performance measurement and management systems: a ... · complexity associated with the technical...
TRANSCRIPT
International Journal of Management Reviews, Vol. 20, 731–754 (2018)DOI: 10.1111/ijmr.12184
Performance Measurementand Management Systems: A Perspective
from Complexity Theory
Simon Okwir, Sai S. Nudurupati,1 Matıas Ginieis2 and Jannis Angelis3,4
Stockholm Business School, Stockholm University, SE-106 91, Stockholm, Sweden, 1GITAM School of InternationalBusiness, Gandhi Institute of Technology and Management, Gandhi Nagar, Rushikonda, Visakhapatnam-530045,
Andhra Pradesh, India, 2Universitat Rovira i Virgili, Tarragona, Spain, 3KTH- Royal Institute of Technology, School ofIndustrial Engineering and Management, Lindstedtsvagen 30, 114 28, Stockholm, Sweden, and 4Research Institute of
Industrial Economics, Grevgatan 34, SE-10215, Stockholm, SwedenCorresponding author email: [email protected]
Complexity negatively impacts the process of continually improving performance man-agement systems (PMSs). The extant PMS literature considers complexity to be a resultof the external environment rather than a user response to that environment. However,this paper argues that organizations generally face internal complexity when adoptingPMSs. Introducing PMSs into an organization can have varied effects in those orga-nizations based on the complexity of an organization’s associated members and itsinteractions. This study aims to understand the emergence of complexities while im-plementing and using PMSs in organizations. From the complexity theory perspective,four system properties (ontological, teleological, genetic and functional) are used tounderstand complexity in PMSs. The paper builds on a systematic literature reviewconsisting of 76 papers and analyses them in the light of exploring sources of complexitywhen implementing and using PMSs. From the outset, complexity is understood to be aresult of the conflict between existing organizational practices and mechanisms and theorganizational controls associated with PMSs. The key findings abstracted six sourcesof complexity in this study: role, task and procedural types of complexity associatedwith the social dimension, and methodological, analytical and technological types ofcomplexity associated with the technical dimension. The study findings contribute tothe current discussion regarding why PMSs typically lag and are not responsive andresilient in emerging contexts. While understanding and exploring all organizationalcontrols that moderate a PMS is useful, organizations should construct the necessarycapabilities, depending on their context and adapt to the changes associated with PMSs.
Introduction
Performance management systems (PMSs) have beenposited as processes that help organizations set goals
This document has been produced with funding from Edu-cation Audiovisual and Culture Executive Agency (EACEA)of European Commission within the framework of the Eu-ropean Doctorate in Industrial Management (EDIM). Theauthors would like to acknowledge the helpful commentsfrom the anonymous reviewers and MUSICA research groupat Stockholm Business School.
and track progress over time. However, growing envi-ronmental and organizational complexity has becomea barrier to implementing efficacious PMSs (Hark-ness and Bourne 2015; Rahbek et al. 2012). Whileexternal environmental complexity has been a focusof several academic studies (Harkness and Bourne2015; Melnyk et al. 2014; Nudurupati et al. 2016),internal organizational complexity has been ignoredby scholars in the extant performance measurementand management (PMM) literature (Braz et al. 2011;Franco-Santos et al. 2007). With the introduction of
C© 2018 British Academy of Management and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd, 9600 GarsingtonRoad, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA
732 S. Okwir et al.
total quality management (TQM) in the 1980s and therevolution of traditional backward-looking account-ing systems in the 1990s, teams of individuals, usingperformance measurement (PM) from within theirfunctional area, became responsible for decision-making. In essence, organizations adopted structuresthat are naturally distributed alongside hierarchiesin which information is exchanged laterally throughthe organization. However, this distribution opposedthe top-down flow of strict hierarchies of commandand control structures that already existed to managethe organization, thus resulting in complexity (Adler2011; Burnes 2005; Lin et al. 2014; Zellner 2008).
According to complexity theory, if organizationsare considered to be complex and non-linear sys-tems, their associated members and their interactionwith subsystems will determine their current and fu-ture behaviour through a self-organizing set of order-generating rules (Brown and Eisenhardt 1997). Evenlight and relatively insignificant turbulence can leadto a huge change with unpredictable consequencesand vice versa. Therefore, PMSs that are introducedinto an organization can have varied effects on thoseorganizations, based on the complexity of their as-sociated members and its interactions. According toStacey (1995), there are three types of these varied ef-fects. In the first type, the PMS has no impact on theorganization, in which case the system soon becomesobsolete. In the second type, the PMS can bring un-controlled instability into the organization, in whichcase it self-destructs. In the final type, the PMS canbring controlled instability, in which case the orga-nization is able to adapt to the change in order tosurvive. Therefore, to understand complexity in thePMS context, it is necessary to understand the roleof PMSs as agents of change (Bourne et al. 2000;Nudurupati et al. 2011).
To begin understanding complexity of a PMS, itis necessary to understand its life cycle from its de-sign through implementation and use (including revi-sions) (Bourne et al. 2000). These process stages havebeen part of previous discussions in several studies(Bourne et al. 2003; Deng et al. 2012; Jaaskelainenand Sillanpaa 2013; Lohman et al. 2004; Nudurupatiet al. 2011; Suprapto et al. 2009). All three stagesare equally important and key to a PMS’s successor failure, depending on the way organizations haveadopted the PMS in different contexts (Bititci et al.2012; Choong 2013; Folan and Browne 2005; Mason-Jones and Towill 2000; Neely 1999). Thus, by un-derstanding how complexity evolves, organizationscan support the process stages by developing ‘best
practices’ in measuring and managing the perfor-mance (Bourne et al. 2000; Melnyk et al. 2014;Nudurupati et al. 2011).
Recently, a few studies have emerged to tacklecomplexity of PMSs in organizations. For instance,Melnyk et al. (2014) proposed a performance align-ment matrix in an attempt to address complexity inthe light of outcomes vs. solutions. Nudurupati et al.(2016) presented a case addressing complexity due toexternal organizational factors in digital economies,which is also echoed by Harkness and Bourne (2015)as well as Roehrich and Lewis (2014). Smith andBititci (2017) identified social and technical controlsto address complexity in organizations. Although themajority of these and past studies proposed betterframeworks, technical controls and management con-trols, only a few studies adopted a theoretical stancefor understanding the complexity of PMSs. It is there-fore necessary to understand complexity and to inves-tigate how it emerges in the stages of PMSs and howorganizations can manage it. Therefore, the overallaim of this study is to understand the emergence ofcomplexities while implementing and using PMSs inorganizations. The purpose of this study is not toidentify another list of social and technical (organiza-tional) controls, but to identify PMSs as systems byunderstanding how their associated properties emergefrom a complexity theory perspective. This studyseeks to understand the complexity of PMSs beforeeducating practitioners in choosing the right organiza-tional controls to moderate a PMS’s behaviour basedon the context.
Pettigrew (1977, 2014, p. 134). views organiza-tions as non-equilibrium systems and argues that un-derstanding organizational change and political pro-cesses requires a systematic approach rather than areductionist approach. However, implementing PMSsin organizations bring in change, so, for understand-ing this change in organizations, a complexity the-oretical view becomes important (Ladyman et al.2013). From a complexity theory perspective, foursystem properties were examined to understand com-plexity in a PMS: ontological, teleological, geneticand functional properties (Anderson 1999; Morel andRamanujam 1999; von Bertalanffy 1969). These foursystem properties are closely associated with the twoseparate but interdependent dimensions, technicaland social controls, identified by Smith and Bititci’s(2017) theoretical framework. This framework is usedas a basis for abstracting findings from this study.
To achieve the study’s overall aim, data were gath-ered from secondary sources relying on a systematic
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 733
literature review (SLR). The SLR method filteredstudies to examine the latent practices used by differ-ent organizations in different operational contexts atthree core process stages. The four system propertiesof complexity were then mapped onto the two typesof controls that exist for implementing and usingPMSs. The findings suggest that the performancemeasurement complexity (PMC) emerges in sixforms,: namely, role, task, procedural, methodolog-ical, analytical and technical complexities that aremapped on to the two dimensions of organizationalcontrol theory, which is a significant contributionto the theoretical foundations of PMM literature. Amajor inference of understanding PMC is to refocushow organizations should systematically select fromthe multiple best practices by examining the uniquecontext in which a PMS is operating. The study’sfindings also contribute to the current discussionon how PMSs should be responsive and resilient inemerging contexts (Bititci et al. 2012; Melnyk et al.2014; Nudurupati et al. 2016).
The rest of the paper is organized as follows. Thenext section presents the background literature andcomplexity theory adopted from other fields (Briscoeet al. 2012; Geraldi et al. 2011). This informs thePMM literature regarding how PMSs can be viewedas complex systems. This is followed by presentationof the method employed in gathering, filtering andanalysing the data. The study then presents the keyfindings obtained from the analysis, followed by athorough discussion. Finally, a conclusion highlightsa summary of the findings and key contributions,which is followed by the limitations of this study andfuture avenues of research.
Background literature
Although the PMM domain has received adequate at-tention from researchers and practitioners, most of thePMSs are still not dynamic and resilient to changesin the internal and external environment of the firm(Melnyk et al. 2014; Nudurupati et al. 2011). As aresult, some of organizations operating in dynamicmarkets are addressing static PMSs while working ondynamic strategies, resulting in complexity and a lackof efficiency in resource allocation. To understand thedynamics of complexity in PMM, it is useful to under-stand the life cycle of PMSs, the technical and socialcontrols for operating PMSs and the complexity the-oretical lens through which PMSs are understood tobe complex systems.
PMM literature
With the dissatisfaction in traditional backward-looking financial accounting systems, a number offrameworks and models have been proposed (seeBititci et al. 2000; Neely et al. 2000). However,few scholars have explored aspects of implementingPMSs, and a three-phase model proposed by Bourneet al. (2000) has been widely accepted in the liter-ature. The first phase is designing the performancemeasures, which are aligned with the organization’sstrategy. The second phase is implementing the mea-sures by putting appropriate systems and proceduresin place to collect and process data that allow mea-surements to be made. The third phase is ensuringthat the measures are used as part of decision-making,while challenging the validity of measures on a reg-ular basis. Since the establishment of this model, anumber of researchers from different disciplines haveworked in this domain to identify the best practicesin these three phases under different contexts (Bititciet al. 2012; Bourne et al. 2003; Choong 2013; Denget al. 2012; Folan and Browne 2005; Garengo et al.2005; Jaaskelainen and Sillanpaa 2013; Lohman et al.2004; Mason-Jones and Towill 2000; Neely 2005;Nudurupati et al. 2011; Suprapto et al. 2009).
Recently, Bititci (2015, pp. 170–187) collated themajority of this work and synthesized it into two broadperspectives: social (art) and technical (science) con-trols. The social controls are posited to be the culturaland behavioural controls achieved through personaltraits, structures or bureaucratic elements and inter-actions. Some of these controls may be implicit andinformal. For instance, effective leadership can em-power people and promote democratic and participa-tive culture while using performance measures. Thetechnical controls incorporate specific methodologiesthat are objective and rational and are employed toreach a specific goal. These are known to be scien-tific, objective and tend to be explicit in nature andinclude a variety of measures, information systems,data collection methods, analyses and visual com-munication (Bititci et al. 2000; Kennerley and Neely2002).
In practice, when not used or implemented ap-propriately, these controls tend to amplify complex-ity during the process stages of PMSs (Bititci 2015,pp.170–187; Bititci et al. 2012; Kauppi 2013; Nudu-rupati et al. 2011; Pekkola and Ukko 2016), resultingin a significant misuse of resources. For example,while reinventing its PMS, Deloitte found that ap-proximately two million hours a year were spent on
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
734 S. Okwir et al.
Figure 1. Framework that typifies social and technical controls (Smith and Bititci 2017)
the formalities of performance management (Buck-ingham and Goodall 2015). Smith and Bititci (2017)proposed a theoretical framework with the technicaland social controls as two dimensions and identifieda number of best practices for PMM, as depicted inFigure 1.
Perspectives of complexity theory
Complexity theory has evolved from systems theory,which emerged from natural sciences that examinedrandomly emergent non-linear interactions in a sys-tem (Burnes 2005; Grobman 2005). Grobman (2005)argues that complexity theory goes beyond systemsthinking and can be applied to understanding themanagement and design of organizations. Complex-ity in an organization is usually triggered by change,whether small or large, and can have varied levels ofconsequences, even when the organizations consistof similar components (Burnes 2005). As discussedearlier, it is useful to explore further how the changetriggers instability in organizations and how they cancontrol that change. According to Bititci et al. (2012),contemporary organizations operate in turbulent en-vironments in which change can be triggered froma number of sources, i.e. globalization, open inno-vation, autopoietic networks, technological disrup-tion, social media, process re-engineering, continuousimprovement and PM.
