ISO 9001 presents a set of fundamental elements that allow an organization to demonstrate its ability to provide products and services that satisfy customers’ needs and meet legal requirements. Implementing and certificating a quality management system based on ISO 9001 can be associated with a number of internal benefits – improved quality, productivity and efficiency, as well as with external ones – access to new markets, increase market share and sales, which, ultimately, could lead to an increase in financial performance. There are numerous empirical research studies showing that ISO 9001 certification attracts a number of benefits including increased corporate financial performance (Corbett et al., 2005; Häversjö, 2000; Heras et al., 2002).

In 1996, the International Organization for Standardization (ISO) published ISO 14001 "Environmental Management Systems", the most important standard in the series of standards dedicated to environmental issues. The purpose of ISO 14001 was to reduce the impact of organizations’ operations on the environment, but also to facilitate sustainable development and international trade, by introducing a system of standardisation recognized globally. Although certification of environmental management systems based on this standard is voluntary, the number of ISO 14001 certifications issued worldwide by the end of 2015 rises up to 319,324 (IOS 2015b).

By means of ISO 14001 certification, organizations convey to different stakeholders a signal about the responsibilities that they assume on the quality of their environmental management system (Jiang and Bansal, 2003). By projecting an image associated with care for the environment, entities hope to improve their competitiveness (Molina-Azorin et al., 2015) and financial performance by increasing productivity and reducing costs (Ullah et al., 2014).

At the internationally level, there is also awareness of the need for implementing and certifying management systems that would ensure a healthy and safe working environment. Issued in 1999 by the British Standards Institution (BSI), after ISO 9001 and ISO 14001,




OHSAS 18001 "Occupational Health and Safety Management Systems" has become the most common standard used for evaluating management processes related to organizations’ occupational health and safety.

Implementing and certifying management systems in compliance with OHSAS 18001 enables organizations to improve their image, but also helps them to assess risks, ensure the health of employees and reduce accidents, which are expected to have a positive impact on business performance.

The three management systems (quality, environmental and occupational health and safety) can operate separately or in an integrated manner (Pardy and Andrews, 2009), within an Integrated Management System (IMS). Regardless of the method chosen, adding environmental as well as health and occupational safety components to quality management systems is presumed to improve companies’ operations by means of cost reductions from eliminating waste of energy and water and avoiding personnel accidents, which can ultimately lead to increased business performance.

However, the operation of three separate management systems proved to be more difficult and harder to align with the organization's strategy, due to duplication of efforts related to documentation, execution of procedures, or verification and control (Zeng et al., 2007).

Benefits can be more obvious in the case of implementing an integrated management system. In the literature, integration of management systems is associated with reducing bureaucracy (Jorgensen et al., 2005; Oliveira, 2013) and documentation (Simon et al., 2011), costs decrease (Asif et al., 2009; Oliveira, 2013), better utilization of human, technical and financial resources (Zutshi and Sohal, 2005; Abad et al., 2014), and improved efficiency, better internal communication and organizational reputation (Zeng et al., 2007).

Romanian companies, in order to be competitive on the market, to cope with changes and developments in technology and to meet the needs and expectations of customers are forced (sooner or later) to implement quality management systems but also environmental and health and occupational safety management systems and to assume certain social responsibilities. In other words, they must obtain certifications of different types (national, European and international) the most commonly used being ISO 9001, ISO 14001 and OHSAS 18001. In addition, certain sectors may use specific standards for those particular industry requirements.

The evolution of certifications obtained by Romania companies in the past 10 years for quality management – ISO 9001 and environmental management – ISO 14001 systems is shown in Figure no. 1.

Except for 2011, the upward trend of the number of certifications issued for Romanian companies from year to year can easily be observed. It is a first sign suggesting that obtaining various types of ISO Certifications has become a concern for companies that understand the need to improve continuously their activities and processes, consider customer needs, and become more transparent in environmental and social matters to meet multiple requirements.


Vol. 19 • No. 44 • February 2017 169

Figure no. 1. The evolution of ISO 9001 and ISO 14001 certifications in Romania

The paper is structured as follows. Section 1 presents the literature review, which highlights the relationship between management systems’ certification and corporate financial performance. Section 2 describes the research methodology, indicating the targeted population, the proposed hypotheses and econometric models. Section 3 presents the research results, whereas the concluding section discusses them in the context of similar international research, highlighting original contributions of the study, as well as its limitations and potential for future research.


There is abundant literature that explores the relationship between certification of management systems and companies’ performance. The concept of "performance" applied in the management of companies is rather vague, as performance is defined in various ways, depending on the level of decision-making, organizational structure, or field of activity. In addition, measures of companies’ performance can be expressed in financial or non-financial indicators. However, granting the polysemy of the concept of performance, evaluating companies’ management primarily involves indicators that are linked to financial performance, which may come in the form of profit and cash flows, or other financial indicators derived from them.

Corporate financial performance is measured based on data from financial statements – balance sheet, income statement, statement of cash flows, etc., but can also refer to market data (e.g. market value of the shares), and can be defined by a variety of indicators (Benner and Veloso, 2008; Martinez-Costa et al., 2009), such as turnover (sales), return on assets (ROA), return on sales (ROS), return on equity (ROE), earnings per share (EPS), earnings before interest, taxes depreciation and amortization: (EBITDA), etc.

The relationship between quality management and corporate financial performance has been documented empirically by many authors, especially since 2000 (e.g. Heras et al., 2002; Sharma, 2005; Morris, 2006; Benner and Veloso, 2008), yet results are (sometimes) different or even contradictory.

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

ISO 14001 752 1454 2269 3884 6863 7418 7394 8527 8744 9302 10581

ISO 9001 6097 9426 9633 10737 15865 16200 14345 18014 18450 18984 20524

0

5000

10000

15000

20000

25000




Heras et al. (2002) conducted a comparative study which analysed the relationship between companies’ profitability and ISO 9000 certification based on two equally numbered samples of companies (from the Autonomous Basque Community, Spain) for a period of five years, one sample of companies featuring ISO 9000 certification, and one sample including non-certified entities. Using return on assets (ROA) as a profitability indicator, computed as the ratio between earnings before interest and taxes (EBIT) and total assets, and the date of ISO 9000 certification, Heras et al. (2002) concluded that ISO 9000 certified companies are more profitable than non-certified ones, confirming the supposition that ISO 9000 certification is a growth driver for profitability.

Sharma (2005) analyzed the impact of ISO 9000 certification on the financial performance of 70 listed companies in Singapore, using as indicators of financial performance: return on sales (determined as the ratio between earnings before interest, taxes and extraordinary items and net sales), the increase in net sales and earnings per share (the ratio of operating profit after tax and the number of ordinary shares issued). The results of Sharma (2005) showed that ISO 9000 certification is associated with improved financial performance of certified companies by significantly increasing the return on sales (ROS), sales and earnings per share (EPS), and proving to be beneficial for the company and its stakeholders. Furthermore, Sharma (2005) observes that the effect of ISO 9000 certification was more important for return on sales than for sales growth, which suggests that enhancing the overall performance of a company is induced largely by improving internal processes due to the implementation and certification of a quality management system.

Benner and Veloso (2008) conducted a study on the relationship between quality management systems represented by ISO 9000 certification and corporate financial performance, based on American automobile dealers which included a representative sample of 75 companies, listed and registered in the US for a period of 10 years, a period that covers a full economic cycle in this industry. Benner and Veloso (2008) have used several indicators to measure the financial performance of companies – two accounting indicators (ROA and ROS) and one based on market values (Tobin’s Q), and their results showed that if the majority of the firms within an industry adopts ISO 9000, later adopters no longer obtain a lasting growth of financial benefits out of this practice. The same authors also showed that ISO 9000 certification has a significant impact on the financial performance of companies – measured by Tobin’Q and ROA (but not ROS), showing that the value of market capitalization of the certified companies tends to grow steadily once ISO 9000 certification is obtained, which is explained by the fact that investors expect higher growth in future profits for companies that have adopted ISO 9000 than from those that have not adopted the standard.

However, not all studies concluded that ISO 9000 certification improve companies’ financial performance. Morris (2006) falls within this category, analyzing the relationship between ISO 9000 certification and financial performance of companies in the American electronics industry – the industry with the most numerous ISO 9000 certifications in the US, using the operating profit before depreciation for measuring profitability. The results obtained by Morris (2006) did not support the hypothesis that ISO 9000 certification leads to superior financial performance, which is explained by the fact that companies may pursue ISO 9000 certification for other purposes than farming quality or financial


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performance, such as aspirations for international sales, competitive pressures or customers’ requirements.

Corbett et al. (2005) found that, after obtaining quality certification, companies had an outgrowth in financial performance, expressed by several indicators including: return on assets, return on sales and turnover growth, compared to other similar companies that were not certified.

A greater increase in profitability for certified companies, compared to uncertified ones, has also been identified in Denmark (Häversjö, 2000). In turn, Duarte et al. (2011), after analyzing the association between several independent variables, including ISO 9001 certification and financial performance for a sample of 3,589 Brazilian companies from different industries, noted that ISO 9001 certification did not affect profitability. However, they identified a causal relationship between quality and environmental management systems certification and revenue growth.

Starke et al. (2012) analyzed the impact of ISO 9000 certification for Brazilian companies during 1995-2006, indicating a positive effect on financial performance measured in terms of sales growth and return on sales (ROS). Fatima (2014), based on a sample of 58 Pakistani companies listed on Karachi Stock Exchange, showed that ISO 9000 certification had an overall favourable impact on companies’ financial performance, although the effect was not felt for small companies.

In turn, Kafetzopoulos et al. (2015), after studying a sample of 287 Greek companies, concluded that ISO 9001 certification does not directly influence business performance, including financial performance, but has an indirect effect by increasing manufacturing quality and operating performance.

While the literature investigating the relationship between ISO 9001 certification and financial performance is quite abundant, the one exploring the effects of certifying environmental and/or occupational health and safety management systems is in its formation phase.

Thus, Hwee Nga (2009), based on a sample of 81 companies listed on Bursa Malaysia during 2005-2007, showed that ISO 14000 certified companies have average returns on equity (ROE) superior to those of uncertified entities, and argued for the need of future research that would capture the impact of the interaction of ISO 9000 and ISO 14000 certifications on companies’ performance. Grolleau, Mzoughi and Pekovic (2013) analyzed the performance of French companies that have implemented quality and environmental management systems, showing that the simultaneous implementation of the two management systems may have a possible synergistic effect that generates higher levels for sales, profit and EBITDA. Instead, for Spanish companies, Heras-Saizarbitoria, Molina-Azorín and Dick (2011) showed that ISO 14001 certification has not resulted in improved financial performance.

Lo et al. (2014), using a sample of 111 companies listed in US and having OHSAS 18001 certification, showed that the implementation of a management system for occupational health and safety led to a significant increase in performance over the expected level (abnormal performance), expressed also in terms of sales growth and profitability.




Integration of quality, environmental and occupational health and safety management systems has been associated with improving the efficiency of an organization (Zeng et al., 2007).

For the Romanian environment, there is no research to date exploring the impact of implementing various management systems on financial performance. As regards the benefits of implementing and certifying management systems, the vast majority of studies focused on increasing competitiveness and customer satisfaction, the targeted companies being small and medium-sized entities (Olaru et al., 2010; Olaru et al. 2011). In the broader context of social responsibility, Miron et al. (2011) discuss the relationship between economic and social performance, suggesting a biunivocal relationship, in the sense that economic performance offers the possibility of assuming social responsibilities, with a negative impact on it on the short-term, effect which will be offset by social performance, that will ensure sustainability for economic performance and competitiveness in the long term.

This paper aims to complement the literature, by providing the first empirical evidence for large companies, listed on BSE and featuring audited financial statements, investigating the impact of ISO 9001, ISO 14001 and OHSAS 18001 certification on their financial performance.


The population of the study is represented by all the companies listed on BSE in the last three years (2013-2015) on both the Premium and Standard tiers, that is 82 companies (22 listed on the Premium category and 60 on Standard). As ISO and OHSAS certification has not been a concern for the financial sector until recently, companies operating in this area were excluded, resulting a total of 67 companies. In the absence of publicly accessible databases, the source data for ISO 9001, ISO 14001 and OHSAS 18001 certifications were the websites of listed companies (reports and copies of certificates) and the BSE website (management reports). The authors performed cross-checks to ensure data accuracy. The evolution of management systems certifications for listed Romanian companies for the period under review (2013-2015) is summarized in Table no. 1.

Table no. 1. Certification of management systems of listed companies

As shown in Table 1, listed Romanian companies are greatly interested in implementing and certifying multiple management systems, a large majority (74.6% in 2015) featuring quality-environmental systems, and over half (58.2% in the last year): having quality-environmental-occupational health and safety management systems. Almost all non-financial listed companies (92% in 2015) implemented a quality management system, certified ISO 9001, and the trend of obtaining new certifications in the analyzed period is evident. This confirms the idea that companies that have already implemented a quality management system want to introduce other management systems, enabling them to

N ISO 9001 ISO 14001 OHSAS 18001 2013 67 60 89.6% 46 68.7% 35 52.2% 2014 67 61 91% 47 70.1% 38 56.7% 2015 67 62 92.5% 50 74.6% 39 58.2%


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minimize risks and strengthen benefits, increasing the effectiveness of processes and resource efficiency, which ultimately could lead to an increase in their performance. Analyzing the data collected, we found that the majority of Romanian listed companies initially obtained ISO 9001 certification, and later on, successively, obtained ISO 14001 and OHSAS 18001 certifications. We also noticed that companies that have later resorted to implementing and certifying a quality management system based on ISO 9001 opted to simultaneously implement it with ISO 14001 or with both ISO 14001 and OHSAS 18001.

Data sources did not allow for establishing in every case the extent to which certified management systems operate separately or within an integrated management system (IMS).

Given that each of the three certifications is presumed to have a positive impact on the performance of the companies, the paper aims to investigate the extent to which the simultaneous functioning of multiple certified management systems can increase the financial performance of companies listed on BSE, the paper’ hypothesis being the following:

• H1: Financial performance is higher for listed Romanian companies implementing and certifying multiple management systems (ISO 9001, ISO 14001 and OHSAS 18001).

• H2: Financial performance of Romanian listed companies is directly proportional to the complexity of the management systems implemented and certified.

To assess the financial performance of listed companies, we have opted for return on assets (ROA: net profit divided by total assets), one of the key performance indicators, which capture the efficiency of assets in producing profits. The source of financial data was Thompson Financials database. Financial data was not available for seven companies, which reduced the population studied to 60 entities.

The two hypotheses were tested based on the following two regression models:

𝑹𝑹𝑹𝑹𝑹𝑹𝒊𝒊𝒊𝒊 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝟏𝟏𝒙𝒙𝑴𝑴𝑴𝑴𝒊𝒊𝒊𝒊 + 𝜺𝜺𝒊𝒊𝒊𝒊 (1) 𝑹𝑹𝑹𝑹𝑹𝑹𝒊𝒊𝒊𝒊 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝟏𝟏𝒙𝒙𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝒊𝒊𝒊𝒊 + 𝜺𝜺𝒊𝒊𝒊𝒊 (2)

Where:

ROAij – Return on assets for company i in the year j, computed as

Net Income ij/Total assets ij;

MSij – Certified management systems for company i in the year j,

Dummy: 1 – Company with multiple management systems certifications (quality-environmental or quality-environmental-occupational health and safety).

0 – Company with only quality management system certification or without any of the three certifications investigated;

MSscoreij – Management system score for company i in year j:

3 – ISO9001, ISO14001 and OHSAS 18001 certifications;

2 – ISO9001 and ISO14001 certifications;




1 – ISO9001 certification;

0- None of the three certifications investigated.

3. Research results

Table no. 2 summarizes the results of the regression analysis for the two models for each of the three years. As shown below, in none of the cases, the coefficient α1 is significantly different from zero, indicating no link between the characteristics of the management systems implemented and certified by listed companies and their performance, and, consequently, suggesting the rejection of the two hypotheses of the study.

Table no. 2. Regression results – whole population

The lack of significant correlations persists even after outliers’ removal. However, following their analysis, it was observed that most outliers come from companies featuring losses. In the literature (Kothari, 2001), losses are considered transient components of companies’ earnings, which contradict the assumption that annual earnings are normally distributed, losses distorting analysis, especially in the capital markets field, often being excluded from investigations (Philip and Raffournier, 2010). In our case, we also bear in mind that losses, as transient elements, can be caused by exogenous events related to the business environment, without being associated with entities’ management (Kothari, 2001). In addition, in the case of listed companies that incur losses, the "big bath accounting" phenomenon was reported to

Model (1) 2013 2014 2015 α0 -.002 (-.062) .004 (.149) -.093 (-.778) α1 -.017 (-.456) .005 (.168) .168 (1.246) F .208 .028 1.553 R2 0.004 .000 .026

Durbin Watson 1.787 1.751 2.705 N 60 60 60

Model (2) 2013 2014 2015

α0 -.019 (-.483) -.011 (-.322) -.072 (-.469) α1 .002 (.141) .008 (.601) .047 (.778) F 0.02 .361 .606 R2 .0003 .006 .010

Durbin Watson 1.832 1.770 2.725 N 60 60 60

Significance level: ***0.01, **0.05, *0.1 Model (1): 𝑹𝑹𝑹𝑹𝑹𝑹𝒊𝒊𝒊𝒊 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝟏𝟏𝒙𝒙𝑴𝑴𝑴𝑴𝒊𝒊𝒊𝒊 + 𝜺𝜺𝒊𝒊𝒊𝒊 Model (2): 𝑹𝑹𝑹𝑹𝑹𝑹𝒊𝒊𝒊𝒊 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝟏𝟏𝒙𝒙𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝒊𝒊𝒊𝒊 + 𝜺𝜺𝒊𝒊𝒊𝒊

Where: ROAij = Return on assets for company i in the year j; MSij = Certified management systems, Dummy: 1-company with multiple management

systems certifications, 0 – Company with only quality management system certification or without any of the three certifications investigated;

MSscoreij = Management system score for company i in year j : 3 – ISO9001, ISO14001 and OHSAS 18001 certifications; 2 – ISO9001 and ISO14001 certifications; 1 – ISO9001 certification; 0-None of the three certifications.

Tests for detecting autocorrelation in residuals performed. Durbin Watson values within normal parameters.


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occur, that is, an overstatement of these losses, as performance targets have already been missed, and such exaggerations relieve future profits of a series of expenses, enabling companies to look more profitable in the future. Consequently, this phenomenon deprives the amounts recorded as losses of their economic content.

In these circumstances, companies that incurred losses during the period analysed were eliminated, and the hypothesis were tested again based on the sample of companies featuring positive returns. Regression results are summarized in Table no. 3.

Table no. 3. Regression results – only positive ROAs

With the elimination of losses, there is a clear improvement of regression models. Significant increases in the explanatory power for the period under consideration are observed (R2 increases from an average of 1% to 11% for model 1, and from an average of 0.5% to 13% for model 2), and coefficient α1 is positive and significantly different from 0, in all cases, indicating a direct relationship between the characteristics of the management system implemented by the listed companies and their performance. Therefore, the two hypotheses are accepted, i.e. there is empirical evidence supporting a possible association between multiple management systems certifications and financial performance of Romanian listed companies, and also a directly proportional increase of their performance with the complexity of the management systems implemented. The explanatory power of model 2 is higher, the type of certifications explaining on average about 13% of the variation in financial performance during the period under review, compared to 11% for model 1, which supports

Model (1) 2013 2014 2015 α0 .012 (.994) 2.713 (2.419) .026 * (1.964) α1 .032 * (.323) 2.908 ** (2.290) .027* (1.805) F 5.395 ** 5.245 ** 3.258 * R2 .124 .119 .081

Durbin Watson 2.327 1.855 2.356 N 40 41 39

Model (2) 2013 2014 2015

α0 .003 (.194) 1.581 (1.112) .018 (1.075) α1 .015 ** (2.535) 1.423 ** (2.567) .012 * (1.898) F 6.424 ** 6.589 ** 3.601 * R2 .145 .145 .087

Durbin Watson 2.379 1.974 2.381 N 40 41 40

Significance level: ***0.01, **0.05, *0.1 Model (1): 𝑹𝑹𝑹𝑹𝑹𝑹𝒊𝒊𝒊𝒊 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝟏𝟏𝒙𝒙𝑴𝑴𝑴𝑴𝒊𝒊𝒊𝒊 + 𝜺𝜺𝒊𝒊𝒊𝒊 Model (2): 𝑹𝑹𝑹𝑹𝑹𝑹𝒊𝒊𝒊𝒊 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝟏𝟏𝒙𝒙𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝒊𝒊𝒊𝒊 + 𝜺𝜺𝒊𝒊𝒊𝒊

Where: ROAij = Return on assets for company i in the year j; MSij = Certified management systems, Dummy: 1-company with multiple management

systems certifications, 0 – Company with only quality management system certification or without any of the three certifications investigated;

MSscoreij = Management system score for company i in year j: 3 – ISO9001, ISO14001 and OHSAS 18001 certifications; 2 – ISO9001 and ISO14001 certifications; 1 – ISO9001 certification; 0-None of the three certifications.

Tests for detecting autocorrelation in residuals performed. Durbin Watson values within normal parameters. Results after outliers’ removal.




the assumption that companies adding to their quality management systems (ISO 9001) environmental (ISO 14001) and occupational health and safety (OHSAS 18001) certifications have a higher incremental performance. In other words, the greater the number of management systems certifications, the higher financial performance of the certified entities.

However, there is a downward trend in the explanatory power patterns during 2013-2015 (R2 annual values feature a decreasing trend), in 2015 the association between ROAs values and management systems certifications becoming only marginally significant. This result can be explained by the increasing number of companies that implement multiple management systems during the analyzed period, knowing that the benefits of implementing such systems are not immediately noticeable, requiring a relevant period of conformity (Heras et al., 2002). Also, another explanation could be that (as proven empirically by Benner and Veloso, 2008) later implementers of management systems do not obtain the same long lasting financial benefits.

In these circumstances, we can accept the two hypotheses of our study, although the results are only valid for profitable companies listed on BSE in the analyzed period.

To test the validity of the models, we controlled for a likely source of interference – the size of the companies that can explain both the appeal for management systems certifications, and the magnitude of financial performance – that is, larger firms are those that are more responsive to ISO 9001 ISO 14001 and OHSAS 18001 certification, and they are also the ones that report higher performance. Therefore, the model included company size (SIZE) as a control variable, operationalized by the logarithm of total assets (we used logarithm as the variable SIZE is not normally distributed: skewness 5.529, std error .215). Regression results for the whole period (2013-2015) are shown in Table no. 4.

Table no. 4. Regression results – only positive ROAs – Control for size effects (panel data)

Model (3) 2013-2015 α0 .006 (.466) α1 .019 ** (2.316) α2 .005 ** (2.405) α3 .006 (.742) α4 .010 (0.211) F 4.927 ***

Adjusted R2 .114 Durbin Watson 2.335

N 123

Model (4) 2013-2015 α0 -.009 (.476) α1 .010 ** (2.601) α2 .004 ** (1.867) α3 .005 (.706) α4 .009 (1.205) F 5.319 ***

Adjusted R2 .124 Durbin Watson 2.357


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As seen in Table no. 4, the variables that capture the features of the management systems certified remain significantly correlated with the performance of the companies in the period under review, even when controlling for scale effects. Therefore, the two hypotheses are confirmed, corporate financial performance is linked to management systems certification and this relationship is not explained by company size.

Conclusions

The purpose of this paper was to explore a possible association between ISO 9001, ISO 14001 and OHSAS 18001 certifications and audited financial performance of Romanian companies listed at BSE. In the international literature, although there is not yet a consensus reached, most studies indicate a positive impact of the implementation and certification of various models of management systems on financial performance. Our study confirms international trends showing that ISO 9001, ISO 14001 and OHSAS 18001 certifications increase financial performance. In particular, the study revealed that listed Romanian companies that have implemented multiple management systems (quality – environmental and/or health and safety) perform better in terms of return on assets (ROA) and that their performance is directly proportional to the complexity of the management systems certified. The positive effect of certifications was proven even when controlling for a potential significant source of interference, namely the size of the companies, which is likely to influence both the degree of certification and the financial performance.

The results should be, however, interpreted with caution, as corporate financial performance may be affected by a variety of complex factors and their influence has not been taken into account. In addition, the results are representative only for profitable companies listed on the BSE, and not for the whole capital market. Nonetheless, our findings provide, for the first time in the literature, objective evidence of a potential positive impact of management systems certifications on the financial performance of listed Romanian companies. Consequently, this paper represents an exploratory study providing a basis for developing future research in the

N 123 Significance level: ***0.01, **0.05, *0.1 Model (3): 𝑹𝑹𝑹𝑹𝑹𝑹𝒊𝒊𝒊𝒊 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝟏𝟏𝒙𝒙𝑴𝑴𝑴𝑴𝒊𝒊𝒊𝒊 + 𝜶𝜶𝟐𝟐𝒙𝒙𝑴𝑴𝑺𝑺𝑺𝑺𝑺𝑺𝒊𝒊𝒊𝒊 + 𝜶𝜶𝟑𝟑𝒙𝒙𝑫𝑫𝟐𝟐𝒊𝒊𝒊𝒊 + 𝜶𝜶𝟒𝟒𝒙𝒙𝑫𝑫𝟑𝟑𝒊𝒊𝒊𝒊 + 𝜺𝜺𝒊𝒊𝒊𝒊 Model (4): 𝑹𝑹𝑹𝑹𝑹𝑹𝒊𝒊𝒊𝒊 = 𝜶𝜶𝟎𝟎 + 𝜶𝜶𝟏𝟏𝒙𝒙𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝒊𝒊𝒊𝒊 + 𝜶𝜶𝟐𝟐𝒙𝒙𝑴𝑴𝑺𝑺𝑺𝑺𝑺𝑺𝒊𝒊𝒊𝒊 + 𝜶𝜶𝟑𝟑𝒙𝒙𝑫𝑫𝟐𝟐𝒊𝒊𝒊𝒊 + 𝜶𝜶𝟒𝟒𝒙𝒙𝑫𝑫𝟑𝟑𝒊𝒊𝒊𝒊 + 𝜺𝜺𝒊𝒊𝒊𝒊 + 𝜺𝜺𝒊𝒊𝒊𝒊

Where: ROAij = Return on assets for company i in the year j; MSij = Certified management systems, Dummy: 1-company with multiple management systems

certifications, 0 – Company with only quality management system certification or without any of the three certifications investigated;

MSscoreij = Management system score for company i in year j : 3 – ISO9001, ISO14001 and OHSAS 18001 certifications; 2 – ISO9001 and ISO14001 certifications; 1 – ISO9001 certification; 0-None of the three certifications.

D2 – Dummy: 1, if j=2014; 0 if j=2013,2015; D3 –Dummy: 1, if j=2015; 0 if j=2014,2015; Variables D2 and D3 introduced for controlling time effects. Tests for detecting autocorrelation in residuals performed. Durbin Watson values within normal parameters. Multicollinearity tests performed: maximum VIF (Variance Inflation Factors) value: 1.3 – within normal parameters. Results after outliers’ removal.




Romanian setting, one possible research direction being the effect of integration of management systems on the performance of Romanian companies.

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COST ANALYSIS OF POOR QUALITY USING A SOFTWARE SIMULATION

Jana Fabianová1*, Jaroslava Janeková2 and Daniela Onofrejová3 1) 2) 3) Technical University of Košice, Košice, Slovakia

Please cite this article as: Fabianová, J., Janeková, J. and Onofrejová, D., 2017. Cost Analysis of Poor Quality Using a Software Simulation. Amfiteatru Economic, 19(44), pp. 181-196


Abstract The issues of quality, cost of poor quality and factors affecting quality are crucial to maintaining a competitiveness regarding to business activities. Use of software applications and computer simulation enables more effective quality management. Simulation tools offer incorporating the variability of more variables in experiments and evaluating their common impact on the final output. The article presents a case study focused on the possibility of using computer simulation Monte Carlo in the field of quality management. Two approaches for determining the cost of poor quality are introduced here. One from retrospective scope of view, where the cost of poor quality and production process are calculated based on historical data. The second approach uses the probabilistic characteristics of the input variables by means of simulation, and reflects as a perspective view of the costs of poor quality. Simulation output in the form of a tornado and sensitivity charts complement the risk analysis. Keywords: poor quality cost analysis, Monte Carlo simulation JEL Classification: C53, C88, D24, L15 Introduction

The problem of maintaining competitiveness of a company is growing in today's dynamic economic environment. Long-term competitiveness requires constantly increasing productivity, efficiency and product quality. This calls for putting new quality management methods and changes in approach into practice. The issue of quality management is the topic of numerous scientific articles. Some scientific work deals with the methods and techniques used in the field of quality on the theoretical level. For example Sokovic, Pavletic and Pipan (2010) introduced characteristics of PDCA tool and Six Sigma (DMAIC, DFSS) techniques and EFQM Excellence Model (RADAR matrix) and showed that some methodologies are more simple and therefore easily to understand and introduce (e.g. PDCA cycle). On the contrary Six Sigma and EFQM Excellence model are more complex and demanding methodologies and therefore need more time and resources for their proper implementation. Schroeder, Linderman, Liedtke and Choo (2008) used the * Corresponding author, Jana Fabianová – [email protected]

AE Cost Analysis of Poor Quality Using a Software Simulation


grounded theory approach and literature to propose an initial definition and theory of Six Sigma. Watson and DeYong (2010) examined the models that have been proposed for design for Six Sigma. Jasti and Kodali (2014) performed the analysis of literature review of lean production. Their study provided a taxonomical and integrated review of articles, put up perspective into the conceptualisation and various critical parameters for research.

Cost analysis due to poor production or process quality is the basis for the proposed changes and improvement in terms of quality management. The aim of one of the phases of the DMAIC method is to describe the current state of the process, its variability and capability. Using software and computer simulation allows incorporating variability and uncertainty of input variables in the analysis and review processes in probabilistic aspects.

This article presents a case study analysis of the production process from the quality aspect using computer simulation Monte Carlo. The structure of the article compounds of a few parts. First part briefly presents case study and defines a problem in quality management. Next part is focused on the methodology how to calculate the cost of poor quality and the method of analysis of the cost of poor quality using Monte Carlo simulation. The last part deals with the calculations and simulation results, results there are compared and the production process is evaluated in terms of sigma level and in terms of the statistical characteristics of the output variables, as well.


Many case studies present application of the methods, practice knowledge or experience in the field of quality. Although, different methods are used in quality management, Six Sigma is a widely used method to improve processes from various industry sectors. Regulation of manufacturing processes by the methods of regulation of production processes is a unique feedback that can be used in the practice of mass and series production according to Panda, Jurko and Pandova (2016). Statistical Process Control has become a mandatory method for the automotive, as well as for other demanding industries. Pugna, Negrea and Miclea (2016) presented a creative solution for improving an assembly process in an automotive company in Romania by using Statistical Thinking and DMAIC Six Sigma methodology. Simanová (2015) introduced the proposal of the application and implementation of Six Sigma in furniture manufacturing. Pribulová et al. (2013) observed quality parameters of the moulding mixtures and determine tolerable content in the moulding mixture. Atanase and Schileru (2014) researched the normative frame, the speciality literature and assessed the perceptions related to service quality in tourism. The Six Sigma methodology has an important place for developing and reducing the actions which do not have inner process in a supply chain. Erbiyik and Saru (2015) explained a general structure of Six Sigma with regard to ‘how to define the complex problems which were encountered in the supply chain’. Aldowaisan, Nourelfath and Hassan (2015) declared that identifying whether the process data were of non-normal distribution was important to more accurately estimate the effort required to improve the process. Widely used assumption of a normally distributed process may lead to erroneous solutions. When a process is exponential, attaining desired performances may require greater quality-improvement effort. Similarly, Hsu, Pearn and Wu (2008) dealt with gamma processes. The substantial increase in quality of decision making according to Janekova, Kovac and Onofrejova (2015) in terms of respect of risk and uncertainty is brought by probabilistic


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methods. An important representative is the Monte Carlo simulation. The main reason for its use is a quantification of the probability distribution for the overall risk of a project.

There is a growing need for operations management models that contribute to the continuous improvement of company processes, among them Lean Manufacturing, Six Sigma and Lean Six Sigma (Drohomeretski et al., 2014). Combining Lean practices with Six Sigma has gained wide popularity in the last years. Whether a combined Lean-Six Sigma approach is the latest management fad, or leads to significant performance benefits is not yet apparent (Shah, Chandrasekaran and Linderman, 2008). Lean Six Sigma is widely adopted in non-manufacturing sectors such as financial, trade, services, etc. The methodology combines the Six Sigma techniques and the Lean Manufacturing principles. Despite growing popularity and impressive outcomes, the Lean Six Sigma model does not always offer the expected results (Dragulanescu and Popescu, 2015). Sagnak and Kazancoglu, (2016) discussed the integration of green lean approach, and identified the limitations of green lean approach. The Six Sigma approach was applied in order to overcome these limitations. Ali and Deif (2014) presented a dynamic model to evaluate the degree of leanness in manufacturing firms. Their model was based on system dynamics approach and presented a “leanness score” for the manufacturing system. Ringen, Aschehoug, Holtskog and Ingvaldsen (2014) explored the relationship between quality and lean as integrated parts of a holistic production system.

