hrm and performance neural.network greek 2007

8/10/2019 HRM and Performance Neural.network Greek 2007

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Interfaces with Other DisciplinesHuman resource management and performance:

A neural network analysis

Eleni T. Stavrou * , Christakis Charalambous, Stelios SpiliotisUniversity of Cyprus, Department of Public and Business Administration, 75 Kallipoleos Avenue, Nicosia, Cyprus

Received 24 May 2005; accepted 1 June 2006Available online 4 August 2006

Abstract

This study utilizes an innovative research methodology (Kohonens Self-Organizing Maps) to explore a subject rela-tively understudied in Europe. It focuses on the connection between human resource management as a source of compet-itive advantage and perceived organizational performance in the European Unions private and public sectors. Whilepractices in these two sectors did not differ signicantly, three diverse but overlapping HRM models did emerge, eachof which involved a different set of EU member states. Training & Development practices were strongly related to perfor-mance in all three models and Communication practices in two. These results show the usefulness of an innovative tech-nique when applied to research so far conducted through traditional methodologies, and brings to the surface questionsabout the universal applicability of the widely accepted relationship between superior HRM and superior business

performance. 2006 Elsevier B.V. All rights reserved.

Keywords: Kohonen self-organizing map (SOM); Articial neural network (ANN); Human resource management; Performance

1. Introduction

The importance of human resource management(HRM) as a competitive tool and the relationshipbetween human resource management and organi-

zational performance has been the subject of sys-tematic research (e.g. see Truss, 2001; Huselid,1995; Delery and Doty, 1996; Becker and Gerhart,1996). Researchers have identied and examinedcertain HRM practices as crucial to developingorganizational competitive advantage (e.g. see

Flanagan and Deshpande, 1996; Pfeffer and Veiga,1999; Ferris et al., 1999). Researchers also foundconnections between HRM and various measuresof organizational performance (e.g. see Truss,2001; Huselid, 1995; Delery and Doty, 1996; Becker

and Gerhart, 1996 ).However, the majority of those studies have beenconducted in the US. The role of HRM elsewhere,especially within Europe, is understudied (see Guestet al., 2003; Cunha et al., 2002; Wood, 1999 ).Brewster (1995) notes the need for identifyingEuropean models of HRM, pointing out that theEuropean business environment differs in manyrespects from the US. European organizationsare less autonomous than US ones due to the

0377-2217/$ - see front matter 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.ejor.2006.06.006

* Corresponding author. Tel.: +357 22892480; fax: +35722892460.

E-mail address: [email protected] (E.T. Stavrou).

European Journal of Operational Research 181 (2007) 453467www.elsevier.com/locate/ejor
mailto:[email protected]:[email protected]


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restrictions they face at national and organizationallevels from cultural environments, legislation, andpatterns of ownership; and at HRM levels from bar-gaining patterns, consultative arrangements and

trade union involvement ( Brewster, 1995; Guest,1990).In addition, previous studies on this subject have

focused on the private sector to the neglect of thepublic sector. However, Duffey (1988) argues thathuman capital can be the greatest source of compet-itive advantage in a nations effort to serve the publicand to be strategically effective within and beyond itsborders. This may be especially true within the EUcontext where member states public sector practicesare inuenced directly by EU Directives as well as byone another. As Wood (1999) argues, the issue of environmental t and contextual variables shouldbe addressed directly in empirical studies.

Finally, existing literature on the subject has beencriticized as to the methods used to explore the linkbetween human resources and performance, sug-gesting that the quality of the research base support-ing this link is relatively weak (Wood, 1999; Gerhartet al., 2000a,b; Wright et al., 2001 ). Much of thisresearch base is conducted through the use of con-ventional statistical methods, which Guest (2001)has described as a traditional research paradigm inneed of much closer scrutiny.

Given the above shortcomings in currentresearch, we attempt to use visualization techniquesbased on Kohonens (1995) self organizing maps(SOMs), to examine the relationship between

HRM as a competitive tool and organizational per-formance in both private and public sector organi-zations in the EU. We hypothesize that superiorperformance will be associated with use of keyhuman resource management practices, and we setout to explore which sets of practices work best inEU private and public sectors. Fig. 1 serves as asummary of the study.

The paper is organized as follows. Firstly, weaddress the various parts of Fig. 1, examining theconnection between human resource managementas a source of competitive advantage and organiza-tional performance and the geographic and organi-zational context in which this linkage may takeplace. Subsequently, we describe the methodologyof the study and continue with a presentation of its results and implications. We conclude with sug-gestions for future research.