While the literature exploring complexity definesit in several ways, the following descriptions are most
commonly used. First, Simon (1996) conceptualizescomplexity through a hierarchical model, arguingthat, when faced with a dilemma of many parts inthe system, the system breaks down into subsystemsuntil a lowest abstraction is reached. Second, Ander-son (1999) extends complex adaptive systems by ar-guing that the strategic direction of a complex systemconsists of establishing and modifying environmentswithin which effective, improvised, self-organized so-lutions can evolve. Based on these works, most at-tention has been focused on trying to determine allthe interactions within the system, why they interactand how they interact. These interactions remain in-fluential when studying complexity. Third, Edmonds(1999) proposes that complexity is the model prop-erty that makes formulating its overall behaviour in agiven language difficult, even when given reasonablycomplete information about its atomic componentsand their inter-relations (Vidal and Marle 2008).
Sahin et al. (2013) defines complexity as a be-haviour that emerges from the way the componentsof the system are interconnected, but not how thecomponents of the system are themselves complex,although the components, people and/or firms are in-deed as complex themselves because they are gen-erally complex adaptive systems (Wilkinson 2006).Similarly, Stacey (1995) argues that organizationsare made up of complex non-linear systems inter-acting with a number of their associated members,which will exhibit a pattern of behaviours. Introduc-ing the understanding of complexity in a PMS would
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 735
influence the existing systems and their associatedagents to produce a new pattern of behaviours, whichshould be controlled for an effective outcome. Thus,in its most basic form, the concept of complexitysuggests that, by understanding the structure andbehaviour of each component within a system, thesystem as a whole could be understood with inter-relations between several components (Anderson1999; Galbraith 1982; Kandjani & Bernus 2012).
According to Anderson (1999), a system has anumber of interactions among its associated compo-nents and agents and with the environment in whichit operates. Using the system perspective to charac-terize complexity is not new (Ladyman et al. 2013).For example, in understanding project complexity,a wide range of empirical studies uses the systemsview to examine complexity (Geraldi et al. 2011;Vidal and Marle 2008). Similarly, by providing in-sights on procurement, a systemic complexity theorywas applied (Roehrich and Lewis 2014). The systemview means examining and categorizing the knownsystem properties, ontological, teleological, geneticand functional, which are real world manifestationsof a particular system. The ontological property rep-resents the internal structures that include leadership,organizational culture and behavioural factors. Theteleological property represents an object in an envi-ronment that aims to reach an objective. The geneticproperty represents the system’s evolution over time.The functional property represents the focal activ-ity to be performed (Boulding 1956; von Bertalanffy1969).
Complexity theoretical lens for characterizingcomplexity in PMM
The PMM literature does not contain many studiesthat examine complexity explicitly in depth. However,a few researchers have studied the impact of com-plexity on PM (Bourne 2015; Harkness and Bourne2015; McAdam and Bailie 2002; Neely et al. 2000).Neely (2005) explores the different ways in whichmanagers can make strategic decisions under com-plex situations; he also proposes different approachesto organizational learning. Bititci et al. (2006) explorethe complex nature of causal links between PM, man-agement styles and organizational culture. In a simi-lar vein, Bititci et al. (2012) argue that practitionershave to address complexity by rethinking the futureof measurement, but they do not explain what exactlyamplifies complexity. Braz et al. (2011) and McAdamand Bailie (2002) highlight complexity in their work,
but they mostly dismiss it as a minor issue. There arenumber of factors that portray complexity as a poten-tial barrier to PMSs’ attaining efficacy (Bititci 2015,pp.170–187; Harkness and Bourne 2015; Paranjapeet al. 2006; Sullivan 2011). The PMM literature alsorefers to complexity when it addresses the evolutionof PMSs. For instance, Bititci et al. (2012) reveal busi-ness trends and how PMM is moving towards chal-lenging operational contexts, thus suggesting that itis a self-learning system.
To understand the sources of complexity in PMSs,they must be considered complex systems, and thequestion of whether managing such systems exhibitssystem properties similar to those described in the lit-erature must be explored (Ladyman et al. 2013; Moreland Ramanujam 1999). These goals are achieved byexploring the PMM literature for practices, processes,policies and mechanisms that are associated with thedesign, implementation and use process stages ofPMSs that match the system properties as defined inthe previous section. It is concluded that, by their def-inition and use, PMSs exhibit similar properties whenviewed as systems, i.e. as discussed in general systemtheory (von Bertalanffy 1969). A PMS is treated as anentity operating in an environment (Vidal and Marle2008) with its four system properties: ontological,teleological, genetic and functional. Table 1 showsthe system properties that are the attributes of a PMS.
It is important to recognize that PMSs are com-plex adaptive social systems (Holland 1975), whichmeans that they are systems in which users contributeto both creating and responding to their environmentto achieve a goal. In this case, the goal is to moni-tor and measure performance in order to improve andcontrol its components. The definition of complexityresonates with PMSs in that, even though there areseveral frameworks available to guide a PMS throughits life cycle and explain exactly what organizationsneed to do, the system behaviour as a whole is unpre-dictable, owing to the complex nature of the organiza-tional interactions that are further aggravated by so-cial and technical controls (Miller and Friesen 1984).
The overall aim of this study is to understandthe emergence of complexities while implementingand using PMSs in organizations. Therefore, thebackground literature has provided some insightson complexity theoretical perspective and identifiedfour system properties that are closely associatedwith social and technical (organizational) controls(Smith and Bititci 2017) when implementing PMM.Most of the organizational controls identified in theliterature are generic, although support PMM in
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
736 S. Okwir et al.
Table 1. Complexity dimensions associated with PMMs
System property Performance management system indicators
Ontological The ontological property is internal structures such as the people component, varied staff, behavioural factors,leadership styles, variety of information, diversity in practices, number of stakeholders, trust, and different views onwhat to measure (Bititci et al. 2012; Nudurupati et al. 2016; Toor and Ogunlana 2010; Wijngaard et al. 2006) andorganization culture (Aguinis et al. 2011; Bititci et al. 2006; Elzinga et al. 2009; Ukko et al. 2007).
Teleological The teleological property specifies goals (Jaaskelainen and Laihonen 2013), objectives (Haponava and Al Jibouri2009; Mol and Beeres 2005) and managerial practices (Angelis and Jordahl 2015). Stakeholder goals (Beer andMicheli 2017).
Functional The functional property considers need-specific methodologies and contingent factors (Lohman et al. 2004; Micheliand Kennerley 2005; Micheli and Mari 2014; Nudurupati et al. 2011) as well as PMS frameworks (Ferreira andOtley 2009).
Genetic The genetic property considers the time factor, evolution of measures (Mol and Beeres 2005), phases of product lifecycles, time factors (Caniato et al. 2014), maturity of key performance indicator, and continuous improvement(Braz et al. 2011; Elg et al. 2013).
organizations. However, the purpose of this studyis to explore the sources of complexity, so thatspecific organizational controls can be identified ordeveloped to increase the usefulness of PMM. AsPMM is a mature field with a number of empiricalstudies emerging every year, collecting evidencefrom extant literature is a good starting point for thisstudy. Using the SLR approach, this study gathersmore data from secondary sources to abstract sourcesof complexity when implementing and using PMM.
Method
An SLR approach was applied to identify and synthe-size the most relevant academic literature in this field.Our work, Therefore, differs from traditional narra-tive reviews because it adopts a detailed, replicableand transparent scientific process that aims to mini-mize bias through exhaustive bibliographical searchesof published studies (Cook et al. 1997; Davis et al.1995; Moustaghfir 2008). An SLR approach providesuseful guidelines that can be followed by other re-searchers in different fields (Cook et al. 1995; Petti-crew and Robert 2006). The chronological evolutionof the research on PMM is examined to see whetherthere is growing interest in academia and hence in theresults presented on the chronological developmentof publications related to this subject. An SLR analy-sis allows researchers to focus on the purpose ratherthan on the utility of publications (Ginieis et al. 2012)and it provides a structured way to summarize variousfindings with minimum bias (Cook et al. 1997). Thisstudy adopted the guidelines proposed by Tranfieldet al. (2003) and Moustaghfir (2008), and presentssix stages, as demonstrated in Figure 2.
Step-by-step process for sample selection
Stage 1. The keywords words associated with PMMare identified from the background literature, scien-tific publications and the authors’ previous experi-ence in the field (Tasca et al. 2010). In line withthe suggestion of Tranfield et al. (2003), more thanone researcher participated in the decisions regardingkeyword selection and their combination to generateeffective search strings. Emphasis was placed on thedegree to which the control factors interactively affectthe process phases of a PMS throughout its life cycleas defined in the literature, the design phase (Denget al. 2012; Lohman et al. 2004), the implementationphase (Bourne et al. 2003; Jaaskelainen and Sillanpaa2013; Suprapto et al. 2009) and the use phase, and asadopted by different organizations in different sectors(Bititci et al. 2012; Choong 2013; Folan and Browne2005; Mason-Jones and Towill 2000; Neely 1999).Therefore, different performance-related strings areused as primary sources, which are concatenated withthe secondary keywords ‘design’, ‘implementation’or ‘use’:
1. ‘performance management’ AND use OR designOR implementation
2. ‘performance measurement’ AND use OR de-sign OR implementation
3. ‘performance assessment’ AND use OR designOR implementation
4. ‘performance indicators’ AND use OR designOR implementation
5. ‘performance appraisal’ AND use OR design ORimplementation
6. ‘performance control’ AND use OR design ORimplementation
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 737
Figure 2. Step-by-step process for sample selection
7. ‘performance complexity’ AND use OR designOR implementation
8. ‘performance strategy’ AND use OR design ORimplementation
9. ‘supply chain performance’ AND use OR designOR implementation
10. ‘performance measurement system’ AND useOR design OR implementation
11. ‘performance management system’ AND use ORdesign OR implementation
12. ‘performance measurement and management’AND use OR design OR implementation
13. ‘organisational performance’ AND use OR de-sign OR implementation
14. ‘organizational performance’ AND use OR de-sign OR implementation
To perform the keyword search, the databases Webof Science (WOS) from Thomson Reuters and Sco-pus from Elsevier were used, because they provide awide coverage of areas within this discipline and pro-vide different searching, browsing and filtering op-tions (Ginieis et al. 2012; Lopez-Illescas et al. 2008).These keyword combinations are searched in the ti-tle, keywords and/or abstract of the paper. Table 2presents the number of articles published in differentjournals in chronological order.
Stage 2. As demonstrated in Figure 2, the initialkeyword combination search yielded 10,589 outputs
in the previous stage, which were further filtered inthis and subsequent stages in a process that incorpo-rates the exclusion criteria established by the researchprotocol (Jones and Gatrell 2014). The use of twodatabases resulted in duplicates, which were removedin Stage 2, resulting in 9251 outputs. As demonstratedin Figure 3, until the late 1990s, only 952 publicationsof PMM (only 10% of total of studies analysed) hadbeen published. The noteworthy increase in publica-tions in this field has been observed since the begin-ning of the year 2000. More specifically during theperiod 2000–2008, 2213 documents were published(24%). Finally, it is important to remark that, in thelast few years (2009–2017), 6086 studies on PMM(66%) have been published. This demonstrates thegrowing importance that this topic has had in the aca-demic literature in recent years. This is one of thefundamental reasons why this study adopted an SLRapproach.
Stage 3. The papers were further filtered to includeonly peer-reviewed journal articles, which reducedthe number to 6091 articles. For instance, Table 2presents the number of articles retrieved using differ-ent keyword search strings in chronological order.
Stage 4. Articles were further filtered by spe-cific journals listed in the categories Manage-ment/Business (WOS), Business, Management and
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
738 S. Okwir et al.
Table 2. Keyword analysis and articles until 2017
Keywords Until 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total
performance management 79 16 22 19 32 28 34 40 37 44 65 65 48 529performance measurement 313 41 58 66 70 77 63 67 61 90 108 102 99 1215performance appraisal;
performance control;performance complexity;performance strategy;supply chain performance;performance measurementsystem; performancemanagement system;performance measurementand management;organisationalperformance;organizational performance
308 36 46 50 65 81 81 90 81 96 122 153 155 1364
performance assessment;performance indicators
595 80 104 112 127 156 154 213 213 238 305 332 354 2983
Total 1295 173 230 247 294 342 332 410 392 468 600 652 656 6091
Figure 3. Number of publications on PMM to 2017
Accounting (Scopus), Operations and TechnologyManagement, General Management, Ethics and So-cial Responsibility. With the increase in the number ofjournals emerging every year, it is becoming difficultto judge the quality of papers. Therefore, the Char-tered Association of Business School (ABS) jour-nal ranking guide and the Australian Business DeansCouncil (ABDC) journal quality list were used to fil-ter the articles further. All journals (irrespective oftheir rank or rating) listed in these guide were usedto filter the list. Using this search strategy in these
databases and guides, the study intends to locate theacademic journals most recognized by the scientificcommunity in the different fields of knowledge. Fur-thermore, this filtering ensures that a high standardis maintained in the performance of the SLR and inits output. Similarly, McGovern (2014) conducted asystematic review based on only four leading aca-demic journals. He justified this decision by notingthat all four journals were ‘considered to be leadingjournals and, as such, might reasonably be expected toexert some influence over their respective subfields’
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 739
Table 3. Evolution of the number of PMM publications in journals WOS/Scopus/ABS/ABDC
Journal Until 2010 2011 2012 2013 2014 2015 2016 2017 Total
International Journal of Operations & Production Management 53 6 2 2 4 – 3 4 74Energy and Buildings 7 4 4 5 3 5 8 8 44International Journal of Human Resource Management 21 6 2 3 3 2 2 5 44Journal of Cleaner Production 8 – 2 1 1 7 9 14 42International Journal of Production Research 18 – 1 2 3 8 5 4 41Public Performance & Management Review 7 11 4 3 2 7 5 2 41International Journal of Production Economics 21 3 2 2 3 5 2 – 38Energy Conversion and Management 5 1 – 4 5 7 8 4 34Reliability Engineering & System Safety 22 – 2 – 7 1 1 1 34Production Planning & Control 15 – – 4 7 2 2 3 33Total Quality Management & Business Excellence 21 1 2 1 2 – 2 3 32Expert Systems with Applications 15 6 2 – 4 2 1 – 30Industrial Management & Data Systems 19 1 3 2 1 – – 3 29Public Administration Review 18 – 1 3 – 2 4 1 29International Journal of Productivity and Performance Management – – – – – 11 8 9 28Journal of Business Research 15 1 1 2 2 3 4 – 28Applied Thermal Engineering 4 – 1 2 2 4 7 6 26Other Journals 384 58 62 64 61 80 76 84 869
Total 653 98 91 100 110 146 147 151 1496
(McGovern 2014, p. 23). This SLR followed a simi-lar path and was organized to consider only journalsrelevant to this discipline and that have publishedat least ten articles on PMM. This resulted in 1496articles. The initial data analysis of 1496 articles re-vealed some useful information that shed light on theresearch trends and direction. For instance, Table 3presents the journals that have published most arti-cles on PMS in chronological order.