The success of Six Sigma improvement process depends on many factors of social, technical and financial character. Mehrabi (2012) considered the evolutionary review of the benefits and challenges of Six Sigma projects and recognized the key and influential elements of the successful approach of Six Sigma method. The study of Arumugam, Antony and Kumar (2013) investigated the impact of two organizational antecedents, (1) Six Sigma resources (technical) and (2) team psychological safety (social), on the success of Six Sigma process improvement projects. Their study empirically established the notion that technical and social supports jointly impacted the success of initiatives such as Six Sigma. Arumugam, Antony and Linderman (2016) examined the interrelationship between Six Sigma project goals, adherence to the Six Sigma method, and knowledge creation. It was found that adherence to the Six Sigma method becomes more beneficial for projects that create a lot of knowledge. Some of the Six Sigma projects failed due to insufficient data and human errors. Hwang (2006) discussed the DMAIC phases to improve the mentioned situation and to avoid the time and cost for sourcing data.

2. Materials and methodology

2.1 Case study description

The case study is focused on the production of components for automotive industry. It is focused on an assembly/production line, which is used for mounting the rear trunk lighting of the license plate – a lightbar (figure no. 1).

The production line consists of seven workstations, served by six workers. In terms of functionality, the line consists of pre-production and assembly section. Pre-production function is directed to the preparation of parts for the assembly of the final product. It has three workstations, welding pins, potting and stove, all served by two workers. The assembly of the final product is performed at four workstations (two screw stations, crimping and test station), served by four workers. The operations at the workplace



pre-production are performed on two pieces simultaneously, at the assembly workplace on four pieces at the same time. Position of a storage buffer with a capacity of 400 units in between the pre-production and assembly workplace allows that the processes must not be fully synchronized. Working place operates on three working shifts, if necessary, the production takes place over the weekend. This part of production cycle reaches a higher number of rejects and waste time caused by higher unplanned stopping or slowing production. Stopping production is conditioned by wrong machine settings, material shortages or machine break. The slowdown is usually caused by training of a new operator on a job, respectively new operators, and by an insufficient number of operators on the line (shift). Stops and slowdown in production are caused mainly due to the assembly operation, which is the bottleneck of a production. Therefore, the idle times and the costs of poor process account only for this particular workplace.

Legend: 2 Light cover 1 Cable harness 3 Structure Lightbar a PCB led + PCB switch + Connector 4 Up and down gasket b Gasket lens 5 Screw c Semi-transparent lens 6 Gasket connector overmolded d Housing switch 8 Gasket cylindrical pin e Microswitch 9 Gasket oval pin

Figure no. 1: Decomposition of the final product

2.2 Problem definition

The aim of this paper is to analyse the total cost of poor quality due to poor production quality and poor organizational quality of processes related to assembly of above mentioned final product – a lightbar for automobile.

Input data consists of actual (historical) data collected from production over a period of 23 working days. The analysis is carried out in two ways depending on the purpose of use. The first, retrospective method specifies total cost of poor quality of the production, deeply analyses rejects rate in time series analysis and sigma level analysis. Second, prognostic approach carries out the calculation of the total cost of poor quality using Monte Carlo simulation and the individual costs are analysed in terms of probability. Both approaches are shown schematically in figure no. 2.


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Figure no. 2: Scheme on cost analysis of poor quality

2.3 Methodology of cost calculation

Cost Analysis caused by poor quality and poor organization of production process is based on a partial evaluation of the costs of rejects and costs of unused disposable time. Thus, the costs associated with poor quality were in this case study of the dual nature:

• Expenses incurred due to a poor production quality (rejects). Rejects signify insufficient production that needs to be replenished. The company incurs costs in an amount of intermediate product on the certain level of completion. In this case, the first part of semi-finished products is discarded after pre-production stage. The average value of rejects at this stage of the production process is EUR 5.5. The other part of the rejects is discarded after an assembly stage. At this level of development, the average value of one reject is EUR 15. The production value, which should be replaced by rejected products, represents the cost of poor product quality. Costs calculation takes into account a fact, that 2 pieces are being processed simultaneously.



Cost of poor product quality:

CPQ/P = QP x VP (1)

CPQ/A = QA x VA (2)

where:

CPQ/P – cost of poor product quality in the pre-production stage,

CPQ/A – cost of poor product quality in the assembly stage,

QP – quantity of products in the pre-production stage,

QA – quantity of products in the assembly stage,

VP – value of the product in the pre-production stage,

VA – value of the product in the assembly stage.

• Costs due to a poor organization of the process. These costs are related to unplanned downtime of workplaces, respectively with the slowdown in activity for reasons that were specified in Chapter 3. The costs associated with poor quality process mean the value for lack of production not being produced, because of an unused time capacity of particular workplaces. Since the time delays were usually caused by problems at the assembly station, this place has to be considered as the bottleneck of the production process. Therefore, lack of production has the same value as the output at the assembly station. The calculation takes into account the fact that the assembly workstation has 4 simultaneous working positions, that is four pieces at the same time, and thus the cost of poor process are multiplied by 4 (see formula no. 3).

Cost of poor process:

CPP = (TL/TA) x VA x n (3)

where:

CPP – cost of poor process,

TL – lost time,

TA – average assembly time

n – number of parts assembled simultaneously.

Total cost of poor quality is the sum of both: costs of poor product quality and cost of poor process quality.

Total cost of poor quality:

TC = CPQ/P + CPQ/A + CPP (4)

Calculation of the cost of poor quality based on historical data

Calculation of the cost of poor quality is based on data obtained by monitoring a selected stage of the production process. Input data are represented by output volume, the number of rejects and idle time. (Table no. 1)


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Table no. 1: Input data

Day Pre-production Assembly Lost time [min]

Output [pcs]

Rejects [pcs]

Rejects [%]

Output [pcs]

Rejects [pcs]

Rejects [%]

1 1,203 158 13.13 1,022 33 3.23 134.0 2 1,185 176 14.85 1,036 25 2.41 115.2 3 1,010 75 7.43 1,111 40 3.60 130.2 4 1,039 70 6.74 924 51 5.52 205.2 5 953 38 3.99 966 3 0.31 195.0 6 919 41 4.46 949 1 0.11 208.0 7 1,032 39 3.78 1,103 4 0.36 174.0 8 950 50 5.26 928 4 0.43 70.0 9 1,095 9 0.82 1,133 16 1.41 60.0 10 1,134 32 2.82 1,034 6 0.58 130.0 11 1,014 29 2.86 1,012 13 1.28 135.0 12 890 15 1.69 1,049 0 0.00 90.0 13 1,065 21 1.97 923 2 0.22 199.0 14 1,086 16 1.47 1,025 2 0.20 142.0 15 998 15 1.50 741 10 1.35 275.0 16 1,010 12 1.19 1,039 8 0.77 145.0 17 1,056 23 2.18 1,081 13 1.20 110.0 18 795 9 1.13 842 7 0.83 620.0 19 1,010 12 1.19 1,039 8 0.77 130.0 20 1,134 32 2.82 1,015 6 0.59 35.0 21 955 11 1.15 672 4 0.60 315.0 22 993 16 1.61 967 7 0.72 180.0 23 781 8 1.02 778 4 0.51 475.0 Σ 23,307 907 22,389 267 4,272.6

Time resources of workplaces are defined by the nominal time fund reduced by the planned breaks. Effective time fund reflects the organization of production, which is continuous, and consist of 3 shifts. Planned breaks relate to each work shift equally. (Table no. 2) Available production time per day is calculated according to following relations:

Tnom/day = 24 h x 60 min = 1 440 min (5)

Tef/day= Tnom/day – 3TSB/shift = 1 245 min (6)

where:

T nom/day – nominal daily time fund

Tef/day – effective daily time fund

TSB/shift – scheduled breaks per work shift



Table no. 2: Scheduled breaks

Reasoning breaks Average scheduled time losses per work shift [min]

Meeting at the beginning of a shift 5 Lunch break 30 Testing MASTER PIECES 5 Restock of a material 10 Final cleaning 5 Preventive maintenance, changing a reference 10 Total average scheduled losses for one shift 65

3. Results and discussions

3.1 Poor quality and poor process cost calculation results

Costing the poor quality is based on specific historical data by monitoring the production process over 1 month (23 working days). (Table no. 3) Data refer to the number of rejects in the pre-production unit, assembly time and recorded time losses. For the purposes of relevant calculation, the losses caused by slow-down production were estimated as complete production stop, based on the percentage estimate of a given slowdown (e.g. in the calculation, 100 min of slowdown in production down to 50% was assumed as 50 min. of lost time). Table no. 1 presents a summary of the input data for production after the above-mentioned adjustment in the time losses.

Table no. 3: Poor quality cost

Unit Pre-production Assembly Output pcs 23,307 22,389 Defects pcs 907 267 Net output pcs 22,400 22,122 Average value of the unit EUR 5.5 15.0

Cost of poor product quality EUR 4,988.5 4,005.0 8,993.5

Average lead time min/pcs 2.2 4.3 Total cost of poor product quality EUR 8,993.5 Disposable time min 28, 635.0 Lost time min 4,272.6 Cost of poor process EUR 59,617.7 Total cost of poor quality EUR 68,611.2

3.2 Analysis of the rejects evolution within a time series

Input data on the quality of production generate the time series and one of the analytical reflections for research can be trend analysis within the flow chart. The time series in terms of rejects trend are shown in figure no.3. A trend line course for the stage of pre-production and assembly has logarithmic character. It is obvious, from the graph that the rejects trend has descending direction and the process shows signs of a gradual stabilization.


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Figure no. 3: Time series plot of the rejects in the pre-production and assembly stage

3.3 Sigma level analysis

Due to the discontinuous nature of the output, a process capability was not measured with parameters Cp and Cpk, but on the basis of data – the number of units and the number of rejects. Next, for both phases of the production process, capability level was assessed through sigma level. Sigma level presents a level of quality production process. Due to the evaluation of a sigma level of a process, indicators as Defects per unit (DPU) and Defect per million opportunities for error (DPMO), valuation and yield sigma level are calculated. Process sigma level was calculated using the Process Sigma Calculator. The result was assumed on 1.5 Ϭ a shift. The resulting values of quality indicators for pre-production, assembly and entire process are presented in table no. 4. The overall level of yield is given by the pre-production stage yield YieldP and assembly stage yield YieldA.

DPU = Total of defects/Total of units (7)

DPMO = (Total of defects /Total of units) x 1 000 000 (8)

YieldTOTAL = YieldP x YieldA (9)

where:

DPU – defect per unit,

DPMO – defect per million opportunities for error,

YieldP – Yield (pre-production stage),

YieldA – Yield (assembly stage),

YieldTOTAL – Yield overall.

Table no. 4: Quality process indicators

DPMO Yield [%] Sigma level Pre-production stage 38,915 96.11 3.26 Assembly stage 11,925 98.81 3.76 Process overall 50,371 94.96 3.14



3.4 Cost analysis of poor quality based on probability characteristics and simulation

Previous approach for calculating the cost of poor quality was provided with real data. This was a retrospective cost calculation, values were known from historical records and the cost of poor quality were calculated relatively reliable. Average values of selected input parameters as the value of semi-finished products, production time or assembly duration time were used for calculations. This approach is relatively common in practice, and its application is described in a number of case studies, e.g. in terms of services company would Dragulanescu and Popescu (2015) in the field of education by Mehrabi (2012) or by Pugna, Negrea and Miclea (2016) in an assembly process. Other applications of probability characteristics and simulation of scientific work appear in the field of dealing with little quality. In terms of assessing process capability in the Six Sigma program when data follows the gamma distribution problem is solved by Hsu, Pearn and Wu (2008). Similarly, the Six Sigma performance for non-normal processes, using mathematical optimization models, addressed Aldowaisan, Nourelfath and Hassan (2015). But in both cases there was no simulation activities. Monte Carlo simulations offer additional opportunities for analysis and use of data particularly in forecasting.

This approach is relatively common in practice, and its application is described in a number of case studies, e.g. in conditions of services company by Dragulanescu and Popescu (2015), in the field of education by Mehrabi (2012) or by Pugna, Negrea and Miclea (2016) in an assembly process. Further applications of probability characteristics and simulation can be found rarely in the scientific work when researching the field of quality. In terms of assessing a capability process in the Six Sigma program where data follows the gamma distribution, the problem has been solved by Hsu, Pearn and Wu (2008). Similarly, the Six Sigma performance for non-normal processes, using mathematical optimization models, was addressed by Aldowaisan, Nourelfath and Hassan (2015). However, in both cases there were no simulation activities. Monte Carlo simulations offer further opportunities for analysis and use of data particularly in forecasting.

Where it is necessary to forecast the amount of the cost of poor quality for the next period, then calculation and analysis of the cost of poor quality should be determined based on the probabilistic characteristics of the input parameters. In the case study, probabilistic characteristics were defined for the input parameters based on historical data. Costing was accomplished by Monte Carlo simulation and the resulting costs are analysed in terms of probability. As selection of the distribution and its characteristics vastly affects simulation results, it is in terms of accuracy significant for calculations. Therefore, a distribution function has to reflect reality as closely as possible. Case study presents the forecasting and the analysis of costs of poor quality assuming that the probability characteristics of the input variables result from the obvious development that was used as a model for estimating the future costs.

3.5 Defining the probability characteristics of the input variables

Based on the recognized facts, distributions for the input variables were defined (table no. 5). For the variables, which course is known from historical data, a probability distribution was defined by the function “Fit” (the best fit to our historical data). The distribution of the remaining variables was defined based on the experience, respectively estimates of real progress.


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Table no. 5: Distribution functions for input variables Variable Distribution Characteristics

Scheduled breaks Triangular Mean 65; Min. 60; Max. 70 Output/day (pre-production) Poisson (fit) Defects/day (pre-production) Geometric (fit) Output/day (assembly) Negative Binominal (fit) Defects/day (assembly) Geometric (fit) Lead time in assembly Triangular Mean 4.3; Min. 4; Max. 4.6

An example of distribution defined by the triangular distribution based on the known data, and the geometric distribution using the function Fit is presented in figure no. 4.

Calculation of the cost of poor process is based on probabilistic characteristics. Unused time fund (Lost Time) is determined as the difference between the effective time fund and standard time required for creating the final output in the observed period (formula 10). For standard time, the average assembly time is considered and the calculation takes into account the simultaneous processing of products in four workplaces. The average daily output value was considered for the calculation, and the distribution was defined according to real progress during the reporting period using the Fit function.

TL = Tef – TP = Tnom – TSB – TP (10) TP = QA/TA (11)

where: TL – lost time, T nom – standard time fund for monitoring period, Tef – effective time fund for monitoring period, TSB – scheduled breaks for monitoring period, TP – time production for monitoring period.

Considered times, other than the standard time fund, are based on the probabilistic nature, see table no. 5. Average installation time appears twice in the calculation (once in the calculation of TP, and once when calculating CPP). In order to avoid in calculation double uncertainty regarding to assembly time, and thus increasing the variability of output, it is considered only as an assumption in calculating TP. In the calculation of CPP, as the value the constant number 4.3 min is considered. Calculation of cost of poor quality is based on formulas (1-4). Prognosis was performed by Monte Carlo simulation.

Figure no. 4: Example of definition of the triangular (left) and the geometric (right)

distribution



3.6 Results of the analysis of the cost of poor quality based on simulation

The simulation was accomplished by defining the probability characteristics for entering the input variables and relationships for each cost calculation. In figure no. 5-7, there are presented simulation outputs – forecast charts for individual costs. Figure no. 5 shows the cumulative cost of poor production quality. According to simulation, the average value of the cost makes EUR 8,703.26, which is very close to the values calculated based on historical data (EUR 8,993.5). At the same time, the graph allows the analysis of the probability when exceeding this value. The probability of exceeding the calculated value of cost proceed at 38.65% (figure no. 5), which agrees also with historical data. That fact declares much higher incidence of rejects in the process of pre-production and also costs on rejects at this stage are lower.

On the contrary, the average value of cost of poor process declared by simulation output is significantly higher (EUR 64,203.66) as the calculation of historical data (figure no. 6). This reflects the fact, that for the lost time value reflects to both, the time recorded during the monitoring of the production process plus idle effective time overall. This result is projected in the graph of total costs (figure no.7), where the difference between the average value from simulation run and calculation according to the data is evident. There is also a high probability (64.95%), that the costs exceed the calculated value.

Figure no. 5: Cost of poor product quality


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Figure no. 6: Cost of poor process

Figure no. 7: Total cost of poor quality

Effect of different assumptions on the overall output – Total Cost of Poor Quality- can be assessed on a Tornado chart (figure no. 8). Chart presents the changes in input factors ± 10% and their impact on overall costs. The strongest impact on the total cost has a continuous assembly time and lost time. These inputs and their variability may significantly affect the rate of utilization of the available time. Idle time fund reflects the cost of poor quality four times, as the assembly operations are carried out simultaneously on four pieces. In the presented case study, due to the high rate of idle time, these variables are dominant determinant of overall costs. Much less impact factor is a failure rate in pre-production stage and at the stage of assembly.



Figure no. 8: Tornado chart

The Sensitivity chart (figure no. 9) presents the contribution of the variables to the variability of output, showing only variables which contribution is greater than 0.1%. In this case, the variability of the total cost was considerably influenced by variability in the occurrence of rejects per day in the pre-production stage (49.6%) and in the phase of assembly (28.1%). The failure rate distribution function was derived from the actual course by Fit function, and it proves significantly higher variability than variables assembly time or volume output.

Figure no. 9: Sensitivity chart

Conclusions

This paper presents a model of analysis and calculation of the cost of poor quality using the software tool. Tracing the production process in terms of the cost of rejects and poor organizational process quality discovers a qualitative-economics view. The analysis presented in the article refers to a phase (Measure) within improvement process of the DMAIC method. Case study presents two approaches to determining the cost of poor quality. One approach from the retrospective view when the costs of poor quality and production process were calculated based on data from the previous period. The second method uses probabilistic characteristics of the input variables and is focused on perspective view on the cost of poor quality. Using simulation software in the second analytical approach allows incorporating the uncertainty of input variables and evaluating


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the resulting parameter from the perspective of uncertainty. The resulting costs values according to both approaches (calculated value in the first case and average value in the second case) were compared and evaluated in probabilistic terms. The contribution of all variables to the variability of output and the impact of the change on the overall costs was presented in both, Tornado and Sensitivity charts.

Retrospective analysis and calculation of the cost of poor quality is an important step in the process of detecting errors. Identification of weak points and the realization of their economic impact must be anticipated in improvement processes. Estimating the future development of costs of poor quality, knowing the effects of inputs uncertainty allows preventing the losses. If the variability of time or performance variables were known in advance, their relation to output would be known and their impact can be verified by means of the simulation. Use of software tools with a range of simulation techniques allows assessing the feasibility of expected or unintended impacts, in advance. Complex combining a number of uncertain variables and monitor their impact on one common target is difficult to implement without computer simulation. Knowing the variables that determine the achievement of objectives, and on the contrary, which one are most threatening for its variability, allows focusing on the right targets and eliminate wasting the resources on non-essential activities. Effective use of simulation software tools in the field of quality management has its important place, even its limits. Its indispensable role here still belongs to the experts who are solely familiar with processes. Correct estimate when defining the distribution of variables, sensitive assigning the uncertainty in simulation experiments, only in the necessary extent, those are prerequisites for the relevant simulation results and their benefits in improving quality.

Acknowledgement

This article was prepared within a research project VEGA 1/0853/16 New project technologies for the creation and implementation of future factories and the authors thank the supporting institution.

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ACHIEVING BUSINESS EXCELLENCE BY OPTIMIZING CORPORATE FORENSIC READINESS

Gojko Grubor1, Ivan Barać2, Nataša Simeunović3* and Nenad Ristić4

1)3)4) Sinergija University, Bijeljina, Bosnia and Herzegovina 2) Singidunum University, Beograd, Serbia

Please cite this article as: Grubor, G., Barać, I., Simeunović, N. and Ristić, N., 2017. Achieving Business Excellence by Optimizing Corporate Forensic Readiness. Amfiteatru Economic, 19(44), pp. 197-214


Abstract In order to improve their business excellence, all organizations, despite their size (small, medium or large one) should manage their risk of fraud. Fraud, in today’s world, is often committed by using computers and can only be revealed by digital forensic investigator. Not even small or medium-sized companies are secure from fraud. In the light of recent financial scandals that literary demolished not just economies of specific countries but entire world economy, we propose in this paper an optimal model of corporative computer incident digital forensic investigation (CCIDFI) by using adopted mathematic model of the greed MCDM – multi-criteria decision-making method and the Expert Choice software tool for multi-criteria optimization of the CCIDFI readiness. Proposed model can, first of all, help managers of small and medium-sized companies to justify their decisions to employ digital forensic investigators and include them in their information security teams in order to choose the optimal CCIDFI model and improve forensic readiness in the computer incident management process that will result with minimization of potential losses of company in the future and improve its business quality. Keywords: computer incident; forensic readiness; forensic alternatives; forensic criteria; greed multi-criteria method; Expert Choice evaluation. JEL Classification: C39, G32, M15 Introduction

In the last few years, cyber threats have become more sophisticated with blended diversified zero day attacks and they practically make ineffective the current container type security position (Casey, 2011; Choo, 2011; FireEye, 2013). Traditional reactive security systems (ISO/IEC 27001:2013) can no longer prevent many computer incidents (CIs). These risks can be mitigated by reducing the opportunities for cybercrime occurrence, making cybercrime * Corresponding author, Natasa Simeunovic – [email protected].

AE Achieving Business Excellence by Optimizing Corporate Forensic Readiness


more difficult to commit and by increasing the likelihood of cybercrime detection and increasing the punishment associated with committing cybercrime (Choo, 2011).

Risk management can be a very helpful tool for companies in achieving sustainable competitive advantage. By implementing CI management, as the element of the overall process of managing risk of fraud, organizations can increase trust level from their shareholders, investors, audit committees, board members, management and society as whole. How to assess and manage risk is well described by Damodaran (2007), Giles (2012), Gibson (2014) and Wu, Chen and Olson (2014). Before defining proactive programs to prevent fraud, especially one which occurs in form of computer incident, it is necessary to understand fraud theory, its schemes and many other facts about fraud in order to make good fraud risk assessment so that digital forensic investigator would be better prepared to detect fraud (Vona, 2012).

Currently, some advanced forensic techniques, such as the unified social graph-based text mining framework to identify digital evidence from chat log data providing algorithms to identify key-terms representing the interests of users, key-users, and key-sessions, etc. can be used (Anwar and Abulaish, 2014). Apparently, for most effective CI management, some automatic forensic tool to image and analyze the data in real time should be used. However, such an automatic DF investigation and analysis tool requires more research and experiments in the future. In this paper, the authors propose a greed multi-criteria method to choose an optimal CCIDFI model in order to increase forensic readiness and mitigate risk of possible corporate fraud which can affect deeply (Dyck, Morse and Zingales, 2013) on achieved business excellence of company.

The article is organized as follows. Literature review of the most relevant findings regarding CI management and adopted MCDM and MCDA methods is given in the first section. Next section explains research methodology presenting the theoretical models, data and approach used in empirical analysis. Section 3 present results and provide discussion, while conclusions are given in the last section.


Proactive network security, including intrusion detection (Kaufman, 2012) and protection systems (IDPSs), strong monitoring, and logging security relevant events from all active network and network security devices into a centralized log server, can provide better network security (Bradford and Hu, 2005; Scarfone and Mell, 2007; Zimmerman, 2010; Alharbi, et al., 2012; Grubor and Njeguš, 2012). In the environment of highly sophisticated threats, high speed changes in attack method and the diversification of vulnerability detection and exploitation tools, even proactive security systems cannot provide protection. Therefore, some predictive (smart) security systems, such as the system of the so called digital ants (Haack, et al., 2011), detecting different types of malware breaking into a computer network over the Internet can be used.

Physical and computer security over assets, records and information present a key aspect of fraud prevention (Giles, 2012). New technological trends are very important, in fact, technology is the key to the success and support which can be exploit for business excellence of organizations (Uhl and Gollenia, 2016). In many companies there is no forensic capacity due to the lack of expensive technology and competent digital forensic investigators. Therefore, choice of the optimal model of corporative computer incident digital forensic


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investigation (CCIDFI) recourses becomes a natural improvement of the CI management process.

A proactive forensic network, in the case of computer incidents, can make digital forensic (DF) investigation and later analysis much easier (Gorzelak, et al., 2011). If there is a huge volume of data that should be analyzed, a distributed digital forensic analysis model can be applied (Martini and Choo, 2014). In addition to the few papers published in this area, some authors (Martini and Choo, 2014) describe in detail the advantages of distributed file system forensics, providing better cost-effectiveness and efficiency for the DF analysis.

As CI management is one of the key strategies in mitigating information security risks, other authors (Ab Rahman and Choo, 2015) have proposed a conceptual cloud incident handling model including computer incident handling; and digital forensics and the capability maturity model for services (Team C.P., 2010) to more effectively handle incidents in the cloud. Generally, integration of the DF investigator into the information security team enables easier later investigation of the computer incident (Kent, et al., 2006). In the case of computer incident, many organizations try to identify the type of incident and recover systems by their own resources. Traditional Corporative Computer Incident Digital Forensic Investigation (CCIDFI) is typically performed by a hired, quite expensive, DF investigator acting as a consultant (Casey, 2011; Steel, 2006). If the attacked computer system cannot be turned off for any reason, a dynamic model of the live digital forensic can be used (Jones, Bejtlich and Rose, 2005). Moreover, if the DF investigator cannot approach the attacked server, for any reason, a technique of more expensive ethical hacking can be applied to remotely access the server and to take images of the HD (Hard drive), RAM (Random Access memory) and network card using a virtual snapshot tool (e.g. Hyper V in Microsoft Server 2012 R2).

In the area of Multiple-Criteria Decision Making (MCDM) and Multiple-Criteria Decision Analysis (MCDA) a variety of approaches and methods have already been published. Many of them have been developed and implemented by specialized decision-making software (Weistroffer, Smith, and Narula, 2005). Some of the MCDM methods have been comparatively studied in the book by Triantaphyllou (2013). Review of the available MCDM methods is completed in the work by Greco, Figueira and Ehrgott (2005). Generally, the MCDM problem can be represented in the criterion space or in the decision space. However, if different criteria are combined by weighted linear functions, it is also possible to represent the problem in the weight space.

Some methods require information on the decision makers’ preferences at the beginning of the MCDM process. This approach is called as prior articulation of preferences that transforms the MCDM analysis into a single criterion problem (Keeney and Raiffa, 1993). Many methods, such as those based on estimating a value function or using the concept of outranking relations, or analytical hierarchy process, and some decision rule-based methods, have tried to solve MCDM evaluation problems using interactive progressive articulation of preferences throughout the solution process (Geoffrion, Dyer and Feinberg, 1972; Köksalan and Sagala, 1995; Köksalan Wallenius, and Zionts, 2011).

The authors of this paper choose the greed MCDM methods suggested by Žižović, et al., (2014) and a new distance based approach to the MCDM problem suggested in Salabun (2015) as a new, integrated approach to decision making regarding forensic readiness in business environment.



2. Methodology

2.1 Functional model of the corporative DF investigation

The functional model of the CCIDFI process should include the same principles, procedures, tools and techniques as those in the law enforcement DF investigation process, with the exception of arresting and sanctioning the attackers. The choice of the optimal CCIDFI process is closely related to the CI management process in companies. Currently, the first response to the computer incident is typically reactive in small and medium private companies. It focuses on re-establishing the information security system, patching vulnerabilities, removing malicious activity, recovering files and the ICT (Information Communication Technology) system, and reporting them to management (Nikkel, 2014). As CI is becoming more complex and typically involves multiple organizations, there is a growing need for CCIDFI readiness as a fundamental part of the CI management process. This CCIDFI readiness should include conceptual models, legal issues, and DF standard operating procedures (SOPs), creating international standards for evidence exchange, and developing technical capabilities for acquisition and analysis of digital evidence (DE) across multiple jurisdictions (Nikkel, 2014). Therefore CCIDFI readiness is a crucial part of security risk management, reducing both the costs of CI response and DF investigation (Nikkel, 2014). Depending on the size, companies can have different capacities for CCIDFI readiness with regard to staff competency and the technologies used. The role of the CCIDFI investigator and analysts in a security team could be summarized as follows:

• First response to the computer incident and documenting the original state of the attacked computer system to provide authenticity of the digital data (DD), determine the type of the CI, and prevent escalation of the attack.

• Organizing and leading the security team in the CI or computer crime DF investigation, providing DD integrity and documenting on each and all activities.

• Applying SOPs to access the attacked computer, taking physical acquisition and forensic images of all the suspect media using writing blockator.

• Collecting all of the relevant DD from the forensic images, recovering as much DD as possible and creating a time line of the attack from the log files of the network and security devices in the logical acquisition phase.

• Analyzing DE traces from the DD sources and constructing legally admissible DE.

• Participating in the interrogation of the suspected interior attackers or eyewitnesses.

• Reconstructing the attack by integrating digital and physical evidence, and reporting them to the manager for further steps – to involve law enforcement or not, depending on its impact on the company’s image.

A competent CCIDFI team can provide valid forensic data for the law enforcement investigator that can cast doubt on the DE’s integrity. Therefore, rigorous documentation of any activities in the CCIDFI process and DE handling must be undertaken for them to be accepted by law enforcement investigators and the DE admitted by judges. As adequate CCIDFI readiness is mainly costly, a team of computer science specialists usually leads the CI investigation and sometimes it can be supported by hired DF investigators as consultants. In this article, the authors suggest the use of the greed MCDM method as the


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optimal CCIDFI model. In a case study, the proposed method is evaluated using the interactive software tool, Expert Choice, to help companies to strengthen their forensic readiness using an optimal or set of optimal CCIDFI model solution alternatives. The results are analyzed in the evaluation section.

2.2 Mathematical model of the greed MCDM method

Any multiple-criteria evaluation problem consists of a limited number of alternatives that must be known before starting of the solution process. Each of the alternatives can be represented by its performance, using multiple criteria or objectives. The criteria can be based on intuition or some other methods and their consequences could be very high. In making the decision to choose an optimal CCDFI model, there are complex multiple criteria and multiple parties involved, such as the DF analyst, prosecutor, judge, victims and attacker that could be deeply affected by the consequences of the CCIDFI results. In the MCDM methods any problem should be properly structured and each chosen alternative explicitly evaluated against multiple criteria. The cost is one of the main criterion for the choice of the CCIDFI model, as in many MCDM methods, too. Some other criteria of the CCIDFI process quality, such as client satisfaction or digital evidence admissibility by judge that could be in conflict with the cost should be considered carefully. Therefore, there is no unique optimal solution due to more than one criterion in the CCIDFI model. It is necessary to choose the most preferred alternative from the available set or to group alternatives into a small set of different preferences, or a set of indifferent or non-dominated solutions. To help decision maker focus on the best alternative from the large set of indifferent solutions, some tools and criteria trade-off are needed (Keeney and Raiffa, 1993).

The MCDM problem may be defined as a process of finding the best alternative or a set of good alternatives for a decision maker. In this article, the MCDM analysis of the determined CCIDFI alternatives against given criteria is undertaken, based on the greed method of MCDM suggested by Žižović, et al. (2014). This method is based on calculating the distance of the given alternative, iA from the hypothetical best alternative, *A , and the hypothetical worst alternative, *A . In this calculation, a number of parameters such as functional value of the criteria and functional description of the alternatives have to be considered. One alternative is better than another if it is closer to the hypothetical best alternative, *A , and further from the hypothetical worst alternative, *A .

Let us have a set of alternatives, 1 2{ , , }mA a a a= that is evaluated against a set of criteria,

1 2{ , , }nC c c c= . The criteria from the given set do not have the same value, therefore a weighted factor, (0,1]kz ∈ is associated with each of the criteria kc , {1,2, , }k n∈ . Here, the weight factor means the level of the criterion value for evaluation of each alternative in the given set of alternatives. Then, each criterion kc , {1,2, , }k n∈ should be associated with a function : [0, )kZ A A× → +∞ that measures the level of the value differences among the alternatives. The Zk,, function associates each pair of alternatives ,i ja a A∈ , with a non-

negative number ( , )k i jZ a a that represents the key performance indicator (KPI) of the Zk

function. For simplicity, for each {1,2, , }i m∈ , let alternative ia be represented as the



arranged n-tuple 1 2( , , , )i i i ina a a a= , or its equivalent 1( )ni ik ka a == , where each

coordinate ija , for {1,2, , }j n∈ , is a non-negative real number that represents the degree

of fulfilment of the criterion jc . Then, the two new sets of the hypothetical alternatives A and A are constructed for further calculation. Thus, for a given set of alternatives 1 2{ , , }mA a a a= , the sets of alternatives A and A are defined as follows (Žižović, et al., 2014):

{ }1(max ) | ({1,2, , }) \ Ø ,nik ki J

A a J P m=∈= ∈ (1)

{ }1(min ) | ({1,2, , }) \ Ø .nik ki J

A a J P m=∈= ∈ (2)

Based on definition (1) set A with a natural arrangement A≤ gives the upper semi-greed

with the highest element max A, denoted by a and called the best alternative. Similarly, set A with natural arrangement A≤ gives the lower semi-greed with the lowest element

min A, denoted bya and called the worst alternative. Now, in the set L A A= ∪ a partial

arrangement that keeps both A and A arrangements can be defined. Here, for all ,a b L∈ , if

Aa b≤ will be a b≤ , and if Aa b≤ will be a b≤ . In the principle, ( , )L ≤ is a partially arranged set that can be considered as a sum of the upper and lower semi-greed. Let ,a b L∈ , where a b or a is covered by b . Then, for each criterion kc , {1,2, , }k n∈

the preferred alternativeb over alternative a in relation to criterion kc can be defined as follows (Žižović, et al., 2014):

**

( , ) ( , ) k kk k

k k

b aa b Z a b

a ad

. (3)

Now, if ,a b L∈ are two arbitrary alternatives, and 1 2: jP a p p p b= = is a path in L from alternative a to alternative b ,then, the current preferred alternative b over alternative a in relation to the criterion kc , alongside path P, if 1( , ) 0k i ip pδ + = for

all { }1, ,i j∈ , and ( , ) 0Pk a bδ = the other vice is defined as (Žižović, et al., 2014):

1

111

11

1( , ) ( , )( , )

jP

k k i iji

k i ii

a b p pZ p p

δ δ−

+−=

+=

= ∑∑

, (4)

or its equivalent:

( , )Pk a bδ =

11,

11 *1 *

11

1 ( , )( , )

jk i ki

k i iji k k

k i ii

p pZ p p

a aZ p p

−+

+−=

+=

−

−∑∑

. (5)


Vol. 19 • No. 44 • February 2017 203

Starting from alternative a to alternative b, the goal L can be reached in more different ways with corresponding current preferred alternatives. So, the preference for alternative b over a in relation to criterion kc can be defined as follows (Žižović, et al., 2014):

( , ) max{ ( , ) | : }Pk ka b a b P a bπ δ= → , and therefore the preference of alternative b over a

can be viewed as n-tuple: 1( , ) ( ( , ))nk ka b a bπ π == .Thus, the distance of alternative a L∈

from the best alternative a is given by:

*1

1

1

1( ) ( , )n

k knk

kk

D a z a az

π=

=

= ⋅∑∑

, (6)

Similarly, the distance of the worst alternative a from the alternative a is given by:

0 *1

1

1( ) ( , )n

k knk

kk

D a z a az

π=

=

= ⋅∑∑

. (7)

Now, for the two alternatives ,a b A∈ it can be said that alternative a is preferred over alternative b if alternative a is closer to the best alternative and the worst alternative is further from alternative a. If both alternatives, a and b, have the same distance from the best and the worst alternatives, then the alternatives a and b are indifferent or they are incomparable.