2. HRM and performance within context

Cascio (1992) suggests that todays organizationsmust gain competitive advantage through the effec-tive utilization of their human resources. Competi-

HUMAN RESOURCE

MANAGEMENT FUNCTIONS

Human Resource PlanningStaffing

Training & DevelopmentCompensation

Communication & Participation

ORGANIZATION

CONTEXT

Private SectorWider Public Sector

GEOGRAPHIC

CONTEXT

ORGANIZATIONAL

PERFORMANCE

Service QualityProductivityProfitability

Superior-performerorganisations

Lower-performerorganisations

Key HRPractices linkedto Competitive

Advantage

Fig. 1. Human resource management as a competitive tool: a framework.

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tive advantage may be dened as the asymmetry ordifferential in any rm attribute or factor that allowsone rm to better serve the customers than othersand hence create better customer value and achievesuperior performance ( Ma, 1999, p. 2). This deni-

tion points out that competitive advantage andsuperior performance are two connected but differ-ent ideas: successful human resource managementcan contribute to superior performance as a sourceof competitive advantage by making organizationsmore effective.

In recent years, those interested in connectinghuman resource management with strategic man-agement often look to typologies of competitiveadvantage for the missing link ( Boxall, 1996). Basedon these typologies, researchers have proposed anumber of human resource practices that could leadto competitive advantage, although none hasproved better than the others in relation to organi-zational competitiveness. As Ferris et al. (1999)note, extant research is subject to many of the samelimitations and offers little consensus with regard toprecisely which HRM practices should be included.In addition, questions are raised in the literature asto whether these practices are universal or contextspecic, and if the latter, which practices are appro-priate for achieving t in which contexts ( Ferriset al., 1999; Boxall and Steeneveld, 1999).

Regardless of the typology used, HR practicesconnected to competitive advantage fall into a num-ber of main categories. We use Stavrou andBrewsters (2005) key practices groupings of HumanResource Management Functions: Planning, Staff-ing, Training & Development, Compensation &Benets, and Communication & Participation.Looking at categories or groups rather than individ-ual HR practices as sources of competitive advan-tage may be more requisite since according toMa (1999:18), the more compound a competitiveadvantage, the more likely it is to have direct impli-cations in the causal chain of performance analysis.In fact, researchers have positively linked HR prac-tices to organizational performance ( Truss, 2001;Pfeffer and Veiga, 1999; Huselid, 1995 ). Specically,human resource management has been linked toincreased productivity ( Huselid, 1995; Ichniowskiet al., 1997; Fox et al., 1999), good customer service(Fox et al., 1999 ), improved efficiency (Becker andGerhart, 1996 ), increased rm value, greater prot-ability (Delery and Doty, 1996; Fox et al., 1999 ) andoverall organizational survival ( Welbourne andAndrews, 1996). Anderson et al. (1997) suggest

organizations must pursue superiority in both cus-tomer satisfaction and productivity, while Savery(1998) and Singh et al. (2000) note that increasingglobal economy pressures compel organizations toplace ever-greater emphasis on productivity

improvement and Zeithaml et al. (1996) emphasizethe importance of service quality on organizationalperformance: all these are sectors where good HRpractices can help give organizations the edge.

The above discussion points to Service Quality,Productivity and Protability as commonly recog-nized measures of organizational performance.Previous research has tended to use them either asseparate measures of organizational performance(Baltes et al., 1999; Huselid, 1995; MacDuffie,1995; Perry-Smith and Blum, 2000 ), or as bench-marks combined into a composite performancemeasure (Sherman et al., 2003 ). Delaney and Hus-elid (1996) used two composite perceptual measuresof organizational performance, which included ser-vice quality and protability, by asking informantsto assess it relative to that of industry competitors.In disciplines such as management accounting,benchmarking measures calculate performance bycombining the three, and have provided superiorresults to traditional one-dimensional performanceassessment methods ( Sherman et al., 2003 ). Forthe purposes of this study, we have considered as

superior-performers those organizations that per-form at the top 10% in all three measures of perfor-mance, and labeled those that do not perform at thetop 10% in all three measures of performance aslower-performers.

2.1. Geographic context

The relevant literature on the subject mainlyinvolves the US context ( Truss, 2001), and the con-tribution of human resource management towardsorganizational effectiveness in Europe has not yetbeen the subject of systematic research ( Sparrowand Hiltrop, 1997 ). However, several researchershave developed a number of mainly descriptiveinternational patterns trying to understand that rolearound the world. In her study of secondary dataanalysis, Filella (1991) found three different patternsof human resource management within the Euro-pean Union. Her Latin group comprised Portugal,Spain, southern France, Corsica, Sardinia and Italy,the UK, the Netherlands, Germany and Switzerlandmade up the central European group while the Nor-dic group included Denmark, Norway and Sweden.