Stage 5. The papers were screened by reading thetitle and abstract to assess their relevance and fit withthe research scope, which resulted in 205 papers.Similar steps have been employed in previous SLRs(Bonatto et al. 2015; McGovern 2014; Tasca et al.2010; Turner et al. 2013). Where an abstract is notclear, it is included for full appraisal.
Stage 6. It is an iterative process at this stage,where the 205 papers are fully appraised to assesstheir suitability to this study using filtering criteria.First, the study included only empirical articleswhere the observations are grounded in practice.Second, the focus of this study is the exploration ofsources of complexity while implementing and usinga PMS. Therefore, from the outset, complexity canbe understood to be a result of the conflict betweenorganizational practices and mechanisms with socialand technical controls associated with PMSs. Thisstage therefore included papers that discuss organi-zational controls that are used to manage PMS in
organizations. Finally, a total of 76 papers werethoroughly analysed and evaluated. These paperswere further supplemented by additional papersobtained through citation tracking together with theauthors’ previous experience where appropriate. Theselected papers from the SLR covered a wide rangeof aspects, such as human resources, manufacturing,measurement systems in small and medium-sizedenterprises (SMEs), financial and non-financialoperations, leaderships styles and motivation at workassociated with PMSs.
Data analysis. The data obtained from these mul-tiple sources were synthesized, and the key researchfindings were abstracted. Multiple sources were ex-amined for common patterns. The explanations ob-tained from the triangulation of consistent patterns,comparable meanings or common aims were inte-grated into themes. The synthesis, pattern-matchingand integration promoted the development of six the-oretical presentations of complexity, which are fur-ther presented in the findings section. While the au-thors emphasized triangulation, the identification ofcommon patterns and the development of integratedexplanations, they did not attempt to quantify anyoccurrences for use in the analysis. The papers ap-praised were grouped by the author(s), purpose, re-search method adopted, PMS life cycle stages ex-plored and, finally, the understanding of the type ofcomplexity abstracted in each study (see Table 4).To identify the emerging complexity, the authors
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
740 S. Okwir et al.
Tabl
e4.
The
inte
ract
ion
betw
een
cont
rolf
acto
rsat
diffe
rent
proc
ess
stag
es
Key
publ
icat
ion
Res
earc
hqu
esti
on/P
urpo
seR
esea
rch
appr
oach
PM
SL
ife
cycl
est
ages
Com
plex
ity
type
Bit
itci
etal
.(20
00)
Dev
elop
sa
mod
elfo
rin
tegr
ated
and
dyna
mic
PM
S,p
rovi
des
acr
itic
alre
view
ofex
isti
ngsy
stem
Cas
est
udy
Use
,Des
ign
Tech
nolo
gica
l
Nee
lyet
al.(
2000
)D
escr
ibes
the
proc
ess
ofde
sign
ing
ape
rfor
man
cem
easu
rem
ents
yste
man
dte
stin
gA
ctio
nre
sear
chD
esig
n,Im
plem
enta
tion
Pro
cedu
ral,
Ana
lyti
cal
Hud
son
etal
.(20
01)
Tode
velo
pm
easu
res
that
driv
eop
erat
iona
lper
form
ance
tow
ards
the
achi
evem
ent
ofst
rate
gic
obje
ctiv
esA
ctio
nre
sear
chD
esig
n,U
seM
etho
dolo
gica
l,P
roce
dura
lN
gaia
ndC
heng
(200
1)To
unde
rsta
ndho
wP
MS
perf
orm
asa
resu
ltof
appl
ying
know
ledg
eba
sed
syst
emC
ase
Stu
dyD
esig
n,Im
plem
enta
tion
,U
seTa
sk,R
ole
Bou
rne
etal
.(20
02)
Toin
vest
igat
eth
eco
ntri
buti
onof
busi
ness
PM
and
hum
anre
sour
cem
anag
emen
tpr
acti
ces
tobu
sine
sspe
rfor
man
ceC
ase
stud
yD
esig
n,Im
plem
enta
tion
,U
seR
ole,
Task
Bou
rne
etal
.(20
02)
Toin
vest
igat
eth
esu
cces
san
dfa
ilur
eof
perf
orm
ance
mea
sure
men
tsys
tem
desi
gnin
terv
enti
ons
inte
nco
mpa
nies
Cas
est
udy
Des
ign
Tech
nolo
gica
l,P
roce
dura
lM
cAda
man
dB
aili
e(2
002)
Toex
plor
elo
ngit
udin
alal
ignm
entb
etw
een
perf
orm
ance
mea
sure
san
dbu
sine
ssst
rate
gyL
iter
atur
ere
view
onP
MM
Use
Pro
cedu
ral
San
tos
etal
.(20
02)
Tosh
owth
ero
leof
syst
emdy
nam
ics
and
mul
ticr
iter
iade
cisi
onan
alys
isA
naly
sis
offr
amew
orks
Des
ign,
Use
Ana
lyti
cal,
Met
hodo
logi
cal
Lew
is(2
003)
Tode
velo
pa
mod
elof
com
pete
nce
asa
tran
sfor
mat
ion
proc
ess,
com
bini
ngre
sour
cean
dac
tivit
yin
puts
into
oper
atio
nalp
roce
sses
that
resu
ltin
spec
ific
com
peti
tive
perf
orm
ance
outc
omes
Cas
est
udy
Des
ign,
Impl
emen
tati
on,
Use
rM
etho
dolo
gica
l,A
naly
tica
l,Ta
sk,
Rol
eL
ohm
anet
al.(
2004
)To
show
how
KP
Isat
vari
ous
leve
lsin
the
orga
niza
tion
can
bein
corp
orat
edin
toon
esy
stem
conv
ince
pote
ntia
luse
rfo
rit
sus
eC
ase
stud
yD
esig
n,Im
plem
enta
tion
,U
seM
etho
dolo
gica
l,A
naly
tica
lB
itit
ciet
al.(
2006
)To
dem
onst
rate
how
exis
ting
PM
may
bead
opte
dto
mea
sure
and
man
age
perf
orm
ance
inex
tend
eden
terp
rise
sL
iter
atur
ein
PM
MU
seP
roce
dura
l,M
etho
dolo
gica
lS
acri
stan
-Dıa
zet
al.
(200
5)To
iden
tify
the
perf
orm
ance
mea
sure
men
tsys
tem
sth
atar
eus
edto
test
thei
rco
rres
pond
ence
wit
hth
eob
ject
ives
that
mot
ivat
edth
ein
vest
men
tsS
urve
yU
seM
etho
dolo
gica
l,A
naly
tica
lFo
lan
and
Bro
wne
(200
5)To
deve
lop
ape
rfor
man
cem
easu
rem
ents
yste
msp
ecifi
cally
desi
gned
for
the
requ
irem
ents
ofth
eex
tend
eden
terp
rise
Ana
lysi
sof
Fram
ewor
ksD
esig
n,U
seM
etho
dolo
gica
l,A
naly
tica
l,P
roce
dura
lG
aren
goet
al.(
2005
)To
clar
ify
whe
ther
chan
ges
inP
MS
are
due
toth
eev
olut
ion
ofth
ege
neri
cm
odel
sor
anat
tem
ptto
intr
oduc
em
odel
ssu
ited
toth
ene
eds
ofS
ME
sL
iter
atur
ese
arch
Des
ign,
Use
Pro
cedu
ral,
Met
hodo
logi
cal
Gre
ilin
g(2
005)
The
use
ofP
Mw
ithi
nth
eG
erm
anpu
blic
sect
orL
iter
atur
ese
arch
Use
,Im
plem
enta
tion
Pro
cedu
ral
John
ston
(200
5)H
owth
eco
nflic
tsre
late
dto
econ
omic
and
soci
alag
ency
wit
hin
part
icul
arpu
blic
sect
orP
Mar
rang
emen
tsca
nw
ork
for
and
agai
nstt
heap
plic
atio
nof
bala
nced
scor
ecar
dst
yle
syst
ems
Cas
est
udy
Use
Rol
e,Ta
sk,
Pro
cedu
ral
Mic
heli
and
Ken
nerl
ey(2
005)
Tore
view
fram
ewor
kscu
rren
tlyde
velo
ped
and
impl
emen
ted
inpu
blic
and
non-
profi
torg
aniz
atio
nsan
dto
iden
tify
the
requ
irem
ents
ofa
fram
ewor
kfo
rne
wco
ntex
ts
Cas
est
udy
Des
ign,
Use
Met
hodo
logi
cal,
Ana
lyti
cal
Mol
and
Bee
res
(200
5)To
stre
ssou
tthe
need
toad
just
perf
orm
ance
man
agem
entt
oth
ede
fici
enci
esin
here
ntin
the
outp
utco
ntro
lsA
ctio
nre
sear
chU
seR
ole,
Task
,P
roce
dura
l
(Con
tinu
ed)
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 741
Tabl
e4.
Con
tinu
ed
Key
publ
icat
ion
Res
earc
hqu
esti
on/P
urpo
seR
esea
rch
appr
oach
PM
SL
ife
cycl
est
ages
Com
plex
ity
type
Nud
urup
atia
ndB
itit
ci(2
005)
Toim
plem
entI
T-P
MS
:ass
ess
the
impa
ctof
IT-P
MS
onm
anag
emen
tand
busi
ness
,ide
ntif
yth
efa
ctor
ssu
ppor
ting
IT-P
MS
that
wer
eim
pact
ing
man
agem
enta
ndbu
sine
ss,e
stab
lish
the
patt
ern
ofoc
curr
ence
ofth
efa
ctor
sim
pact
ing
onm
anag
emen
tand
busi
ness
Cas
est
udy
Des
ign,
Impl
emen
tati
on,
Use
Rol
e,Ta
sk,
Pro
cedu
ral,
Met
hodo
logi
cal,
Ana
lyti
cal
Turn
eret
al.(
2005
)T
his
pape
rde
scri
bes
how
perf
orm
ance
mea
sure
sw
ere
sele
cted
and
then
impl
emen
ted
intw
oS
ME
sin
Cen
tral
Sco
tlan
dL
iter
atur
ere
view
Use
Ana
lyti
cal
Wou
ters
and
Spo
rtel
(200
5)To
inve
stig
ate
the
role
ofex
isti
ng,l
ocal
perf
orm
ance
mea
sure
sin
the
proc
ess
ofde
velo
ping
and
impl
emen
ting
anin
tegr
ated
perf
orm
ance
mea
sure
men
tsys
tem
Cas
est
udy
Use
,Im
plem
enta
tion
Rol
e,Ta
sk,
Pro
cedu
ral
Sha
rma
and
Bha
gwat
(200
6)To
deve
lop
afr
amew
ork
that
mea
sure
san
dev
alua
tes
perf
orm
ance
Sur
vey
Use
Ana
lyti
cal
Wij
ngaa
rdet
al.