The relations (6) and (7) are partially arranged. If decision makers want to have total arrangement the following relation can be constructed:

For all a A∈ , a difference is defined as:

0 1( ) ( ) ( )D a D a D a= − . (8)

Total arrangement can be defined for all ,a b A∈ , if and only if:

( ) ( )D a D b> , (9)

Where a is preferred over b, and

( ) ( )D a D b= (10)

Where a is indifferent to b.

In this article, the greed MCDM method (Žižović, et al., 2014) is adopted and evaluated by the Expert Choice interactive software tool. Choice of the optimal CCIDFI model in small and medium organizations is set up as the goal of MCDA. Using Expert Choice interactive software tool integrated with greed MCDM method presents a novel approach to decision making process regarding digital forensic readiness in business environment.

The inability of the security mechanisms of the reactive and even proactive information security systems to discover and permanently prevent repetition of a sophisticated attack is the main reason for choosing an optimal CCIDFI model. The next step in the maturing



process of CI management is hiring an experienced DF investigator as a consultant. Integration of an expert with digital forensic knowledge, tools and skills into the security team is a mature CI management process (Kent, et al., 2006). As it is quite expensive for the average company, the choice of an optimal CCIDFI model seems to be a natural solution. A typical CI management model is shown in figure no. 1 (Cichonski, et al., 2013).

Figure no. 1: Typical model of the CI management process

Source: Cichonski et al., 2013

In improving CCIDFI readiness some alternatives and their functional descriptions can be considered (table no. 1).

Table no. 1: Functional description of the chosen CCIDFI alternatives

iA Functional description of the CCIDFI alternatives

1A Command line forensic tool application by system/network administrator as first response to the computer incident/crime without use of DF science principles and procedures.

2A Provision of an open source or commercial DF tool for CCIDFI and application of DF principles and procedures by competent information security specialist, or hired DF consultant that provides authentic DD for potential DF analysis.

3A Provision of a tool for CCIDFI as in 2A and application of DF science principles and procedures by a competent DF investigator that is integrated into the information security team providing authentic DD for potential DF analysis.

4A Combined alternative 3A with a system of proactive DF of the computer network – strong monitoring and logging of forensically relevant data in all active network devices, for easier future DF investigation.

5A Proactive network forensic system integrated with central log server and DF tool as in alternatives 2A and 3A .

6A Ethical hacking and live forensic system integration into alternative 5A for DF investigation of running web servers.


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iA Functional description of the CCIDFI alternatives

7A

Integrated 5A and 6A alternatives with predictive security mechanism – expert systems, data mining techniques etc. – for DF investigation and analysis of most sensitive computing systems such as Cloud Computing in real time.

Even though the order of the alternatives is not important for many MCDM methods, the authors, in table no. 1, suggest their ascending values starting from 1A to 7A .These alternatives can be evaluated using various criteria. Let us assume that we evaluate the seven CCIDFI alternatives using four main criteria, based on the greed MCDA method (Žižović, et al., 2014) and the Expert Choice tool. The criteria for evaluation of the alternatives can be chosen according to their importance and represented by the KPI or weight factors in the average company (table no. 2).

Table no. 2: Chosen criteria and their normalized weight factors for each of the CCIDFI alternatives

kC The criteria for evaluation of the CCIDFI alternatives

KPI or weight factors (0,1]kz ∈

1C Cost of the CCIDFI process 0.9

2C Forensic image authenticity 1.0

3C Forensic investigator/analyst competency 0.7

4C Quality of the DE for admission in court 0.8

Source: Realized by authors based on Žižović et al., 2014.

Among all possible solutions of the CCIDFI alternatives, the one that performs well in all considered criteria should be the ideal choice. As there is unlikely to be a single best solution for all the considered criteria, a trade-off between the criteria is usually necessary. Functional descriptions of the criteria for the evaluation of each alternative are given in table no. 3.

Table no. 3: Functional descriptions of the chosen criteria

kC Functional descriptions of the chosen criteria

1C

Cost increases using commercial DF tools, network forensics, the number of incompatible platforms and new devices with unknown architecture and hardware, and by the virtual environment

Cost decreases using known file systems, familiar DF techniques and SOPs

2C To be admitted in court, the forensic acquisition and analysis must preserve the integrity of the DE in the custody chain

3C The competency of the DF investigator and analyst is relevant for DE admission by a judge



kC Functional descriptions of the chosen criteria

4C The quality of the DE, such as relevance, completeness, corroboration, harmfulness and coverage for the case, is required for legal admission of the DE

Suggestions for the preferredkC weight factors for the optimal CCIDFI alternative

evaluations are shown in table no. 4.

Table no. 4: SuggestedkC weight factors for evaluation of the CCIDFI alternatives

iA kC weighted factors

1C 2C 3C 4C

1A 20 15 9 8

2A 18 17 12 12

3A 15 19 16 18

4A 12 20 18 18

5A 11 20 18 19

6A 10 20 19 19

7A 9 20 20 20

In this article, an assumption is made that all criteria are maximized and ranked from 1 to 20, where 20 is the best, and 1 is the worst weight factor. Certainly, it is possible to set up some other criteria for the CCIDFI alternative evaluations, but the authors of this paper considered that these criteria are appropriate for the CCIDFI optimal model choice in many practical cases.


Evaluation of the suggested alternatives, based on the greed MCDM method was undertaken by the multi-objective decision Expert Choice support tool, based on the mathematical Analytic Hierarchy Process (AHP) theory, first developed at the Wharton School of the University of Pennsylvania by one of Expert Choice's founders. The AHP helps decision making process by using both empirical data and the subjective judgment of the decision makers, providing them by a structure to evaluate the values of various criteria and the preferences of alternative solutions. Using what-if and sensitivity analyses, and pair wise comparisons to derive more accurately priorities than other MCDM methods, the Expert Choice combines these priorities for each solution to get the overall priorities of the alternatives and to determine how a change in the criterion value would affect choice of the alternatives (Barfod, 2014).

The choice of the preferred solutions among all the alternatives can be based on their relative value in Distributive mode, or on a single best alternative in Ideal mode of the Expert Choice tool.


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In the Distributive mode, the normalized weight of criterion is distributed among all the alternatives. Then, all criteria weight is divided up into proportions that corresponds to the relative priorities of the alternatives. The Ideal mode maintains the rank of the best alternatives, unlike the Distributive mode. In it, the preferred alternatives are divided by the largest value among them and multiplied by the weight of the corresponding parent node. In that way, the most preferred alternative receives entire group priority given by the criterion immediately above it. The other alternatives receive a proportion of the parent node weight. The alternative that is best for all the criteria obtains value of one, while the other alternatives obtain proportionately less values. All the alternatives together give the sum more than one (Teknomo, 2006).

In the case study, the weight factors of the kC criteria changed a few times to choose the optimal CCIDFI alternative or set. The comparative review of the relative impact among criteria

kC in the accomplishment of the main goal is shown in figure no. 2.

Figure no. 2: Comparative review of relative impact among criteria

Source: Authors calculations using Expert Choice Interactive Software tool

The set of optimal CCIDFI alternatives according to the established goal of this MCDM is shown in figure no. 3.

Figure no. 3: Set of optimal CCIDFI alternatives




3.1 Evaluation of the results

To obtain the optimal solution set that could be acceptable for many companies of different sizes, an analysis of the Expert Choice results using both the Ideal and Distributive modes was undertaken. The same input parameters were used in both modes (figures no. 2 and no.3). The goal, criteria and CCIDFI alternatives that were evaluated in the Ideal mode are represented in figure no. 4.

Figure no. 4: The goal, criteria and CCIDFI alternatives in Ideal mode Source: Authors calculations using Expert Choice Interactive Software tool

The comparative values of criteria kC , their influence and the results of the alternatives analysis, both given in percentages, are shown in figure no. 5.

Figure no. 5: Comparative values of criteria

kC , their influence and results of alternatives’ analysis


In the Ideal mode, three alternatives, 3A ,

4A and 7A , have values of 15% and represent a set of optimal solutions. The values of the other alternatives are as follows 5A and 6A (14.9%),

2A (13.3%), and 1A (11.8%). Having a set of optimal alternatives, in addition, another three alternatives are analyzed to simplify the CCIDFI choice for the companies using only one or two criteria.

The summarized results of the “Head to Head” analysis by the Expert Choice software tool are given in table no. 5. The values all of the kC criteria for each pair of alternatives are compared relating to each criterion.


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Table no. 5: Head to head analysis of the A3, A4 and A7 alternatives

kC Weight head to head between alternatives 3A ,

4A and 7A The best solution(s)

1C 3A >

4A ; 3A > 7A ;

4A > 7A ; 3A

2C 3A <

4A ; 3A < 7A ;

4A = 7A ; 4A , 7A

3C 3A <

4A ; 3A < 7A ;

4A < 7A ; 7A

4C 3A =

4A ; 3A < 7A ;

4A < 7A ; 7A

The results of the analysis confirmed that the 3A alternative becomes the optimal solution for

the company choosing the 1C criterion as the most preferred. For companies choosing 2C as

the preferred criterion, the set of alternatives, 4A and 7A , is the optimal solution. The

authors suggested that alternative 7A should be chosen as it is most favorable from the forensic investigator and analyst point of view. The alternative 7A is the optimal solution

for the choice of the 3C and 4C criteria.

Combining two or more criteria and obtaining the results, and the optimal solution using Expert Choice, analysis becomes a more complex process due to choice of the starting set for the optimal solution from three criteria by increasing kC . The ‘performance sensitivity’ analysis in Ideal mode (figure no. 6) shows how the alternatives were prioritized relative to other alternatives with respect to each criterion as well as overall.

Figure no. 6: The ‘performance sensitivity’ analysis in Ideal mode


The goals, criteria and alternatives that were analyzed by the Expert Choice tool in Distributive mode are shown in figure no. 7.



Figure no. 7: The goals, criteria and alternatives analysed in Distributive mode


Compression of the criteria values, their impact on the alternatives and the results of the alternatives analysis (both in %) are given in figure no. 8.

Figure no. 8: Compression of criteria values, their impact and results of analysis

(in %) Source: Authors calculations using Expert Choice Interactive Software tool

Analyzing the results of the evaluation in Distributive mode it can be seen that there is only one optimal solution,

3A (15.1%), and the other solutions of the CCIDFI alternatives are as follows:

4A (14.9%); 5A and 7A (14.8%); 6A (14.7%); 2A (13.5%), and 1A (12.2%). The

‘performance sensitivity’ analysis in Distributive mode, displayed in figure no. 9, shows how the alternatives were prioritized relative to other alternatives with respect to each criterion as well as overall.


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Figure no. 9: The ‘performance sensitivity’ analysis in Distributive mode Source: Authors calculations using Expert Choice Interactive Software tool

Conclusions

In an environment of highly sophisticated threats and exploitation tools, and methods of attack, CI management becomes an inevitable part of a company’s management process. Due to the complexity of management phenomenon, the first response and the CI investigation require integration of the DF investigator into the information security team. In many private companies there is no appropriate capacity for CI management due to the lack of expensive technology and a competent DF investigator. That is why the choice of the optimal CCIDFI model becomes the most important activity in the CI management process. In this paper, use of the greed MCDM method in choosing the optimal model, combined with the Expert Choice software tool whose purpose is to evaluate an optimal set of the CCIDFI model alternatives is suggested by the authors as a new, integrated approach that aims to accomplish companies’ missions and sustain world-class results in order to become more competitive. The intention of given research was to help SMEs in managing their risk of fraud committed by using information technologies through optimization of forensic readiness of companies.

It is supposed that the CCIDFI alternatives should include similar DF investigation resources, principles, procedures and tools to those used by law enforcement, with the exception of arresting and sanctioning the attackers. As various companies have different capacities for CCIDFI, depending on size and CCIDFI readiness, the suggested approach in the choice of the optimal CCIDFI model alternatives or set can help companies to make better decisions. The main purpose of this work is to help company managers to validate their decision to employ a DF investigator and analyst as part of the information security team to improve the CI management process, increase forensic readiness and prevent possible future losses which, together with previously mentioned elements, have direct impact on company's business excellence.

In order to improve CCIDFI readiness, the authors considered seven crucial alternatives and four criteria for their evaluation. The ideal choice, among all possible solutions of the



CCIDFI alternatives, should be the one that performs well in all considered criteria. In the Ideal mode, three alternatives,

3A (provision of a tool for CCIDFI as in 2A and application of DF science principles and procedures by a competent DF investigator that is integrated into the information security team providing authentic DD for potential DF analysis),

4A (combined alternative 3A with a system of proactive DF of the computer network – strong monitoring and logging of forensically relevant data in all active network devices, for easier future DF investigation) and 7A (integrated 5A and 6A alternatives with predictive security mechanism – expert systems, data mining techniques etc. – for DF investigation and analysis of most sensitive computing systems such as Cloud Computing in real time), represent a set of optimal solutions.

The results of the analysis confirmed that Alternative 3, as the most preferred, becomes the optimal solution for the company choosing the first criterion – “Cost of the CCIDFI process”. For companies that chose “Forensic image authenticity” as the preferred criterion, the set of alternatives, 4A and 7A , is the optimal solution. Looking from the forensic investigator's and analyst's point of view, the authors suggested that Alternative 7 should be chosen as it is most favorable. As for the choice of “Forensic investigator/analyst competency” and “Quality of the DE for admission in court” criteria, the Alternative 7 is the optimal solution. Analyzing the results of the evaluation in Distributive mode it can be seen that Alternative 3 is the only optimal solution.

Experimental verification by the Expert Choice software tool, both in Ideal and Distributive mode, and evaluation of the results confirmed that an optimal CCIDFI alternative or set of them can be determined and can help small and medium-sized companies to strengthen their CCIDFI readiness and CI management processes in order to assess and manage risk and achieve or improve their business excellence.

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QUALITY MANAGEMENT IN PROJECT MANAGEMENT CONSULTING. A CASE STUDY IN AN INTERNATIONAL CONSULTING COMPANY

Eduard-Gabriel Ceptureanu1, Sebastian-Ion Ceptureanu2*,

Cristian-Eugen Luchian3 and Iuliana Luchian4 1), 2), 3), 4) The Bucharest University of Economic Studies, Romania

Please cite this article as: Ceptureanu, E. G., Ceptureanu, S. I., Luchian, C. E. and Luchian. I., 2017. Quality Management in Project Management Consulting. A Case Study in an International Consulting Company. Amfiteatru Economic, 19(44), pp. 215-230


Abstract The present paper addresses quality management from the specific perspective of project management consulting service providers, in the framework of large infrastructure projects. Because of their supposed superiority in knowledge and experience, project management consultants have an ultimate responsibility for the proper implementing of the project. Therefore, quality management in consulting organizations should focus on critical success factors. As there is no consensus yet regarding the most important aspects of the consulting activity on which depend the achievement of the project aims, there is scope for further investigating this subject. Here, the case of a project management consulting organization involved in large infrastructure projects in Romania, Bulgaria, Moldova, Ukraine and Serbia is analyzed. Data collected through a questionnaire-based survey among international consultants and support personnel suggest that factors related to leadership style and communication skills are more closely tied to the success of the project than more technical aspects. The results constitute an empirical evidence of main success factors for specialized consulting services in project management and can be useful in improving business and project performance and achieving business excellence. Keywords: quality management, consulting services, critical success factors, business performance JEL Classification: M10, O1, L2

Introduction

Ambitious infrastructure development programs adopted in Central and Eastern European Countries (CEECs) have created new market opportunities for project management consultants. For CEECs the infrastructure is among the most important factors of national * Corresponding author, Eduard-Gabriel Ceptureanu – [email protected]

AE Quality Management in Project Management Consulting. A Case Study in an International Consulting Company


or regional economic competitiveness, together with macroeconomic stability, goods market efficiency, labor market potential and level of workforce training (World Economic Forum, 2013); scholars have indeed demonstrated the positive relationship between infrastructure and economic and social development (Ihuah, et al., 2014).

However, the capacity to realize a sustained rhythm of infrastructure development depends on the ability to attract financing resources (mainly EU funds), which in turn require compliance with high standards in project design and implementing. This generates a demand for high quality project management consulting services. On the other hand, consulting organizations are selected based on their reputation, or, in other words, the success of the projects they assisted in the past.

Quality management systems were implemented for infrastructure projects on a large scale, especially for companies managing multiple or mega projects in construction. Although many studies have been conducted to study quality management practices within various industries, there is a distinct deficiency of relevant studies on consulting industry as a whole and consulting for infrastructure in particular since researchers are more interested in analyzing quality and costs of projects (Soetanto, et. al., 2001) rather than quality management. Simultaneously, more efforts should be made toward analyzing the case of business services quality and eventually designing an instrumental framework for quality assessment.

For infrastructure, there is evidence that implementing quality management systems may improve communication between all stakeholders involved, cuts costs and improve control of subcontractors, resulting in improved productivity, better margin and increased market share (Motwani and Kumar, 1996). However, most of the studies were not specifically designed to examine performance criteria for projects in consulting industry. Some studies suggested that implementation of ISO standards can be beneficial for companies by improving overall performance and quality awareness (Mo and Chan, 1997; Parsa and Keivani, 1997; Thelen, 1997).

Consultants and their teams have to properly identify and understand the impact of each project success factors in to improve performance and increase project success rate. In this way, the chances of achieving project objectives could increase substantially considering the classical constraints of time, resources and budget.

1. Theoretical background

Efficient implementation of quality management in projects is important for improving performance for virtually all consulting company. In the literature, there is a significant number of studies investigating the effects of various quality management practices on quality (Dow et. al., 1999), operational performance (Samson and Terziovski, 1999) or business performance (Adam et. al, 1997).

Literature on consulting for project management suggests that there are many factors that influence a project’s success and there is no consensus on a particular set of critical success factors. Providing consulting for successful implementation of a project usually include a wide variety of criteria. However, in the simplest terms, the success can be considered to incorporate four basic issues. A project is generally considered to be successfully implemented if: a) it is finished in time (time criterion); b) the cost does not exceed the


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planned budget (monetary criterion); c) achieve all the goals initially set (efficacy criterion); d) it is accepted and used by the customers for whom the project is intended (customer satisfaction).

Most of the results demonstrates that quality management practices are significantly linked with quality and operational performance, but they have unclear effects on business excellence (Arias, et al., 2014). Another study (Forker, et. al, 1996) emphasize that quality support a company to acquire competitive advantages by fulfilling client needs. Nevertheless, those performance indicators are not appropriate for infrastructure projects. Sharma and Gadenne (2002) emphasized relationship between quality management system and organizational and business performance in general.

However, consulting for project management has changed over the years (Ceptureanu, 2015). Müller and Jugdev (2012) made a comprehensive review of the literature on this topic for the last 40 years and reach the conclusion that there is a significant increase in the set of factors affecting the success of a project and considered by those involved, in our case the consultants. The 80s were marked by an emphasis on planning and control tools, as well as customer relations focus and project team professionalization (Morris, 1988; Pinto and Slevin, 1988). Performance evaluation criteria such as time, budget and functionality have dominated this period and turned gradually to more subjective factors such as customer satisfaction. The 90s have shown an increasing interest in behavioral and interpersonal factors and increased awareness of how project management is about managing human resources to achieve results, not necessarily about managing work itself (Turner and Müller, 2006). A subsequent study by Müller and Turner (2007) showed that differences in projects’ success interpretations depends specifically on the local culture and complexity of the projects.

Generally, it has been accepted that time, quality and cost are the main factors in the performance measurements of a project (Barkley and Saylor, 1994) while additional criteria have been suggested to be considered by others (Kumaraswamy and Thorpe, 1996), including quality of works or fulfilling stakeholders’ requirements.

Today, for consultants, project's success and implicitly quality of consulting service is commonly considered as a combination of:

• Project’s critical success factors, variables that can be acted upon to increase success chances (Morris and Hough, 1987; Turner and Zolin, 2012);

• Criteria for measuring projects’ success, what are those measures on which the success of the project is evaluated (key performance indicators) (Wateridge, 1995). Evidently, project management consulting should focus on these elements.

Success means different things to different people and in the case of consulting for project management, the situation is no different (Ceptureanu, 2015). One common hypothesis is if a project is completed on time, within budget and agreed quality, then the project is considered successful. The existing evidence suggests that this is far from the truth. Since the late 60s, project management researchers have tried to discover what factors lead to project success (Baker, et al., 1974:25, 1988; Pinto and Slevin, 1988; Lechler, 1998). Most of the initial studies have focused more on the reasons leading to project failure than its success (Balachandra and Raelin, 1984; Hall, 1980), so consultants would focus on these factors to provide qualitative consulting services.



According to Pinto and Slevin (1988) full completion within budget and with a satisfied customer are common elements that measure the success of a project, hence the consultant should emphasize these factors. However, they suggest that there are few topics in project management, which are discussed so often and yet so rarely agreed to, which makes consulting companies to consider various factors when are offering their services. Yet the precise specifications of these services (applying and submitting a proposal, monitoring, actual project management, managing subcontractors etc.) may be a source of competitive advantage and business excellence, main consulting companies offering comprehensive sets of services.

The success of consulting services for an infrastructure project can be indicated by project performance. The performance of it will be dependent on various factors including its complexity, contractual arrangements, competency of project manager and consultant etc. Most project objectives, however, include multiple criteria, including time, cost, quality and safety. Consulting in project management usually must compromise between these criteria, adding an additional one, total cost of service for beneficiary. Additionally, the consultant must consider if compromises are agreed by the project manager and client, the project could still be accepted as a success, even if some of the objectives have not been fully met.

Shenhar, et al. (2002) analyzed 127 different projects, arguing that the critical success factors are not common to all projects, for various reasons. A seemingly obvious reason is that not all projects are alike. Each project is different and every project operates in its own environment. As an example, they suggest that projects of considerable uncertainty must be managed by consultants differently from projects with a lower degree of uncertainty.

The first systematic classification of critical success factors is done by Schultz, et al. (1987), identifying two groups of factors – strategic and tactical – which determine performance of the project in different phases of the project life cycle. For example, the strategic factors include support from top management and good planning of the project. Tactical factors include customer consulting, selection and training of human resources. Moreover, Pinto and Slevin (1988) increased the range of success factors by considering the specifics of the various stages of the project life cycle.

We considered important for our research the following factors, based on Leon approach (Leong et. al, 2014):

• Cost factor is the degree to which a project is completed within the estimated budget (Bubshait and Almohawis, 1994). Final or total cost has been used in the literature as measurement for project performance on a regular basis (Andi and Minato, 2003).

• Time factor measure a project in terms of completion. For a consultant, it is crucial to complete project on time (Lim and Zain Mohamed, 2000). Moreover, implementation according to schedule is considered as one of the main requirements by clients (Latham, 1994).

• Quality factor, in consulting industry, represents the totality of features required for a provided service to satisfy a given need of client (Parfitt and Sanvido, 1993). For consultants, quality emphasizes the capability to establish requirements with conformance to the quality standard (Ganaway, 2006) and is related to non-conformance report in the ISO 9000 standard, set out in Clause 4 of the ISO 9000.


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• Client’s satisfaction means the ratio between actual outcome and the expected outcome (Locke, 1970). This factor has become a challenging issue for consultants because it includes all clients’ dissatisfactions like overspend in project cost, delay of completion or poor quality. Quality is always closely related to the measurement of clients’ satisfaction (Soetanto and Proverbs, 2004).

Research has shown that impact success factors can vary in different phases of the project life cycle and in terms of success measures identified by consultants. Alexandrova and Ivanova (2012) presents a list of critical success factors relevant for projects with European financing, factors that were identified by literature review and from a pilot study performed in Bulgaria in 2012. The resulting critical success factors are as follows:

• Project Manager Competence; • Support from the contracting authority; • Clarity of project objectives; • Support of top management; • Team members’ competence; • Level of motivation for project’s team members; • Effective communication between project stakeholders; • Quality of subcontractors; • Accuracy in documenting and archiving project information; • Effective coordination of project activities; • Compliance with the rules and procedures established by the contracting authority; • Systematic control of project implementation; • Access to organizational resources; • SMART planning; • Competence and adequate support from external consultants.

This paper uses this list as a starting point to assess critical factors to which we have added 6 new factors considering literature and our own expertise. They are as follows:

• The purpose of the project is well defined; • Objectives are clear and accepted; • Significant support from management; • Parties involved in the project have the required expertise for project implementation; • The risks are identified and managed; • Management style of project manager is essential for project success.



2. Research Methodology

2.1 Background

The analysis was performed in an international counselling company. The company (we agreed not to disclose its name, hence in the paper it will be simple called “company”) has its Central and Eastern Europe coordinating office in Bucharest and operates in the following countries: Albania, Armenia, Austria, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Estonia, Georgia, Greece, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Lithuania, Macedonia, Malta, Moldova, Montenegro, Poland, Romania, Russia, Serbia, Slovakia, Tajikistan, Turkey, Turkmenistan, Ukraine, Hungary and Uzbekistan.

For this paper, two key business sectors for the company, namely transport (10 projects) and environment (water, wastewater and waste management – 6 projects) were considered, for the following countries: Romania, Bulgaria, Republic of Moldova, Ukraine, and Serbia. The sample consists of company’s employees involved in various roles in 16 European funded infrastructure projects, in the above-mentioned countries.

2.2 Sample size and structure

To determine the sample size consisting of employees with the role of consultant or support staff involved in infrastructure projects of public interest with European funding the

following formula applies (Cătoiu, 2009), e²

qpz²n ∗∗=

where:

n = sample size;

z = confidence level; z = 0.95

p = proportion of people engaged in projects (estimation); p = 0.80

q = complement of p, i.e. the percentage of cases that do not possess the attribute (i.e., those not engaged in projects) and is determined by the relation 1 – p; q = 0.20

e = maximal perceived error, e = 0.05

After applying the formula, we consider that the investigated sample will include 58 respondents i.e. 57.7.

The sampling method used was random sampling (probabilistic) method because all people are part of the research unit have equal opportunities to be selected and placed in the sample research. Once picked one respondent, it is no longer considered for selection following sample components.


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2.3 Questionnaire design and testing

The research was done using an individual, highly structured and dissimulated questionnaire (Cătoiu, 2009). The questionnaire includes questions about project performance important for consultant (questions Q1, Q2_1, Q2_2, Q2_3, Q2_4, Q2_5 and Q2_6, Q3 and Q4), questions about the factors that impact the success of a project (Q5 – Q23). The questionnaire consists of closed questions. Scale types used in the performance of questions in the questionnaire is Likert scale questions. The questionnaire was tested for formulation and complexity on three people chosen randomly from the sample. The individuals selected for the sample were contacted either directly by e-mail (Romania) or indirectly through a company representative in the target country in question. This solution was chosen to provide a speedy data collection. Questionnaires were filled in electronic format provided and returned via e-mail. Once the data were collected, they were analyzed using SPSS 17.0 statistical program.

2.4. Data analysis

Quality management variables influence on project performance was tested with regression analysis. (Table no. 1)

Table no. 1: Regression result

Quality management variable Standard error Significance Cost factor 0.004 0.022 0.842 Client satisfaction 0.177 0.022 0.000 Non-conformance report 0.064 0.041 0.115 Time factor 0.344 0.048 0.000

We also performed Reliability Analysis, which involves identifying the degree of precision that measures a characteristic scale. This step was carried out using Cronbach's coefficient alpha for internal consistency, which indicates the inter-item consistency of the scale is based on the average analyzed and the correlations between the items of the scale. (Table no. 2)

Cronbach’s alpha is commonly used as a measure of the internal consistency of how well the items in the set are correlated to each other. It is not uncommon for researchers to suggest a threshold value of 0.7 (Nunnally and Bernstein, 1994). According to the internal consistency index value (0.694) shown in table no. 3, the variables analyzed show a very good correlation, which means that the items were accurately perceived (correctly) and have left no room for interpretation by all respondents, suggesting a high internal consistency as well as a high level of reliability of the survey instrument.



Table no. 2: Item-Total Statistics

Scal

e M

ean

if It

em

Del

eted

Scal

e V

aria

nce

if It

em D

elet

ed

Cor

rect

ed It

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Tot

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latio

n

Squa

red

Mul

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C

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n

Cro

nbac

h's A

lpha

if

Item

Del

eted

Q1. Pease specify if from your point of view “successful project management” is the same thing as “successful project”.

69.36 20.310 .128 .272 .680

Q2_1. Consulting for a project is considered successful when project is completed in time 68.58 19.803 .310 .646 .642

Q2_2. Consulting for a project is considered successful when it project implemented in its budget 68.45 20.067 .270 .670 .648

Q2_3. Consulting for a project is considered successful when all project objectives are achieved 68.04 20.258 .395 .539 .635

Q2_4. Consulting for a project is considered successful when the strategic objectives of the company who is implementing the project are achieved

68.49 19.180 .468 .449 .621

Q2_5. Consulting for a project is considered successful when the project is accepted and used by the beneficiary 68.47 18.809 .479 .635 .617

Q2_6. Consulting for a project is considered successful when project stakeholders are satisfied 68.47 19.032 .410 .603 .627

Q3. Consulting for a project could be still considered successful if it is implemented in a longer period of time than initially planned, with a budget over the estimated one but it achieves all its objectives and it is accepted and used by the beneficiary?

69.16 21.658 .027 .348 .688

Q5. For consultant, a project to be successful – clarity of its scope and goals are … 67.91 21.158 .236 .325 .652

Q6. For consultant, a project to be successful – achieving its objectives is … 67.80 21.385 .303 .354 .649

Q7. For consultant, a project to be successful – top management support is… 68.00 21.148 .247 .361 .651

Q8. For consultant, a project to be successful – access to the organization’s resources is… 68.27 19.832 .518 .427 .623

Q9. For consultant, a project to be successful – support from the contracting authority is… 68.11 21.136 .149 .275 .664

Q10. For consultant, a project to be successful – following the contracting authority's rules and procedures is 68.20 20.200 .452 .426 .631

Q11. For consultant, a project to be successful – the quality of the sub-contractors' services is 68.07 22.069 .050 .452 .672

Q12. For consultant, a project to be successful – the quality of the contractors' works is … 67.82 21.781 .194 .467 .657

Q13. For consultant, a project to be successful – the works execution control is… 67.80 22.274 .077 .435 .665

Table no. 3: Cronbach alpha coefficient of internal consistency Reliability Statistics

Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items

.663 .694 17


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3. Results

Correlation determined the links between any given project performance indicator and quality management factors. These shows significant but weak correlation with client satisfaction and time factor and very weak correlation with other significant project performance indicators. (Table no. 4)

Table no. 4: Correlation result

Variable Quality management variables Cost factor Not significant Client satisfaction Weak Nonconformance (quality) factor Very weak Time factor Weak

One of the most interesting results of the research is that more than half of respondents (53% overall) believe that successful project management is the same as "successful project". As such, for most of those involved in implementation of projects, regardless of their position (project manager, consultant or support staff position), to successfully manage a project is equivalent to fulfil customer expectations. In other words, the consultants involved in providing consulting in project management do not make a distinction between the two concepts. The causes are, in our opinion, multiple: even if a project does not fulfil objectives set by contract, a common occurrence in infrastructure projects are financial corrections and budget overruns, it does not affect fees of consulting firms but the project beneficiary. Often the success fee is charged to the contract, and any corrections are caused by legal issues, frequent changes in legislation, especially in public procurement making it difficult successful completion of a project.

Another interesting fact is that of those who did not agree with the statement (32% total) only 15% are project managers (team leader, project manager or project responsible) or have a position involving management responsibilities (Deputy Team leader).