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This separation is similar to that of Hofstedes(2001) more general cultural classication, whichsuggested the presence of Latin, Anglo-Saxon andNorth European cultures.

In a different descriptive study of secondary data

analysis, Sparrow et al. (1994) developeda worldwidepattern about the importance of human resourcemanagement practices for organizations. Theyformedveclusters of countries, which were analyzedacross fteen areas of human resource management.The rst cluster included countries of the Anglo-Saxon business culture (the UK, Australia, Canadaand the USA), while the second cluster consistedsolely of France and the third of Korea. The fourthcluster included Brazil, Mexico and Argentina, andthe fth consisted only of Japan. Moss-Kantersworldwide survey of HRM-relevant issues (quotedin Sparrow andHiltrop, 1997 ) reaches similar conclu-sions and identies similar groupings, with the mainexception of clustering Germany in with France.

These different models and geographic clusters of human resource management practice may reectthe different foci of the researchers, as well as a vari-ety of cross-border political, economic, social andcultural considerations which create convergenceor divergence among various business structurespractices. Sparrow and Hiltrop (1997) claim that,while a clear model of European human resource

management does not exist yet, many large andsome medium-sized European successful organiza-tions already display some distinct pan-Europeanforms of human resource management principles.

2.2. Organizational context

Besides the geographical context, Brewster et al.(1993) report that the relative strength of the privateand public sectors across Europe represents animportant context. Countries like Italy have partic-ularly large public sectors that are institutionallyseparate from private sectors, leading to a socialfocus in public sector personnel management, whilein some other European countries the separationmay not be so distinct ( Filella, 1991). In either situ-ation, the traditional human resource managementrole of record keeping and dealing with functionalpersonnel issues in a public organization is beingquestioned due to the changes that both privateand public organizations have been facing(McHugh et al., 1999; Klingner and Lynn, 1997 ).

As a result, researchers and administrators havedebated the appropriateness of transferring effective

managerial processes from the private to the publicsector, based on the question of whether the twosectors are fundamentally dissimilar or not. But acritical evaluation of 34 empirical studies on thesubject found little evidence of fundamental differ-

ence, and suggested that public sector managerscan derive useful lessons from private sector man-agement provided they understand the determinantsof private sector performance clearly ( Boyne, 2002).Along similar lines, Braddon and Foster (1996) sug-gest a strategy through which private sector man-agement processes are used towards a moreeffective and efficient public sector, a transitionwhich can be facilitated through the appropriatehuman resource management functions and theircontribution towards organizational effectiveness(Brown et al., 2000).

3. The study

As noted above, the majority of extant researchrelating to the role of HRM as a competitive toolis rooted in the US private sector; nevertheless, itprovides a foundation for exploring the Europeanprivate and public sector context as well. Our gen-eral aim is to use SOMs to help understand the rela-tionship between human resource management andorganizational performance among organizations in

the private and wider public sectors of the EU. Wepropose the conceptual framework shown in Fig. 1to explore the following research questions:

1. Do superior-performer organizations form a spe-cic pattern that distinguishes them from lower-performer ones on the basis of the HR functionsidentied in Fig. 1? If so:(a) Is this pattern the same throughout the

European Union or does it differ?(b) Is this pattern similar or different in the pri-

vate and the wider public sectors?2. Which HR functions contribute most to the dif-

ferentiation between superior and lower perform-ing organizations?

3.1. Measures

The independent variables Key HR Practices ,grouped into ve Human Resource ManagementFunctions , are operationalized into 80 questionsmeasured on a binomial scale as to whether the spe-cic practice is used or not (yes or no).



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The Organization Context variable is also bino-mial (private versus wider public sector). Speci-cally, we separate organizations into thosefor-prot private sector businesses and those orga-nizations and services that belong, all or in part to

the government.The Geographic Context involves the countriescomprising the European Union prior to May2004. Luxemburg was not included as it did nothave sufficient organizations, and organizations inthe former East and West Germany were studiedseparately as we wanted to take into account thesocio-economic differences of the two former states.