(200
6)To
link
conc
epts
from
orga
niza
tion
alan
dso
cial
psyc
holo
gyto
prod
ucti
onpl
anni
ngan
dco
ntro
lA
ctio
nre
sear
chU
seTa
sk,P
roce
dura
l
Yil
maz
and
Bit
itci
(200
6)To
com
pare
the
PM
ofm
anuf
actu
ring
and
tour
ism
indu
stri
esfr
oma
valu
ech
ain
pers
pect
ive
Con
cept
ual
Use
Ana
lyti
cal
Ukk
oet
al.(
2007
)To
inve
stig
ate
the
impa
cts
ofP
Mon
man
agem
enta
ndle
ader
ship
Cas
est
udy
Use
Rol
es,T
ask,
Pro
cedu
ral
Rib
eiro
-Car
pine
tti
etal
.(20
08)
Tom
odel
for
PM
Mof
acl
uste
rba
sed
onth
eco
ncep
tsof
the
Bal
ance
dS
core
card
and
othe
rm
odel
sA
ctio
nre
sear
chD
esig
n,U
seM
etho
dolo
gica
l,Te
chno
logi
cal
Pong
atic
hata
ndJo
hnst
on(2
008)
Toex
plor
eth
epo
ssib
ilit
yth
atso
me
degr
eeof
mis
alig
nmen
tbet
wee
npe
rfor
man
cem
easu
res
and
stra
tegy
,far
from
bein
gco
unte
rpro
duct
ive
30in
terv
iew
sU
seP
roce
dura
l,M
etho
dolo
gica
l,R
oles
,Tas
kB
road
bent
and
Lau
ghli
n(2
009)
Stu
dies
the
inte
rrel
atio
nshi
psam
ong
stra
tegy
syst
ems,
PM
Ms
and
orga
niza
tion
alch
ange
prog
ram
mes
wit
hin
Pett
igre
w’s
mod
elC
once
ptua
lD
esig
n,Im
plem
enta
tion
,U
seA
naly
tica
l,P
roce
dura
l,M
etho
dolo
gica
lH
apon
ava
and
AlJ
ibou
ri(2
009)
Toid
enti
fypr
oces
s-ba
sed
KP
Isfo
rus
ein
cont
rolo
fth
epr
e-pr
ojec
tsta
geC
ase
stud
yan
dli
tera
ture
revi
ewD
esig
n,U
seA
naly
tica
l,Te
chno
logi
cal
Elz
inga
etal
.(20
09)
The
role
ofbe
havi
oura
lfac
tors
inth
eus
eof
PM
Ms
Cas
est
udy
Use
Rol
e,Ta
sk,
Pro
cedu
ral
Ferr
eira
and
Otl
ey(2
009)
Tode
scri
beth
est
ruct
ure
and
oper
atio
nof
PM
Ss
ina
mor
eho
list
icm
anne
rO
bser
vati
onD
esig
n,Im
plem
enta
tion
,U
seP
roce
dura
l,M
etho
dolo
gica
l,R
oles
,Tas
kH
anse
n(2
010)
The
pape
rpr
esen
tsan
anal
ysis
ofth
ere
solu
tion
ofor
gani
zati
onal
exte
rnal
itie
sth
roug
hth
eus
eof
nonfi
nanc
ialp
erfo
rman
cem
easu
res
for
plan
ning
Com
para
tive
case
stud
yU
seP
roce
dura
l,M
etho
dolo
gica
lTo
oran
dO
gunl
ana
(201
0)To
inve
stig
ate
the
perc
epti
onof
the
KP
Isin
the
cont
exto
fa
larg
eco
nstr
ucti
onpr
ojec
tin
Tha
ilan
dS
urve
yU
seA
naly
tica
l
van
Vee
n-D
irks
(201
0)To
exam
ine
the
impo
rtan
ceth
atis
attr
ibut
edto
ava
riet
yof
fina
ncia
land
non-
fina
ncia
lper
form
ance
mea
sure
sS
urve
yD
esig
n,U
seA
naly
tica
l
Adl
er(2
011)
Toex
amin
eho
wP
MM
sar
ede
sign
edto
mee
tand
supp
ortt
heim
plem
enta
tion
ofa
conf
ront
atio
nst
rate
gyC
ase
stud
yU
se,I
mpl
emen
tati
onR
ole,
Pro
cedu
ral
(Con
tinu
ed)
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
742 S. Okwir et al.
Tabl
e4.
Con
tinu
ed
Key
publ
icat
ion
Res
earc
hqu
esti
on/P
urpo
seR
esea
rch
appr
oach
PM
SL
ife
cycl
est
ages
Com
plex
ity
type
Bra
zet
al.(
2011
)To
show
diffi
cult
yan
dco
mpl
exit
yof
revi
ewin
gan
dup
dati
ngan
ener
gyco
mpa
ny’s
PM
Sfo
rit
sm
arit
ime
tran
spor
tati
onar
eaA
ctio
nre
sear
chD
esig
n,Im
plem
enta
tion
,U
seA
naly
tica
l,M
etho
dolo
gica
lJa
inet
al.(
2011
)P
rese
nts
ada
taen
velo
pmen
tana
lysi
s(D
EA
)ba
sed
appr
oach
for
PM
and
targ
etse
ttin
gof
man
ufac
turi
ngsy
stem
sC
ase
stud
yU
seA
naly
tica
l
Pavl
ovan
dB
ourn
e(2
011)
Topr
opos
ea
theo
reti
calm
odel
ofm
easu
rem
ento
npe
rfor
man
ceR
evie
wof
PM
syst
ems
Use
Tech
nolo
gica
l
Sil
lanp
aa(2
011)
Toid
enti
fyth
efo
cale
lem
ents
ofpe
rfor
man
cein
Finn
ish
wel
fare
serv
ice
orga
niza
tion
sC
ase
stud
yU
seM
etho
dolo
gica
l
Tung
etal
.(20
11)
Toex
amin
eth
eas
soci
atio
nbe
twee
nth
eus
eof
mul
tidi
men
sion
alpe
rfor
man
cem
easu
res
and
fact
ors
that
affe
ctth
eef
fect
iven
ess
ofP
MS
sS
urve
yU
seP
roce
dura
l,R
ole,
Task
sV
alm
oham
mad
iand
Ser
vati
(201
1)To
desi
gnan
dim
plem
enta
perf
orm
ance
man
agem
ents
yste
mus
ing
thir
d-ge
nera
tion
Bal
ance
dS
core
card
(BS
C)
toco
mpa
rean
dev
alua
teso
me
stra
tegi
cm
easu
res
ofth
eco
mpa
nyag
ains
ttho
seof
ale
adin
gco
mpa
ny
Cas
est
udy
Des
ign,
Impl
emen
tati
on,
Use
Tech
nolo
gica
l,M
etho
dolo
gica
l
Cha
lmet
aet
al.(
2012
)To
prop
ose
am
etho
dolo
gyfo
rde
sign
ing
and
impl
emen
ting
PM
Sad
apte
dto
the
char
acte
rist
ics
ofS
ME
sC
ase
stud
yIm
plem
enta
tion
Met
hodo
logi
cal,
Ana
lyti
cal
Rah
bek
and
Sud
zina
(201
2)To
outl
ine
the
anat
omy
offi
rms
whi
chad
optc
ompr
ehen
sive
perf
orm
ance
mea
sure
men
tsys
tem
sin
orde
rto
gain
anun
ders
tand
ing
ofho
win
tern
al(o
rgan
izat
iona
lcap
abil
itie
s)an
dex
tern
al(p
erce
ived
envi
ronm
enta
lun
cert
aint
ies)
fact
ors
shap
eP
Mpr
acti
ces
Sur
vey
Use
Tech
nolo
gica
l,A
naly
tica
l
Tati
cchi
etal
.(20
12)
Topr
ovid
ere
sear
chgu
idel
ines
for
buil
ding
aP
MM
syst
emth
roug
ha
refe
renc
efr
amew
ork,
and
toid
enti
fym
ajor
desi
gnch
alle
nges
Cit
atio
nan
dco
-cit
atio
nan
alys
isD
esig
n,U
seM
etho
dolo
gica
l
Ben
Had
jSal
emM
ham
dia
(201
3)O
npe
rfor
man
cean
dhe
alth
ines
sm
easu
rem
entp
ract
ices
ina
Tuni
sian
soft
war
eec
osys
tem
Sur
vey
Use
Pro
cedu
ral,
Ana
lyti
cal
deW
aala
ndK
ourt
it(2
013)
The
bene
fits
expe
rien
ced
byor
gani
zati
ons
inpr
acti
ceaf
ter
intr
oduc
ing
PM
MC
ase
stud
yIm
plem
enta
tion
,Use
Rol
es,P
roce
dura
l
Elg
etal
.(20
13)
Toco
ntri
bute
toth
ekn
owle
dge
base
onho
wP
Mdr
ives
impr
ovem
ents
inhe
alth
care
prac
tice
Cas
est
udy
Des
ign,
Use
Ana
lyti
cal,
Met
hodo
logi
cal
Jaas
kela
inen
and
Lai
hone
n(2
013)
Toid
enti
fypr
acti
calw
ays
toov
erco
me
the
spec
ific
PM
chal
leng
esof
know
ledg
e-in
tens
ive
orga
niza
tion
sA
ctio
nre
sear
chU
seR
ole,
Task
Jaas
kela
inen
and
Sil
lanp
aa(2
013)
Toev
alua
tefa
ctor
saf
fect
ing
the
succ
ess
ofth
em
easu
rem
ents
yste
mim
plem
enta
tion
inth
eco
ntex
tof
two
case
serv
ices
wit
ha
spec
ific
mea
sure
men
tob
ject
Cas
est
udy
Des
ign,
Use
Rol
e,Ta
sk
Ver
nada
teta
l.(2
013)
Topr
opos
ene
wP
MM
fram
ewor
kba
sed
onva
lue
and
risk
Cas
est
udy
Des
ign,
Use
Met
hodo
logi
cal
Can
iato
etal
.(20
14)
Und
erst
andi
ngw
hata
reth
em
osta
dopt
edin
dica
tors
,wha
tare
the
key
elem
ents
char
acte
rizi
ngth
eim
plem
enta
tion
proc
ess
and
wha
tare
the
diff
eren
ces
Cas
est
udy
Use
Ana
lyti
cal
Gar
engo
and
Sha
rma
(201
4)To
inve
stig
ate
the
role
ofco
rpor
ate
gove
rnan
cest
ruct
ure
asa
PM
Sco
ntin
genc
yfa
ctor
Cas
est
udy
Use
Rol
es,T
asks
,P
roce
dura
lL
aiho
nen
etal
.(20
14)
Toin
vest
igat
eth
eim
plic
atio
nsof
the
netw
orke
dan
dop
enna
ture
ofth
ese
rvic
ebu
sine
sson
PM
Cas
est
udy
Des
ign,
Impl
emen
tati
on,
Use
Pro
cedu
ral (C
onti
nued
)
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 743Ta
ble
4.C
onti
nued
Key
publ
icat
ion
Res
earc
hqu
esti
on/P
urpo
seR
esea
rch
appr
oach
PM
SL
ife
cycl
est
ages
Com
plex
ity
type
Mel
nyk
etal
.(20
14)
Tore
solv
eth
eis
sue
of‘F
it’
ofth
ere
vise
dm
easu
res
toth
ene
eded
stra
tegy
Del
phia
ppro
ach
Use
,Im
plem
enta
tion
Ana
lyti
cal,
Met
hodo
logi
cal
Upa
dhay
aet
al.
(201
4)To
inve
stig
ate
the
role
ofpe
rfor
man
cem
easu
rem
ents
yste
ms
inor
gani
zati
onal
effe
ctiv
enes
sin
the
cont
exto
fth
efi
nanc
ials
ervi
ces
sect
orw
ithi
na
deve
lopi
ngco
untr
y
Sur
vey
Use
Rol
e,Ta
sk
Tayl
oran
dTa
ylor
(201
4)In
vest
igat
esth
ein
flue
nce
ofor
gani
zati
onsi
zeon
the
effe
ctiv
eim
plem
enta
tion
ofP
MS
sC
ase
stud
yIm
plem
enta
tion
Pro
cedu
ral
Spe
kle
and
Ver
beet
en(2
014)
Tost
udy
the
use
ofpe
rfor
man
cem
easu
rem
ents
yste
ms
inth
epu
blic
sect
orS
urve
yU
seA
naly
tica
l
Ang
elis
and
Jord
ahl
(201
5)To
com
pare
man
agem
entp
ract
ices
inpr
ivat
ean
dpu
blic
lyow
ned
elde
rly
care
hom
eS
urve
yU
seM
etho
dolo
gica
l,P
roce
dura
l
Bit
itci
(201
5)To
expl
ore
how
visu
alst
rate
gyan
dpe
rfor
man
cem
anag
emen
ttec
hniq
ues
impa
ctP
MM
prac
tice
sof
orga
niza
tion
sA
ctio
nre
sear
chU
seM
etho
dolo
gica
l
Can
onic
oet
al.(
2015
)To
cons
ider
how
and
tow
hate
xten
titi
spo
ssib
leto
inte
rpre
taP
MS
asa
typi
cal
cont
rolm
echa
nism
Cas
est
udy
Des
ign,
Use
Rol
es,T
asks
,Ana
lyti
cal
Pekk
ola
and
Ukk
o(2
016)
Toex
amin
eho
wP
MS
can
faci
lita
teco
llab
orat
ive
wor
kdy
nam
ics
inne
twor
kC
ase
stud
yD
esig
nM
etho
dolo
gica
l,A
naly
tica
l
Car
lsso
n-W
alle
tal.