In terms of the “successful project” concept confirmed the issues identified by the bibliographical study which defines this concept. All aspects record high percentage (over 70% of respondents), of which nearly matches the achievement of project objectives unanimity (91%) of those questioned in. While that is important, a project to satisfy all stakeholders (77%) than only customer for that project is executed (75%). Furthermore, the results achieved to question Q3 which respondents were asked to confirm or deny the claim such that a project can be considered successful if conducted over a period of time, with a budget exceeding the initial estimate but achieve all objectives initially established and accepted and used by the beneficiary for the project is intended to show that 60% of them are "agree" and "strongly agree" while 23% still have not formed an opinion (disagree nor disagree with the statement). By correlating these results with those recorded question Q4 which sought to determine the frequency of similar cases to the one shown in Question Q3 in project management in the target area at the time of the research, which shows that 52% of those surveyed believe that this happens "often" or "very often" and 38% believe that the only times you encounter a similar case although projects exceeding the period and estimated budget chances are that they be considered "successful projects" if they met their objectives and are accepted by the recipient.



Analyzing the responses received in relation to the factors that have the greatest impact on the consulting service and the success of a project respondents produced the following ranking based on percentages recorded for each of the items in terms of maximum impact on the success of a project (marked as "very important") (Table no. 5):

Table no. 5: Ranking of critical success factors

No. Critical success factor Question Total score %

1 Leadership style of consultant Q22 86.7 2 Consultant team competence Q17 78.3 3 Fulfil the purpose and objectives of the project Q6 76.7 4 Quality of consultant control over contractor activities Q12 76.7 5 Quality of consultant control over activities implemented Q13 76.7 6 Consultant competence Q16 75 7 Quality and effectiveness of communication between project stakeholders Q23 71.7

8 All parties involved (contracting authority, consultant, building contractors, sub-contractors, etc.) must have the required expertise for project implementation

Q14 70

9 Effective coordination of project activities by consultant Q19 68.3 10 Identifying and managing risks by consultant Q15 63.3 11 Support for consultant from top management Q7 60 12 Support from the contracting authority Q9 56.7 13 Quality control of services provided by sub-contractors Q11 56.7 14 Motivating consulting team Q18 51.7 15 Fulfilment of rules and procedures established by the contracting authority Q10 41.7 16 Access of consultant to organizational resources Q8 36.7 17 Accuracy in documenting and archiving Q21 36.7 18 Flexibility in planning project activities Q20 31.7

Comparing these results with those acquired by Alexandrovna and Ivanova (2012) these are:

• Consultant Competence => 81.8%

• Compliance with the rules and procedures => 78.0%

• Consulting team competence => 66.7%

• Quality of services provided by sub-contractors => 66.7

• Support from the top management => 64%

(Results from initial study)

• The management style consultant (leadership) => 86.7%

• Consulting team competence => 78.3%

• Fulfill the purpose and objectives of the project => 76.7%

• Quality of sub-contractors => 76.7%

• Control of execution => 76.7%

(Own study results)


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4. Discussion

Most of the companies adopt quality management to become competitive by improving quality of their products or services (Deming, 1982; Garvin, 1988; Steeples, 1992) while consulting companies implement quality management systems to assess and improve project performance, their main business.

Various studies (Ahmed et. al, 2005) demonstrate that infrastructure contractors will hire a consulting company to ensure the overall and specific quality of the project, usually detailing their requirements in the contract. That means consultant will have a checklist to evaluate project performance from client perspective. According to our research, client satisfaction and time factor have shown significant positive relationship with quality management variables. At the same time, client satisfaction and time factor have weak but significant correlation. That further proves that time criteria are always important for clients’ satisfaction, as is proven in other studies (Parasuraman, et al., 1988).

On the other hand, cost factor does not show significant or positive relationship with quality factor. For cost the result is not significant because infrastructure projects are complex and usually involve additional cost (e.g. due to variation orders, changes in legislation etc.). Some of the costs will be covered by main contractors when project is completed and the source of the problem is hard to identify.

Concerning the second part of our research, only "consultant team competence" emerge in both our study and Alexandrova and Ivanova’s among the most important factors that impact quality of consulting service. The fact that in our study “quality of consultant control over contractor activities” and “quality of consultant control over activities implemented” appear in the top five factors is normal and to be expected given that type of project analyzed – infrastructure, regardless of industry: transport, environment – water and waste water, waste, energy etc.

The most important result of the second part of our study is that "management style of consultant" is ranked first, differentiating from "competence" (factor ranked no. 6 in the ranking), which means technical knowledge of consultant. This result is part of the trend outlined by studies in recent years arguing that the project manager identifies leadership as a very important critical success factor. Kerzner (Atencio, 2013) argues that "project managers are often selected or not depending on their management style (leadership)". Also, the link between project success and ability and leadership style of the person managing it was identified and demonstrated in a study led by Muller and Turner (2007). Atenció (2013) mentions that although his driving style is considered a success factor at the organizational level for a long time, however, this concept has been adopted relatively recently in project management. This assertion is supported by other authors like Dvir, et al. (2005), Turner and Muller (2005; 2006) or Jiang (2014).

In the aforementioned study, Jiang (2014) states that if we look at things from the perspective of the model developed by Yang (2011) consultant leadership style influence project success through teamwork and the results of this study – "consulting team competence" ranked as the second most important critical success factor – which indicates the importance of team and teamwork.

Another interesting aspect is that of the factors analyzed more than 50% of the respondents eight of factors relates to human resources (Q22, Q17, Q16, Q23, Q14, Q7, Q9 and Q18) only three are related to project management (Q6, Q19 and Q15) and the remaining three relate to quality assurance (Q12, Q13 and Q11).



Based on these results a conceptual model to improve overall quality of consulting services was developed. It is based on fourteen critical success factors that have registered more than 50% of total received answers is graphically illustrated as in Figure no. 1. In this model the beneficiary is a central, regional or local public authority, which access European funding and wants to run a particular program or project to make an investment that meets the needs of the population in a given area, which can be a territorial-administrative unit (ATU), several ATUs, a county, region or entire country. The consultant is a specific consulting firm that was selected following a procurement process (public tender). It could be a local or international company or an association of companies. Both entities offer their support to project team, which are responsible for implementing the project and how the contractor meets its contractual requirements.

The study shows unequivocally that the project management capacity of consultants and beneficiaries has not improved in the last several years which is a significant signal. Given the multitude of short courses available for example in Romania, many of them financed by Human Resources Development programs, and trainings organized by European programs’ management authorities for their employees supervising or monitoring projects, apparently, the results should have been different. However, 60% of respondents did not consider that consultants’ capacity to manage projects has improved.

BeneficiaryCompetency

ConsultantCompetency

Project teamCompetency

Project managerCompetency

Constructor/Sub-contractorCompetency

• Quality w orks• Quality services

Fulfilling goal and objectives

Efficient activity coordination

Risks identification and management

LeadershipStyle

Support

Support

Figure no. 1: Conceptual framework for critical success factors in assuring

quality in infrastructure projects consulting services

For those who answered affirmatively, they were consequently asked to determine the most important 3 elements that have led to improved capability to successfully manage an infrastructure project among both beneficiaries and consultants. First was ranked factor "better coordination and communication with stakeholders and the project team" (42%), the second "personally extremely experienced" (33%), and the third "experience of the


Vol. 19 • No. 44 • February 2017 227

consultants’ firm" (25%). It follows that through the improved communication between parties involved in project implementation, the ability of consultants to manage projects has improved. It is obvious that this is the result of lessons learned from project implementation.

Another important aspect is the use of increasingly better prepared consultants. This statement has to be understood by taking into account that most respondents were local experts. More than five years ago, the proportion of international experts was higher compared to the present situation in which only the team leader or a highly-specialized consultant are foreign experts with international experience. The third aspect should be considered in the same context, of the transition from teams mostly international (at least in terms of key experts of the projects) to teams of local consultants and experts who have accumulated enough experience to work in European funded projects in key positions.

Concerning beneficiaries, the issues identified are related to lessons learned from the implementation of European projects over the years. Moreover, public authorities have received formal and informal training, received or were involved in the transfer of knowledge and on-the-job training throughout the implementation of the projects, in various projects components and specific activities. The fact that communication between customer and consultant have improved should be understood in the context of enabling a balance of powers. An experienced beneficiary will communicate much better with a consultant because he knows what he wants and how to get to it. Also, such an entity agrees easier to solutions provided by the consultant and as such communication process is improved.

Conclusions

One topic analyzed the relationship between project performance indicators and quality management variables. The findings indicate that client satisfaction and time factor have positive and significant relationship with quality management implemented while other project performance indicators do not seem to show significant correlation.

Although this study was conducted in a single company, this is an international consulting company specialized in providing support for infrastructure projects, with a presence in all countries of Eastern Europe. To make research possible, we used Leong approach and Alexandrovna and Ivanova approach for the 2 parts of our research.

We believe that our paper contributes to quality management and project performance literature, providing a point of view from consultants involved in infrastructure projects. There are relatively few international players able to apply for such projects, making the research more interesting. Moreover, this framework includes leadership style of consultant as a critical success factor has been identified in the research as the most important in the context in which it was conducted. As such, this paper demonstrates, with the necessary limitations, how important is the leadership style in the context of specific projects in the context of infrastructure projects with European financing. And this is happening in a context where there are sophisticated project management tools available which apparently, for some, make project management a matter of competence and tools rather than human resources.

We also hope the study results represent an impetus for theorists to bring new evidence in this direction but also for practitioners to focus their efforts on these key areas. For the latter, it is important to understand the importance of leadership style for both main



consultant and consulting team. Practice and reality on the ground showed that leadership style it is more important than technical competence for main consultant.

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Contents Economic Interferences Timing of Proactive Organizational Consulting: Difference between Organizational Perception and Behaviour ...................................................... 232 Ichak Adizes, Mladen Cudanov and Dusanka Rodic Estimation of Relationship between Inflation and Relative Price Variability: Granger Causality and ARDL Modelling Approach .................................................... 249 Saghir Pervaiz Ghauri, Rizwan Raheem Ahmed, Jolita Vveinhardt and Dalia Streimikiene Determinants of Foreign Direct Investment in Romania: a Quantitative Approach ................................................................................................. 275 Calcedonia Enache and Fernando Merino

AE Timing of Proactive Organizational Consulting: Difference between Organizational Perception and Behaviour


TIMING OF PROACTIVE ORGANIZATIONAL CONSULTING: DIFFERENCE BETWEEN ORGANIZATIONAL PERCEPTION AND BEHAVIOUR

Ichak Adizes1, Mladen Cudanov2 and Dusanka Rodic3*

1)Adizes Institute, Santa Barbara, California, USA 2),3)The Faculty of Organizational Science, Belgrade, Serbia

Please cite this article as: Adizes, I., Cudanov, M. and Rodic, D., 2017. Timing of Proactive Organizational Consulting: Difference between Organizational Perception and Behaviour. Amfiteatru Economic, 19 (44), pp. 232-248

Article History: Received: 19 July 2016 Revised: 12 November 2016 Accepted: 21 December 2016

Abstract The aim of this paper is to show organizational perceptions and behaviours regarding proactive search for consultancy services. Management consulting is operating primarily through the analysis of perceived and existing organizational problems, but on the other hand it could be considered as a creation of value for organizations through the application of knowledge, techniques and, assets to improve organizational performance which is in the unknown and hidden potential part of organizational Johari window. Perceptions of organization are shown through managers and consultants perceptions survey in southeast Europe region. From the other side as an empirical base of real managerial behaviours there is a database of companies which had been proactively asking for consultancy services in southeast Europe region. This research is based on Adizes Institute database, which is the reason we have introduced the Adizes Corporate Lifecycle model theory. We will show the difference of perceptions at what stage in lifecycle organizations should engage consultant help and reality, when companies actually do that. We will use data analysis results to discuss whether timing when organizations decide to proactively engage consultants is the right time for such engagement. Keywords: Management consulting, Adizes Corporate Lifecycle stages, managerial perceptions, organizational reality, Johari window in organizational consultancy context JEL Classification: L29, L84, M10

*Corresponding author, Dusanka Rodic – [email protected]

Economic Interferences AE

Vol. 19 • No. 44 • February 2017 233

Introduction

Among various contemporary definitions of management or business consulting services, early definition example of the Association of Consulting Management Engineers (ACME) stands out, defining management consulting as the professional service performed by specially trained and experienced persons in helping managers identify and solve managerial and operating problems of the various institutions of our society, and this professional service focuses on improving the managerial, operating, and economic performance of these institutions (Kipping and Clark, 2012). Management consulting develops and changes along with the technology and industry so the following definition and its explanations must be considered. Management consulting has long been recognized as a useful professional service that helps managers to analyse and solve practical problems faced by their organizations, improve organizational performance, learn from the experience of other managers and organizations, and seize new business opportunities (Kubr, 2002). As a process, management consulting could be stated as a way of practice in helping organizations to improve their performance, operating primarily through the analysis of existing organizational problems and the development of plans for improvement. Management consultancy could be considered as a creation of value for organizations through the application of knowledge, techniques and assets to improve organizational performance.

Management consulting industry developed with the rise of management as a unique field of study, and first consulting firm dated in 1886 and it was Arthur D. Little Inc. in USA. But the expansion in management consultancy industry was in 1980s and 1990s.The industry than stagnated in 2001 and recovered after 2003, with a current trend towards a clearer segmentation of management consulting firms. Management consulting is today divided into numerous specialized domains, such as supply chain management, logistics management, change management, information technology consulting, enterprise integration, mergers and acquisitions, workforce optimization, human resource consulting, financial consulting, taxation and advisory consulting, audit and assurance consulting, virtual-digital management consulting, strategic communication consulting, and many others, which could overlap between. Larger diversified consultancies, such as Booz Allen Hamilton, Accenture, Delloite, McKinsey, Ernst and Young, PricewaterhouseCoopers, Bain and Company cover wider domains, while smaller organizations usually specialize. Current challenges lie in critiques, such as numerous business books published with techniques which could be helpful for improvement of organization, but do not work in real cases, impression management – presented and promoted false benefits – raised potential client’s expectations and not fulfilling it. Also, the rising of digitalized world is one of the biggest challenges for every industry so for consultancy as well, now when lots of knowledge is viral and accessible, competition is not just from large consulting companies, but also from smart individuals.

The main reason of hiring consultants is solving of major problems which could not have been solved by management, mainly by gaining external (objective) advice and use of services/specialized expert knowledge which organization lacks, but has been externally confirmed in practice many times. Consultants can function as bridges for information and knowledge, and that external consultants can provide these bridging services more

https://en.wikipedia.org/wiki/Organization

https://en.wikipedia.org/wiki/Performance_management

https://en.wikipedia.org/wiki/Information_technology_consulting

https://en.wikipedia.org/wiki/Human_resource_consulting

https://en.wikipedia.org/wiki/Virtual_management



economically than client firms themselves (Bessant and Rush, 1995). If managers and outside advisers work out in advance what is expected of each party during their work together, the chances of solving problems are improved. He suggests that managers and consultants structure the engagement according to a hierarchy of goals – which proceeds from the most basic objective, providing information, to the most sophisticated, permanent improvement of organizational effectiveness. The best way to move up that hierarchy is for executives and advisers to work together to identify needs and develop solutions (Turner, 1981). Ringland and Shaukat (2004) summarized research that found management consultancies were employed for five key reasons:

• Organizations need specialist knowledge (industry-specific knowledge, organization-specific or some issue-specific knowledge),

• Organizations need implementation (organization has constrained resources, or it is inexperienced in that type of project, or organization need effective and efficient work done),

• Organization needs validation – assurance (of already done internal work or for decisions or implementation, and from the other hand a better image in front of its clients),

• Organizational need for independence/impartiality (objectivity and distance from “centralized” corporate politics),

• Organizational need for blaming somebody (when internal work goes wrong, or where stakeholders need someone to “sacrifice”, so called “scapegoat”).

We can distinguish proactive engagement, where organizations hire consultants without significant external enforcement, and reactive engagement where main reason is external pressure – e.g. late stage crisis, external entity or legal obligation. Still, there is misunderstanding regarding exact time when consultants should be engaged. Organizational perception for any of five above stated reasons will directly influence actual consultant engagement chance. That perception changes during lifecycle, and depends on number of other factors, e.g. organizational performance, culture and management style. It is appropriate to assume that organizations near the “Prime” lifecycle phase will diminish chance to accept external knowledge and engagement due to enlarged self-confidence and even arrogance. Often it is more important to unlearn, than to learn (Nystrom and Starbuck, 2015), which is even harder in that stage. Individual perception may be different from structural organizational factors and hinder engagement of consultants. Employees may learn that organization needs outside help when organization is still unable to learn and execute that knowledge, because individual learning does not guarantee organizational learning (Senge, 1990). We have found this issue interesting, and with data from confidential consultant’s reports available, have decided to examine following hypothesis:

HYPOTHESIS 1: There is significant difference in distribution of probabilities between perception of lifecycle phases when proactive consulting should be procured, and actual proactive engagement of consultants in organization.

This hypothesis can be reformulated into form harder to prove by quantitative methods, stating that organizations often do not engage consultants at the right time because they


Vol. 19 • No. 44 • February 2017 235

lack knowledge or fail to confess about problems or crisis existence at the first time. So, contrary to common practice in business, organizations may not engage consultants when they learn there is, or expect future development of an issue or crisis. Instead of that consultants should proactively engage organizations and offer them their knowledge, because in most cases it is the knowledge organizations lack in the first place. However uncommon that approach in business is, it is currently dominant in medicine, where focus of efforts shifts toward primary care, mostly on prevention, just like in quality management (Jarrett, 2016). So instead of waiting for medical problem to develop, show and demand costly, timely and risky treatment at secondary or tertiary healthcare level, healthcare workers are preventing it by proactive engagement. Maybe consultants should try the same.

1. Methods

Empirical background of this article is twofold. First part is based on questionnaire answered by 119 managers from companies in Southeast Europe region fitting similar Balkan economy outlook (Savoiu, Taicu and Cudanov, 2016), about perception when do companies proactively search for consultancy services help. Questionnaire had 7 questions, and was sent without bias to 311 random participants within social network of researchers. With total of 119 valid replies, response rate was solid 38.26%. Second part is based on secondary data from consulting experience and case studies of 72 companies from the same region which proactively asked for consultancy services. Case studies were consultant’s Syndag® – organizational diagnosis reports database from the Adizes Institute, and multiple case study, as well as extraction of secondary data is method supported in business research (Zikmund et al., 2012).

Syndag® reports are usually about thirty pages long of structured and organized content and results from organization diagnosis workshop. The name represents abbreviation for Synergetic Diagnostic workshop which is the first phase of eleven phases of the Adizes program for organizational transformation. It involves the whole management team of directors of all main functional units and the CEO. The consultants do not do themselves the diagnosis but facilitate through a structured process to identify from the participants the potential improvement points for organization. In today’s accelerating change world, just as organizations change from mechanistic (Weber, 2009) to more organic (Burns and Stalker, 1961), organizational consulting follows the sequence in process of organizational diagnosis, changing from external consultant analysis into method of group consultants and management sessions. The consultants lead the participants in classification of the potential improvement points to identify at which stage on the Adizes Corporate Life cycle curve (Adizes, 2004).

To check our hypothesis, we have used chi-square goodness of fit test (Krishnaswamy, Sivakumar and Mathirajan, 2004) to test difference between two distributions of data. Conclusions were provided using inductive and deductive reasoning, following guidelines appropriate for research in business context (Saunders, Lewis and Thornhill, 2011, p. 501). Construct validity as an extent to which an operationalization measures the concept it is supposed to measure (Cook and Campbell, 1979) is analysed by qualitative means, because this questionnaire was originally developed for this research and there is no comparable



data or study on the validity of questionnaire, and because only one method to measure the concept was used, limiting the of advanced statistical analysis of construct validity proposed by Bagozzi, Yi and Phillips (1991). Questionnaire was developed following the guidelines from Saris and Gallhofer (2014) as “concept by intuition”, in order for our questions to measure real impressions of the participants. Second part of our research was primary data, derived directly from observation of consultants. When estimating their accuracy and reliability we bear in mind that consultants worked at least three days in observed organizations, that during the process around one hundred ”Potential Improvement Points” or PIPs were identified (Raz, 2009). Also, process of assessing organization is standardized, timing is planned literally to the minute and guidelines are the same for all consultants, who are required to pass exams in standardized skills and knowledge. Finally, companies paid substantial consultant fees for each of those reports with conclusions and estimations, among which important one is current lifecycle phase. Based on that we conclude that accuracy and reliability of data was acceptable.

External validity of the study is mostly limited by our samples in both empirical pillars, focused on Serbia, with 16.81% in questionnaire and 27.78% in analysis coming from neighbouring countries of Croatia, Slovenia, Macedonia, Bosnia & Herzegovina. This limits generalization to the Western Balkan countries, but since Uhlenbruck and De Castro (2000) found many similar trends in the economies of Central and Eastern Europe, generalization of results may be applied to the organizations in countries of Central and Eastern Europe which have finished or are still in the process of transition.

2. Results

Questionnaire replies were first normalized, and then average probability was calculated for each lifecycle phase. Results of the questionnaire are presented in the following figure, and follow expected pattern where proactive consulting is more actively sought during early phases of the lifecycle, peaks in “Prime” and “The Fall“ stages, and then decline during the later stages.

From the analysis of Adizes Institute ASEE database of 72 client companies which proactively searched for consultancy services, companies proactively asked for consultancy services help when they were in following Adizes Corporate Lifecycle stages in percentages: Infancy (5.56%), Go-go (51.39%), Adolescence (30.56%), The Fall (1.39%), Aristocracy (1.39%), Recrimination (8.33%) and Bureaucracy (1.39%). Probabilities are presented in the following figure, and it is observable how strong discrepancy exists in “Go-go” and “Adolescence” phases where actual engagement is much higher than presumed, as well as in “Prime” and “The Fall” stages where actual engagement is much less than presumed. It is very interesting that in Recrimination phase values are practically the same. (Figures no. 1 and no. 2)


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Figure no. 1: Perceived probability of proactive search for consultant services

per each Adizes Corporate Lifecycle Stage

Source: questionnaire results

Figure no. 2: Actual probability of proactive search for consultant services

per each Adizes Corporate Lifecycle Stage

Source: analysis of consultant reports



Figure no. 3 shows differences between phases, and values were calculated by subtracting actual percentage in that phase from expected percentage. Next, we will examine if differences in distribution are due to the chance.

Figure no. 3: Difference between perceived and actual probability of proactive search

for consultant services per each Adizes Corporate Lifecycle Stage

Source: questionnaire results – analysis of consultant reports

In order to examine if difference observed is due to chance we have used χ2 (chi-squared) goodness of fit (GOF) test. First we have checked assumptions required for the test:

• Existence of categorical variable – Phases of Adizes lifecycle have been used as categorization for estimated and actual probabilities. As ordinal variable, because there is clear order of phases, it satisfies requirements.

• There is no relationship between observations and participants in the research.

• Groups of categorical variable is mutually exclusive. In reality, lifecycle phase is closer to continuous variable than to discrete, because consultant reports often describe lifecycle phase as “in-between”, describing characteristics of two consecutive phases. In several cases, reports even found noteworthy characteristics of more than two phases which were not consecutive. In order to eliminate that issue, former Adizes consultant has independently classified each case in the category which was found most representative.

• Minimum of 5 expected frequencies in each group. Since there are severe deviations in the actual distribution among different phases, there are seven categories which were not


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represented with at least five frequencies. In order to overcome this issue, those categories were merged and treated as single category, both on the survey data and actual data.

Afterwards data was processed using R-Studio software package. Results are presented in tables no. 1 and 2. Table no. 1 presents perceived probabilities of proactive consultant engagement in the observed lifecycle phase, followed by actual frequency of consultant engagements in the next row. All other seven phases besides Go-Go, Adolescence and Recrimination are aggregated and treated as one, due to the conditions for Chi-squared analysis.

Table no. 1: Values and residuals

Lifecycle phase / consultant engagement

data Go-Go Adolescence Recrimination All other

phases

Questionnaire results – normalized values 11.388% 11.500% 8.756% 68.357%

Actual occurrence – frequencies 37 22 6 7

Actual occurrence – expected results 8.199146 8.279798 6.304178 49.216878

Standardized residuals 10.0582182 4.768158 -0.1211474 -6.0176799

Table no. 2: Chi-squared test results

χ2 (chi-squared) degrees of freedom P-value 160.13 3 <2.2e-16

3. Discussion

Our results show there is significant difference of distribution between perceived timing for proactive consultant engagement and actual consultant engagement. P value of <2.2e-16 indicates very little probability that results are due to chance. Also, residual values of 10.0582182; 4.768158; -0.1211474; -6.0176799 show how much approximately standard deviations is the difference between compared distributions in respective categories, and deviations above three are considered large, so our conclusion is that in recrimination phase there is general accord between perception and behaviour, but in all other lifecycle phases there are large deviations. In order to discuss further why could organizations ask for consulting mainly in “Go-Go”, “Adolescence” and “Recrimination” stage we will first review organizational lifecycle theory.

3.1 Main organizational lifecycle models

In 1950, Kenneth Boulding (Boulding, 1950) first suggested the concept of organizational life cycles presenting most general model of lifecycle (Ionescu and Negrusa, 2007). It was followed by Haire (1959) idea that there is uniform path most organizations follow in their



development. Pioneer review research was done by Quinn and Cameron (1983) and it identified nine early models. And each of them emphasizes different aspects and factors as important and valid to be measured and evaluated through organizational change in timeframe from its beginnings to the maturing stages (or end). Here we will list nineteen models:

• Basic model lifecycle with birth, youth and maturity/decline (Boulding, 1950).

• Organizational life cycle model based on research in government bureau (Downs, 1967): Downs' model views government organizations as moving from the establishing of legitimacy, to innovation and expansion, and then to formalization and control (Quinn and Cameron, 1983).

• Organizational life cycle model in based on research in private companies (Lippitt and Schmitdt,1967), corporations progress through three stages of development: birth, youth-developing, stability and reputation, and maturity.

• Organizational life cycle based on strategy and structure (Scott, 1971), Scott's model indicates that organizations progress has 3 stages, from informal "one-man-shows", to formalized bureaucracies, and then to diversified conglomerates.

• A Life cycle theory of a firm (Mueller and Dennis,1972).

• Greiner’s model (Greiner, 1972) organizational grow through evolution and revolution has five stages: creativity, direction, delegation, coordination and collaboration stage.

• Torbert’s model (Torbert, 1974) is a model of organizational development based on the individual "mentalities" of organizational member.

• Lyden’s model (Lyden, 1975) is based on organizational functional problems and have four stages.

• Lavoie and Culbert`s stages of organization and development (Lavoie and Culbert, 1978).

• Katz and Kahn (1978) base their model on the elaboration of organizational structures that develop over timeframe of growth, and they suggest that three main stages occur in organizational life cycle: primitive system stage, stable organization stage and elaborative supportive structure stage.

• Adizes organizational life cycle model (Adizes,1979) compares the life cycle of a company to life cycle of living organism, and has 10 stages, a life cycle curve and its separation on growth and maturity phases.

• Kimberly’s model (Kimberly, 1979) is based on creation and development of medical schools and has four stages.

• Quinn and Cameron (1983) propose four stage model stating entrepreneurial, collectivity, formalization and control stage and elaboration of structure stage.


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• Five stage model of small business growth (Lewis and Churchill, 1983) recognize existence, survival, success, take-off and resource maturity as main stages, but do not elaborate on decline process.

• Miller and Friesen (1984) recognize birth, growth, maturity, revival and decline as five stages of growth, without much focus on the pitfalls of described phases but with elaborated empirical research of different characteristics of situation, strategy, structure and decision making process.

• Another influential five stages of small business growth model (Bruce and Scott, 1986) identifies inception, survival, growth, expansion and maturity, along with the characteristic crisis during growth, but does not elaborate decline either.

• Whetten (1987) describes organizational growth and decline process as main phases of the organization.

• Five stage empirical life cycle model (Lester, Parnell and Carraher, 2003) recognizes existence, survival, maturity, renewal and decline, resembling general Miller and Friesen viewpoint and giving more slightly more focus on decline.

• Hanks (2015) presents recent analysis of lifecycle and offers integrated stage and process model and content.

In this paper we will focus on Adizes organization life cycle model (figure no. 4) due to available data. In focus of whole Adizes methodology is change as perpetual process. The corporate life-cycle stages that Adizes (2004) describes in his model provide a useful basis for understanding a fundamental perspective of organizational development, change, and the principles which causes organizational aging. Adizes corporate life cycle theory identifies two common causes, one is that everything even stars (which are not living beings) as its own life cycle, and the other common denominator is change and disintegration as a natural consequence of changing process. Adizes adopted the approach in chaos theory that problems are manifestations of disintegration caused by change (Masterpasqua and Perna, 1997), and then he used that approach to look at the organizational developmental stages through the nature of problems organization is overcoming. The essence is in this conclusion according to Adizes is: if we assume that every system has its own life cycle, as they change progressing along their life cycle, system follows predictable patterns of behaviour. At each stage, system manifest certain struggles, certain difficulties or transitional problems-they must overcome.

Whenever an organization makes the transition from one lifecycle stage to the next, difficulties arise. When an organization expends energy to make effective transitions from old to new patterns of behaviour, these are normal problems. If an organization expends energy inward in futile attempts to remove blockages to change, it is experiencing abnormal problems which usually require external therapeutic interventions. An organization can solve its normal problems with its own internal energy, setting processes in motion and making decisions that will overcome the problems. An organization cannot avoid those normal problems because it needs to learn and develop its capabilities. Normal problems are transitional in nature: You encounter them, solve them, learn from them, and readily move on. Abnormal problems slow the organizational progress, retarding organizational



ability to develop. They frustrate and entrap it in a particular stage of the life cycle. Organizations with normal problems do not require external interventions (Adizes, 2004).Organizations with abnormal problems need interventions from outside, this is the point where the organizations should become aware that they need to proactively search for help of consultancy services. After short explanation of ten Adizes corporate lifecycle stages, we will focus on real situation from database of companies, and see in which stages they proactively search for consultancy services and help, and we will elaborate why.

Figure no. 4: Adizes Corporate Lifecycle

Source: Adizes, 2004

3.2 Perceptions of organizational reality

Johari Window model (Luft and Ingham, 1955) is made for individual use to help individuals to see themselves in context of self-perception and perception of others in terms what is known and not known (figure no. 5). Johari window could be extended on organizational level in perspective of an outside world (Joglekar, 2015) and further developed (Mainiero and Tromley, 1994) to show where management consultancy services are needed and useful. If we look at the diagram below which represent Johari window for organizations in context of organizational transformation consultancy services, logically the “unknown to the organization” quadrants are where organizational consultancy services are needed and helpful. The area of Quadrant 2, where we have organizational latent undeveloped possibilities from one side and available knowledge, specific, universal methodology, experience and skills of organizational consultants on the other side, is obvious field for helping the organization to develop its flexibility by activation its latent internal organizational forces, energy and learning capacity.


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Interesting is that if organization decide to work on this Quadrant 2 area, it could not just open the learning and change process possibilities, but it could be also used to get organization in condition of rejuvenating itself and preventing of aging on the road to stage of Prime. But for the aiming to Prime stage (top organizational performance) and continuous and healthy organizational growth through the exact amount of flexibility and controllability (Adizes, 2014), organization should also work on Quadrant 4 in Johari window. The value system of a group and its membership may be noted in the way “unknowns” in the life of the group confronted. So called “Unknown aspect of an organization” could be achieved through the change management process implementation in every segment of an organization, from processes to structure, communication of vision and values to culture artefacts and deep beliefs which at the end of the change process become the most valuable asset. In starting the change process, it is something which could not be described and defined – so called intangible and by the end of organizational transformation process it becomes awakened, meaningfully defined internally in organization and impossible to be copied from competitors. It becomes an asset portfolio of key success factors which could not be replicated so easily because of uniqueness of change management implementation process in every organization combined with specific organizational cultural psycho-social DNA, where this organization was firstly aware that need help for improvement, then willing to learn and grow to the full potential and beyond known limits.

Explaining Johari window adapted for organizations in context of organizational transformation consultancy services, we could notice that perception is very important. Perception of organization what is known and what is unknown for itself is actually the perspective of people, of employees and management of this particular organization. It is usually perception of – key people (owners and top managers), who get the information from their employees and colleagues from their own perception. The important question than imposes to us, does the perception of an organization (its management) corresponds with the reality in terms when organization have need for change and consultancy services. Does the organization have perception what is “unknown to them” and when it is the right timing to proactively work on it?

Relating this concept and our research results, we can explain unusually high trend to proactively engage consultants in “Go-go” and “Adolescence” phases as a “sweet spot” between size of Quadrant 1 in Johari window, organizational modesty to accept outsider’s superiority in solving some important internal issue (or even lack of arrogance in some cases) and sense of urgency for observed problems and issues. Those three factors are related similar to series-factor model, so if one factor diminishes all output diminishes along, making it easy to dismiss proactive consultant engagement. Modification of Johari window is a factor we underline as influential, because it is easy for growing organization to become less aware of its problems, especially on the system level of whole organization, due to forming of organizational silos which hinder communication within (Daft, 2009).



Figure no. 5: Johari window applied to organizations in terms

of organizational consultancy services Source: developed according to Luft and Ingham (1955)

Conclusions

This paper presents differences in perceptions of organizations and actual engagements regarding timing for asking for consultancy services in Southeast Europe region. Based on database of 72 companies from the region which proactively asked for consultancy services and questionnaire answered by 119 managers and consultants from companies in Southeast Europe we conclude that in growing stages, significantly more than in aging stages, the majority of the companies in Southeast Europe region have been proactively asking for consultancy services, specifically in Go-Go and Adolescence stage. If we take into

Unknown to organization

Unknown to envirnoment Known to envirnoment

Known to organization


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consideration the results from questionnaire for managers and consultants there is statistical significant difference in perceptions and reality.