Finally, the dependent variables Organizational Performance measures are service quality, produc-tivity and protability. These three measuresinvolve the perceptions of study participants as tothe performance of their organization in compari-son with that of competitors leading to their placingas superior (top 10% in all three measures) organi-zations and lower-performers (not top 10% in allthree measures). The use of a perceptual perfor-mance measure, even though not optimal, is accept-able and consistent with prior research (e.g. Smithand Barclay, 1997; Perry-Smith and Blum, 2000;Jap, 2001). Pearce et al. (1987) found that seniormanagers perceptions of performance were consis-tent with nancial and other measures. The choice

of 10% as the minimum for excellence relates thisstudy to other disciplines which use a similar deni-tion within-100% evaluation scales ( Hax and Wilde,1999; Rank and Hirschl, 2001; Kerr and Beaujot,2002; Blum and Clegg, 2003).

3.2. Sample

The present study draws on data generated by theCranet Network questionnaire (see Brewster et al.,2004). The samples in each country have beenselected from lists provided by the national federa-tions, such as chambers of commerce or national sta-tistical services. Researchers ensure that all sectors of the economy are represented so that samples in theCranet database are demonstrative of the popula-tion of organizations in each country. The overallresponse rate averaged 17%. For the present study,the nal sample for the EU member states was4759, of which 3559 were private sector businessesand 1200 were public sector (governmental orsemi-governmental) organizations. The unit of anal-ysis was the organization and the respondent was thehighest-ranking officer from the corporate HR team.

Although data for this study come from a singlesource, the Cranet research teams data collectionmethods, include certain steps to minimize the effectsof single-method bias, as suggested by Podsakoff et al. (2003). Respondents were guaranteed anonym-

ity to increase the accuracy of the responses to thequestionnaire. Criterion measures were placed in dif-ferent sections of the questionnaire from predictorvariables which were posed at the end of the ques-tionnaire after a set of demographic variables.Finally, criterion variables were posed in a set of dif-ferent response formats from predictor variables.

In addition to these safeguards, Huselid andBecker (2000) explain that the validity of single-source measures depends on the size of organiza-tions in the sample, the expertise of the sourceresponding to the questions and the clarity of itemscomprising the survey. The Cranet survey meetsthese requirements: the average number of employ-ees in the organizations of our study was 1546; therespondents were members of the corporate HRteam; and the international Cranet team took greatcare in the methods and procedures used to makethe questionnaire specic and clear, leaving littleroom for ambiguity.

3.3. Procedure

This study explores the HR-performance rela-tionship within the EU through Kohonens Self Organizing Map (SOM). While new to the eld of human resource management, this methodologicalapproach has been used successfully across differentdisciplines and lines of research ( Moreno et al.,2006; Deichmann et al., 2003; Veiga et al., 2000;Smith, 1999; Mazanec, 1995 ).

Self-Organizing Maps (SOMs) belong to thebroad category of unsupervised neural networksand the basic idea behind them is competitive learn-ing (Kohonen, 1995 ). This procedure is superior toother methods. Thus it provides a visual representa-tion of the relationships that exist in the originaldata, while avoiding creating articial clusters(Deichmann et al., 2003; Mazanec, 1995 ).

SOMs have additional advantages which makethem appropriate in the current study. First, theyare able to outperform the traditional data reductionand clustering techniques, in both speed and qualityof solution ( Smith, 1999). Second, they have thecapacity to operate on very large samples and needno a priori assumptions about the distribution of the sample (Deboeck and Kohonen, 1998 ). Using



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SOMs helps overcome structuring task problemsassociated with nding the appropriate underlyingdistribution and the functional form of the underly-ing data. (Such problems are often encountered, forexample, when using cluster analysis.) Finally, SOMs

are useful in visually examining the relationshipbetween input data and identifying important pat-terns and clusters. In the current research, it wasthe most appropriate tool to examine the relationshipbetween superior- and lower-performing organiza-tions, public and private ones, and the HR-perfor-mance relationship in different EU countries.

The basic SOM consists of M neurons located ona low- (usually two-) dimensional grid. The two-dimensional map grid of neurons (neuron space)can be visualized to reect spatial properties of theoriginal data ( Kohonen, 1995 ). The SOM algorithmis iterative. Each neuron i has a d -dimensional(d represents the number of variables) prototypevector m i = [m i 1 m i 2 mid ] which represents a sam-ple unit in the original data set. Prototype vectors of the SOM are trained in such a way that they repre-sent the original data set in a substantially smallersize and they can form a map where e ach prototypevector is a unit (neuron) of that map. 1