(201
6)To
expl
ore
the
role
ofP
MS
inth
eco
-exi
sten
ceof
diff
eren
tins
titu
tion
allo
gics
ina
foot
ball
orga
niza
tion
Cas
est
udy
Use
Rol
e
Lai
hone
nan
dPe
kkol
a(2
016)
Toex
amin
eho
wut
iliz
atio
nof
perf
orm
ance
mea
sure
sin
flue
nces
supp
lych
ains
Act
ion
rese
arch
Use
Pro
cedu
ral,
Rol
e,Ta
sk
Zha
nget
al.(
2016
a)To
prop
oses
am
odel
inw
hich
the
prod
ucti
onan
dus
eof
perf
orm
ance
info
rmat
ion
are
sepa
rate
dS
urve
yU
seM
etho
dolo
gica
l,A
naly
tica
l
Pell
inen
etal
.(20
16)
Toid
enti
fyte
nsio
nsan
dco
ntra
dict
ions
insi
tuat
ions
whe
reth
ebe
nefi
tsof
both
vert
ical
and
hori
zont
alin
tegr
atio
nas
stra
tegi
esfo
rpr
ophe
tC
ase
stud
yU
seM
etho
dolo
gica
l,A
naly
tica
l
Zha
nget
al.(
2016
b)To
expl
ore
the
role
ofIC
Tin
tegr
atio
nbe
twee
nin
ter
and
intr
aro
les
Sur
vey
Use
Tech
nolo
gica
l,M
etho
dolo
gica
lK
ang
etal
.(20
16)
Topr
opos
ea
mul
ti-l
evel
stru
ctur
efo
rid
enti
fica
tion
and
anal
ysis
ofK
PIs
and
thei
rin
trin
sic
rela
tion
ship
sin
prod
ucti
onsy
stem
sD
ocum
enta
naly
sis
Use
,Des
ign
Task
,Ana
lyti
cal
Nud
urup
atie
tal.
(201
6)To
expl
ore
prac
titi
oner
thou
ghts
onho
wP
MM
shou
ldbe
impr
oved
indi
gita
lec
onom
ies
Cas
est
udy
Des
ign,
Use
Rol
e,A
naly
tica
l,M
etho
dolo
gica
l
Forc
ada
etal
.(20
17)
Pre
sent
sth
ero
leof
com
mun
icat
ion
key
perf
orm
ance
indi
cato
rsS
urve
yU
seR
ole,
Task
Bee
ran
dM
iche
li(2
017)
Toex
amin
eth
ein
flue
nces
ofP
MS
amon
gno
t-fo
r-pr
ofit(
NF
P)
orga
niza
tion
sC
ase
stud
yU
seR
ole,
Task
Pavl
ovet
al.(
2017
)To
inve
stig
ate
how
PM
Sin
tera
ctw
ith
hum
anre
sour
ceS
urve
yD
esig
nR
ole,
Task
,Pro
cedu
ral
Sm
ith
and
Bit
itci
(201
7)To
pres
entt
heim
port
ance
ofth
ein
terp
lay
betw
een
PM
S,e
mpl
oyee
enga
gem
ent
and
over
allp
erfo
rman
ceA
ctio
nre
sear
chU
seR
ole,
Task
,Pro
cedu
ral,
Ana
lyti
cal,
Met
hodo
logi
cal,
Tech
nolo
gica
lG
ovin
dan
etal
.(20
17)
Toin
vest
igat
ew
hich
indi
cato
rsne
edpr
iori
tyD
ocum
enta
naly
sis
Des
ign,
Use
Ana
lyti
cal
Shi
nan
dK
onra
d(2
017)
Toin
vest
igat
eif
high
-per
form
ance
wor
kan
dor
gani
zati
onal
.Per
form
ance
indi
cate
sca
usal
ity
Sur
vey
Use
Task
,Rol
es,P
roce
dura
l
Hw
ang
etal
.(20
17)
Toin
vest
igat
eth
ew
orka
bili
tyof
inte
rnet
ofth
ings
for
capt
urin
gre
alti
me
data
for
PM
SC
ase
stud
yD
esig
nTe
chno
logi
cal,
Ana
lyti
cal,
Met
hodo
logi
cal
Dew
etti
nck
and
Vro
onen
(201
7)To
the
cons
eque
nces
ofli
nem
anag
ers’
deci
sion
son
PM
SS
urve
yU
se,I
mpl
emen
tati
onR
ole,
Task
,Pro
cedu
ral
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
744 S. Okwir et al.
Figure 4. Sources of complexity in PMM dimensions (Adapted from Smith and Bititci 2017) [Colour figure can be viewed atwileyonlinelibrary.com]
independently reviewed each of the manuscripts withthe four system properties described in Table 1 in thelight of social and technical controls.
Key findings
The findings reveal that most of the studies identifiedeither social or technical controls as a foundationfor an effective PMS. For example, after analysing76 empirical studies, Franco-Santos et al. (2012)classified unique contemporary PM features intothree categories: people’s behaviour, organizationalcapabilities and performance consequences. Theyhighlighted the necessity of understanding how or-ganizations respond to dynamic situations. Similarly,Braz et al. (2011) studied the process of reviewingand updating a company’s existing PMS to reflectchanges in the environment. They identified that PMSusers, assessing of performance indicators and the es-tablishment of goals are key. Many other researchersindicated that supporting frameworks, informationsystems, data-collection methods, analysis and visualcommunication were technical controls for effectivePMSs. However, none of these studies had a criticalview of the complexity theoretical lens. Therefore,this study aims to understand the emergence ofcomplexities while implementing and using PMSs inorganizations. Smith and Bititci’s (2017) theoreticalframework identified social and technical controls toassess the maturity of PMM in organizations using
an organizational theory perspective. This studyidentified six sources of complexity that are closelyassociated with the social and technical dimensionsof Smith and Bititci’s (2017) framework and usedit as a reference model. As demonstrated in Figure4, six sources of complexity were abstracted fromthis study: role, task and procedural types of com-plexity were associated with social dimension, whilemethodological, analytical and technological types ofcomplexity were associated with technical dimension.
Social complexity
The SLR analysis abstracted the first theme, socialcomplexity, which is associated with leadership, hier-archical structures, empowerment, trust, motivationat work, employee behaviour, training, skills, trustand culture. These characteristics closely match theontological property of complexity. With the evolu-tion of a PMS, some of these social controls improveand become more fine-tuned and mature, thus con-necting to the genetic property of complexity. Mostof the studies that explored social controls identifiedthem as foundational for PMSs at every process stage.For example, Elzinga et al. (2009) set out to identifyfactors that influence the use stage of PMSs and arguethat behavioural factors in different roles within anorganization are the most important factors at the usestage of a PMS. In another study (Shin and Konrad2017) on human resource management, social con-trols were also identified as high-performance work
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 745
practices (HPWS), high involvement practices thathelp organizations to achieve a better performance.
Role complexity. According to command and con-trol theory, organizations are established on the basisof hierarchical relationships with a clear flow of au-thority, to allow their entities to achieve economicperformance and goals (Rizzo et al. 1970). Whena PMS with a democratic culture is adapted to anorganization, it can lead to conflict, incompatibilityand ambiguity regarding existing roles, thus creat-ing role complexity. From a theoretical perspective,if organizations do not control behavioural factorssuch as empowerment, autonomy, trust, communi-cation and training, role complexity could develop.From the analysis, role complexity can be further ex-plained as the relationship/conflict between differentteam roles and individual roles and how they shouldbe appropriately allocated. Toor and Ogunlana (2010)highlighted the complexity of social controls in theconstruction industry. They revealed the differing per-ceptions on a construction project, leading to conflict-ing requirements regarding what to measure. Further-more, in another study, Beer and Micheli (2017) gavea contribution on how performance management in-fluences the attention and actions at an individualmicro level, which also reveals social controls at anindividual level.
Task complexity. Organizations establish the knowl-edge, skills and resources needed for an entity todemonstrate satisfactory performance (Wood 1986).Therefore, a task in an organization must be clearlydefined; without this definition, there is often sub-stantial ambiguity and conflict, leading to task com-plexity. From our sample, this is also echoed by Adler(2011), who argued that clarity regarding a task andtask efficiency are particularly essential to organiza-tions that are trying to be price minimizers or costleaders. However, the introduction of new measuresor new ways of measuring and managing often bringambiguity and conflict with existing knowledge, skillsand resources, thus leading to task complexity. Taskcomplexity emerges not only from a lack of clarity ontasks, but also from inter-relationships and the con-flicts between them; Therefore, it is necessary to ex-plore mature social controls associated with task com-plexity. Adler (2011) studied the design of PMSs forconfrontation strategies and found that programmesthat focus on developing empowered, multi-skilledteams of self-governing and well-coordinated indi-viduals lead to task efficiency.
Procedural complexity. While many authors (Bititci2015, pp.170–187; Smith and Bititci 2017) acknowl-edge the need for social controls for an effective PMS,there is little consensus regarding how to strike theproper balance between the level of control over in-dividuals and the level of freedom or autonomy givento them. While organizations have a number of rulesto control the way they operate, introduction of aPMS will initiate turbulence and will create substan-tial ambiguity and conflict. Procedural complexityemerges when there is a lack of information regard-ing priorities or the course of action when there is achange in the routine. This complexity occurs whenthe new processes and their impact are not explainedto employees. With mature social controls such asautonomy, empowerment, communication and multi-skills (the benefits of PMS), organizations can self-organize and adapt to this change while also creatingnew rules, regulations and policies for operating aneffective PMS, without which procedural complexitywould develop. Social controls were also mainly iden-tified in the study by Dewettinck and Vroonen (2017):drawing from signalling theory, theory of plannedbehaviour and social exchange theory, they investi-gated social controls such as attitudes, employee con-cern, job satisfaction and engagement. It was foundthat the antecedents and the outcomes of front-linemanagement’s enactment of performance manage-ment activities were moderated by managers’ span ofcontrol.
Technical complexity
The analysis from the SLR abstracted the secondtheme, technical complexity, which is associated withframeworks or models, information systems, data col-lection methods, analysis and visual communication.These characteristics are closely associated with thefunctional and teleological properties of complexity.The teleological property represents an objective ofthe system, while the functional property representsthe methods of achieving that objective. With theevolution of PMSs, some of these technical controlsimprove and mature, thus mapping onto the geneticproperty of complexity. Technical controls are for-mal and more explicit than social controls. Perfor-mance management systems have a specific goals,such as improving performance, learning or controland, Therefore, they use specific methodologies, tech-nology and analyses to reach these goals (Bititci 2015,pp.170–187).
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
746 S. Okwir et al.
Methodological complexity. When PMSs are imple-mented in organizations, there is often conflict be-tween their objectives and associated measures. Inmost of these cases, organizations are less informedregarding which method should be used in choos-ing measures (often using subjective measures) andwhich philosophy to use in selecting the type of mea-sures (often using objective measures). These sit-uations usually lead to methodological complexity.Methodological complexity often relates to a con-flict between an approach to choosing measures (suchas quantitative vs. qualitative) and the difficulty en-countered in selecting the type of key performanceindicators (KPIs), their calculations and the numberof KPIs to be used. Santos et al. (2002) investigatedhow system dynamics and multicriteria decision anal-ysis can enhance the effectiveness of selecting mea-sures during the design and implementation of a PMSwhile taking input from all stakeholders. From theirfindings, they recommended that the use of such ap-proaches provides a means for addressing method-ological complexity in organizations.
Analytical complexity. When implementing PMSsin organizations, there is often difficulty in under-standing each measure, its influence on other mea-sures, and its impact on the organization’s strategy asa whole. These relationships are often underminedin organizations, leading to analytical complexity(Suwingnjo et al. 2000) Analytical complexity is of-ten associated with a lack of understanding regard-ing cause and effect relationships between measures,data presentation, sophisticated charts and graphs,and visual screens. Therefore, organizations shoulduse more scientific and objective methods such asmathematical and simulation modelling, systems dy-namics, cause and effect analysis, correlations andregression for designing and using specific measures.These analytical approaches restrict subjectivity, am-biguity and conflict between measures (Santos et al.2002). While studying the use of PMSs in the pub-lic sector, Spekle and Verbeeten (2014) identifiedtechnical controls, such as contractibility, clarity ofgoals and undistorted performance metrics, that cre-ate performance effectiveness. In another study byHwang et al. (2017), the workability of the internetof things (IoT) for capturing real-time data was inves-tigated: technical controls such as simulation results,planned and actual production data, timestamp dataacquired by IoT were identified leading to analyticalcomplexity. In an attempt to improve supply chainperformance in another study (Govindan et al. 2017),
technical controls such as collaborations and informa-tion exchanges, customer relationship managementcompetitiveness, organizational-level innovation andsupply chain reliability were found to using fuzzyAHP method for analysis, which is purely technicalcontrol leading analytical complexity.
Technological complexity. The way in which we areoperating in digital economies is changing with theadvent of technological developments (Nudurupatiet al. 2016). While the way customers are engagingwith businesses is changing, the way in which or-ganizations are gathering data using advanced tech-nologies is also evolving. The implementation of aPMS in this context is no exception, and organiza-tions’ failure to use technology appropriately leads totechnological complexity. Technological complexityoften emerges when there is no effective coordina-tion and tighter integration between the selection ofand investment in technology such as IT systems andthe needs of the business. Turner et al. (2005) ex-amined the implementation of performance measuresand recommended that PMSs could enhance businessperformance if the implementation is well structured,resourced and focused on improving the capability oftechnical controls. In another study, technical controlssuch as measurement frameworks, information sys-tems and reporting mechanisms using balance score-cards took greater precedence (Lohman et al. 2004).