The interesting question is why gap in perceptions exist in minds of key people in organizations and even consultants whose job is to help managers in diagnosing and overcoming organizational problems. We have discussed that in the light of corporate lifecycle theory and Johari window, explaining gaps and blind spots in organizational knowledge. Practical contribution of our research can be in opening managers to the idea their organizations sometimes lack the knowledge on a binary decision that consultants should be engaged, let alone what should be their task and project description.

The brief explanation of results found in real cases is in following facts:

• Organizations seek more for consultancy services in Go-Go phase, because they are in the Founders Trap (Adizes, 2004) or lost their way.

• The hardest transition along the growing part of Adizes Corporate Lifecycle curve is between Go-Go and Adolescence in overcoming normal and abnormal problems of growth. Also, from those three phases where the majority of companies from sample are found (Infancy, Go-Go and Adolescence) are full of pathological deviations of the phases. There are: Infant mortality a threat from Infancy stage, Founders trap – in Go-Go stage and Divorce in Adolescence stage which could lead into Premature aging and going directly to aging part of Lifecycle curve or to Unfulfilled Entrepreneur deviation. Most of companies from sample were located in these three growing stages, actually in areas on Adizes Corporate Lifecycle model which include problems characteristic for certain stage, its deviation and even previous stage (company passed through).

• Interesting point is that some of the companies from sample had characteristic problems in aging stages, but they never actually went through Prime stage, and this could be a trap even to consultants if they do not pay attention to this fact that even though the company shows the signs of aging, could go to aging part of the Adizes Corporate Lifecycle curve by deviation, but its location when organization proactively search for consultancy service is designated in growing stage.

• Organizations are less aware of need for help in phases of Aristocracy and Bureaucracy because they are into the Finzi-Contini syndrome (Adizes, 2004) where powered group dynamic of management team overruled each one of them in actual personal awareness of situation. Aristocracy companies are rich and sleepy and lulled into the image of success. In Bureaucracy companies are usually subsidized by political forces, and because of that they do not have pressure to change.

• In phase of Recrimination, organizations usually hire consultants for financial restructuring. As Adizes does not do that, thus we do not have data about this location on Adizes Corporate Lifecycle model.

This is one of the limitations of this paper because our data is biased to what Adizes methodology® can do. Other limitations might be “blind area” in the Adizes institute itself, besides knowledge regarding Recrimination phase – maybe organizations perceive this organization as best for engaging in “Go-go” and “Adolescence phases. This limitation



might be solved by using multiple data sources from number of major and acknowledge consulting companies, but since consulting firms usually keep their knowledge to themselves, and often are legally bound to non-disclosure we would recommend other approach to future researchers trying to find original empirical data unbiased by single source. Survey participants might not know Adizes lifecycle and methodology, so part of answers can have bad influence to accuracy and reliability of estimations. Companies from our sample might be biased by strategic environment and redirected to solving more urgent economic issues (Jednak and Kragulj, 2015). Also, research can be extended beyond borders of South East Europe with larger sample which would extend generalization. Our main contribution might be that key organization decision makers will consider successful quality management and healthcare system concepts of prevention as a parallel to engaging consultants. Rational patient should visit doctor regardless of feeling perfectly well in order to prevent possible issues, because doctor might know something that patient doesn’t. Quality should be ensured early in the process, not controlled at the end when everything is over and there are no easy, cheap and quick solutions. Consultants should not only be engaged when organization thinks there is the need for their services, but even when organization feels no need – they might know something organization does not. After all, that is one of the main reason for consultant engagement.

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ESTIMATION OF RELATIONSHIP BETWEEN INFLATION AND RELATIVE PRICE VARIABILITY: GRANGER CAUSALITY

AND ARDL MODELING APPROACH

Saghir Pervaiz Ghauri1, Rizwan Raheem Ahmed2, Jolita Vveinhardt3 and Dalia Streimikiene4∗

1) 2) Indus University, Karachi, Pakistan 3) 4) Lithuanian Sports University, Kaunas, Lithuania

Please cite this article as: Ghauri, S. P., Ahmed, R. R., Vveinhardt, J. and Streimikiene, D., 2017. Estimation of Relationship between Inflation and Relative Price Variability: Granger Causality and ARDL Modelling Approach. Amfiteatru Economic, 19(44), pp. 249-274


Abstract The objective of this research paper is to examine the relationship between relative price variability and inflation by using consumer price index (CPI) of Pakistan. The outcomes of the research further divided into food and non-food groups too. The monthly data of CPI was taken from the Pakistan Bureau of Statistics, from August 2001 to July 2011 (with 2000-01 base) for 92 composite commodities with 12 sub-groups. We employed the Granger causality testing approach for the evaluation of any possible influence of one indicator to another. In this scenario, it is viable to state that there is a presence of causality and bidirectional feedback between the variables or the two variables are independent. The major issue is to identify a suitable statistical method that enables us to analyze the association among the variables. The findings of this study demonstrated that there is a probable relationship between inflation (DPt) and both un-weighted measures of price variability (VPt and SPt) for the whole items that have been considered for the analysis. Apart from that, this association also exists between food and non-food categories of CPI basket.

Keywords: CPI basket, consumer price index, food & non-food group, inflation, Granger-causality, relative price variability JEL Classification: B23, E30, E31, E52

Introduction

The relationship of relative price variability with inflation, monetary, and income development have attracted noteworthy consideration in terms of investigation of the ∗ Corresponding author, Dalia Streimikiene – [email protected]

AE Estimation of Relationship between Inflation and Relative Price Variability: Granger Causality and ARDL Modelling Approach


economy on theoretical and empirical basis (Chronis, 2016; Gu et al., 2016; Lis and Zwierzchlewski, 2016; Skare and Benazic, 2015; Krajewski, 2005). Though empirical and theoretical models have usually forecasted an affirmative relationship, however, the direction of this association has not been substantiated through these empirical investigations. Although, number of studies has been proposed in the support of positive association between the variables, several researches have also reported an adverse association between variation in prices and inflation. In the view of Reinsdorf (1994) the relationship between these variables is adverse during the decade of 1980s with respect to the United States of America. On the other hand, Fielding and Mizen (2000) and, Silver and Ioannidis (2001) reported similar results for different European countries as well. On the contrary, theoretical similarities such as cost of menu and asymmetric proof predict an affirmative association between relative price variability and inflation. The empirical studies have outlined variety of outcomes in this respect, which is related to trivial and significant relationship between the two variables (Baglan et al., 2016; Blejer, 1983).

Beginning with the research of Parks (1978), which indicated that relative price variations witness an increasing trend more during the periods of decrease in price as compared to the situation in which prices increase. The different directions of the study have raised questions regarding the functional type of association and offered proof of a quadratic association. The proof of the relationship related to threshold changes somewhat from one country to another and depends upon the type of inflation RPV nexus. Cukierman (1982; 1983) has provided a relevant survey regarding the undertaken subject matter. However, the classic elaboration supports the association between relative price variability and level of inflation. Although, different sectors might react differently to the nominal shocks that they suffer in the market. As the fixed cost is associated with the changes in the level of prices; therefore, the prices of goods change only at separated and isolated intervals, which develops deviations in relative prices.

Baglan et al. (2016) attempted to understand the power of relative price variations (RPV) in explaining inflation in Turkey by using monthly micro-pricing data of 128 Turkish goods and services in 13 different cities/regions of the country. Semi-parametric regression revealed a hump shaped relationship between the two measures with RPV at it’s the highest when inflation was registered at 20%. The result was consistent with tariff rate models that features Calvo (1983) pricing with an endogenous bond erection and zero-state rise in prices.

The association between rise in the prices of goods and services, and variations in the prices across different countries have made substantial interests for the researchers and financial economists (Parks, 1978; Vining and Elwertowski, 1976). The leading argument vis-à-vis causality direction has always been in discussion. The causal link between one way or the other has been ascertained by the models and theories, and it is further suggested that certain distinct factors may also cause RPV and inflation concurrently.

Dabús and Cerioni (2000) have studied the causality in the association between inflation and RPV with respect to 6 Latin American countries those have a history of suffering from inflation. This study focuses on four inflationary regimes. According to the findings the affirmative association has been seen in most of the cases along with efficient rise in RPV at greater inflation. The results of the Granger causality test illustrated a rapid causality, and Granger causality from inflation to RPV. They further concluded the significant affect of relative price variability and inflation in all the cases.


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In the context of Pakistan, the association between inflation and relative variation in prices was evaluated in different researches. One research was undertaken by Mohsin and Gilbert (2008), which was concerned with the estimation of the prices conjunction of city in overall CPI for the time period of July 2001 to June 2008. Another study in this respect was undertaken by Akmal (2012), which explored the association between the inflation and variations in the relative prices in Pakistan by employing groups of products. Both of these researches ignored the causal association between these variables while modelling the relationship between the independent and the dependent variable. Nevertheless, the evaluation of the literature revealed that some researchers have applied Granger’s causality test to establish the cause and effect importance between these variables and the direction in which the relationship is flowing.

The aim of this study is to use consumer price index (CPI) inflation to explain variations in the relative prices of varied consumer goods and services. It is suggested that relative variations in prices move in tandem with the variations in national inflation with some anomalies. Changes in the national CPI from one period to the next in a time-series are analyzed using stable distributions to understand and document their unique characteristics in order to extrapolate and superimpose those characteristics into explaining variations in relative prices amongst consumer goods.

The current paper is one of the few researches, which has reflected on the association between RPV and inflation on consumer price index data of Pakistan on monthly basis for the period of ten years for overall and also for twelve sub groups. The remainder of paper consists on previous literature, estimation techniques, findings and results, and conclusions.

1. Theoretical approach

The research of Parks (1978), which is concerned with the transformations in the relative prices and inflation fundamentally motivated various researchers to examine the association between these two variables. The research of Glejser (1965) identified an important factor related to changes in relative prices for the period 1953-1959 for 15 countries, and those were part of OECD. Similarly, a research was conducted by Okun (1971), which took into consideration 17 countries of OECD from the period of 1951-68 and concluded the significant association between mean inflation and standard deviation of the GDP deflator.

According to Chronis (2016) stable distribution is a useful statistical tool that has found its way into plotting and explaining unique phenomena in several social science disciplines including economics. Repeat sampling allows us to capture the nature, trend and characteristics of our series to explain the variable under study. Since inflations generally are stable in nature and several indicators give prior warnings of impending inflations, they have a stable trend, thus our choice of the “stable distributions” model to study its characteristics.

Gu et al. (2016) tried to understand the marginal impact of inflation in explaining variations in prices of goods in several Chinese provinces. Regression analysis showed that the relationship was significant with high variations in inflation explaining the relative differences in prices in the Chinese market. Low variations in inflation did poorly explaining the relative price variations. This result substantiates the economic policy objective of setting low inflation targets in China, as high inflation causes severe distortions in prices.



According to Wolman (2016), US relative prices registered varying trends which need to be captured and controlled by adjusting the cost prices of goods and services. Adjusting for the cost prices would take care of severe price spikes and variability of certain goods thereby helping to predict inflation with somewhat accuracy. The US service industry has experienced dramatic increases in prices relative to the durable goods industry. Since a CPI index is a conglomeration of several goods and services, price adjustments work to reduce distortions in cost prices and help to ascertain the real inflation (adjusted for cost prices).

According to Ahmed et al. (2016) the low income developing countries’ monetary policy is to stabilize their business cycles to achieve economic stability. The researchers have found out that several developing economies aspiring for a middle income status have added an additional objective of setting inflation targets to influence relative prices of goods and services. The study further reveals that variations in relative prices of most of the goods and services do not accurately explain the distortions in inflation and discrediting previous studies, those showing a positive and stable relationships between relative price variability (RPV) and inflation.

Baglan et al. (2016) examined the relationship between relative price variability and inflation with the help of employing micro price data on a monthly basis for 128 commodities in 13 different regions of Turkey for the time period from 1994 to 2010. There is a unique characteristic of the taken data time series including annual inflation in the range of 0% to 90%. Projections in this respect highlighted a significant and an affirmative relationship between level of inflation and RPV. It was also evaluated that the highest amount of price variability is gained when inflation on the yearly basis is around 20%. Therefore, it can be concluded that these results are in lined with metropolitan alike menu cost (price) model that comprises on Calvo (1983) pricing through internal assembly bond.

Hajzler and Fielding (2014) have theorized that substantial empirical literature exists that establishes an association between inflation and relative price variations (RPV), and relative inflation variability (RIV) across goods and situations. While early empirical theory on relative inflation variability has its bases on the signal-extraction models, contemporary relative price variability has produced results inconsistent with signal-extraction theory. In particular, while RIV is rising in the utter prices of inflation shocks, RPV is a negative monotonic function of inflation shocks. They show that consumer search theory offers a potential explanation for these apparently contradictory observations.

Ghauri et al. (2014) analyzed the role of pricing mediators as emphasized in relative price transformations in reaction to the features of supply and demand. They have also analyzed the model of Parks (1978) on the basis of monthly data, which is related to CPI of Pakistan. The results have highlighted that variations in the relative prices was decided generally through the factors of supply as it has been highlighted in unforeseen inflation. The factors of demand such as changes in the real income of a country were explored as insignificant for the determination of the level of RPV.

Akmal (2011) examined the relationship between inflation and relative price variability with the help of non-aggregated CPI data for Pakistan. The application of statistical techniques indicated the ambiguous association between relative variations of prices and inflation. On the other hand, the systematic regression method indicated that association is not significant throughout all the rolling samples. Apart from that, it may not be appropriate to adopt anti-inflationary policies if the relationship between inflation and RPV is


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insignificant. The studies of Parsley (1996), and Lastrapes (2006) have the opinion that the increase in the level of inflation lead towards increase in RPV only in the condition if it leads towards increase in the threshold value. In one of the researches undertaken by Head and Kumar (2005) investigated the monetary examination by applying Calvo (1983) type model, and results have been stipulated the inverse connection between inflation and relative price variability in Japan and China.

Tommasi (1992), Caglayan et al. (2008), Debelle and Lamont (1997) established a V-shaped association between inflation and relative price variability. The studies also reported the U shaped outlined that the inverse association is identified among the countries that have low inflation irrespective of information technology adoption. It has been observed among targets of high inflation only after the process of adopting the information technology. Nevertheless, no shift has been observed among the targets of high inflation.

Choi (2010) through his research applied judgment regarding the association between RPV and inflation on the basis of non-aggregated data for consumer price index for the US and Japan. They explored a negative association between rise in prices and changes in relative prices. On the contrary, the association was not stable over a period of time and it changed in a significant manner with the transformations in the inflationary practices. As a result of that, the results of the general price setting between inflation and relative changes in prices are in contradiction with the prevailing theoretical models which predict a positive relationship regarding menu prices.

Balderas and Rogelio (2009) examined the remittances and relative piece variability that affect the inflation of 32 Latin American countries for the two years. They have used panel data of 224 observations with different modelling techniques. The Findings suggest that there is an affirmative association between overall level of prices and transfer of funds. On the other hand, when they non-aggregated the overall level of prices with respect to the type of expenditure into 7 different categories of activities of the industry, they concluded a significant positive association only in education and house hold sectors, but it is inconclusive to establish the overall association between RPV and remittances. Rather, the five important sectors demonstrated a negative association.

It has been evaluated that in both short and long run, Ukoha (2007) explored significant affect of overall inflation on the price variability of agricultural products through 1970-2003 in Nigeria. The fluctuations in the level of prices of agricultural commodities estimated the significance of this association in in the long-term in Nigeria. In another interesting research by Nath (2004), in which modern correlations techniques were employed to compare the long-term and short-term relationships between RPV and Inflation. In both instances the relationships were found to be positive and the research emphasized the need, to incorporate special features in the models to bring the short and long term results into harmony and coherence.

Another study conducted by Chang and Cheng (2000) that examined the discrete data set of the prices of the United States after the period of war and highlighted the association between variability levels in the prices inflation. As a first step they utilized a model to evaluate the variations in the level of prices, which are subject to unforeseen inflation and the changes in inflation. Afterwards, they explored the proof that RPV has a noteworthy association with inflation as well as the fluctuations in the level of relative prices.



Furthermore, they emphasized upon the fact that prices of oil continue to be the strong shudder in the development of this association.

Silver and Ioannidis (2001), and Fielding and Mizen (2000) came up with the similar outcomes for different European countries. This original course of study has raised objections regarding the prevailing functional mode of the association, and demonstrated the quadratic relationship. The robust threshold effects changes from one country to another. Balk (1985) attempts to determine the direction of causality between relative inflation and relative prices fell flat on its facade when evidence he collected pointed to an inconclusive direction of the above mentioned relationship.

The research of Jaramillo (1999) suggested that the affects of inflation on relative price variability (RPV) is always significant when it is lower than 0. In the same way, Carabello et al. (2006) reported that the situation of positive relationship differs in Argentina and Spain where the positive relationship is stronger when the rate of inflation is high and reached at an alarming rate in Argentina. According to Caglayan and Filiztekin (2003) the association is significantly dissimilar in higher and lower inflationary periods in the case of Turkey. However, during the higher inflation period, the relationship is week between RPV and inflation. According to Bick and Nautz (2008) an adverse implications of inflation have been indicated on relative price variability only in the situation if it enhances the value of threshold in the United States.

The Outcomes that were derived from the region of Europe by Nautz and Scharff (2012) evaluated that inflation leads towards reduction of RPV just in the case of extremely low or high inflation. The affects of RPV and inflation have been examined by Choi and Kim (2010) in case of the United States, Japan, and Canada. Becker (2011) studied the European countries’ data, and Fielding and Mizen (2008) undertook research in the context of the US. In addition to that, in one of the researches related to the impact of inflation aiming on the association of inflation and its linkage with RPV. Choi et al. (2011) evaluate a set of data consisting of 20 emerging countries that comprised of 12 targeters and 8 non-targeters which also involve Turkey during the period of moderation They indicated that the causative association between RPV and inflation is inverse, and in majority of the cases, these results are consistent with previous researcher such as, Fielding and Mizen (2008), and Choi and Kim (2010). Lach and Tsiddon (1992) discussed about the influence of inflation, which has on the food costs that vary for the tenure of 6 years in Israel. This impact was found much unexpected as compared to other studies. Loy and Weaver (1998) got the similar outcomes in Russia, the result depicted that a predictable inflation actually persuaded disturbance in prices, not an unpredictable inflation.

Cukierman and Wachtel (1982) researched about the outline which was based on Parks’ study excluding the allowed inflation anticipations that had variations across marketplaces. According to the different markets, the balance prices have variance and predictions of inflation may also vary as well. There was a direction shown the favourable relationship between cost that varies and predictable inflation. According to their study, either the collective demand or supply shocks are the reason behind this variation which resulted in variability of price.

Fischer (1982) discussed about the economies of Germany and the United States and their inflation and price relation to get the knowledge about inflation and its cost. According to


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him, these two factors are the pillar for both the economies. The VAR model assessments ended that due to policy reactions to supply shocks, the inflation and inconsistently decreased.

Venning and Elwertowski (1976) discussed that there was a connection between changes in prices and inflation instability in the USA. The results demonstrated that there was a link between wholesale and consumer price index and it showed the inconsistency of difference in price through integration of each sub-index of the main sequence and at every stage the variance was estimated. Due to some pitfalls of calculations, this research was criticized. However, Granger causality test was used by Ashley (1981), and concluded that variations in price difference because of variations in the inflation.

2. Data and Methodology

Through the Pakistan Bureau of Statistic, the CPI was derived from the month of August 2001 to July 2011. There were 92 goods with 12 subgroups. This study requires both, the prices of goods and their weights. That is why 92 indices for food and non-food groups have been taken.

Unit Root Test. In this test, the order of combination of the individual series is included, various measures for this test have been established. Augmented Dickey-Fuller (ADF) test (1979; 1981) was the effective one. It depends on refusing a null hypothesis of this test which shows that it favors the alternative hypothesis. This test is generally calculated by following regression:

(1)

where y represents time series, t shows time period, the first difference operator is Δ, a constant is αo, and n shows the finest no of lags in the dependent variable, and e represents the random error terms.

Data construction and variables. To create the data of the variables, the data set is used, which is required for this research. Following variables must be defined first. Let Pt becomes the Consumer price index of every good in month t. After that the inflation in month t is described as:

(2) Variance (VPt) which is one of the relative price variability (RPV) in month t is described as:

(3)



Where , the average change in prices (averaged through 92 composite stuffs) in time period t. This should be also considered that i is an indexes consumption goods, and then n is denoted the number of goods.

This shows that the variable is actually the standard deviation change in price1. And the other one is the skewness of changes in prices for all goods in month time period t is expressed as follows:

(4)

When the construction of the series is made, the pair wise Granger Causality test is verified for entire CPI, non food, and food groups.

Vector autoregressive (VAR) models. The way of causality is tested in this model. This is actually different from the concept which is common nowadays. It shows the ability of one variable to forecast the other one. Assume two variables, yt and xt, which are affecting themselves with spreader lags. This model can notice the link which these variables have between them. So it can be stated that as:

• Xt is caused by yt

• Yt is caused by xt

• Two way comments which shows the causality between the variables

• These two variables are independent in nature

Now the cause and effect relationship which the variables have between each other must be tested and statistically detected by any relevant technique.

Granger Casualty Analysis. This analysis is to test the causality of the time series data, and this model was established by Granger (1969). A variable xt is caused by yt, incase if it can forecast the xt with greater number of accuracy with the usage of yt past value.

The first step of this test is the calculation by the VAR model and given as follows:

(5)

1 All these estimates are un-weighted, in this research we also build these as weighted estimates. The weighted measures are estimated as provided the basis by Parks (1978). Therefore, the weighted mean inflation in time period t is described as weighted variance is defined as and weighted skewness is defined as where wit is known as the relative weight of the items i in time period t, and, .


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(6)

Where e1t and e2t are known as the uncorrelated white noise error expressions. In our taken model following four different cases to be happened:

Case 1: yt is caused by xt because there is a difference between lagged x terms in equation (5) and zero, and there is not a difference between lagged y terms in equation (6) not and zero.

Case 2: xt is caused by yt, as there is a variance amongst the lagged y expressions in mathematical equation (6) and zero, and there is not a difference between lagged x terms in equation (5) not and zero.

Case 3: two-way causality is there. because there is a difference between sets of x and y terms and zero in equation (5) and (6).

Case 4: yt is dependent of xt because there is a difference between both sets of x and y and zero in equation (5) and (6).

Then the following steps are included in this test also;

Measuring the model of VAR with the help of Eview’s utility test. Applying the test of Granger causality. Here null hypothesis shows that xt is not caused by yt. If there is a situation that value of F computed > F critical value, thenceforth, the null hypothesis rejected, and it is finalized that xt is caused by yt.

An Autoregressive Distributed Lag (ARDL) Modeling Approach. Illustration for the association for ARDL testing approach has developed as specified below:

(7)

Wald coefficient restriction estimation used to test the presence of valid long run association by taking the null hypothesis of . In this test we equated the F computed value with the group of critical values from 4.93 to 5.73 for the lag 1 (at 5% critical level), as formulated and proposed by Pesaran et al. (2001). A computed value of F is greater than the higher level, which shows that there is a relation between the variables.


The quick view on the data exhibits the positive association between the variables, and can be observed from the scatter plot diagrams for the pairs of the variables. Since we have used two measures of relative price variability i.e. Vpt, & Spt, for overall and sub-groups of CPI data so we draw 12 pairs with Dpt for all series (see Figure no. 1 and Figure no. 2). By viewing both figures 1 and 2, we can say that there is positive and strong relationship between almost all pairs of Dpt vs. Vpt and Dpt vs. Spt for overall and subgroups of CPI data.



Figure no. 1: Scatter Diagram for Dpt and Vpt of sub-groups- Unweighted


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Figure no. 2: Scatter Diagram for Dpt and Spt of sub-groups – Unweighted

Augmented Dickey-Fuller testing approach. The important step to way forward is to check the Stationarity of the data time series. Since, Granger causality approach requires that all the data series have to be stationary. Therefore, for this purpose we used unit root test, and ADF is the most widely used test, thus, ADF test has employed in order to check the Stationarity of all the data series (Dpt, Vpt, & Spt for overall and sub-groups of CPI data). All series used in this study, the results of ADF show that null hypothesis has rejected for non-stationarity at 5% level of significance. The non-stationary at level (became stationary at 1st difference) includes two sub groups of Dpt, non-food and cleaning laundry and personal appearance (g9) and the other rest are stationary at level. So Granger causality is not applied to those pairs associated with these (Dpt vs. Vpt & Dpt vs. Spt) series.



Akaike Information Criteria (AIC). Since the Granger causality test is multifaceted to the no of lags that is incorporated in the regression. Therefore, the Akaike information criteria (AIC) is applied to identify the appropriate no of lags (see Figures no. 3, 4 and 5). Since we have used two measures of relative price variability i.e. Vpt, and Spt, for overall and sub-groups of CPI data, therefore, we draw 12 pairs with Dpt for all series (see Figures no. 3, 4 and 5). The figures no. 3 and no. 4, depict that there is positive and strong relationship between almost all pairs of Dpt vs. Vpt and Dpt vs. Spt for overall and subgroups of CPI data.

Figure no. 3: Plots of the Inflation (Dpt) for overall and other sub-groups

of CPI – Unweighted


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Figure no. 4: Plots of the Relative Price Variability (Vpt) for overall and other sub-

groups of CPI – Unweighted



Figure no. 5: Plots of the Relative Price Variability (Spt) for overall

and other sub-groups of CPI – Unweighted

Granger Casualty results for CPI overall and Groups (Vpt vs. Dpt & Dpt vs. Vpt) – Unweighted Measured. After that these requirements have been satisfied, Granger-causality is used, and calculated results are reported in Table no. 1. Elaborating the Table no. 1, overall and transport & communication group shows one-way causation from Vpt to Dpt, which means Vpt has power to predict Dpt. Whereas Non-food groups has two-way causation, i.e. both variables have predicting power both ways. The Granger causality of Dpt to Vpt showed improved result because out of 12 groups including overall 5 groups rejecting the null hypothesis of non-food groups by the Granger causality test, but important group i.e. overall accepting the null hypothesis.


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Table no. 1: Granger Casualty results for CPI overall and Groups (Vpt vs. Dpt & Dpt vs. Vpt) – Unweighted Measured

Granger Casualty results for CPI overall and Group (Spt vs. Dpt & Dpt vs. Spt) – Unweighted Measured. Table no. 2 shows the analysis, with the help of the other estimate of relative price variability which is Spt the result is overall recovered in food group of CPI Data. In both groups’ data shows two-way causation for Dpt and Spt variables. The result is promising and shows that both variables have power to predict each other.



Table no. 2: Granger Casualty results for CPI overall and Group (Spt vs. Dpt & Dpt vs. Spt) – Unweighted Measured

In this study we also used weighted measures of Dpt, Vpt and Spt as used by Parks (1978). In order to view relationship between the variables calculating by using the weighted measures, can be seen by drawing the scatter diagrams (See Figures no. 6 and 7) for the pairs of the variables.


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Figure no. 6: Scatter Diagrams for Dpt and Vpt of other sub-groups – Weighted



Figure no. 7: Scatter Diagrams for Dpt and Spt of other sub-groups – Weighted

An Autoregressive Distributed Lag (ARDL) Modeling Approach. This method is used to find out the long run relationship which is there in between pairs of series. The result of F-computed for pairs Dpt vs. Vpt for non-food group is 9.28, Dpt vs. Vpt for g9 sub-group is 9.96, whereas for Dpt vs. Spt for non-food group is 13.78 and for g9 19.99. These F-computed values are much higher than upper bond for lag 1 (at 95% CI) that demonstrated the long-term association between these variables.

The groups which are left, to find out the lag length criteria, Akaike Information Criteria (AIC) is applied, because Granger causality test is complex enough. (see Figures no. 3, 4 and 5).

Granger Casualty results for CPI overall and Groups (Vpt vs. Dpt and Dpt vs. Vpt) – Weighted Measured. After these requirements have been completed, there is a computation of Granger-causality tests and the outcomes are mentioned in tables no. 3 and 4. The pairs which are of Vpt vs. Dpt and of Spt vs. Dpt for overall and other sub-groups of CPI data for weighted measures. From table no. 3, one-way causation can be seen for overall CPI from Vpt to Dpt. Other strong one-way causation reported in CPI sub-group of Fuel and lighting from Dpt to Vpt. 8 from the 12 sub-groups have shown that there is long-term association, which is very encouraging in the way that each of the variable has predicting power to other variable.


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Table no. 3: Granger Casualty results for CPI overall and Groups (Vpt vs. Dpt and Dpt vs. Vpt) – Weighted Measured

Granger Casualty results for CPI overall and Groups (Spt vs. Dpt & Dpt vs. Spt) – Weighted Measured. The result in table no. 4, which is obtained by using other measures of relative price variability i.e. Spt also depict the same result as of using Vpt. Therefore, for weighted measures there is no significant difference between using the two measures of relative price variability (see also Figures no. 8, 9 and 10).



Figure no. 8: Plots of the Inflation (Dpt) for overall

and other sub-groups of CPI – Weighted


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Figure no. 9: Plots of the Relative Price Variability (Vpt) for overall




Figure no. 10: Plots of the Relative Price Variability (Spt) for overall



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Table no. 4: Granger Causality result for CPI overall and Groups (Spt vs. Dpt & Dpt vs. Spt) – Weighted Measured

Conclusions

The purpose of this research is to identify the linkage of inflation with the price variability. Data set used for this study is overall and 12 sub-groups of CPI basket. For relative price variability two measures Vpt and Spt were used and these measures were calculated using weighted and un-weighted methods.

According to the ARDL testing approach, the long-run relationship is existed between these variables. And the outcomes of Granger-causality tests concluded that the one-way causality could be noticed for overall CPI from Vpt to Dpt. Other strong one-way causation reported in CPI sub-group of Fuel and lighting from Dpt to Vpt. Out of 12 sub-groups 8 groups show the long run relationship between each other, which is very encouraging in a way that each of the variable has predicting power to another variable.

It is further concluded that un-weighted measures have strong predicting power as compared to weighted measures. Between two measures Vpt having better predicting power as compared to Spt. Among the 12 sub-groups almost eight, Dpt have predicting power with respect to Vpt. There is two-way causation in overall and food group by using Spt un-weighted measure of relative variability. It is finally concluded that can predict DPt using the information in Spt or vice versa and for non-food group using un-weighted measure of relative price variability Vpt has similar association with Dpt. The results of this study provide the background of the modeling relationship between Dpt, Vpt and Spt.

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Vol. 19 • No. 44 • February 2017 275

DETERMINANTS OF FOREIGN DIRECT INVESTMENT IN ROMANIA: A QUANTITATIVE APPROACH

Calcedonia Enache1* and Fernando Merino2

1)University of Agronomic Sciences and Veterinary Medicine, Bucharest, Romania 2)University of Murcia, Murcia, Spain

Please cite this article as: Enache, C. and Merino, F., 2017. Determinants of Foreign Direct Investment in Romania: a Quantitative Approach. Amfiteatru Economic, 19(44), pp. 275-287


Abstract This study aims to examine the dynamic relationship between foreign direct investments (FDI) and economic growth, using the Structural Vector Autoregressive model, in the period 2007-2014. The results of the econometric model show that the trajectory of FDI has its own origins, with reduced influences from economic growth. Another important conclusion is that there is a unidirectional causal relationship from the economic growth towards FDI, more precisely the influence of FDI on economic growth does not have a systematic, anticipatory nature. These results were achieved in the condition that, in the analyzed period, the net inflows of FDI were influenced by the lack of certainty on the sustainable re-launching of the economic growth both domestically and internationally, the segmentation of the financial market, the domestic structural reforms. Keywords: foreign direct investment, economic growth, vector autoregressive model, Sims-Bernanke decomposition, Granger causality test JEL classification: C180; E430; F21 Introduction

Foreign direct investments (FDI) play an important role in the Romanian economy, perceived as a factor that boosts economic growth, a supplement of local investment and an important financier of the current account deficit. In addition, they carry technological flow vectors, with influence on the productivity, employment and profitability of domestic companies.

The net inflows of FDI accounted for 6.3 percent of GDP in 2008, 0.8 percentage points higher than the previous year. The growth was mainly due the capital increases performed at a series of companies which were stimulated by the effects of the full capital account liberalisation and the introduction of the flat corporate income tax. In a global environment dominated by uncertainties and major risks, the share in the GDP of the net inflows of FDI

* Corresponding author, Enache Calcedonia – [email protected]

AE Determinants of Foreign Direct Investment in Romania: a Quantitative Approach


ranged from 2.8 percent in 2009, followed by a downward trend over the next two years, to 1.3 percent. The increase of the share of the short term foreign debt in the total foreign debt from 19.2 percent in 2009 to 23.1 percent in 2011, accompanied by an unpredictable fiscal regime represented an appreciable source of instability for the Romanian economy, which in some cases resulted in the restriction or cessation of the activities initiated by foreign investors. Later, in the period 2012-2014, the net inflows of FDI fluctuated at around 1.9 percent GDP, while the relatively high volume of international reserves helped to increase Romania's credibility abroad. Thus, the forex reserve registered an average annual growth rate of 3.2 percent, covering six months of future imports of goods and services at the end of 2014, which favoured a stable exchange rate for the RON / EUR, with a significant contribution to lowering the annual inflation rate and ensuring financial stability. In 2014, the net inflows of FDI were directed mainly to the manufacturing industry (38.4 percent of the total), real estate transactions (13.7 percent), constructions (13.0 percent), information technology and communications (10.5 percent). The main origin of foreign inflows was the Netherlands, Austria, Germany, Cyprus and France.

From a microeconomic point of view, the relevance of these figures is even clearer. Since 1997, the net inflows of FDI have been an important change factor in Romanian firms (Hunya, 2002). According to the statistics analysed by the National Bank of Romania, in 2014 the companies that received flows as FDI created approximately 50.0 percent of the value added by non-financial companies, accounted for 24.9 percent of the number of employees in the economy, with a contribution of 70.9 percent on exports of goods and 64.7 percent of imports of goods. For this category of companies, the return on equity was maintained during the period 2011-2014 in the range of 9.1-12.1 percent, while at the level of non-financial companies this indicator was in the range of 8.2-11.2 percent. In 2014, the degree of indebtedness was at 2.3 compared to 2.5 in 2013 and 1.5 in 2011. In August 2014, the non-performing loans ratio was of 15.7 percent, 6.3 percentage points below the average on economy. Then, given the importance of these companies in the Romanian economy, the study of FDI inflows is an interesting topic that deserves further research.

This study aims to examine the dynamic relationship between FDI and economic growth in recent years. This is a hot topic in the analysis of FDI, since as UNCTAD (2016) reports, in the last years FDI flows have a lower growth impact since a large part are attributed to corporate reconfiguration more than greenfield investments.

It must be noted that the studied period (2007-2014) is characterised by a deep financial crisis that reduced FDI flows worldwide from USD 1902 billion in 2007 to USD 1277 billion according to UNCTAD. Different explanations have been signalled for this reduction, but as UNCTAD (2010) explains, the lack of funding for this main investors, the turmoil in financial markets that obscures price signals which are determinants on the decision to invest may explain the reduction in the worldwide FDI flows. Then, an analysis of this period, different than Misztal (2010) will help to know the role of FDI in a completely different economic situation.

The methodology to be employed is the Vector Autoregressive (VAR) that appeared with Sims’ studies (1980). The VAR methodology can be considered a generalization of both the univariate autoregressive model, because the dependent variables are lags of the explanatory variables and the simultaneous equations, because a system of equations is simultaneously estimated. In addition, each equation includes lags of other endogenous variables. In general, the model does not include exogenous variables, as is done in


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modelling (Păuna, 2007). The VAR techniques can be applied only to stationary series (Albu et al., 2003). The main purpose of the VAR analysis is to evaluate the effects of various shocks on the variables of the system. Each variable is affected by its own innovations, and the innovations in the other variables (Boţel, 2002).

The study has the following structure: Part Two reviews the specialized literature regarding the analysis of the relationship between FDI and economic growth. Part Three is dedicated to the VAR methodology. Part Four shows the time series used, the structure of the causal relationships between the variables and the modelling stages. Part Five contains the results from the econometric model and their interpretation. Part Six presents the final conclusions.


The analysis of the relationship between FDI and economic growth is a widely researched topic in the specialized literature. Studies in the field have shown that both developing countries and developed countries encourage the attraction of FDI, even though their objectives are diverse.

Campos and Kinoshita (2002), Blonigen and Wang (2004) demonstrate that FDI positively influence the well-being, growth and development of the host countries. Bengoa and Sanchez-Robles (2003), conducting a panel data analysis for 18 countries in Latin America between 1970-1999, found that there is a positive link between FDI and economic growth, provided that there is adequate human capital, economic stability and liberalized markets in the host countries. Instead, Balasubramanyam et al. (1996), using cross-section data through a procedure based on fixed effects for a group of 46 developing countries in Africa, Asia and Latin America for the period 1970-1985, show that FDI positively affect the economies that apply strategies to promote exports and negatively those implementing import substitution strategies. Furthermore, Alfaro et al. (2004), using the cross-section OLS regressions for a sample of 20 OECD countries and 51 non-OECD countries for the period 1975-1995, notice that the development of the local financial markets is decisive to the existence and extent of the positive effects of FDI. In another study, Hansen and Rand (2004) examine the effects of FDI on the per capita income and economic growth in states across the USA for the period1977-2004, by means of the Markov chains. The authors conclude that FDI that mainly employ labour and located in rich countries generate higher revenues, while FDI using particularly capital and which are positioned in poor countries do not exert an influence on economic growth. Borensztein et al. (1998) making a study on 69 developing countries for the period 1970-1989 by applying a cross-country regression framework, conclude that the size of the positive effects of FDI on economic growth depends on the education level of the workforce in the host country so that it can capitalize the spillover effects that include such capital flows. Similarly, Carkovic and Levine (2002), applying the generalized method of moments on a set of 72 countries for the period 1960-1995, reveals that FDI do not exert an independent, robust influence on economic growth. Resorting to the VaR methodology, Rădulescu (2013) analyses the influence of FDI on the economic growth in some countries in Central and Eastern Europe, for the period 2000-2010. The results indicate that the impact of FDI on economic growth is important in the Czech Republic, both in the short and long term, while in Poland, the short-term impact is more significant than the long-term. In Bulgaria and Romania, the situation is the opposite. The impact on growth can only be seen in the long term, but on a smaller scale than the



impact in the first two countries mentioned above. In completing the picture, the studies of the Keller and Yeaple (2009), Lipsey and Sjöholm (2004) show that FDI bring advanced technology and performant management to the host countries. In this context, the local companies, competing with the foreign ones, are forced to improve their business, either by observing and taking over technologies, organizational practices, strategies which are used by multinational companies (MNCs) (Blomstrom and Kokko, 1998), or by attracting employees trained by MNCs (Meyer, 2004; Spencer, 2008). According to Moran (quoted by Andrei, 2013), instead of connecting the value of the domestic currency resources to that of FDI, MNCs can crowd local producers out of business and replace the imports of raw materials. Moreover, Mencinger (2003), analysing the economies of 8 candidate countries to joining the European Union in the period 1994-2001, suggests that FDI negatively affect economic growth. In this regard, the author argues that, in case of the investigated countries, FDI had as main purpose the acquisition of fixed assets from the patrimony of public institutions and the revenues thus obtained did not finance productive investments and generate a growth in imports and thereby the current account deficits. In addition, Kholdy and Sohrabian (2005), Javorcik (2004), Grilli and Milesi-Ferretti (1995) consider that FDI do not affect economic growth.

When the connection between FDI and economic growth is studied, a notable problem is the potential endogeneity between them. Thus, on the one hand, the bilateral causality is tested and, on the other hand, a system of equations is estimated within which the equation associated with FDI may include variables such as economic growth, interest rates, foreign exchange, infrastructure.

Chowdhury and Mavrotas (2006) examine the connection between FDI and economic growth in Chile, Malaysia and Thailand in the period 1969-2000, using the long-run Granger causality test proposed by Toda and Yamamoto (1995). The results show that in Malaysia and Thailand there is a mutual causal relationship between the two variables, while in Chile a unidirectional causal relationship from growth to FDI was identified. Choe (2003), using a panel VAR model to examine the connection between FDI and economic growth in 80 developed and developing countries in the period 1971-1995, finds that there is a bidirectional causal connection between the studied variables, with clearer effects from growth to FDI. Later, Moudatsou and Kyrkillis (2011) studied the connection between FDI and economic growth both in developed EU countries and in some ASEAN developing countries, namely Indonesia, Singapore, Philippines and Thailand in the period 1970-2003, with the aid of the co-integration and error-correction technique. The authors reached the conclusion that FDI cause economic growth only in Finland and Indonesia, while in all the countries included in the analysis economic growth motivates FDI. In case of the Romanian economy, Misztal (2012) using a VAR model to assess the relationship between FDI and economic growth in the period 2000-2009, notes that FDI substantially influenced economic growth.

A series of authors, such as Ruxanda and Muraru (2010), Ford et al. (2010), Ghosh Roy and Van den Berg (2006), Li and Liu (2005), applied models with simultaneous equations in which both economic growth and FDI are treated as dependent variables. The results show that FDI especially target the countries holding large markets. Moreover, the human capital and absorption capacity are essential in order for FDI to have a positive impact on economic growth. Given that the investigated variables tend to become explanatory, the


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promotion of human capital and technical progress encourage the attraction of FDI, which drive economic development and competitiveness.

2. The model

Our objective is to use a model capable to identify the interactions between the macroeconomic variables. The theoretical model is similar to that presented by Enache (2005).

Yt is considered as a VAR model of p (VAR(p)) order in the form:

Yt = Щ1Yt-1+Щ2Yt-2+…+ЩpYt-p + et (1)

where:

Yt is a (nx1) vector of endogenous variables; Щi is (nxn) coefficient matrix, for pi ,1= ; et is (nx1) vector of errors with M(et)=0 and the variance-covariance matrix M(etet

T)=Σe.

The VAR(p) process is stationary if the polynomial defined from determinant det(In-Щ1Z -…-ЩpZp) ≠ 0, for │z│≤1 has the roots outside the unit circle in the complex plane (Hamilton,1994).

Equation (1) can be written using the lag operator lag Ljyt =yt-j in reduced form, as follows:

(In – Щ1L – Щ2L2 – … – ЩpLp)Yt = et or: (2)

Щ(L)Yt = et (3)

From the form of equation (1) unrestricted VAR we can obtain a VAR restricted equation (Pfaff, 2008):

ЩYt = Щ1*Yt-1+Щ2

*Yt-2 +…+Щp*Yt-p +Φut (4)

where ut are the structural innovations, M(ut,utT)=Σu is the variance-covariance matrix, the

coefficients of the Щi* matrix, for pi ,1= are structural coefficients, that generally differ

from their counterparts in reduced form, Φ is the diagonal matrix.

Multiplying equation (4) with Щ-1 we obtain:

Yt = Щ-1 (Щ1*Yt-1 + Щ2

*Yt-2 +…+ Щp*Yt-p+Φut) (5)

Equation (5) can be re-written as:

Yt = Щ1Yt-1 + Щ2Yt-2 +…+ ЩpYt-p+ vt 6)

where vt =Щ-1Φut makes the connection between the two forms. The variance-covariance matrix is: Σv=Щ-1ΦΦTЩ-1T.

In order to determine the structural innovations, the minimum k(k-1)/2 zero restrictions must be set to the coefficients of the Щ matrix in equation (5) → The Щ matrix reveals the interdependency connections between the variables included in the model. In this regard, the Sims-Bernanke decomposition (1986) can be used for short-term innovations, admitting the free distribution of the zero restrictions within the Щ matrix.



t

p

jjtjit

p

ijt uYbZaZ 1

11

111 ∑∑

=−−

=

+++= α

t

p

jjtjit

p

ijt uYbZaY 2

12

122 ∑∑

=−−

=

+++= α

The results of the VAR model are:

• The impulse response function (IRF) examines the impact of an innovation of a standard deviation of the residuals of a variable on the future evolution associated with each

variable in the model. IRF is expressed by the relationship: ht

ht

uy

υ=∂∂ + , where υij, the

element of the υh matrix highlights the impact that the increase by one unit of the uj,t variable at the time t has on the yt+h variable, if the other variables in the system exert a constant action.

• The variance decomposition (VD) determines, by percentage, the specific weight in the variance of a variable that is the result of their own shock and of the shocks from the other variables in the system.

• The Granger causality test (1969) shows whether there is a statistical connection between the data sets of variables Y and Z. It can be said that Y Granger causes Z, whether a forecast of Z performed based on the past values of Z and Y is better than a forecast made only based on the values of Z in the previous period. The Granger causality test is based on the following regression equations:

(7)

(8)

that assumes that the errors, u1t and u2t, are uncorrelated. Testing the null hypothesis Y does

not Granger Cause Z, i.e. H0: ∑=

=p

jjb

11 0 , it is carried out using the F-test.

3. Data description, modelling Economic literature has two different indicators for the size of an economy: GDP and GNP. While GDP measures the value of the production inside the country borders, GNP measures the value of the production of those economic agents that are resident in those countries. Obviously, the difference emerges from the fact that part of the production developed in the country is done by inputs which are no residents in the country (and reverse). Given the low value of the income of labour (this is trans-border workers) most of it comes from the income of foreign capitals invested in the country. GNP is usually preferred (see, for example the methodology of the Human Development Index, or the criteria for European Cohesion and Regional Funds), since it is a better proxy of the income available for the citizens of that country.

In order to highlight the reaction of the FDI to various innovations in the economy, we estimated a VAR model, using the quarterly data from the period 2007-2014. In this regard, we included the following variables in the model:

FDIG Net inflows of FDI (percent of GDP) RGNP Real gross national product growth rate (percent) Pr Central Bank policy rate (percent)


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The data series were taken from periodicals of the National Bank of Romania. The data series for the FDIG and the RGNP were seasonally adjusted using the TRAMO/SEATS method. The Augmented Dickey – Fuller test (Dickey and Fuller, 1979) and the Kwiatkowski-Phillips-Schmidt-Shin test (Kwiatkowski, Phillips, Schmidt and Shin, 1992) reveal that the three variables are stationary (Table no. 1).

Table no. 1: Evaluation of the series integration order

Notes: The tests are performed for the series in levels. The tests contain constant, constant and trend. The series is stationary if the value obtained is less than at least one of the critical values in absolute value.

The choice of the optimum number of lags in order to estimate the VAR model was made using the Akaike (1974, 1976), Schwarz (1978) and Hannan-Quinn (1979) criteria. The Akaike and Hannan-Quinn criteria indicated 2 lags. Since the VAR is stable, and the tests carried out on the errors reveals that they are distributed normally, they are not heteroscedastic and auto correlated, we opted for the Schwarz criterion, who selected a period as optimum lag (Table no. 2).

Table no. 2: VAR lag order selection criteria

The structure of the causal relations between FDIG, RGNP and Pr is presented in Table no. 3.

Table no. 3: Щ matrix structure

Variable Augmented Dickey – Fuller test Kwiatkowski – Phillips – Schmidt – Shin test

Specification t-Statistic Specification t-Statistic FDIG RGNP

Pr

c,t c

c,t

-4.789 -3.065 -4.833

c c c

0.699 0.114 0.624

Test critical values 1 percent level 5 percent level 10 percent level

MacKinnon (1996) c c,t -3.7 -4.3 -3.0 -3.6 -2.6 -3.2

Kwiatkowski-Phillips-Schmidt-Shin (1992) c

0.7 0.5 0.3

Lag LogL LR FPE AIC SC HQ 0 136.2033 NA 1.48e-08 -9.514524 -9.371788 -9.470888 1 211.1540 128.4868 1.34e-10 -14.22528 -13.65434* -14.05074 2 225.7799 21.93887* 9.21e-11* -14.62713* -13.62798 -14.32168* 3 229.8270 5.203386 1.40e-10 -14.27335 -12.84599 -13.83700 4 242.9162 14.02423 1.19e-10 -14.56545 -12.70988 -13.99818

* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion

FDIG RGNP Pr FDIG 1 1 1 RGNP 0 1 0

Pr 0 1 1



The imposed restrictions show that over a quarter, the FDIG are influenced by the RGNP and Pr, while the latter responds to the developments in the RGNP. In addition, each variable is influenced by itself.

4. Estimates and results

The impulse response functions to an innovation in the three series, simulated on the basis of the estimated model, are shown in Figure no. 1.

Figure no. 1: The impulse response functions

The reaction of FDIG is strong to its own shocks, after the first 7-8 quarters remaining at a high level. FDIG respond positively to the shocks in the RGNP, peaking in quarter 2, following a period of gradual reduction in their intensity. In addition, FDIG respond positively to the shocks in the Pr in the first two quarters and then negatively. It should be noted that only the effect of its own shocks are statistically significant, indicating that the trajectory of FDIG has its own origins, with reduced influences from the other two variables included in the model. The shocks derived from FDIG and from Pr have no significant effects on RGNP (the confidence intervals include the zero value and the innovations are located close to this value). Also, the positive response of Pr to the FDIG shocks is not statistically significant. Similar conclusions can be drawn from studying the variance decomposition. Thus, at a horizon of 12 quarters, the Pr is the most important explanatory factor for the change in the FDIG, after their own innovations (figure no. 2). Moreover, in the same time frame, the variation of RGNP is caused in proportion of only 5 percent by the FDIG shocks,

-2

-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10 11 12

Response of FDIG to Shock FDIG

-2

-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10 11 12

Response of FDIG to Shock RGNP

-2

-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10 11 12

Response of FDIG to Shock Pr

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10 11 12

Response of RGNP to Shock FDIG

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10 11 12

Response of RGNP to Shock RGNP

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10 11 12

Response of RGNP to Shock Pr

-.002

-.001

.000

.001

.002

1 2 3 4 5 6 7 8 9 10 11 12

Response of Pr to Shock FDIG

-.002

-.001

.000

.001

.002

1 2 3 4 5 6 7 8 9 10 11 12

Response of Pr to Shock RGNP

-.002

-.001

.000

.001

.002

1 2 3 4 5 6 7 8 9 10 11 12

Response of Pr to Shock Pr

Response to Structural One S.D. Innovations ± 2 S.E.


Vol. 19 • No. 44 • February 2017 283

respectively, 12.1 percent by the Pr shocks. Also, the increased explanatory capacity of its own shocks to the Pr variation decreases from 82.6 percent at a horizon of two quarters to 23.4 percent at a horizon of 12 quarters.

Figure no. 2: Variance decomposition Next, we tested the Granger causality (table no. 4).

Table no. 4: The Granger Causality Test

. Notes: 1. The basic hypothesis tested is: the variable in the line is not Granger caused by the variables in the columns. 2. The figures represent the probability (p-value). 3. The figures in bold indicate the rejection of the basic hypothesis at a 5 percent significance level.

Both the RGNP (the p-value test: 0.0005), and the Pr (the p-value test: 0.0378) Granger cause the FDIG, in other words, the future values of FDIG are explained by the past values of the other two variables included in the model. Instead, RGNP is not Granger caused by FDIG (the p-value of the test: 0.5409). More precisely, the influence of FDIG on RGNP does not have a systematic, anticipatory nature. Moreover, it appears that FDIG Granger causes Rd (the p-value of the test: 0.0012).

These results were achieved in the condition that, in the analysed period, the net inflows of FDI were influenced by the lack of certainty on the sustainable re-launching of the economic growth both domestically and internationally, the segmentation of the financial market, the domestic structural reforms. According to Roman (2014), the bureaucracy, the high administrative costs, an uncertain fiscal climate and the size of the underground economy make Romania less attractive to foreign investors compared to the other neighbouring countries such as Serbia, Bulgaria or Croatia.

In the period 2008-2009, the services and construction sectors have attracted an average of 57.4 percent from the final FDI stock, compared with 61.8 percent in the EU 10 average (except for Poland), which led to the creation of an unsustainable model of economic

FDIG RGNP Pr FDIG 0.0005 0.0378 RGNP 0.5409 0.1573

Pr 0.0012 0.0395

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Percent FDIG variance due to Shock FDIG

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Percent FDIG variance due to Shock RGNP

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Percent FDIG variance due to Shock Pr

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Percent RGNP variance due to Shock FDIG

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Percent RGNP variance due to Shock RGNP

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Percent RGNP variance due to Shock Pr

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Percent Pr var iance due to Shock FDIG

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Percent Pr var iance due to Shock RGNP

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Percent Pr variance due to Shock Pr



development, cantered on imports and investments that rely on imports, which are the main cause of trade and currency imbalance. Attracting FDI flows to the non-tradable sectors was achieved in terms of obtaining high profit rates of the short term, mainly from financial activities, with a speculative nature. In the period 2010-2014, the industry and information technology and communications sectors, that generating high added value benefited from an increase in FDI flows, focusing on average 52.2 percent of the final FDI stock, amid a high uncertainty of foreign investors regarding the international financial system and the potential consequences in Central and Eastern Europe countries. With the triggering of the economic crisis, the large withdrawals of foreign capital generated the contraction of the investment volume, the average annual variation in equity participations (distributed dividends and reinvested earnings) fluctuated in real terms ranging from -47.1 percent 129.4 percent between 2010-2011 to 2012-2014.

Between January 2009 and June 2010, in the context of the financial crisis, most companies with FDI opted for external financing. In order to reduce the interest rates on credits in the national currency, the Central Bank policy rate gradually decreased from 6.25 percent in January 2011 to 2.75 percent in December 2014, amid the annual inflation rate decreasing and the very low values of the interest rates in the Eurozone and the USA. In these circumstances, in the period 2009-2014, the share of the net loans from foreign direct investors in the final FDI stock was placed within the range from 27 to 32.2 percent. According to Georgescu (2013), regardless of the structure of the net loans by maturities, the FDI enterprises have high levels of indebtedness. The impact on the real economy of the reduced FDI flows and the deterioration of the profitability parameters of the companies with foreign capital operating in Romania reflects on our country's external financial framework.

Conclusions

In the present study, we used the Vector Autoregressive Model to evaluate the effects exerted by the RGNP and the Pr on the FDIG in Romania. The results of the Granger causality test indicate that the evolution of FDIG it is preceded by the evolution of the other two variables included in the model. According to the impulse response functions, only the effects of own shocks on FDIG are significant, indicating that the trajectory of FDIG has its own origins, with reduced influences from the other two variables included in the model.

Similar conclusions arise from the analysis of the variance decomposition. Thus, it appears that, at a horizon of 12 quarters, the fluctuations in FDIG is explained in the proportion of 77.2 percent by its own shocks. Moreover, at the same time horizon, the FDIG shocks explain an insignificant proportion of the fluctuation of RGNP. Also, it is worth mentioning the influences of FDIG and RGNP on Pr at a horizon of 6 quarters.

These results were achieved given that the investigated period was affected by a strong financial crisis, which determined an important shortage of financial funds internationally as well as a great reduction in international FDI. In this context, in the period 2009-2011, in Romania, the deterioration of the business environment, the increase of the share of the short-term external debt in the total external debt, accompanied by an unpredictable fiscal regime were significant barriers towards attracting FDI. It should also be pointed out that, throughout the period 2008-2014, the activity of FDI enterprises was completed with a net loss (reduced appropriated dividends from net profits), which resulted in some cases in the restriction or cessation of the activities initiated by the foreign investors.


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Results presented in this paper suggest a set of interesting future research lines. First, the analysis of the FDI-RGDP link could be expanded to include the destination sector, since not all of them have the same possibility to increase GDP, as well as the kind of FDI flows distinguishing those ones aimed for greenfield investments from those ones that are part of corporate restructuring or the acquisition of previously existing firms. Second, the different effect that origin of the FDI flows could also be an interesting topic for research. FDI inflows from technologically advanced countries that may go associated to a more advanced technology or managerial skills may generate a different effect on the Romanian growth possibilities than those ones generated in less advanced countries.

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Contents Amfiteatru Economic recommends Innovation – key to economic growth

An Assessment of the First Round Impact of Innovation Industries on Europe’s Regional Economies ........................................................................ 289 Adrian Oţoiu, Ramona Bere and Cătălin Silvestru Development and Operationalization of a Model of Innovation Management System as Part of an Integrated Quality-Environment-Safety System ............ 302 Dorin Maier, Irmer Sven-Joachim, Astrid Fortmüller and Andreea Maier

Best practices

Best Practices for Integrating the Romanian Small Farmers into the Agri-Food Chain ..................................................................................... 315 Dan Boboc, Gabriel Popescu, Mirela Stoian, Georgiana-Raluca Lădaru and Dan Cosmin Petrache The Market for Ideas. For Common Sense Elites .............................................. 327 Octavian-Dragomir Jora and Alexandru Georgescu

Amfiteatru Economic recommends AE

Vol. 19 • No. 44 • February 2017 289

AN ASSESSMENT OF THE FIRST ROUND IMPACT OF INNOVATION INDUSTRIES ON EUROPE’S REGIONAL ECONOMIES

Adrian Oţoiu1∗, Ramona Bere2 and Cătălin Silvestru3

1),2), 3) The Bucharest University of Economic Studies, Romania

Please cite this article as: Oţoiu, A., Bere, R. and Silvestru, C., 2017. An Assessment of the First Round Impact of Innovation Industries on Europe’s Regional Economies. Amfiteatru Economic, 19(44), pp. 289-301


Abstract This paper attempts to give an economic perspective of the impact of the innovation industries. The estimation method used is that of panel data modelling, based on data from regions from European countries, including countries from Central and Eastern Europe, for which exploratory analysis was conducted on the effects of employment counts, number of companies, and wages per capita, in computer and related activities and research and development industries. Higher employment in both industries have positive effects on total employment and GDP/Capita. No sizable displaced workers effects can be seen, as higher employment and wages/capita in innovation industries are accompanied by higher employment and lower unemployment at regional level. Positive effects can be observed for both young and mature workers, and are stronger for the latter, pointing out to strong potential spillover effects. Number of local companies is not a relevant indicator for assessing the influence of research and development activities. All these effects point to the sustainability of innovation industries, which not only lead to increase of GDP per capita, but also show positive spillover effects, increase employment and reduce unemployment. The results for countries from Central and Eastern Europe (CEE) have been to some extent less significant, due to several objective factors. The results should also be viewed in the framework of the transition and catch-up period that characterizes the evolution of the CEE economies. The positive effects of strong growth are primarily reflected in GDP growth, and it may be that it takes a while for these effects to propagate in the rest of the economy in terms of job creation and sizable reduction of unemployment. While the current analysis revealed some of the first-round impacts of the innovation industries, much work remains to be done in matching these effects with other determinants of employment and unemployment, which can improve existing models with relevant empirical elements. Keywords: regional growth, innovation, research and development, computer and related industries, labour market, employment, unemployment

JEL Classification: R11, R23, O32, L86 ∗ Corresponding author, Adrian Oțoiu – [email protected]

AE An Assessment of the First Round Impact of Innovation Industries on Europe’s Regional Economies


Introduction

The question as to what are the drivers of economic growth is more and more relevant since most EU (European Union) countries experience only modest GDP/capita growth. While the latest EC (European Commission) forecast signals an acceleration of growth due to positive factors, it acknowledges the fact that structural factors and crisis-related economic weakness still weigh upon its medium-term performance (European Commission, 2015a). This is also true for the global economy, which will see moderate acceleration of growth (European Commission, 2015a).

Another major issue affecting many economies is the proper functioning of the labour markets. Mass unemployment is affecting many economies, and especially the youth, for which economies cannot create proper opportunities that will enable them to enter the labour market. Apart from the historical perspective, while unemployment has been persistently high since the 70s until 2000 (Blanchard, 2006), more recent evidence shows that unemployment in Europe has fallen since the 2010-2011 peaks, but it is still close to historically high levels, with long unemployment spells and worse terms of employment for youths and prime-aged individuals (European Commission, 2015b).

These major issues have drawn the attention of the EU, which has addressed them in its policies. Through public policies, which represent one of the key means for protecting the values of a country (Lefter, 2015), policy makers should focus on overcoming these issues. The Europe 2020 sustainable growth strategy has among its key targets an increase in R&D spending to 3% by 2020, achieving a 75% employment rate for workers aged 20-64, at least 40% of 30-34–year-olds completing third level education, and 20 million fewer people in or at risk of poverty and social exclusion (European Commission, 2015c). Innovation and its digital agenda, are placed among its top 7 flagship initiatives (2012) as tools that will enable the achievement of these objectives. The potential of the digital economy, which grows seven times faster than the rest of the economy, should be exploited to deliver the economic and social benefits is a sustainable way (European Commission, 2014a). Also, innovation is seen as a main engine for generating growth and jobs, increase the competitiveness of the EU, solve challenges relating to population ageing (European Commission, 2014b).

The impact of innovation industries on labour markets has not been thoroughly explored from the classical point of view of job gains or losses, most likely because this is likely to be similar with the impact of technological change. There are two competing effects: innovation and ICT, by increasing productivity, can lead to job losses and displacement of workers in certain industries, whereas their expansion can create, directly or indirectly, new jobs, that need to be explored.

Considering the central roles that innovation and the digital economy play in the EU’s future economic and social development, it was considered of utmost importance to inquire about their real impact on growth and jobs. Using a quantitative approach, the impact of two industries was examined, more specifically of computer and related activities, and research and development, on employment rates, unemployment rates and GDP/capita, at


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regional level, which, according to the Nomenclature of territorial units for statistics (NUTS) used by Eurostat, corresponds to NUTS2 regions.

Our goal is not only to see to what extent these industries affect labour markets and GDP at regional level, but also to make a comprehensive analysis that examines most of the EU regions in both New and Old Member States (mostly defined as former Communist countries; to promote an empirical approach where the explanatory power of the basic economic indicators is fully explored, and see which of the explanatory variables (employment counts, wage levels, number of companies) are relevant. The last item is especially important, considering the fact that there is a significant conceptual ambiguity with respect to the dimensions of market-led innovation at local and regional level (Moulaertab and Sekiac, 2003).

1. Theoretical aspects regarding effects of innovation industries on economic growth

The literature review concerning the role of the innovation and the digital economy is vast and a comprehensive review is well beyond the scope of this paper. However, the intent is to pin down the key outcomes for the subsequent analysis of the impact of innovation and digital industries at regional level and thus create a perspective for the current analysis.

The labour market impact of jobs in innovation industries appears to be large. In a working paper, European Commission (2014b) states that while ICT represents 4.8% of the EU economy, it generated about half of productivity growth, and about 1/3rd of GDP growth. A review study (OECD 2004, cap 4 and cap 5) point out to multiple effects of ICT, that raise capital productivity, and effect lower transaction costs and more rapid innovation that boost economic growth. The same study (OECD, 2004) shows that ICT capital added between 0.3 and 0.8 percentage points of GDP and labour productivity growth in OECD over the 1995-2001 period.

In a meta-analysis of the effects of growth, Kokko, Tingvall and Videnord (2015) show the fact that most studies indicate a significant correlation between R&D spending and economic growth in both the EU and the US. The ICT industry effects economic growth not only through its own activity, but also to the spillover effects on other industries (Oulton, 2012). In his study, Oulton (2012) shows an average increase in annual long-term growth for 15 EU countries of 0.26 percentage points due to production of ICT goods, and 0.52 percentage points due to ICT use (spillover effects). If ICT intensity would reach the levels of USA or Sweden, there could be a further increase in long-term growth due to spillover effects by 0.21 percentage points, based on Oulton (2012). Overwhelming evidence on the ICT as a source of growth, based on an analysis of 102 countries for the period 1976-2005, is also presented by Vu (2011), which also shows the strategic role in improving ICT penetration, and enhancing its effects on growth through fostering innovation and exchange. In their analysis on a sample of 158 regions that form parts of 18 European countries over the 1998-2008 period, Moutinho, Oliveira and Coelho (2015) built a regional innovation system, in which R&D employment, technological capacity and knowledge intensity are shown to increase GDP and Gross Value Added, and decrease both total unemployment and youth unemployment in a significant way. García-Muñiz and Vicente (2014) show a more insightful perspective of the ICT role in the EU economy, by showing that ‘ICT sector has then the capacity to foster technological diffusion and the



generation of innovation in the European Union’, but that low efficiency levels are recorded for those industries with the highest value creation.

The role of the technological change induced by the innovation industries can be ambiguous, with multiple effects that can act in opposite directions. Bartel and Sickerman (1993) show that in industries with high rates of technological change workers retire later, and that a sudden unexpected increase in technological change leads to early retirement. The same authors show that industries with high technological change command a higher wage premium (Bartel and Sickerman, 1999) after controlling for education and other personal characteristics of workers. In a review of three recent papers, it is argued that displacement of workers due to technological advances (The Economist, 2015) is often overrated, and that it does not automatically affect the low-skilled workers, unless their jobs can be easily automated. Autor (2015) cited by The Economist (2015) claims that the new jobs appearing from the use of technology sometimes more than compensate job losses due to substitution of workers with computers.

2. Methodology

The scope of the present analysis on the impact of innovation industries on the regional economies from Europe, was defined, based on the insights and findings from the literature review, with particular focus also on CEE countries.

Besides the effects of the innovation industries on economic growth, it was also considered important to incorporate effects on local labour markets. There is a major stream of research that shows that economic growth is not an end in itself, but one of the components of well-being and societal progress. Among other significant factors of socio-economic well-being is employment, as a means for providing households with their most important source of income (Oțoiu, Țițan and Dumitrescu, 2014).

Unemployment rates are also important from a sustainability point of view, as the adverse effects of persistent employment, in particular youth employment, are not to be neglected. Layard, Nickell and Jackman (2005) showed that unemployment is associated with loss of income and increased social inequality, and increase psychological and social problems. This is especially important in the context of massive retirements of the baby boomers, which will exit the labour market and may not be so easily replaced by youths with little or no relevant work experience.

All these outcome variables are important in the context of regional development. Lengyel and Rechnitzer (2013) show that unemployment rates play a major role in defining regional competitiveness, which is defined as a principal component of employment rates, available incomes, and labour productivity. Regional competitiveness, in turn, is highly correlated with GDP/capita (Lengyel and Rechnitzer, 2013), hence the role of these variables as drivers of regional growth in Central and Eastern Europe.

Thus, data used to conduct the analysis are from Eurostat, with taking into consideration European Union countries. The analysis targeting in particular Central and Eastern Europe (CEE) countries took into account the following countries: Bulgaria, Czech Republic, Estonia, Latvia, Lithuania, Hungary, Poland, Romania, Slovakia and Slovenia. Data was used at regional level, which according to the Nomenclature of territorial units for statistics (NUTS) corresponds to NUTS2 level. The 2008-2011 period was selected for conducting


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the analysis, this period was chosen with taking into account data availability, and the definition of industries. Thus, since 2008, there is a better definition of the innovation industries, which makes the focus of the current exploratory analysis. For the current analysis, two sections, which can be considered as innovative industries, were selected: J and M sections. Section J groups ‘production and distribution of information and cultural products, provision of the means to transmit or distribute these products, as well as data or communications, information technology activities and the processing of data and other information service activities’ (Eurostat, 2008) which were previously scattered under broader sections. Section M groups scientific research and development, with professional, scientific and technical activities being separated from other activities.