Discussion on PMC
From a complexity theory perspective, a PMSis considered a complex adaptive social system(because it consists of a large number of ele-ments whose interactions create new behaviours thatcannot be predicted by a complete analysis of the in-dividual elements (Sahin et al. 2013). A PMS hasits own life cycle, and it interacts with a numberof other elements exemplifying the levels of com-plexity at different times. Therefore, coercively con-trolling such an adaptive system can have a negativeimpact on the process of measurement and manage-ment (Bititci 2015, pp.170–187). However, the SLRreveals abundant literature arguing the need for dif-ferent factors, controls and best practices to measureand manage performance. Overall, the analysis fromthe SLR suggests that complexity exists in a PMSthrough its life cycle, but varies based on how manycombinations of organizational controls and elementsinteract with it. At one extreme, an overly controlled
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 747
process can leave individuals unable to cope witha complex situation owing to rigidity. On the otherextreme, if individuals are not given enough guid-ance (or too much autonomy) to measure and manageperformance, the process may be adversely affectedowing to variability.
Adler (2011) found that both technical and socialcontrols were necessary to address the implemen-tation of a confrontational strategy. The technicalcontrols included strategies for cost leadership,differentiation and confrontation. The social controlsincluded training, development, multiskilling and col-lective responsibility. In studying the advantages anddisadvantages of using PMM tools and techniques, deWaal and Kourtit (2013) identified two main reasonsfor their use, which focused on controls and strategy.They recommended that management needs to takethe explicit advantage of PMM when designing aPMS, and they stressed the advantages of socialcontrols during and after implementation. However,in another study, Sharma and Bhagwat (2006) ex-plored PMS implementation at both small and largefirms. They presented a framework that suggestsinformation systems as a foundation for growth and away to drive strategy. The study focused on technicalcontrol factors and neglected to address the influenceof social factors. While Smith and Bititci (2017)broadly divided most of these controls under theclassification of technical and social controls, strikinga balance between the two is important to mitigatecomplexity. Performance measurement complexity isa type of complexity that emerges internally, breedswithin and through the short- and long-term routinesof managing PMS and sustains unnoticed. Therefore,it is necessary to understand systematically how PMCemerges to identify its root causes before prescribinga solution. As demonstrated in Figure 4, the goal of anorganization is to mitigate PMC by striking the rightmixture and balance of technical and social controlsto move into the top right quadrant of the theoreticalframework.
Although it is widely accepted that complexity isa result of several external influences and factors thataffect the organization and the PMS, this study hasdemonstrated that complexity is generated throughthe interaction of PMSs with several internal elementsduring its evolution. Harkness and Bourne (2015)identified internal factors such as ambiguity, a lackof control, unpredictability and information issues,which interact with PMSs as a precursor to com-plexity. Furthermore, research into PMM has beenlimited to the interplay between what is measured
(Micheli and Mari 2014) and how it is controlled(Canonico et al. 2015; Mol and Beeres 2005) andthe process of updating, analysing, and acting on per-formance data (Bititci 2015, pp. 170–187; Bourneet al. 2000; McAdam and Bailie 2002). Hence under-standing complexity from its ontological, functional,teleological and genetic properties will identify newdimensions in moderating complexity in PMM. Fromthis perspective, this study identified six sources ofcomplexity using a theoretical framework from com-plexity theory in the context of PMM. As discussedearlier, complexity may emerge in varying forms andin varying intensities at different stages of a PMS,depending on the context.
While studying the state-of-the-art of PM, Greiling(2005) suggests that, in order to keep the motivationand participation rate high with a PMS, it is necessaryto concentrate on a few relevant indicators and thatmeasurement for measurement’s sake is not a goalin itself. Therefore, motivation acts as an importantsocial control, particularly during the use stage of aPMS. A lack of motivation in employees can lead torole complexity, as they do not perform their jobs ef-fectively. This role complexity is also exemplified byconflicting interests, different ambitions and differentmeasures of success. Similarly, several studies haverecognized that a lack of standardization generatestask and procedural complexity (Jaaskelainen and Sil-lanpaa 2013; Nudurupati and Bititci 2005; Ukko et al.2007). While the three complexities discussed in theprevious section, i.e. role, task and procedural, ap-pear to be technical at first, the maturity of socialcontrols such as motivation, leadership, training andskills, empowerment, self-regulation, trust and hier-archy plays a significant role in addressing the on-tological property of PMSs, thus moderating socialcomplexities. Ukko et al. (2007) found that it wasthe role of leadership skills and the manager’s com-mitment as social controls that were crucial in align-ing the manager’s and employees’ perspectives on thestrategy and improving performance.
When measures are selected with lack of clarityor are poorly and ambiguously defined at the de-sign stage, they may not reflect the strategy of theorganization at the use stage, resulting in a com-plex situation (Courty and Marschke 2003). Kelmanand Friedman (2009) argue that resources are usuallydeployed for actions related to improving measureswhile neglecting other aspects of the business, whicheventually manifests in a vague complex situation.Nudurupati et al. (2016) argue that a lack of strategicintent in resource allocation for a PMS may create a
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
748 S. Okwir et al.
technological mismatch with the business needs andresult in a complex situation. The teleological andfunctional properties associated with such PMSs maybe affected, leading to methodological, analytical ortechnological complexities. Mature technical controlssuch as a balanced set of metrics, a high degree ofdefined casual relationships, strategic measures de-ployed to lower levels, targets and incentives linkedto strategic objectives, measures and their trends re-ported in an accessible manner, and regular and fre-quent performance reviews play a significant role inmoderating technical complexities.
From complexity theory, the genetic property of aPMS represents how it evolves over time. From theSLR and its analysis, the genetic property is moreassociated with exploring the maturity and validity ofKPIs over time. This property also incorporates theinteraction of PMSs with other elements such as con-tinuous improvement, changing programmes and ex-ternal fluctuations that influence the organization. Forexample, Angelis and Jordahl (2015) studied the ma-turity of various performance management practicesat the use stage, which maintains the evolution of thesystem. As a PMS matures, it provides standard man-agement practices; however, as Kennerley and Neely(2002) state, ‘measurement systems should be dy-namic; they have to be modified as times change’. Inthis study, the authors examined the factors that affectthe evolution of PMSs over time, including both socialand technical controls, which are both internal and ex-ternal to the system. The interaction of the continuousimprovement framework for PMSs was studied at thedesign stage (Hudson et al. 2001). Similarly, Garengoet al. (2005) presented a model of how PMSs evolveover time. This study also explored all three processstages and found that ‘the models developed in thelast 20 years are more horizontal, process-orientedand focus on stakeholder needs’. This demonstratesand validates the genetic property of PMSs, and un-derstanding this property is vital to their success. Sim-ilarly, the business trends in the external context alsoinfluence organizational strategy, leading to the needfor change in the PMS over time (Bititci et al. 2012;Melnyk et al. 2014). Therefore, the moderation ofcomplexity that stems from a PMS is not a linear taskand needs to be controlled over time with feedbackloops.
From the findings, social controls employed inPMS also appear to create a versatile conditionfor adapting technical controls. Managing PMSs islargely socially constructed rather than technicallyconstructed and operated (Johnston 2005, p. 514).
Social controls are assumed to be purely concernedwith the human aspects, such as group dynamics,relationships, commitment, leadership, authenticity,behaviours, values and trust. Therefore, as more or-ganizations become democratic, the focus of controlshould shift from command and control to somethingmore participative (Bititci et al. 2012; Bourne et al.2000; Mol and Beeres 2005). Then, through demo-cratic freedom, a new way of thinking can be encour-aged among the employees.
This study attempted to shed light on the reasonswhy PMSs become successful in some organizationswhile failing in others, even if they consist of similarcomponents. Therefore, changes to these organiza-tional controls and their interaction with PMSs andother elements will give rise to new and unpredictablebehaviours. This signals that the system is exhibitingcomplex properties that are unique and grow organ-ically in that particular context, which ultimately re-quires unique monitoring and control. The lack ofpredictability of the usefulness of organizational con-trols also may exhibit unintended complexity.
Conclusion
The key motivation for this study was the PPM liter-ature’s attribution of a majority of complexity issuesto the external environment while neglecting inter-nal environmental perspectives. The purpose of thisstudy is to identify the PMS as a system by under-standing how its associated properties emerge froma complexity theory perspective. The study focuseson understanding the complexity of a PMS over itslife cycle in order to shed light on moderating its be-haviour. From the background literature, this studypresented the PMM literature, which is dominated byorganizational controls and best practices regardingwhat to measure, how to control and how to managethe process of updating, analysing and acting on per-formance. The study also explored how social andtechnical controls amplify complexity at the threecore PMS process stages, and it presented new per-spectives of complexity theory, i.e. the ontological,teleological, functional and genetic characteristics ofa PMS.
Through a review of 76 PMM papers, the studydescribed how complexity reproduces in a PMS overits life cycle. More specifically, the study showed thatPMSs consist of a large number of interconnected andinterdependent elements, which evolve over time andadapt to changes in the internal environment, making
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 749
them a complex social adaptive system. Changes inthe internal environment such as plurality in practicesmay negatively impact the PMS process, the numberof controls that interact at each process stage, and thenature of these interactions. This makes it difficultto predict what will be important to measure in thefuture and how to measure and manage it. There isgreater unpredictability and ambiguity in the system,often lacking the relevant information for decision-making. The results seem to emulate the issues offit, as shown by Melnyk et al. (2014), in which theorganization’s measures are not synchronized withits strategy and its environment, leading to a complexsituation. Therefore, organizations require more toolsand techniques to survive complex situations.
The four characteristics identified in the study wereused in examining the PMM literature and resulted insix forms of complexity, namely, role, task, proce-dural, methodological, analytical and technical com-plexities, which is a significant contribution to the-ory. A major implication of understanding PMC isto recast how organizations should systematically re-spond to the plurality of best practices by examiningthe unique context in which a PMS is operating. Thestudy findings contribute to the current discussionaddressing why PMSs typically lag and are not re-sponsive and resilient in emerging contexts (Bititciet al. 2012; Melnyk et al. 2014; Nudurupati et al.2016). While it is useful to understand and exploreall organizational controls in moderating PMC, theorganizational controls may have varying impacts onorganizations even when they consist of similar com-ponents and are operating in the same industry. Us-ing the insights from this paper, organizations shouldbuild the capabilities to choose the appropriate or-ganizational controls, depending on the context andshould adapt to the changes associated with PMSs.
This study sets new directions for PMM researchersand practitioners, as it identifies sources of complex-ity that will be complementary in prescribing socialand technical controls in organizations for mitigatingcomplexity. This study opens several avenues for fu-ture research on PMS complexity as well as on PMMin general. First, the definition of PMC, as outlined inthis paper, challenges the PMM literature by definingthe foundations of complexity in performance man-agement, which also calls for a unique definition ofcomplexity in PM. Future research should also seek toexplore PMC in its two streams, i.e. social complexityand technical complexity. Second, this study investi-gated the mix between social and technical controlsand how such a mix should be treated. As a contin-
uation of this research, further studies may explorewhich specific factors and interactions between theminduce complexity and to what extent. Additionally,more empirical research may unlock critical eventsfrom the external environment that are contingent onthe practice of measurement and tend to induce com-plexity in the internal environment.
While interactions between the social and techni-cal dimensions are difficult to manage, their divisionsand interactions are quite crucial to facilitating re-sponsiveness and dynamism in organizations. Havingexplored PMC, the study results suggest that com-plexity theory is an essential element for studyingPMM complexity. In this paper, PMC was exploredusing the system perspective in vast complexity the-ory. For future studies, more perspectives could beexplored: for example, using the hierarchical modelby Simon (1996) that could break down PMM systeminto subsystems. It also suggests that, by understand-ing how complexity emerges, managers may rethinkhow they can better organize their use of controls tomanage a PMS over its life cycle. Based on the SLR,the study has demonstrated how complexity emergesand is amplified in organizations, and it has attemptedto identify ways of moderating complexity. Just likeany research, SLR has its own limitations of whichsome papers will be missed out, despite selecting acomprehensive list of search strings. In order to mit-igate this limitation, we followed citation tracking toensure that important missed papers were recovered.The SLR in this study is completely reliant on previ-ous published papers that may have different purpose.Therefore, we focused more on the objective of eachpaper to see its relevance to this study. Similarly, arti-cles were filtered based on the appropriateness of theselection criteria. This study has attempted to miti-gate this limitation by following thorough researchprotocols (Moustaghfir 2008; Tranfield et al. 2003).We urge more empirical studies to show how orga-nizational controls and their interaction can actuallymoderate complexity over time. While many authors,such as Johnston (2005) and Bititci et al. (2012) ac-knowledge the need for social controls at each pro-cess stage, there is little consensus regarding how tostrike the proper balance between the level of con-trol and the level of freedom or autonomy given toindividuals.
ReferencesAdler, R.W. (2011). Performance management and organi-
zational strategy: how to design systems that meet the
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
750 S. Okwir et al.
needs of confrontation strategy firms. British AccountingReview, 43, pp. 251–263.
Aguinis, H., Joo, H. and Gottfredson, R.K. (2011). Why wehate performance management – and why we should loveit. Business Horizons, 54, pp. 503–508.