Expanding the series with data from the corresponding industries, classified using NACE Revision 1 would have caused a significant break in series that would make estimation unreliable, as the innovation industries were regrouped into new sections that are not comparable, as acknowledged by Eurostat (2008).

The analysis variables are:

Dependent variables (DV):

• GDP/capita expressed in both purchasing-power parity (PPP) and Euros, to account for disparities between the New and Old Member States.

• Employment rates: for workers 15-25 (youths entering labour market) and workers aged 25 and over.

• Unemployment rates: for workers 15-25 (youths entering labour market) and workers aged 25 and over.

Independent Variables (IV) for a) scientific research and development and b) computer programming, consultancy and related activities industries:

• Number of local units;

• Wages and salaries per capita;

• Number of employees.

Data for industry-specific indicators is not complete due to the relatively small dimension of these industries. For the industries chosen, scientific research and development (NACE Revision 2 code M72) and computer programming, consultancy and related activities (NACE Revision 2 code J62) there is no data available on their share in manufacturing employment. Data is scarce and covers only some countries for the following indicators investment per person employed, and gross investment in tangible goods.

Based on a synthesis of the influences that innovation industries will have on growth and jobs, the scope of the current analysis is presented in table no. 1. These indicators are not, by any means, intended to be an exhaustive list of the items that define and quantify the impact of an industry on economic development.



Table no. 1: Innovation industry specific indicators and their hypothesized influence on economic indicators of sustainable regional development

Innovation Indicators at industry level (IV) Economic indicators

of sustainable regional development (DV)

Number of Local Units

Wages and salaries per

capita

Number of Employees

GDP/Capita + ? + Employment rate + ? + Unemployment rate - ? -

Note: ”+” positive influence; ”-” negative influence; ”?” influence that could not be presumed at the beginning of the analysis

The estimation method used is random-effects panel data models, estimated with the plm R package (Croissant and Millo, 2008). Effects represent the NUTS regions for which data is available. The choice of random effects was considered appropriate since the results will have a higher degree of generalization given the large number of regions, as opposed to a limited number of fixed effects with distinct characteristics.

The unbalanced nature of the panels, which did not allow estimation to be done on both individual and time effects, was overcome by using time dummy variables (represented by the factor variable time).

The general equation for the analyses performed is the following:

(1)

with DV is one of the dependent variables, IV the pair of the independent variables for the two industries, k is the index of the industries, i the index of individual fixed effects, t is the time index, β represents the coefficients of the regression parameters, αi is the country constant, δt is the time constant (corresponding to base level year 2008) and uit is the random error.

The use of the word model was explicitly avoided when defining the analysis approach, as the intent was to analyse the effects of these industries alone on the major indicators on key regional economic indicators. A model that will include all relevant variables stemming from standard economic theory and accommodate the indicators relevant for these industries would require work way beyond the scope and length of the current paper.

3. Results of analysis and discussions

The econometric analyses have revealed that, in most cases innovation industries have a sizeable impact on the dependent variables, as shown by coefficients of determination of over 20% that in most cases are even above 33%. This is in line with the exploratory nature of the analysis and confirms the fact that the activity in innovation industries is not the sole driver behind the evolution of the DVs, but that they should be taken into account when modelling changes in GDP, employment and unemployment.


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The employment counts in the scientific research and development (R&D) (table no. 2) appear to have a positive effect on the GDP/Capita expressed in PPP and on employment counts for both youths and mature workers, and a negative effect on unemployment rates. This is in line with expectations, and shows the sizeable positive effect of this industry on Europe’s regional economies. However, a closer examination gives more insights about its effects.

Table no. 2: Impact of employee counts on regional economic indicators

Dependent variable Scientific research and development

(M72)

Computer and related activities

(J62) R2

Employment age 15-25 (000) 1.7657e-03* 1.4662e-03*** 0.32 Employment age 25+ (000) 9.6286e-03*** 4.0870e-03*** 0.14 Unemployment rate age 15-25 (%) -7.4985e-04*** 1.4554e-05 0.36 Unemployment rate age 25+ (%) -2.2028e-04** 3.0739e-06 0.28 GDP/Capita, expressed in purchasing power parity (PPP) 2.2025e-01** 1.4924e-01*** 0.39

GDP/Capita, EUR 1.5716e-01 1.7872e-01*** 0.34 Note: Levels of significance based on calculated p-values: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

First of all, results indicate that high disparities in the GDP/capita across the EU render estimation of impact of R&D useless, and that an analysis done on comparable data can help show the actual impact of this industry. The impact of having 100 more employees in R&D is 22 EUR/Capita GDP growth expressed in PPP.

While effects on both employment and unemployment are significant and confirm the working hypotheses, it can be seen their huge difference with respect to young versus mature workers. The decrease in unemployment rates triggered by increased employment in R&D is more than three-fold for youths (0.75 percentage points decrease for an increase of 1000 employees in this sector) as for mature workers (0.22 percentage points only). An increase in employment of 1000 R&D workers effects an increase of approximately 9630 mature workers in a region, and only 1766 young workers in a region. It is very likely that spillover effects can account for this difference.

From the perspective of employment counts, direct effects for computer and related industries (CI) are similar. Comparable results were obtained for GDP/capita, albeit the impact of having 100 more workers in this industry adds 15-18 EUR to it.

These explanations may account for the CI employment counts influence on employment. Additional CI workers are associated with increased employment for both youths (1.5 additional workers for one CI hire) and mature workers (4 additional workers for one CI hire), but the increase for the latter are less than half than the one effected by R&D. Finally, there is no evidence that CI employment is a solution to decreasing unemployment from a first-round effect perspective.

The number of local companies is also significant when assessing the impact of these industries on regional economic indicators. Results of the analysis are shown in table no. 3.



Table no. 3: Impact of number of local firms on regional economic indicators


(M72)


(J62) R2

Employment age 15-25 (000) -1.3511e-02 6.8778e-03*** 0.23 Employment age 25+ (000) -0.0320846 0.0115280*** 0.06 Unemployment rate age 15-25 (%) 0.00590400* -0.00088440*** 0.34 Unemployment rate age 25+ (%) 1.9050e-03* -3.1559e-04*** 0.27 GDP/Capita, PPP 1.6394e+00 5.2430e-01*** 0.34 GDP/Capita, EUR 2.28464. 0.47876*** 0.28

Note: Levels of significance based on calculated p-values: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

It seems that only for CI results are within expectations, and that they match the results obtained for the number of workers. One more CI entity adds 0.5 EUR to the GDP/capita, a result in line with the one obtained for employment counts if one is to consider that the average local CI firm has 4.3 employees.

Strong estimates are obtained for employment and unemployment, where 1 extra CI firm generated 12 jobs for the mature workforce, and 7 jobs for the youth. Unemployment for mature workers decreases by 0.3 percentage points, and by 0.9 percentage points for youth, mostly in line with results obtained for employment counts.

For R&D, it seems that there are no immediate significant effects on GDP/Capita and employment, probably due to the less dynamic nature of company creation which cannot be related to economic opportunities. It seems that a higher number of companies is associated with increases in unemployment rates, which may indicate a job destruction effect. However, further analysis should be conducted on this, as these results are not consistent with the other results obtained for this industry, and there may be other effects at play that may explain them.

A third dimension used to assess the first–round effects of innovation industries on the economic indicators of regional economy is related to average wages and salaries. This investigation is potentially trickier because of the potential competing effects (positive spillovers vs. job destruction and mass unemployment) that can tip the balance in favour of one or the other.

The results (table no. 4) are consistent with results obtained for the influence of the number of employees. It appears that, indeed, higher wages incorporate a premium for higher productivity, and that there are no adverse effects triggered by higher wages in these industries.

Table no. 4: Impact of wages and salaries on regional economic indicators


(M72)


(J62) R2

Employment age 15-25 (000) 4.3277e-05 1.8257e-04* 0.22 Employment age 25+ (000) 2.28464. 0.47876*** 0.28 Unemployment rate age 15-25 (%) -3.5410e-05* -1.4951e-04*** 0.37 Unemployment rate age 25+ (%) -1.2529e-05* -4.3403e-05*** 0.28 GDP/Capita, EUR 2.6403e-02*** 1.1527e-01 *** 0.37 Note: Levels of significance based on calculated p-values: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


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An increase of 1000 EUR in wages triggers increases in GDP/capita in both industries, However, increases in R&D salaries are much stronger than those in Computer and related activities by about 5 times for each GDP/capita measure, and ranges from 115 EUR for CI to 26 EUR for R&D.

The employment effects are significant for CI, and extremely strong for the mature workers, as an increase of 1 EUR is associated with an increase of about 500 extra workers. By comparison, a similar increase of 1000 EUR leads to an irrelevant increase of about 2 extra workers for youth. A much stronger effect is observed for R&D, where a 1 EUR increase is associated with 2285 extra mature workers, albeit the result is significant at a 10% level only.

Decreases in unemployment are markedly different in the two industries, especially for youths, where the decrease due to CI is about 43 times stronger than for R&D: 0.15 percentage points for a 1000 EUR decrease for the former vs 0.035 for the latter. For the mature workers, these decreases are much smaller, both below 0.043 percentage points.

An initial assessment of results on CEE countries shows that these industries had no significant impact on the unemployment rates and on employment of the core working age population (over 25 years). Therefore, results where these effects were not significant at all were not shown in the tables. Weak results were also obtained for youth employment, which, with one exception, show counterintuitive results that may be due to other developments not captured in this analysis that deserve future research. With one exception, the R&D sector had no significant impact on growth, which may be due to the embattled evolution of this sector in the CEE countries. However, computer and related industries (CI) variables all show strong positive effects on growth.

The impact of employee counts for CI industry on GDP (Table no. 5) are in line with those obtained for Europe as a whole. The impact on youth employment is very small with a low level of significance and impact, and may be due to other factors behind the dynamic of both variables that are not captured in the present analysis.

Table no. 5: Impact of employee counts on regional economic indicators – CEE countries


(M72)

Computer and related activities (J62) R2

Employment age 15-25 (000) -0.0032381 -0.0013088. 0.51 GDP/Capita, PPP 4.1713e-03 4.0052e-01*** 0.66 GDP/Capita, EUR -5.5190e-02 2.0261e-01*** 0.64 Note: Levels of significance based on calculated p-values: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The number of local R&D firms from CEE countries seems to increase local employment by a sizeable extent, however, the impact appears to be limited (table no. 6), as, for example, an extra 1000 firms would trigger an increase in youth of only 107. However, the difference between this result and the one obtained for Europe is an encouraging development. The negative relation with GDP/capita is counterintuitive and can best be explained by the much stronger dynamic of the latter. By contrast, CI firms have a significant positive influence on GDP, almost three times stronger than for Europe as a whole. While statistically significant, the result for youth employment has little impact as



an extra 1000 firms would presumably lead to an insignificant decrease of 9.6 young employees.

Table no. 6: Impact of number of local firms on regional economic indicators – CEE countries


(M72)


Employment age 15-25 (000) 0.1068871*** -0.0095639*** 0.56 GDP/Capita, PPP -10.59470*** 1.45579*** 0.68 GDP/Capita, EUR -3.82524. 0.54311*** 0.62 Note: Levels of significance based on calculated p-values: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Wages and salaries from CEE countries in the M72 (R&D) sector show a weak negative impact on the employment counts of the youth (table no. 7). This may in fact express a disconnect between youth job creation and R&D wages, pointing out to dynamics that are completely different and unrelated e.g. youth employment may be affected by strong emigration while R&D may grow a little, or that youth employment is booming triggered by strong growth while R&D sector is growing much slowly. For CI industries there is a strong positive impact as a 1 EUR increase in wages leads to 7.6 EUR growth in GDP/Capita, which is much larger in PPP terms. However, CI wages not to affect employment of youths, showing that there are no strong negative labour substitution effects. (Table no. 7).

Table no. 7: Impact of wages and salaries on regional economic indicators – CEE countries


(M72)


Employment age 15-25 (000) -0.386606. -0.010945 0.52 GDP/Capita, EUR -1.7128 7.6148*** 0.64 GDP/Capita, PPP 18.5662 11.1276*** 0.62 Note: Levels of significance based on calculated p-values: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Conclusions

The effects of innovative industries of key economic indicators show that both R&D and computer and related industries have a significant positive influence on the GDP and the labour markets.

In most cases, one may see that more employees in these industries translate into higher GDP/Capita, especially for R&D. This is also consistent with significant increases in employment numbers for both youths and mature workers. However, the first-round effects are stronger for R&D; in the case of unemployment, CI employment numbers had no impact on it.

Number of local companies is a significant indicator only for CI industries. Results are, however, not much different than those obtained for employment counts, adjusted with an average of 4.3 employees per firm. By contrast, number of companies is an irrelevant


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indicator for the R&D activities, with weak and counter-intuitive results. This indicates that a more thorough investigation on the nature of employment in this industry is needed, which may also improve the reliability of business statistics on it.

Wages and salaries per capita are also bringing another perspective on the effect of these industries on GDP/capita and labour market indicators. The findings are consistent with those for employment numbers, which confirms the wage premium for increased productivity pointed out in the literature. Another salient feature of this indicator is that it does a better job in evidencing short-term first-round effects in the case of CI. It seems that these effects, that are more immediate, are better matched with the evolution of wages, albeit there could be the case that CI industries have a more moderate wage growth than R&D. It may be that increases for relatively low wages that are not tightly related to market conditions of the latter are immediately visible, and that the risks associated with R&D activities are passed on to employees as their regular wages are lower that the productivity increases they normally effect.

All these effects point out to the sustainable nature of the innovation industries on growth. Not only do they trigger an increase in GDP/Capita, but they also point out to positive spillover effects that exceed their size, increase employment, and decrease unemployment. The same effects can be seen for the youth; while the magnitude is smaller than for the older workers it may be that some of the spillovers consist of helping young unexperienced workers getting their first job(s).

Results obtained for Eastern European (EE) countries were, to a certain extent, less significant and less reliable due to several objective factors. First, data was scarcer due to the fact that, in these developing economies, the two innovation sectors were, to a certain extent less represented, a fact that explains the relative scarcity of the data (less than 25% of the observations for the dataset for all European countries). The second factor is given by the final stages of the transition period, with significant macroeconomic imbalances that accompanied the journey towards a fully functional market economy, that, for most countries, were also affected by the Great Recession. Therefore, in many cases, estimations were made using fixed effects models in order to address data scarcity and reliability of results. The choice of fixed effects, as opposed to random effects, is justified by the fact that, given the issues mentioned above, they refer strictly to the countries and time periods analysed, thus enabling us to draw more reliable conclusions about the available data.

Results should be also viewed within the transition and catch-up period that characterizes the evolution of the CEE economies. The positive effects of strong growth are primarily reflected in GDP growth, and it may be that it takes a while for these effects to propagate in the rest of the economy in terms of job creation and sizable reduction of unemployment.

The current analysis concentrates merely on the first round effects of the variables for which data availability allows drawing a conclusion that refers to most NUTS2 regions. Thus, a higher level of activity in these industries is supposed to increase GDP/Capita and employment, and decrease unemployment. The effect of wage levels in these industries is to be determined; on one hand it is possible to have the same effects as for the other variables for GDP, as higher wages can reflect higher productivity. However, for labour markets, it is not clear that there will be substitution effects triggered by these industries which can decrease employment and increase unemployment, or whether there will be an increase in employment and a decrease in unemployment due to spillover effects.



While the current analysis has uncovered some of the first-round impacts of the innovation industries, much work remains to be done in matching these effects with other determinants of employment and unemployment, which can improve existing models with relevant empirical elements.

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AE Development and Operationalization of a Model of Innovation Management System as Part of an Integrated Quality-Environment-Safety System


DEVELOPMENT AND OPERATIONALIZATION OF A MODEL OF INNOVATION MANAGEMENT SYSTEM AS PART OF AN INTEGRATED

QUALITY-ENVIRONMENT-SAFETY SYSTEM Dorin Maier1*, Irmer Sven-Joachim2, Astrid Fortmüller3 and Andreea Maier4

1)4) Technical University of Cluj Napoca, Romania 2) 3) Bucharest University of Economic Studies, Romania

Please cite this article as: Maier, D., 2017. Development and Operationalization of a Model of Innovation Management System as Part of an Integrated Quality-Environment-Safety System. Amfiteatru Economic, 19(44), pp. 302-314


Abstract The results of the research, presented in this article, have the purpose to contribute in the area of innovation management and its relation to other management systems. The research objective is to give to organizations a model of innovation management system as part of an integrated management system. Defining the model of innovation management system is based on the four perspectives of the balanced scorecard tool, namely: financial perspective, learning and development, internal processes and customers. In the process of defining the model several steps were taken, such as: literature review, data collection from the enterprise level through a questionnaire, analysis and correlation of data and finally the proposed model has been operationalized. In order to have a working model that can be easily adopted by any interested company, the research was deepen to the organizational processes, identifying nine processes specific for innovation that exist in an organization. Each of the nine processes have been operationalized using a SIPOC model (Suppliers − Inputs − Process − Outputs − Customers). The system for managing innovation may therefore have a positive influence on the ability and awareness of enterprises in innovation actions, and by defining this model the research conducted in this article helps to improve the efficiency of innovation, with direct implications in business performance. Keywords: integrated management systems, innovation management system, quality management system, the performance of organizations JEL classification: O32, M21

Amfitearu Economic recommends AE

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Introduction

The results of the research presented in this article come in a period characterized by the globalization of business, this involving many changes in the economy, in communications, in personal and organizational areas. The current socio − economic context is marked by a dynamic which force companies to find new ways to maximize profit and thus increase their performance.

Periods of progress and growth are followed by periods of crisis and economic instability having a domino effect, so the restrictive nature of resources is being felt ever more strongly in terms of a high consumer demand and also the environment protection issue and finding alternative resources is becoming a priority. Significant changes are taking place within companies, generated just by the diversity needs to adapt, the businesses around the world, at local or national specific culture.

Not long ago, quality and productivity were the key elements for the competitiveness of an organization (El Sakka, 2013); today it requires a new strategy: the strategy of innovation – innovation of products and services, innovation processes and production methods, organizational structure innovation etc., all requiring the existence of an adequate management of innovation (Xu, 2010).

Integrated management systems perspective requires the orientation of managers of various organizations toward reformulating strategies and redefine the mission by changing the vision. Integration of multiple management systems brings most diverse benefits for organization, this benefits can be translated into a more efficient organization activity and, consequently, increase business performance.

An organization oriented towards becoming more effective and optimizing the processes and the use of resource has a high chance of becoming competitive in a highly competitive environment. With as many organizations are competitive, the more the whole local, regional or even national business environment becomes more competitive, ultimately leading to increased economic competitiveness.

The existence of an adequate innovation management and its inclusion in an integrated quality − environment − safety management system already implemented, can be considered a viable solution to increase the performance of an organization. The novelty, of the research in this article, is also given in terms of lack of studies for this subject in the literature.

The presentation of the results are structured in three part, in the first part are the results obtained by applying the questionnaire, in the second part the model of innovation management system is presented and in the final part is the process of operationalized it. Before presenting the results a short literature review is performed and the research methodology is presented. The end of the article is reserved for a part of conclusion and discussion.


Organizations survival and prosperity in a globally increasingly competitive environment is an intense subject addressed in the literature. Researchers have tried to find solutions and methods to meet the new requirements imposed by economic globalization, so it came to the development and implementation of several management systems such as quality management system, information management system, environmental management system, occupational health and safety management system etc.



Managers had to comply with the new requirements and to find solutions in order to resist on the market, in the first phase, and then to become competitive (Brad, 2008). Implementation of various management systems was a solution in this regard. Quality management system already had a huge spread and implementation of other management systems has become just a matter of time (Maier, 2013).

Commitment to produce and offer quality services, to preserve and protect the environment and to take care of the safety and health of employees has become a very important part in the long-term strategy and image of organizations (Olaru, 1999). By addressing these issues, organizations take responsibility for their work and this is felt in trust and loyalty of partners and at the same time the economic indicators are improving (Jorgensen, 2006).

An integrated management system must include both common elements of the chosen management systems and also specific elements whose definition can be performed from the external environment requirements. Regarding the common elements, it appears that approximately 80% of the workload is similar for all management systems, which is the most important argument pleading for their integration (Suditu, 2005).

Integration is the complete harmony and alignment of organization strategy and operations. This means that different departments and levels speak the same language and are tuned to the same wavelength. Most studies in literature on the integration of management systems focused on the integration of quality management system, environmental management system, health and safety management system and the information security management system (Asif, 2008).

The small number of enterprises in Romania that have implemented an integrated management system made us to limit the research only on models of integration the quality management systems, environmental management system and the occupational health and safety management system.

A common part of the three reference documents, is that it takes into account the needs and requirements of customers and also the needs and acceptance of all stakeholders. Each of the three management systems are focused on achieving goals this make measurable the progress in implementing those policies. For this reason, developing and implementing an integrated management system, cannot be achieved without taking into account the correlations that exist between the three management systems (Wilkinson, 2001).

Integration modality is chosen according to the situation in the enterprise at a certain time. The concepts that can be used for integration of management systems, can be: the additional / auxiliary / helper model, partial integration model, common elements model and integration model based on processes (Jorgensen, 2006).

The same global economic context, in addition to management systems it appears more natural and necessary the need for innovation. The importance of innovation for enterprises is felt especially by fierce competitiveness both locally and especially globally. Many specialists (Varis, 2010) states that innovation becomes mandatory not only for success in business but also for the survival of businesses, and in these circumstances any research aimed at introducing innovation into everyday practice businesses is always welcome (Maier, 2014).

The major importance of innovation leads to attempt to develop solutions for easier deployment within enterprises (Piiranien, 2010). Finding solutions is a difficult process just


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because of the novelty and hardly predictable nature of innovation. Innovation is by definition creating something new, bringing added value or profit for the enterprise, in the same time innovation requires allocation of resources, both material and human, and the result of the innovative process is difficult to control or predict (Perdomo, 2009).

The research conducted in this article is intended to create some links between innovation and integrated management systems by proposing a model of innovation management system as part of an integrated quality-environment-safety management system. This topic is not yet treated in the literature probably because until recently there was no innovation management system. In recent years it was developed at EU level the Innovation Management System CEN / TS 16555-1: 2013 (Real Cloud Project, 2013). This document was not developed with the intention of being used for certification organizations in terms of innovation, this is part of a series of seven parts to be developed under the general title of Innovation Management.

The successful implementation of innovation management system, proposed by the technical specification CEN / TS 16555-1: 2013 is given by the organization's ability to respond to internal and external conditions influencing innovation capacity and the factors influencing the innovation process at organization level such as: the level of involvement of the organization's top management, the innovation strategy, factors favoring innovation process or different management techniques for innovation (Finn Kollerup, 2015).


The main objective of the research presented in this article was to propose a model of innovation management system as part of an integrated quality-environment-safety management system and in this sense the research was divided into several components.

A first component was the one related to identification at organizations level the data on the current state regarding the implementation of integrated management systems, awareness of innovation and its implementation at organization levels and the identification of possible connections between management systems vision and innovation management from managers point of view.

The second component of the research aimed to analyze and identify patterns in the literature of management systems integration and models for managing innovation. The next part of the research aimed at defining and operationalizing the model of innovation management system as part of the integrated quality – environment – safety management system and the significance of this model in the current economic climate.

Data collection among companies in Romania was done through a questionnaire developed by the authors. The studied phenomenon was innovation process and integrated management systems. The statistical community was made up of enterprises in Romania through their specific activity are potential suppliers of innovations and the statistical unit was the general manager or manager with responsibility for innovation or management systems.

The questionnaire was divided into three parts, the first part for identification elements and information about respondents, the second part to identify the existing management systems in the organization, the models of integrated management systems and the main benefits of them. The third part included questions about the identification on each segment of the degree of innovation in the enterprise and the main barriers to innovation.



To establish the sample were used data provided by the Statistical Yearbook of Romania, so were achieved a total of 7988 enterprises in the North-West of Romania by size according to number of employees. It took into consideration a 95% and a maximum permissible error of +/- 5%, so that for 7988 the population of enterprises were obtained a minimum sample of 381 companies. Given the minimum sample size over 500 questionnaires were sent for which was obtained a few number of answers, namely 122 questionnaires completed and validated. Data from the questionnaire did not significantly influence the process of defining the model of innovation management system, it only played a supporting role, for information about the general situation in practice. For these reasons the obtained data were considered in the research although the response rate was very low.

Defining the model of innovation management system as part of the integrated quality-environment-safety management system is based on four perspectives of the balanced scorecard tool, namely, financial perspective, internal processes, learning and development, customers. For the model defining several steps were follow:

• analysis of the literature on innovation management models, at international level and checking for a model of innovation management system or other attempts to create connections between innovation and management systems;

• analysis of the results collected by questionnaire and their correlation with data obtained from literature review to identify the expectations of the direct beneficiaries of the proposed model;

• the effective defining and operationalize of the model of innovation management system as part of the integrated quality – environment – safety management system.

Operationalization of the model was done by extending the research to organizational processes where nine organizational processes specific to innovation were identified. The new organizational processes have been operationalized with a SIPOC template (Suppliers -Inputs – Process – Outputs – Customers).

SIPOC method is an effective tool for analysing organizational processes to improve their action. This summarizes the inflows and outflows of one or more organizational processes in a synthetized form, a table. The acronym SIPOC (supplier, input, process, output, customer) comes from the name of the specific layout information contained in this instrument i.e. suppliers, inputs, processes, outputs and customers. All these elements are found in a tabular form.

The most important information from a SIPOC model refer to the following:

• Suppliers – systems, people, organizations or other sources of material, information or other resources that are consumed or transformed in the process.

• Input – materials, information and other resources provided by suppliers that are consumed or transformed in the process.

• Process – set of actions and activities that transform inputs into outputs. • Outputs – products or services that the process produce and the customer uses. • Clients – individuals, groups of individuals, companies, systems and processes

downstream processes that receive the outputs.

By choosing the SIPOC method can be created the basis for the processes definition and characterization, for measuring and evaluating them. It creates also the basis for analyzing and identifying areas for improvement or change. Using this method to evaluate the specific


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processes of innovation at the organizational level is essential in the process of increasing the efficiency of these processes. The information provided for each process are helpful both to resources management and also in remodeling processes whose outputs are not powerful enough.

3. The research results

3.1 The results obtained from applying the questionnaire

By applying the questionnaire were obtained information about the existence of integrated management systems at the enterprise level. From the received responses a very small percentage, 3% of enterprises had not implemented a management system and in case of integrated management systems almost 70% of respondents had already implemented a model of integration of their own. Most of integrated management systems are made up of quality management system. Implementing the integrated quality-environment-safety management was made in about 40% of enterprises surveyed.

Also by applying the questionnaire were obtained some information about the existing situation on the importance of innovation, the importance of external factors on innovation and even identify issues that lead to block the innovation or significantly influence the decision to innovate.

The main factors that have a negative effect on innovation capacity are those related to corruption, excessive bureaucracy and development of legislative framework. Although these factors do not have a direct influence on the innovation process their impact is felt by the effects for the enterprise, often managers are busy finding solutions to problems caused by these external factors and in consequence there is a lack of time allocation for innovation, the time needed to be transformed into added value and then in to profit.

Benefits that managers have in mind when they want to implement an integrated management system was another item in the questionnaire. It was formulated so that respondents could choose from a list of benefits and to obtain more conclusive choices were limited to a maximum of five benefits of the 11 benefits including the option "Other" benefits that were not listed. Interpretation of results was done by analyzing the number of choices for each benefit separately and are shown graphically in Figure no. 1. In the set of benefits choices, a benefit related to increasing innovation capacity was introduced, aiming to analyze the importance given to innovation and innovation expectations especially in the context of implementing an integrated management system.

Looking at the Figure no. 1 it can be observed that the benefit with the highest percent of choice is the one for a more efficient management system 16%, followed by increasing business profitability (13%) and reducing the maintenance costs for the management systems (11%). The increased capacity of innovation is not considered one of the main benefits of an integrated management system, although the current economic context propels innovation as vital for future businesses. Not considering innovation as part of the integrated management system is yet another reason to find solutions for implementing it in the existing models of integrated management system.



Figure no. 1: The benefits of implementing an integrated management system

Analyzing the results of applying the questionnaire study on barriers that influence the activity of innovation in enterprises, identifies that innovation is not treated as a priority activity, there are several barriers that lead to decisions not to innovate. There is a high percentage of abandoned projects from the design phase, 30%, and most times even if innovation activity is initiated almost 80% of projects are significantly delayed and then abandoned. The main factors that block the activity of innovation are the company's own lack of funds, lack of qualified personnel, the market dominated by established enterprises and even the lack of demand for innovative products. All these factors must be considered in the development of the model of innovation integration as part of an integrated management system.

3.2 Defining the model of innovation management system as part of the integrated management system

The end result of the conducted research was materialized by defining the model of innovation management system as part of the integrated quality – environment – safety management system in a globalized economy (Figure no. 2). The existing model can be seen as a range of tools, techniques and methodologies that help companies to adapt to the market circumstances and challenges, in a systematic way. It can help enhance enterprise performance and can positively influence the outcome of performance evaluation of enterprises in Romania and worldwide.

16%

11%

9%

9%5%6%

13%

9%

9%

8%5%

a more efficient management system

reducing maintenance costs

reducing the time needet formanagement systemsa better focus on the businessobjectivesincreasing the capacity of innovation

reducing the conflicts related toresponsabilitiesincreasing the business profit

a better comunication betweendepartments and managersa better equilibriun between theconflictual objectivesfaciliting the HR development

Other


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Figure no. 2: The proposed innovation management system as part of the integrated quality – environment – safety management system

The structure of the model shown in Figure no. 2 was chosen to highlight the main features of innovation and the common elements that influence an integrated management system. A number of entities outside the enterprise act and shape the integrated management system, like: customers, as direct beneficiaries of the company activities; the suppliers, that influence the entire activity and possibilities of enterprises; the government, that regulates and restricts the organization possibilities; universities and research institutes, which may have an important role particularly in terms of innovation, and ultimately other organizations active in the market that contribute to the development of the competitive environment and thus causes the enterprise to achieve and exceed its limits.

The research has identified that a strategic approach of the innovation process involves primarily a proactive attitude regarding the characteristics of internal organizational environment and also of the external environment that is in constant change. Thus in the composition of the model of innovation management system are nine organizational processes specific for innovation that will be detailed below. The companies that will use the proposed model must constantly analyze their innovation strategy so that it can meet with the internal needs and to be compatible with the requirements of the external environment, this includes also the requirements of organizations that innovate.

The novelty of the integrated management system is the inclusion of innovation management system and for this reason in order to emphasize this, was presented its main features. It can



be observed that as part of the innovation system composition are: policy, strategy and process of innovation and also the evaluation and results of the innovation process. Regarding innovation strategy is based on vision and the objectives of innovation, innovation plan, the means to achieve such objectives and identify the resources necessary for innovation. When it comes to the innovation process four main types of innovation must be taken into account: marketing innovation, process innovation, product innovation and innovation in human resources.

Another characteristic that can be drawn from the analysis of the model of integrated management system is that all stakeholders should innovate to achieve a high level of performance for the overall system. For each category of stakeholders must be developed specific solutions for each innovation subsystem.

Deadlines embedded in the strategy refers usually to: date of commencing implementation of the strategy, intermediary terms which marks the deadlines of significant progress in achieving the objectives set, the final term of the strategy.

Enterprise resources approach assumes that performance differences between organizations are due to differences from organization specific valuable resources, profitable resources and skills that cannot be easily imitated or substituted. The model of innovation management system, together with the other three parts of the integrated quality-environment-safety management system, it is hoped to be a factor for success in all areas of an organization and also by having the vision of innovation will help to bring added value for the ones that use it.

3.3 Operationalization of the proposed model of innovation management system using the SIPOC method „Suppliers –Inputs – Process – Outputs – Customers”

The operationalization of the model of innovation management system started from a systematic approach, designed to help an organization to optimize its activity processes to achieve more effective results, found in the literature under the name of improving business processes.

The processes approach must be seen from the perspective of the chain value, i.e. each process is influenced by the previous one and brings value to the next one. A purposeful approach particularly in the context of strategic management and organizational management in general, is that the performance of the organization can be seen from the key processes carried out to achieve its goals. The processes are structured as a set of actions, conscious, correlated, geared towards getting a result or achieving a goal, through which a number of inputs are transformed into outputs.

The practical implementation aspects of the organizational processes were done by identifying and formulating nine organizational processes specific for innovation in order to define a model of innovation management system as part of the integrated management system, a tool to increase competitiveness and business success.

The nine processes are: P1 – Identification of ideas to increase competitive advantage through innovation; P2 – Development of strategy and innovation policy of the company; P3 – Implementation of policy and strategy for innovation in the research and development activity within the company; P4 – assessment of innovation impact on customers, on environment and on the activity of the organization; P5 – Establishing the final version in


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order to start the innovation process; P6 – Establishment the development plan for the chosen variant by running the innovation process; P7 – Conducting the innovation process; P8 – process of control, monitoring and periodic evaluation of the results; P9 – analysis of the results and proposing solutions to improve the processes for future innovation.

Each process has been introduced in a tabular SIPOC type layout, resulting nine layouts, one for each process. The applying scheme for the SIPOC model is presented in figure no. 3. The results of applying the method for each process is detailed in the following paragraphs.

The first organizational process is P1: Identification of ideas to increase competitive advantage through innovation. This is a basic process in providing market position and a favourable level of competitiveness for the organization. Identifying the innovation needs envisages a permanent analysis of the evolution of the business in economic – financial terms, an analysis of the product portfolio, the productivity and quality of products and services, the technological characteristics of equipment, methods, the production processes and recipes used, the assessment of ideas for business development and the increase competitive advantage derived from different sources: employees, external environment, customers.

Figure no. 3: The applying scheme for operationalization of the organizational processes through SIPOC method

For the process P2: strategy development and innovation policy of the company, without a strategy for innovation, the interest and attention is dispersed. The most innovative companies will be the best of the best. They do not innovate just to even others, they create a vision and a goal that, if achieved, will create products that will prove their superior performance and bring to hold a distinct position in the market.

Organizational process number three P3: Implementation of innovation policy and strategy in the research and development activity within the company, comes as a continuation of the process two and aims to develop and transform the ideas of innovation strategy into innovative results for the enterprise such that they bring additional performance to the organization.

The next organizational process is the number four P4: Evaluation of the impact of innovation on customers, the environment and the activities of the organization, this process takes into account the analysis of all technical reports, feasibility studies, economic analyzes in order



to choose the variant that will be the subject of innovation. Evaluation has the greatest potential of contribution to innovative technologies and products, when achieving success is not guaranteed and the implementation of the economic agents is not without obstacles.

The next step, after technical evaluation and the impact of innovation is the process P5: Establishing the final version in order to start the innovation process. This process involves many specific organizational activities so as to maximize the positive effects brought by innovation.

The organizational process P6: Establish the development plan of the chosen variant by running the innovation process is a complex process that has many specific activities such as setting strategy and implementation plan; establishment of scenarios depending on the risks identified; establishing organizational hierarchy, the flow of documents, format documents; procedure for resolving conflicts; determine the accepted risk / failure degree; establishing the management of additional unexpected changes; the safeguards on quality; determining how monitoring post implementation by providing consultancy services and improvement; development during the implementation of studies and / or technical-economic analyzes and assessments.

The next organizational process is the process P7: Performing the process of innovation. If all the above processes were conducted in an effective and efficient manner it can be created the premises that all barriers and risks for innovation at this time can be successfully overcome. In this process activities targeting in particular the transition from the idea stage to finite stage by preparing innovation to market.

P8: process of control, monitoring and periodic evaluation of the results requires a technical-economic audit of completed activities and results. This process must take place throughout the implementation period and must be a managerial support to constitute substantially to the technical or economic decisions.

The last organizational process analyzed in P9: Analysis of results and proposing solutions to improve processes for future innovation. Substantiation of a rehabilitation or modernization plan for technology in response to the market changes, it is necessary for the decision regarding whether to continue to use the existing technology or focus on the development of new technologies.

The conduct way, the performance, the efficiency and the effectiveness of these processes have a major influence on the success of innovation activity. It can be also notes that these processes are those that influence powerful the other processes upstream or downstream of them in the specific flow of processes in the organization.

Conclusions

The research carried out in this article had its starting point the current socio-economic context characterized by the globalization of business and the challenges that businesses must respond. Innovation is now considered a viable solution to the new requirements in this context.

The purpose of this research was to create links between innovation and integrated management systems by proposing a model of innovation management system as part of an integrated quality-environment-safety management system. The small number of enterprises


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in Romania that have implemented an integrated management system is one of the limitations of this research and for this reason the research were concerned only for quality management systems, environmental management system and occupational health and safety management system.

The subject of the research presented in this article is barely addressed in the literature, the authors identified as the main reason for this, that only recently was developed a proposal for an innovation management system, as the one of quality management system.

With the proposed model of innovation management system companies can make strategic planning and implement them, thus highlighting the differences between the actual performance of the company and its objectives.

The innovation management system may therefore have a positive influence on the ability and awareness of enterprises in innovation actions, and by defining this model the research conducted in this article helps to improve the efficiency of innovation, with direct implications for business performance of organizations.

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December 2014]. Brad, S., 2008. Vectors of innovation to support quality initiatives in the framework of ISO

9001:2000. International Journal of Quality & Reliability Management, 25(7), pp.674-693.

Dicționar de certificare ISO, n.d.. Sistemul de management. [online] Available at: <http://dexcert.ro/iso-14001/sistemul-management-iso-14001.html> [Accessed at 20th February 2013].

El Sakka, S., 2013. The impact of applying quality management system and environment standard on organization performance an application on SME’S in Egypt, European Journal of Business and Management, 5(19), pp.82-86.

Finn Kollerup, 2015. The European Innovation Management Standard. [online] Available at: <http://finnkollerup.com/2015/09/20/use-the-european-innovation-management-standard-as-your-innovation-checklist//> [Accessed at 23th July 2015].

Jorgensen, T.H, Remmen, A. and Mellado, M. 2006. Integrated management systems – three different levels of integration, Journal of Cleaner Production, 14(8), pp.713-722.

Maier, D., Olaru, M., Weber, G. and Maier, A., 2014. Business Success by Understanding the Process of Innovation. In: s.n., The 9th European Conference on innovation and Entrepreneurship – ECIE. Belfast, UK, 18-19 September. Belfast: s.n.

Maier, D., Olaru, M. and Maier, A., 2013. Integrating concepts of innovation and creativity – a key to excellence in business. In: s.n. The 8th European Conference on Innovation and Entrepreneurship. Brussels, Belgium, 19-20 September. Brussels: s.n.

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Perdomo-Ortiz, J., Gonzalez-Benito, J. and Galende, J., 2009. An analysis of the relationship between total quality management based human resource management practices and innovation, The International Journal of Human Resource Management, 20(5), pp.1191-1218.

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Purcărea, I. and Olaru, M., 2011. Paving the Path for Innovation: the case of Romanian SMEs. In: s.n., The 6th European Conference on Innovation and Entrepreneurship – ECIE 2011. Aberdeen, UK, 15-16 September 2011. Aberdeen: s.n.

Real Cloud Project, 2013. The European Standard for Innovation Management (UNE-CEN/TS 16555-1:2013) has been recently published. [online] Available at: <http://www.realcloudproject.com/the-european-standard-for-innovation-management-une-cents-16555-12013-has-been-recently-published/> [Accessed at 23th July 2015].

Suditu, C., 2005. Aspecte specifice privind implementarea unui sistem integrat calitate – mediu – securitate, Simpozionului naţional „Calitate – Management – Integrare europeană”, Bucureşti, Romania

Varis, M., 2010. Types of innovation, sources of information and performance in entrepreneurial SMEs. European Journal of Innovation Management, 13(2), pp.128-154.

Wilkinson, G. and Dale, B.G., 2001. Integrated management systems: a model based on a total quality approach, Managing Service Quality: An International Journal, 11(5), pp.318-330.

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BEST PRACTICES FOR INTEGRATING THE ROMANIAN SMALL FARMERS INTO THE AGRI-FOOD CHAIN

Dan Boboc1, Gabriel Popescu2, Mirela Stoian3∗, Georgiana-Raluca Lădaru4

and Dan Cosmin Petrache5 1)2)3)4)5)The Bucharest University of Economic Studies, Romania

Please cite this article as: Boboc, D., Popescu, G., Stoian, M., Lădaru, G.R. and Dan Cosmin Petrache, 2017. Best Practices for Integrating the Romanian Small Farmers into the Agri-Food Chain. Amfiteatru Economic, 19(44), pp. 315-326


Abstract In the modern market economy, agri-food chains were imposed and strengthened due to unprecedented evolving recorded by supply and demand of food in recent decades, dynamic driven by market fragmentation, on the one hand, and specific processes of consumption and consumer emancipation, on the other hand. The objective of the paper aims to bring to the fore the best practices for support the Romanian small farmers to facilitate their access to the market, which is often dominated by large distribution networks. Reality has shown that financial support is not enough, even if farmers use modern and efficient technological systems, and that it is a need for an efficient integration of small producers in the agri-food sector. To highlight the best practices that can be used by Romanian small farmers were used information derived from: literature study on problems regarding the food sector; analysis of studies conducted by the consulting companies specialised in this field; analysing the consequences of recent legislation concerning the marketing of food products, namely its implementation in our country, especially in terms of small agricultural producers and their access to the Romanian market.

Keywords: best practices, agricultural producers, retail, agri-food chain JEL classification: M11, O11, O13, Q13 Introduction

Currently, the globalization exerts a pressure on food organisations and their specific ways to interact, including the inside of the agri-food sector. If a few decades ago supply chains and thereafter food chains acted autonomously and independent, currently are established interconnections and complex relationships, inside and outside the chains, both at the level of its components and their assembly level.

∗ Corresponding author, Mirela Stoian – mí[email protected]


AE Best Practices for Integrating the Romanian Small Farmers into the Agri-Food Chain


Food markets exert an ongoing pressure on the agri-food chains, requiring innovation, effective management of flows and coordination. The supply is incessantly diversifing, competition is intensifying and in such a context, quality, price and especially quality / price ratio assume a greater importance. Due to the manifestation of all these phenomena, specific processes of development and integration of agri-food sector can be considered not only an opportunity / challenge to rural areas, but also a threat. Thus, regarding small farmers, whose resources are limited, including in terms of access to modern technologies and informations, their relationships with market may be substantially affected by major competitors who activate on the market. Distribution networks requests large and homogeneous lots, and continuous delivery, which often small farmers can not offer as requested

Finding the most favorable solutions for integrating small producers in the agri-food sector is an important challenge of the current period. In our country, land ownership atomization of the 90s led to the emergence of numerous food suppliers that initially were oriented to street markets and fairs, adding them later with an alternative form of distribution, the wholesale. Thus it emerged and developed a new entity, the small distributors. In this background, the defining element of the food chain – the consumer, begun to orient the branch from the agri-food chain "production" according to its own requirements. Thus, it is observed a differentiation in purchasing options depending on many factors: age, consumption area (urban / rural), education level, purchasing power etc. These factors led to an increasingly obvious segmentation of the market and therefore consumption.

The present paper will refer the readers a set of theoretical aspects on the complex issue of food production chains, after which, following the analysis of Romanian food chains evolution, will inventory a set of best practices for the integration of Romanian small farmers in chains, which is so needed in the actual context.


The need to ensure an appropriate food contributed to assuming specific roles in food production and distribution processes to the final consumer (Barrett, 2001). Given that, in any society, both the food availability and price are closely related variables to the political factor (Henson and Loader, 2001), not infrequently, imbalances in ensuring food needs have generated conflicts.

In the twentieth century, developed countries have achieved high performance in food production and distribution, which have helped to meet household demand, respecting also the quantity and quality criteria specific to each product / product group / segment of the market etc. (Vellema and Boselie, 2003), and many national strategies have increased the attention of commodities supply to the population. Unfortunately, developing countries recorded a poor performance of the agricultural sector in the 80s and, in this context, wider gaps from developed countries were produced at technical and economic levels.

In the first half of the last century food problems were mainly at quantitative level, as they aimed at satisfying the national demand. The solutions consisted of increasing production capacities and storage. In the 60s of the same century, with the increase in production needed to meet demand for the increasingly urban agglomerations – and not only – is imposed significant changes in route, flows and processes traveled by food to final


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consumers. In such a context, analysis, planification and evaluation made at the chain level were absolutely necessary (Gittinger, 1972).

Agri-food chains (Figure no. 1) are important components of the agri-food sector, which reflects a specific socio-economic reality and involves a group of stakeholders and entities whose sole purpose is to satisfy the increasing and diversified demand for food in various markets.

Agricultural producers

↓ ↓ ↓

Carriers, negotiators, importers, exporters, others

↓ ↓ ↓

Post-harvest activities, industrialization

↓ ↓ ↓

Wholesalers, importers, exporters, others

↓ ↓ ↓

Retailers

↓ ↓ ↓

Final consumers

Figure no. 1: The component entities of the agri-food chain

Source: FAO, 2010

The agri-food chain brings together several levels, the most important being: agricultural production; harvesting and post-harvesting activities; transport; storage; conservation; transformation/processing; distribution and consumption (figure no. 2). In the reviewed literature we find numerous approaches to the structure of the food chain, where the number and the specific entities and the relationships established between branches can differentiate, by product, sector production, spatial or temporal operation area etc. (FAO, 2010).



Figure no. 2: Agri-food chain – participants and processes

Source: Luning, 2002, p.74

We believe that the adoption of best practice systems for production and management of systems for monitoring the technical and economic results, with innovative approaches, will ensure the proper functioning of the agri-food chain, and implicitly market competitiveness. A proper collaboration between small farmers, food industry and the distribution network requires continuous learning, synergy, trust, compliance etc.

2. The evolution of Romanian agri-food chains

The analysis of the Romanian agri-food chains evolution in the last decade, aimed, primarily, the period between the timing of product councils to date, and reveals an important development of marketing activities, and particularly retail food in the overall context of retail sector development. As it can be seen from analysis of data listed in Table no. 1, retail with food products registered a significant growth in the 2008-2015 period, reaching a value of 17.4 billion euros.

Table no. 1: Retail trade in food products in Romania (billion euros)

Indicator 2008 2009 2014 2015

Total food retail (indicator value includes sales of non-food hypermarket, their share ranging between 10 and 20% of their total)

15,3 13,2 16,0 17,4

Source: developed by authors based on data from the National Institute of Statistics, TEMPO-Online database

Preproduction

Production

Harvest, transport, storage

Transformation

Distribution

Planning


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We are also witness to a change in the structure of food retail, the weight of large sales areas registering a significant and continuous growth (table no. 2).

Table no. 2: The structure of the food retail in Romania (%)

Indicator 2008 2009 2010 2011 2012 2013 2014 2015 Traditional trade 59 58 56 53 50 49 48 45 Modern trade, which: 41 42 44 47 50 51 52 55 The main players (hypermarkets, supermarkets, minimarkets, discounters and cash & carry)

34 35 37 39 42 43 45 47

Other retailers (gas stations, retail sales of cash & carry networks, estimated at 10-20% of their total; other smaller international traders)

7 7 7 8 8 8 7 8

Source: Ardelean, 2016

The sales of the "main players" as outlined in Table no. 2 evolved in the same period, from 2008 to 2015, from 5.2 to 8.2 billion euros (Table no. 3), the average annual growth being of around 7%.

Table no. 3: Sales development of the biggest players in food retail in Romania (billion euros)

Indicator 2008 2009 2010 2011 2012 2013 2014 2015 The main players (hypermarkets, supermarkets, minimarkets, discounters and cash & carry)

5,2 4,7 5,1 5,4 6,0 6,6 7,1 8,2

Source: Ardelean, 2016 Modern food trade (represented by hypermarkets, supermarkets, minimarkets, discounters and cash & carry) grew in Romania in recent years at the expense of traditional trade. Our country joins neighbouring countries and beyond, as it can be seen from the data analysis presented in Table no. 4. In the first quarter of 2016, Romanians have bought food from: hypermarkets 29%; supermarkets 17%; minimarkets 2%, discounters 11% and cash & carry 1% (Vaschi, 2016).



Table no. 4: Food retail structure in 2015 in Romania and other European countries (%)

Specification Czech Republic Slovakia

Hungary

Poland

Romania

2015

Romania first quarter

2016

Modern trade, which:

69

66

62

71

55

60

Hypermarket 33 27 24 14 27 29 Supermarket 17 24 18 17 15 17 Minimarket - - - 13 2 2 Discounter 18 14 18 26 10 11 Cash & Carry 1 1 1 1 1 1

Traditional trade 31

34

38

29

45

40

Source: Vaschi, 2016

We believe that, in the context described above, which has generated a differentiation of purchase options, supplying the food in the European Union (EU) also from other countries has led to a greater diversification of supply, which has created a strong pressure on local producers of food, who are in increasingly fierce competition with foreign competitors. The context was in favor of foreign investments specific to the free market, retail over large areas (or the great distribution), represented by multinationals, in which for years some local products (fruits and vegetables, for example) hold decreasing shares in sales.

The effects of expansion and diversification of food supply on the production branch have created a series of transformations in the chain that will bring the consumer in a situation of increasing comfort and safety. This is due to the appearance of some quality filters at purchasing goods from large retailers, led by authorisation requirements according to international standards of quality suppliers, namely to have quality certification (ISO 9000). Also, sales in large stores, provides a grouping of products families, classes and categories (ultra fresh, fresh, frozen, dried, etc., for example), which facilitates the consumer choice. On the other hand, manufacturers have been forced by the market to calibrate, prepackage and offer merchandise on different quality categories and, very importantly, mandatory taxed. This had the immediate effect of taxing the agri-food economy, with beneficial influence on the state budget, but also on upstream and downstream distribution companies, which were able to deduct expenses in a legal financial mechanism. Thus it were created the premises for the reorganisation of small farmers and their transformation into farmers or businesses able to access various forms of government support or European funds. Other effects of this important transformation mechanisms were reflected in cost reduction in the domestic farms, which led to increased competitiveness of Romanian products.

We believe it is time the local agro-food sector to be “forced” to make the transition from the initial form of organisation to a more complex implementation of market measures, and


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running in a similar way to stock exchanges dynamic. Thus it is reinforcing the first segment of the food chain, the one of "pre-production" that will be decisive in ensuring balance in the sector and will act as a relief for the contradictions that arise between segments production / processing and processing / distribution ones. On this background, formed in the pre-accession of our country to the EU, the new-formed Councils on the product, have evolved and turned into "interbranch organizations", with extended structures aggregating all chain: pre-production, production, processing and distribution, plus related activities (inputs upstream: fertilisers, additives, packaging, research area represented by: research stations, higher education companies specialised in intra and extra community trade; companies to promote products etc.) (Order no. 1446/2014, Order no. 269/2011, Order no. 160/2011, Order no. 101/2011, Order no. 99/2011, GEO no. 103/2008, Ordinance no. 55 / 2000). This process of consolidation can contribute significantly to maintaining the marketing of domestic producers.

It therefore finds that by building and strengthening the product pathways were created the premises and the organisational environment for crossing subsistence households to legal entities, and to stimulate the association. But things are still at the beginning and must be complemented with government support, generated in its turn by agricultural policies, legislative and fiscal facilities able to provide support to domestic producers in the competitive market in which the adjustment process can be lengthy.

The causes for the heavy adaptation can be both objective: differences in rural-urban areas, migration of labor to urban, poor training of rural inhabitants, the rate of getting old; and subjective: fear of association, local corruption phenomena, all summed leading to slowness of the implementation of market mechanisms in the segment "production" of the sector.

In this specific context intervenes the "preproduction" segment of the chain. That is, given that in the vast distribution is found in recent years that more than 2/3 of fruits and vegetables, but not only, offered in the EU and other countries, the question arises: what are the causes? And so it is a new contradiction in the sector, apart from the classical producer / processor and processor / distributor: a contradiction within the segment "distribution", between the local producers and major distribution chains. And how this dispute is manifested through free market mechanisms in which the consumer is the main actor and where the price is determined by supply / demand balance, the two major entities were grouped in Association of Major Retailers in Romania (AMRR), on one hand and Interbranch Organizations in the Agricultural Products (IOAP), on the other hand.

We consider therefore that the development and maturation of agri-food chain, which represents more than half the actors in the production for each product, could create a significant force of negotiation in the market, able to decipher shortcomings and to find practical solutions to support domestic production.

And as any contradiction may become a factor of progress by stimulating finding solutions, IOAP found at least two directions to improve its presence in the distribution segment, namely increased productivity and increasing pressure on the vast distribution within purchase. On the other hand, equally legitimate is the AMRR approach: vast distribution is only a trader in a free market where EU products are not counted in import but to intra-Community trade, and besides the price offered by the provider to purchase, is requested amount, continuity in quality and delivery.



In this context, to stimulate Romanian food production chains, at the intervention and under IOAP pressure, the regulatory mechanisms involved segment "preproduction" at parliamentary level by the Committee on Agriculture, which provided a valuable tool for OIPA, Law 150 / 2016 amending Law 321/2009 on food marketing through large retail – appearing obligation to take a minimum of 51% of Romanian products – fruits and vegetables, along with other commercial regulations. This market measure imposed legislative and immediately effective, with the desire to be an incentive for local production by forcing its application immediate, despite the generosity of support for local products, has created huge tensions in the market by disrupting specific mechanisms, but paradoxically had effects also inside Romanian agri-food sector. There were created, in fact, prerequisites for depth research in every chain to find the causes, and obviously to formulate appropriate solutions.

Thus, it is found a strong polarisation of the actors in the segment "production", which led to contradictions between the major manufacturers – most advantaged by the legislation, on the one hand and small producers, on the other hand. The latter, not having significant power production, does not have access to modern trade network, and especially in hypermarkets, still having the only alternative to become suppliers to large manufacturers. Consequently, for small farmers the price will not be dictated by supply and demand in the market but the major manufacturers. But this contradiction can become a factor of progress, through the intervention of the regulatory mechanisms of the department "preproduction" and all appropriate agricultural policies. Starting from the case which gave rise contradiction, namely the polarisation of producers in the sector, are created preconditions for a real revival of domestic production: support smallholders in their attempt to become legitimate providers in the great distribution. Thus will appear the premises for solving the potential contradictions arising within the agri-food sector.

3. Best practices for integrating the small farmers into the agri-food chains

Proper functioning, accountable and performance of the agri-food sector requires a comprehensive approach. We appreciate that stimulating local production, local and regional economic development involve the establishment and functioning of organizational and administrative entities that facilitate market access for products produced on small farms. Add that it is required a reshaping of information flows and relationships established between different actors of the market, so that small producers in the composition of the new models to have a place and a role determined with precision. Also operation in the agri-food sector performance requires consideration of an increasing number of consumers concerns regarding the safety and quality of food; production methods and technologies used; quantities of waste resulting from the processing of agricultural raw materials; genetically modified organisms; traceability and others (Humphrey and Oetero 2000 Omta, 2001; Maican, 2015).

Currently, large chains of modern retail develops strategies and specific projects to encourage the Romanian products,and own brands also, but they are not sufficient to ensure performance in all branches of the food chain in general and for small farmers in particular.

In our view, powerful integration of small agricultural producers in the Romanian agri-food sector can be supported by implementing the following best practices:


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a) product councils

Since 2005, it began to coalesce early forms of organization of local producers in the so-called " product councils " entities, capable of interacting with competitors abroad and having at the same time, legitimacy in front of the authorities (Ministries of Agriculture, Finance, European Funds, Trade, etc.) and have points of view that matters at both governmental and market levels (Order no. 1282/2005, Order no. 1289/2005);

b) branch organizations

Compared with the product councils, which were the first associative forms and meant the active germ for food production chains -, – the interbranch organizations Products (OIPA) are structures that actively participates in drafting laws and regulations. They are created as the vector of cooperation, so hard to identify.

The advantages of this form of organization can be found in: food business financing schemes; wide circulation of information on specific areas; participation in the lawmaking process; harmonisation of relations in the pre-production chain, all of which are forming the mechanisms for regulating the sector. Faced with these important advantages, more and more small and middle producers are attracted to the benefits of presence in the market, but to become a part in the decision to vote, they have to be part of regional organizations of producers;

c) producer groups

Producer groups can play an important role in the planning of agricultural production to adapt it to the quantitative and qualitative market requirements. They can also facilitate the supply of inputs and can make a significant contribution to the marketing of products produced by farmers as advantageous as possible for them.

Advantages of a producer group are many for the component entities , among these are: better management of production costs; enabling the exploitation of large areas of land and, therefore, application of modern technologies, performance; improving communication; greater bargaining power; accessing European funds or, where appropriate, bank loans; production promotion etc .;

d) storage, sorting, packing and marketing cooperatives

Given that the market calls for a constant supply from the point of view of accessing them throughout the year, and in terms of quality standards, production sale made by small producers is a challenge in many cases. Consequently, providing conditions for storage and subsequent sorting, conditioning, packaging etc. is a necessity;

e) organic farming and traditional production

Both in modern trade and in the traditional one, is an increasing demand for quality products, for products certified organic by default, and for the traditional ones. Small producers can capitalise in terms of efficiency these two market segments with significant potential for growth, which we can highlight through the indicator “number of operators certified in organic farming”, which was raised from 3155 to 12231 in the period 2010-2015 ( MARD, 2016);



f) European funds for agriculture

National Rural Development Programme (RDP) 2014-2020 includes a total of 15 funding measures and is primarily aimed at bridging the gaps that currently exist between agriculture practiced in Romania and the EU member countries. The budget allocated RDP 2014-2020 is worth 9.85 billion euros (RDP, 2016).

g) proposing regulatory interventions inside chains, which can be materialised in creating agri-food economic entities

In our opinion, the new agri-food entities, may initially have state capital, and in their composition should be included a specialist with technical training, able to assist a larger group of small producers and a specialist with economic training, able to search the market and recommend cultivating varieties required by the market in general and retail trade network in particular. A minimum endowment of such a center with a space equipped with means of sorting and wrapping can be conducive to a finished product marketable and compatible with products from outside Romania. Also, the products unused fresh and highly perishable (fruit, vegetables) can be processed in these centers. In addition, it will be solved other two hotspots of the agri-food sector – goods moving under taxed and small producers can coagulate around this structure for association and cooperation, bringing efficiency and rural empowerment. After gathering in associative forms and transforming them into businesses, these centers can be leased from the state by the associations, entering to their own patrimony.

We appreciate that these new economic entities are the best possible practice today, the priority purpose of their implementation being represented by easier access to food chains of small producers. In fact, the new entities can implement best practices and all the above. Also we appreciate that these entities represent also a novelty in the domestic agri-food sector.

Conclusions

The aim of this work was to bring to the fore how support should be implemented for Romanian small agricultural producers to facilitate their penetration on the agri-food chains. From those specified in the work it is showed that there are not sufficient only financial forms of support for productions performing, assuming that they correctly manages production technologies. What they lack is precisely the relationship / collaboration with upstream production or preproduction and the downstream, the relationship with the distribution segment.

Implementing best practices such as product councils; interbranch organizations; producer groups; cooperatives storage, sorting, packing, marketing; organic farming and traditional production; European funds for agriculture can contribute to the integration of Romanian small farmers in agri-food sector.

We consider that in this context it can be made a strong development, in accordance with the market economy principles of the whole agri-food chain and it can be diminishing the contradictions between domestic producers and distribution, which will have benefic effects on the entire Romanian agri-food economy.


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THE MARKET FOR IDEAS For Common Sense Elites

Octavian-Dragomir Jora1 & Alexandru Georgescu2 1) The Bucharest University of Economic Studies, Romania

2) University Politehnica of Bucharest, Romania

A truth that is so well known that to reference it has become a cliché is that Romanian society is in a long and arduous transition, beset by internal setbacks and by external challenges. Having chosen a Euro-Atlantic future, through integration in the EU and NATO, Romania finds that, along with opportunities, numerous uncertainties and risks have manifested themselves along the way. To get in was not enough. We must leverage our new status to the best of our abilities and build the pillars of a successful society, from economics to rule of law and social policy, every one of these creating some sort of capital, whether financial, social or institutional that will lead to the fulfilment of the common good and to higher living standards. However, we have arrived on the scene in a period when old certainties are being shaken up and the status-quo’s lacks and faults are being exposed, with the attendant disruption, uncertainty and crisis of confidence. The roosters are coming home to roost for the West and its particular models and Romania must, regardless of what shall happen in the future, be confident and able enough not just to unthinkingly emulate the West, but to critique this heritage in order to ensure that we have kept what is best and most useful to place our development on a more sustainable footing.

During these troubled times, we have launched The Market for Ideas

THE MARKET FOR IDEAS www.themarketforideas.com

Frequency: issued every two months (print), in continuous flow (online) ISSN (print) 2537 – 2610

ISSN (online) 2558 – 8206

Octavian-Dragomir Jora Founder Editor in Chief Alexandru Georgescu Deputy Editor in Chief

Mihaela Iacob Editorial Coordinator

Issued under the aegis of the Romanian-American Foundation for the Promotion of Education and

Culture (RAFPEC)

http://www.themarketforideas.com/

AE The Market for Ideas. For Common Sense Elites


(www.themarketforideas.com) – a both print and online magazine for consumers and producers of ideas –, addressed to elites and, crucially, to people who strive for that status. It was self-evident to the masterminds of established democracies in the West that, beyond their role in guiding their nation, they also served as models for people to follow on the road to forming a better citizenry. That emulation started with engagement – first with ideas and then with society itself. The role of the magazine is to offer an outlet for a new level of communication of ideas and policies, without enforcing an ideological constraint on contributors, save those of common sense, decency and demonstrable argumentation. It was established under the patronage of the Romanian-American Foundation for the Promotion of Education and Culture (RAFPEC), whose projects, the most famous of which is the Romanian-American University, pursue an osmosis of appropriate values, models and knowledge from the US – and its global system of civilization – to Romania and vice versa, guided by an ethos of voluntary work and exploring new ideas through a visionary rather than utopian mindset. The Market for Ideas was enacted through the extraordinary support of Professor Dumitru Miron, Ph.D., President of the RAFPEC Board of Directors.

The role of The Market for Ideas is, in the first place, descriptive and, in the second place, prescriptive. Some of our contributors may describe reality or potential scenarios in a novel way that arouses the interest of their audience and leaves readers with a better view of certain issues. Other contributors embark on a prescriptive course, advocating for an articulate policy, a structured even if not definitive idea or simply a working insight that is useful for a stated goal. They can follow-up on the declaration with argumentation, proofs, appeals to accumulated wisdom and other devices. Having made these arguments, we hope to see the results of our platform for deliberation and honing of arguments also enter into the wider society and the public sphere, and then possibly returning for further refinement. When multiple contributors address the same topic, or go through a more detailed analysis of a subject, The Market for Ideas compiles “editorial dossiers”, which may be single-issue affairs or continuous additions. This will amount to an accessible body of work on various topics for a wide readership of many different professional backgrounds, but with the intellectual means of approaching the subject and understanding its nuances.

Ultimately, The Market for Ideas sets out to develop and exploit a niche that Romanian media has left, for the most part, untouched – middlebrow or upper middlebrow commentary on issues of major importance for our nation and our civilization. The Romanian market serves the infotainment and news areas, as well as the realm of specialists working on a different level compared to laymen. What it has lacked is middle ground where specialists and people with deep insights neither simplify their ideas until they are devoid of content and nuance, nor remain arcane and impenetrable. Such literature is the quintessence of an engaged middle class of diverse means and training, which we would like the majority of our society to become, demanding a certain level of continuous interest and accumulated knowledge and rewarding them appropriately, not least of all with the means to exert purposeful influence on our leadership. Surprisingly, it seems to us that the closest Romanian media has come to a middlebrow market is in the hobbyist department, where fans of cars, gadgets and videogames expend intellectual resources on understanding the various intricacies of their chosen passions. What was chronically flawed was the area of commentary in the civic fields, which should interest the average citizen – social issues, economics, international relations, politics, geopolitics, history of thought and peoples and many others.


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If we look towards the American market, we can see that the golden age for US middle class and, via a circuitous but well understood route, this democracy came in an era when various civil society groups published middlebrow commentary to persuade and to mobilize the readers in favour of one policy or another. For instance, during its heyday, National Review, the flagship of American conservatism, had over one hundred thousand subscribers, but its ideas extended far beyond its still small readership through its influence on public discourse. It is still influential, along with middlebrow publications such as Foreign Affairs, Claremont Review, The Atlantic, The New Yorker and even the venerable The Economist. Today, the American mainstream media may be in a crisis of finance and credibility, but the “modus operandi” of the great persuaders in society has stayed the same, only moving online and exploring new business models. The backdrop to the recent American election, with its spectacle and scandal, was that of a sustained online effort lasting several years to critique the current political, social and economic consensus and formulate alternatives which were then boiled down to the slogans and memes that won Donald Trump the election. Behind the apparent frivolity of online argumentation and trolling stood a very serious effort, both from the right and the left, to lay down coherent arguments for their respective visions. It is the modern day evolution of when an influential American magazine would write an article critiquing some policy or another, and thousands of its readers would send letters to their elected officials demanding explanations or expressing their misgivings. If Romania were to have a pluralistic and open society that pursues its collective interests with meaningful input from the population, this would be the cycle of deliberation, dissemination, upward communication and refinement that influencers must attend.

The Market for Ideas strives to become a small tribute to the idea of such a society. It will feature in its pages both well-established and developing intellectuals, Romanian as well as foreign. Publishing exclusively in English was an early choice to provide an initial filter to any potential reader and to garner more attention and implicit prestige for its high production values. We also sought a better engagement with contributors, foreign and Romanian, who are thus able to relate to their contribution and consider it part of their permanent portfolio through its publication in an international language. Also on our minds were foreign readers who want a direct take on what Romanian elites are thinking and how they are reacting to events within and beyond our borders. For The Market for Ideas to succeed, it needs to become a true market for ideas, rather than the display window it currently is. We need to develop a community of readers who are also contributors of ideas to our print and online editions. We need the tools to not simply churn out new content, leaving the old one out of sight, but to consider the work as an evolving body of knowledge. And, finally, we need for part of our output to consist in critiques of already published ideas, meaning that our readers should want to deliver high quality rebuttals to ideas presented in the magazine, forming an asynchronous debate that is not unlike chess by post.

Beyond sustainability, our success will be measured not in issues sold or page views, but in the influence and spread of ideas which a presence in our pages will afford to the writers.

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