Anderson, P.W. (1999). Complexity theory and organizationscience. Organization Science, 10, pp. 216–232.
Angelis, J. and Jordahl, H. (2015). Merciful yet effective el-derly care performance management practices. MeasuringBusiness Excellence, 19, pp. 61–69.
Ben Hadj Salem Mhamdia, A. (2013). Performance mea-surement practices in software ecosystem. InternationalJournal of Productivity and Performance Management,62, pp. 514–533.
Beer, H.A. and Micheli, P. (2017). How performance mea-surement influences stakeholders in not-for-profit organi-zations. International Journal of Operations & ProductionManagement, 37, pp. 1164–1184.
Bititci, U.S. (2015). Managing Business Performance. Hobo-ken, NJ: John Wiley.
Bititci, U.S., Garengo, P., Dorfler, V. and Nudurupati, S.(2012). Performance measurement: challenges for tomor-row. International Journal of Management Reviews, 14,pp. 305–327.
Bititci, U., Mendibil, K., Nudurupati, S.S. and Garengo,P. (2006). Dynamics of Performance Measurement andOrganizational Culture. International Journal of Op-erations & Production Management, 26. Available at:https://doi.org/10.1108/01443570610710579
Bititci, U.S., Mendibil, K., Martinez, V. and Albores, P.(2005). Measuring and managing performance in extendedenterprises. International Journal of Operations & Pro-duction Management, 25, pp. 333–353.
Bititci, U.S., Turner, Ut. and Begemann, C. (2000). Dynam-ics of performance measurement systems. InternationalJournal of Operations & Production Management, 20, pp.692–704.
Bonatto, F., Resende, L.M.M. De, Betim, L.M., Pereira, R. DaS. and Agner, T. von (2015). Performance management inhorizontal business networks: a systematic review. IFAC-PapersOnLine, 48, pp. 1827–1833.
Boulding, K.E. (1956). General systems theory – the skeletonof science. Management Science, 2, pp. 197–208.
Bourne, M., Mills, J., Wilcox, M., Neely, A. and Platts,K. (2000). Designing, implementing and updating per-formance measurement systems. International Journalof Operations & Production Management, 20, pp. 754–771.
Bourne, M., Neely, A., Mills, J. and Platts, K. (2003). Imple-menting performance measurement systems: a literaturereview. International Journal Business Performance Man-agement, 5, pp. 1–24.
Bourne, M., Neely, A., Platts, K. and Mills, J. (2002). Thesuccess and failure of performance measurement initia-tives. International Journal of Operations & ProductionManagement, 22, pp. 1288–1310.
Braz, R.G.F., Scavarda, L.F. and Martins, R.A. (2011). Re-viewing and improving performance measurement sys-tems: an action research. International Journal of Produc-tion Economics, 133, pp. 751–760.
Briscoe, G., Keranen, K. and Parry, G. (2012). Understand-ing complex service systems through different lenses: anoverview. European Management Journal, 30, pp. 418–426.
Broadbent, J. and Laughlin, R. (2009). Performance manage-ment systems: a conceptual model. Management Account-ing Research, 20, pp. 283–295.
Brown, S.L. and Eisenhardt, K.M. (1997). The art of contin-uous change: linking complexity theory and time-pacedevolution in relentlessly shifting organizations. Adminis-trative Science Quarterly, 42, pp. 1–34.
Buckingham, M. and Goodall, A. (2015). Reinventing per-formance management. Harvard Business Review, 93,pp. 40–50.
Burnes, B. (2005). Complexity theories and organizationalchange. International Journal of Management Reviews,7(2), pp. 73–90.
Caniato, F., Luzzini, D. and Ronchi, S. (2014). Purchasingperformance management systems: An empirical investi-gation. Production Planning and Control, 25, pp. 616–635.
Canonico, P., De Nito, E., Esposito, V., Martinez, M., Mercu-rio, L. and Pezzillo Iacono, M. (2015). The boundaries ofa performance management system between learning andcontrol. Measuring Business Excellence, 19, pp. 7–21.
Carlsson-Wall, M., Kraus, K. and Messner, M. (2016). Per-formance measurement systems and the enactment of dif-ferent institutional logics: insights from a football organi-zation. Management Accounting Research, 32, pp. 45–61.
Chalmeta, R., Palomero, S. and Matilla, M. (2012). Method-ology to develop a performance measurement system insmall and medium-sized enterprises, International Jour-nal of Computer Integrated Manufacturing, 5, pp. 716–740.
Choong, K.K. (2013). Understanding the features of perfor-mance measurement system: a literature review. Measur-ing Business Excellence, 17, pp. 102–121.
Choong, K.K. (2014). Has this large number of performancemeasurement publications contributed to its better under-standing? A systematic review for research and applica-tions. International Journal of Production Research, 52,pp. 4174–4197.
Cook, D.J., Mulrow, C. and Haynes, R. (1997). Systematicreviews: synthesis of best evidence for clinical decisions.Annals of Internal Medicine, 126, pp. 376–380.
Cook, D.J., Sackett, D.I. and Spitzer, W.O. (1995). Method-ological guidelines for systematic reviews of randomizedcontrol trials in health care from the Potsdam Consultationon Meta-analysis. Journal of Clinical Epidemiology, 48,pp. 167–171.
Courty, P. and Marschke, G. (2003). Dynamics ofperformance-measurement systems. Oxford Review ofEconomic Policy, 19, pp. 268–284.
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 751
Davis, D.A., Thomson, M.A., Oxman, A.D. and Haynes, R.B.(1995). Changing physician performance: a systematic re-view of the effect of continuing medical education strate-gies. JAMA, 274, pp. 700–705.
de Waal, A. and Kourtit, K. (2013). Performance mea-surement and management in practice: advantages, dis-advantages and reasons for use. International Jour-nal of Productivity and Performance Management, 62,pp. 446–473.
Deng, F., Smyth, H. and Anvuur, A. (2012). A critical reviewof PMS in construction: towards a research agenda. InSmith, S.D. (ed.), Proceedings of the 28th ARCOM Confer-ence, Edinburgh, September 2012. Reading: Associationof Researchers in Construction Management, pp. 807–816.Available at: http://discovery.ucl.ac.uk/1358228/.
Dewettinck, K. and Vroonen, W. (2017). Antecedents andconsequences of performance management enactment byfront-line managers. Evidence from Belgium. Interna-tional Journal of Human Resource Management, 28,pp. 2473–2502.
Edmonds, B. (1999). Syntactic measures of complexity. Doc-toral thesis, University of Manchester, Manchester, UK.
Elg, M., Palmberg Broryd, K. and Kollberg, B. (2013). Perfor-mance measurement to drive improvements in healthcarepractice. International Journal of Operations & Produc-tion Management, 33, pp. 1623–1651.
Elzinga, T., Albronda, B. and Kluijtmans, F. (2009). Behav-ioral factors influencing performance management sys-tems’ use. International Journal of Productivity and Per-formance Management, 58, pp. 508–522.
Ferreira, A. and Otley, D. (2009). The design and use ofperformance management systems: an extended frame-work for analysis. Management Accounting Research, 20,pp. 263–282.
Folan, P. and Browne, J. (2005). A review of performancemeasurement: towards performance management. Com-puters in Industry, 56, pp. 663–680.
Forcada, N., Serrat, C., Rodrıguez, S. and Bortolini, R.(2017). Communication key performance indicators forselecting construction project bidders. Journal of Man-agement in Engineering, 33, pp. 4017033.
Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V.,Mason, S., Marr, B., Gray, D. and Neely, A. (2007). To-wards a definition of a business performance measurementsystem. International Journal of Operations & ProductionManagement, 27, pp. 784–801.
Franco-Santos, M., Lucianetti, L. and Bourne, M. (2012).Contemporary performance measurement systems: a re-view of their consequences and a framework for research.Management Accounting Research, 23, pp. 79–119.
Garengo P. and Sharma M.K. (2014). Performance measure-ment system contingency factors: a cross analysis of Italianand Indian SMEs, Production Planning and Control, 25,pp. 220–240.
Garengo, P., Biazzo, S. and Bititci, U.S. (2005). Performancemeasurement systems in SMEs: a review for a research
agenda. International Journal of Management Reviews, 7,pp. 25–47.
Galbraith, J.R. (1982). Designing the innovating organiza-tion. Organizational Dynamics, 10, pp. 5–25.
Geraldi, J., Maylor, H. and Williams, T. (2011). Now, let’smake it really complex (complicated). International Jour-nal of Operations & Production Management, 31, pp. 966–990.
Ginieis, M., Sanchez-Rebull, M.V. and Campa-Planas, F.(2012). The academic journal literature on air trans-port: analysis using systematic literature review method-ology. Journal of Air Transport Management, 19, pp. 31–35.
Greiling, D. (2005). Performance measurement in the publicsector: the German experience. International Journal ofProductivity and Performance Management, 54, pp. 551–567.
Grobman, G.M. (2005). Complexity theory: a new wayto look at organizational change. Public AdministrationQuarterly, 29, pp. 351–384.
Govindan, K., Mangla, S.K. and Luthra, S. (2017). Priori-tising indicators in improving supply chain performanceusing fuzzy AHP: insights from the case example of fourIndian manufacturing companies. Production Planning &Control, 28, pp. 552–573.
Hansen, A. (2010). Nonfinancial performance measures, ex-ternalities and target setting: a comparative case studyof resolutions through planning, Management AccountingResearch, 21, pp. 17–39.
Haponava, T. and Al Jibouri, S. (2009). Identifying keyperformance indicators for use in control of pre projectstage process in construction. International Journal ofProductivity and Performance Management, 58, pp. 160–173.
Harkness, M. and Bourne, M. (2015). Is complexity a barrierto the practice of performance measurement? In Proceed-ings of the Performance Management Association Confer-ence, Auckland.
Holland, J.H. (1975). Adaptation in Natural and ArtificialSystems. Cambridge, MA: MIT Press.
Hudson, M., Lean, J. and Smart, P. (2001). Improving con-trol through effective performance measurement in SMEs.Production, 12, pp. 804–813.
Hwang, G., Lee, J., Park, J. and Chang, T-.W. (2017). De-veloping performance measurement system for Internetof Things and smart factory environment. InternationalJournal of Production Research, 55, pp. 2590–2602.
Jaaskelainen, A. and Laihonen, H. (2013). Overcomingthe specific performance measurement challenges ofknowledge-intensive organizations. International Jour-nal of Productivity and Performance Management, 62,pp. 350–363.
Jaaskelainen, A. and Sillanpaa, V. (2013). Overcoming chal-lenges in the implementation of performance measure-ment. International Journal of Public Sector Management,26, pp. 440–454.
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
752 S. Okwir et al.
Jain, S., Triantis, K.P. and Liu, S. (2011). Manufacturing per-formance measurement and target setting: a data envelop-ment analysis approach. European Journal of OperationalResearch, 214, pp. 616–626.
Johnston, J. (2005). Performance measurement uncertaintyon the Grand Canal: ethical and productivity conflicts be-tween social and economic agency? International Jour-nal of Productivity and Performance Management, 54,pp. 595–612.
Jones, O. and Gatrell, C. (2014). Editorial: the future of writ-ing and reviewing for IJMR. International Journal of Man-agement Reviews, 16, pp. 249–264.
Kandjani, H. and Bernus, P. (2012). Towards a cybernetic the-ory and reference model of self-designing complex collab-orative networks. Collaborative Networks in the Internetof Services, 380, pp. 485–493.
Kang, N., Zhao, C., Li, J. and Horst, J.A. (2016). A hierarchi-cal structure of key performance indicators for operationmanagement and continuous improvement in productionsystems. International Journal of Production Research,54, pp. 6333–6350.
Kauppi, K. (2013). Extending the use of institutional the-ory in operations and supply chain management research:review and research suggestions. International Journalof Operations & Production Management, 33, pp. 1318–1345.
Kelman, S. and Friedman, J.N. (2009). Performance improve-ment and performance dysfunction: an empirical exami-nation of distortionary impacts of the emergency roomwait-time target in the English National Health Service.Journal of Public Administration Research and Theory,19, pp. 917–946.
Kennerley, M. and Neely, A. (2002). A framework of the fac-tors affecting the evolution of performance measurementsystems. International Journal of Operations & Produc-tion Management, 22, pp. 1222–1245.
Ladyman, J., Lambert, J. and Wiesner, K. (2013). What isa complex system? European Journal for Philosophy ofScience, 3, pp. 33–67.
Laihonen, H., Jaaskelainen, H. and Pekkola, S. (2014). Mea-suring performance of a service system from organizationsto customer-perceived performance. Measuring BusinessExcellence, 18, pp. 73–86.
Laihonen, H. and Pekkola, S. (2016). Impacts of using a per-formance measurement system in supply chain manage-ment: a case study. International Journal of ProductionResearch, 54, pp. 5607–5617.
Lewis, M.A. (2003). Analysing organisational competence:implications for the management of operations. Interna-tional Journal of Operations & Production Management,23, pp. 731–756.
Lin, Y.H., Chen, C-.C., Tsai, C.F.M. and Tseng, M-.L.(2014). Balanced scorecard performance evaluationin a closed-loop hierarchical model under uncer-tainty. Applied Soft Computing Journal, 24, pp. 1022–1032.
Lohman, C., Fortuin, L. and Wouters, M. (2004). Designing aperformance measurement system: a case study. EuropeanJournal of Operational Research, 156, pp. 267–286.
Lopez-Illescas, C., Moya-Anegon, F. and Moed, H.F. (2008).Coverage and citation impact of oncological journals inthe Web of Science and Scopus. Journal of Informetrics,2, pp. 304–316.
Mason-Jones, R. and Towill, D.R. (2000). Designing, imple-menting and updating performance measurement system.International Journal of Operations and Production Man-agement, 20, pp. 754–771.
McAdam, R. and Bailie, B. (2002). Business performancemeasures and alignment impact on strategy. InternationalJournal of Operations & Production Management, 22,pp. 972–996.
McGovern, P. (2014). Contradictions at work: a critical re-view. Sociology, 48, pp. 20–37.
Melnyk, S.A., Bititci, U.S., Platts, K., Tobias, J. and Ander-sen, B. (2014). Is performance measurement and manage-ment fit for the future? Management Accounting Research,25, pp. 173–186.
Micheli, P. and Kennerley, M. (2005). Performance measure-ment frameworks in public and non-profit sectors. Produc-tion Planning & Control, 16, pp. 125–134.
Micheli, P. and Mari, L. (2014). The theory and practiceof performance measurement. Management AccountingResearch, 25, pp. 147–156.
Miller, D. and Friesen, P.H. (1984). A longitudinal studyof the corporate life cycle. Management Science, 30,pp. 1161–1183.
Mol, N.P. and Beeres, R.J.M. (2005). Performance manage-ment in a setting of deficient output controls. InternationalJournal of Productivity and Performance Management, 54,pp. 533–550.
Morel, B. and Ramanujam, R. (1999). Through the look-ing glass of complexity: the dynamics of organizations asadaptive and evolving systems. Organization Science, 10,pp. 278–293.
Moustaghfir, K. (2008), The dynamics of knowledge assetsand their link with firm performance. Measuring BusinessExcellence, 12, pp. 10–24.
Neely, A. (2005). The evolution of performance measure-ment research. International Journal of Operations & Pro-duction Management, 25, pp. 1264–1277.
Neely, A. (1999). The performance measurement revolution:why now and what next? International Journal of Opera-tions & Production Management, 19, pp. 205–228.
Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M.,Bourne, M. and Kennerley, M. (2000). Performance mea-surement system design: developing and testing a process-based approach. International Journal of Operations &Production Management, 20, pp. 1119–1145.
Ngai, E.W.T. and Cheng, T.C.E. (2001). A knowledge-basedsystem for supporting performance measurement of AMTprojects: a research agenda. International Journal of Op-erations & Production Management, 21, pp. 223–233.
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
PMM Systems 753
Nudurupati, S.S. and Bititci, U.S. (2005). Implementationand impact of IT-supported performance measurementsystems. Production Planning & Control, 16, pp. 152–162.
Nudurupati, S.S., Bititci, U.S., Kumar, V. and Chan, F-T.S.(2011). State of the art literature review on performancemeasurement. Computers and Industrial Engineering, 60,pp. 279–290.
Nudurupati, S.S., Tebboune, S. and Hardman, J. (2016). Con-temporary performance measurement and management(PMM) in digital economies. Production Planning andControl, 27, pp. 226–235.
Paranjape, B., Rossiter, M. and Pantano, V. (2006). Per-formance measurement systems: successes, failures andfuture – a review. Measuring Business Excellence, 10,pp. 4–14.
Pavlov, A. and Bourne, M. (2011). Explaining the Effectsof Performance Measurement on Performance: An Or-ganizational Routines Perspective. International Journalof Operations & Production Management, 31, pp. 101–122.
Pavlov, A., Mura, M., Franco-Santos, M. and Bourne, M.(2017). Modelling the impact of performance managementpractices on firm performance: interaction with humanresource management practices. Production Planning &Control, 28, pp. 431–443.
Pekkola, S. and Ukko, J. (2016). Designing a performancemeasurement system for collaborative network. Interna-tional Journal of Operations & Production Management,36, pp. 1410–1434
Pellinen, J., Teittinen, H. and Jarvenpaa, M. (2016). Per-formance measurement system in the situation of simul-taneous vertical and horizontal integration. InternationalJournal of Operations & Production Management, 36, pp.1182–1200.
Petticrew, M. and Robert, H. (2006). Why do we need sys-tematic reviews? Systematic Reviews in the Social Sci-ences: A Practical Guide, Chapter 1. Oxford: Blackwell,pp. 1–27.
Pettigrew, A.M. (1977). Strategy formulation as a politicalprocess. International Studies of Management & Organi-zation, 7, pp. 78–87.
Pettigrew, A.M. (2014). The Politics of OrganizationalDecision-Making. Abingdon: Routledge.
Pongatichat, P. and Johnston, R. (2008). Exploringstrategy-misaligned performance measurement, Interna-tional Journal of Productivity and Performance Manage-ment, 57, pp. 207–222.
Rahbek, Gjerdrum, Pedersen, E. and Sudzina, F. (2012).Which firms use measures? International Journal of Op-erations & Production Management, 32, pp. 4–27.
Ribeiro-Carpinetti, L.C., Cardoza Galdamez, E. and CecilioGerolamo, M. (2008). A measurement system for manag-ing performance of industrial clusters. International Jour-nal of Productivity and Performance Management, 57, pp.405–419.
Rizzo, J.R., House, R.J. and Lirtzman, S.I. (1970). Role con-flict and ambiguity in complex organizations. Administra-tive Science Quarterly, 15, pp. 150–163.
Roehrich, J. and Lewis, M. (2014). Procuring complex perfor-mance: implications for exchange governance complexity.International Journal of Operations & Production Man-agement, 34, pp. 221–241.
Sacristan-Dıaz, M., Alvarez-Gil, M.J. and Dominguez-Machuca, J.A. (2005). Performance measurement systems,competitive priorities, and advanced manufacturing tech-nology: some evidence from the aeronautical sector. In-ternational Journal of Operations & Production Manage-ment, 25, pp. 781–799
Sahin, E., Vidal, L.-A. and Benzarti, E. (2013). A frame-work to evaluate the complexity of home care services.Kybernetes, 42, pp. 569–592.
Santos, S.P., Belton, V. and Howick, S. (2002). Adding valueto performance measurement by using system dynamicsand multicriteria analysis. International Journal of Oper-ations & Production Management, 22, pp. 1246–1272.
Sharma, M.K. and Bhagwat, R. (2006). Performance mea-surements in the implementation of information systemsin small and medium-sized enterprises: a framework andempirical analysis. Measuring Business Excellence, 10,pp. 8–21.
Shin, D. and Konrad, A.M. (2017). Causality between high-performance work systems and organizational perfor-mance. Journal of Management, 43, pp. 973–997.
Sillanpaa, V. (2011). Performance measurement in welfareservices: a survey of Finnish organisations. MeasuringBusiness Excellence, 15, pp. 62–70.
Simon, H.A. (1996). The architecture of complexity: hier-archic systems. Sciences of the Artificial, 106, pp. 467–482.
Smith, M. and Bititci, U.S. (2017). Interplay between perfor-mance measurement and management, employee engage-ment and performance. International Journal of Opera-tions & Production Management, 37, pp. 1207–1228.
Spekle, R.F. and Verbeeten, F. (2014). The use of perfor-mance measurement systems in the public sector: effectson performance. Management Accounting Research, 25,pp. 131–146.
Stacey, R.D. (1995). The science of complexity: an alterna-tive perspective for strategic change processes. StrategicManagement Journal, 16, pp. 477–495.
Sullivan, T. (2011). Embracing complexity. Harvard Busi-ness Review, (14 sept 2017), pp. 89–93. Available at:http://opus.bath.ac.uk/26603/.
Suprapto, B., Wahab, H.A. and Wibowo, A.J. (2009). The Im-plementation of balance score card for performance mea-surement in small and medium enterprises: evidence fromMalaysian health care services. Asian Journal of Technol-ogy Management, 2(2), pp. 76–87.
Suwingnjo, P., Bitici, U. and Carrie, A.P. (2000). QuantitativeModels for Performance Measurement System. Interna-tional Journal of Production Economics, 64, pp. 231–241.
C© 2018 British Academy of Management and John Wiley & Sons Ltd.
754 S. Okwir et al.
Tasca, J.E., Ensslin, L., Ensslin, S.R. and Martins Alves, M.B.(2010). An approach for selecting a theoretical frameworkfor the evaluation of training programs. Journal of Euro-pean Industrial Training, 34, pp. 631–655.
Taticchi P., Balachandran K. and Tonelli F. (2012). Perfor-mance measurement and management systems: state ofthe art, guidelines for design and challenges. MeasuringBusiness Excellence, 16, pp. 41–54.
Taylor, A. and Taylor, M. (2014). Factors influencing ef-fective implementation of performance measurement sys-tems in small and medium-sized enterprises and largefirms: a perspective from Contingency Theory. Inter-national Journal of Production Research, 52, pp. 847–866.
Toor, S.-R. and Ogunlana, S.O. (2010). Beyond the ‘irontriangle’: stakeholder perception of key performance in-dicators (KPIs) for large-scale public sector developmentprojects. International Journal of Project Management,28, pp. 228–236.
Tranfield, D., Denyer, D. and Smart, P. (2003). Towards amethodology for developing evidence-informed manage-ment knowledge by means of systematic review. BritishJournal of Management, 14, pp. 207–222.
Tung, A., Baird, K. and Schoch, H.P. (2011). Factors influ-encing the effectiveness of performance measurement sys-tems. International Journal of Operations & ProductionManagement, 31, pp. 1287–1310.
Turner, T.J., Bititci, U.S. and Nudurupati, S.S. (2005), Im-plementation and impact of performance measures intwo SMEs in Central Scotland. Production Planning &Control, 16, pp. 135–151.
Turner, N., Swart, J. and Maylor, H. (2013). Mechanisms formanaging ambidexterity: a review and research agenda.International Journal of Management Reviews, 15,pp. 317–332.
Upadhaya, B., Munir, R. and Blount, Y. (2014). As-sociation between performance measurement systemsand organisational effectiveness. International Jour-nal of Operations and Production Management, 34,pp. 853–875.
Ukko, J., Tenhunen, J. and Rantanen, H. (2007). Perfor-mance measurement impacts on management and lead-ership: Perspectives of management and employees. Inter-national Journal of Production Economics, 110, pp. 39–51.
Valmohammadi, C. and Servati, A. (2011). Performancemeasurement system implementation using BalancedScorecard and statistical methods. International Journal ofProductivity and Performance Management, 60, pp. 493–511.
van Veen-Dirks, P. (2010). Different uses of performancemeasures: the evaluation versus reward of production man-agers. Accounting, Organizations and Society, 35, pp.141–164.
Vernadat F., Shah L., Etienne A. and Siadat A. (2013).VR-PMS: a new approach for performance measurementand management of industrial systems, International Jour-nal of Production Research, 51, pp. 7420–7438.
Vidal, L.-A. and Marle, F. (2008). Understanding projectcomplexity: implications on project management. Kyber-netes, 37, pp. 1094–1110.
von Bertalanffy, L. (1969). General System Theory: Foun-dations, Development, Applications. New York, NY:Braziller.
Wilkinson, I.F. (2006). The evolution of an evolutionary per-spective on B2B business. Journal of Business & Indus-trial Marketing, 21, pp. 458–465.
Wood, R.E. (1986). Task complexity: definition of the con-struct. Organizational Behavior and Human Decision Pro-cesses, 37, pp. 60–82.
Wijngaard, J., de Vries, J. and Nauta, A. (2006). Performersand performance: how to investigate the contribution of theoperational network to operational performance. Interna-tional Journal of Operations & Production Management,26, pp. 394–411.
Wolf, F.M., Shea, J.A. and Albanese, M.A. (2001). Towardsetting a research agenda for systematic reviews of evi-dence of the effects of medical education. Teaching andLearning in Medicine, 13, pp. 54–60.
Wouters, M. and Sportel, M. (2005). The role of existing mea-sures in developing and implementing performance mea-surement systems. International Journal of Operations &Production Management, 251, pp. 1062–1082.
Yilmaz, Y. and Bititci, U.S. (2006). Performance measure-ment in the value chain: manufacturing v. tourism. Inter-national Journal of Productivity and Performance Man-agement, 55, pp. 371–389.
Zellner, M.L. (2008). Embracing complexity and uncer-tainty: the potential of agent-based modeling for environ-mental planning and policy. Planning Theory & Practice,9, pp. 437–457.
Zhang, H., Van de Walle, S. and Zhuo, Y. (2016a). Does trustin the performance measurement organization influencehow public managers use performance information? Pub-lic Performance & Management Review, 40, pp. 409–430.
Zhang, X., Van Donk, D.P. and van der Vaart, T. (2016b).The different impact of inter-organizational and intra-organizational ICT on supply chain performance. Interna-tional Journal of Operations & Production Management,36, pp. 803–824.
C© 2018 British Academy of Management and John Wiley & Sons Ltd.