At each such training step, one sample unit xfrom the original data set is chosen randomly and asimilarity measure is calculated between it and all

the prototype vectors of the map. The simplestway is to nd the neuron whose prototype vectoris closest to the sample unit in terms of Euclideandistance. This neuron is called the Best MatchingUnit (BMU) for that sample unit. Next, the SOMis trained by updating the prototype vectors, mov-ing the prototype vectors of the BMU and its topo-logical neighbors closer to the sample unit in theoriginal data set. The update rule for the prototypevector of neuron i ism i t 1 m i t a t h bi t x m i t

where t denotes discrete time ( t = 0,1,2, . . .), a(t)is the learning rate (a suitable monotonicallydecreasing function of t, 0 < a(t) < 1), and hbi (t) isa neighborhood Kernel in the two-dimensionalmap grid of neurons centered on the BMU. In par-ticular, a(t) = a(0) * (1 t/T ) and h bi t e d

2bi =2 r t

2

(Gaussian neighborhood function). T is the numberof training steps, a(0) is the initial learning rate, r (t)

is the neighborhood radius at time t, and d bi is thedistance between neuron b(BMU) and neuron i onthe map grid.

During training, the SOM behaves like a net thatfolds onto the cloud formed by training data, tak-

ing up its exact shape. Because of neighborhoodrelationships, prototype vectors of neighboring neu-rons are pulled to the same direction, and thus pro-totype vectors of neighboring neurons resembleeach other. The most efficient tools in the visualiza-tion of the cluster structure of SOM are distancematrices, the most widely used of which is theU -matrix. The U -matrix holds the Euclidean dis-tance in the original data set between the prototypevectors of a neuron with its neighbors in the neuronspace, as well as the average Euclidean distance inthe original data set of each map neuron to itsneighbors ( Ultsch and Siemon, 1990 ). [For a dem-onstration of the methodology followed, pleaserefer to the Appendix .]

4. Results

First we explored the data to uncover clusteringpatterns among EU organizations by training anSOM on the whole sample of the 4759 organiza-tions. 2 The SOM consisted of 360 neurons on a 24by 15 map grid, with hexagonal lattice and

Gaussian neighborhood function. The resultingU -matrix visualization, which is created on thewhole sample, is shown in Fig. 2, and acts as thefundamental graphic display for identifying clustersof organizations based only on the way they use thesets of HR practices. High values are represented bydarker shades and low values by lighter ones. Highvalues on the U -matrix indicate cluster borders anduniform areas of low values indicate clusters them-selves. From this gure, we identied four regions labeled 1, 2, 3 and 4 which represent differentcombinations of intensity of the ve HR functionsused by EU organizations.

Given the results in Fig. 2, we visualized the posi-tion of superior and lower-performer organization son the map for each EU member state separately. 3

1 SOMs and other visual display maps were implemented usingSOM-toolbox software, which can be implemented in the Matlabprogram.

2 The whole data sample was used for the purpose of trainingKohonens SOM. However, only top-performers and lowerperformers (as dened in Section 3.1) were used for subsequentanalysis on the maps and other visualization.

3 These visualizations were too numerous to be depicted in thepresent study. However they are available on request from thelead author.

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This allowed us to visualize the relationship of keyHR practice use to superior/lower performance onthe map for each EU member state separately, aswell as their relationships with organizationalgroupings shown in Fig. 2. Fig. 3ac are summa-ries which represent the emerging common geo-

graphical clusters of this relationship. From thesevisualizations, superior-performers among EUmember states formed three clusters (Clusters A, Band C, see Fig. 3) which represent similar combina-tions of use of the ve HR functions. These encom-pass parts of regions 2, 3 and 4 in Fig. 2. Region 3 of Fig. 2 is covered by all models and includes themajority of superior-performer organizations, whileregion 1 includes the fewest superior-performerorganizations.

Cluster A, located in the bottom-left of the map(Fig. 3a), includes superior-performers in Swedenand Finland; Cluster B, in the bottom half of themap (Fig. 3b) includes superior-performers in theUK and the former W. Germany; while Cluster C,in the bottom-right of the map ( Fig. 3c) includessuperior-performers in Greece, Portugal, Spain,Italy, France, Ireland and Belgium. Denmark, Aus-tria, The Netherlands and the former East Germany(GDR) did not appear in any clusters.

Next, we proceeded to explore whether theseclusters were similar for both private and public sec-tor EU organizations. These results are concen-trated and shown in Fig. 4, which shows the

position of organizations on the U -matrix by cluster(also demonstrating a graphical conrmation of thethree clusters), type (private versus public) and per-formance (superior-performers versus lower-per-formers). According to Fig. 4, with the exceptionof public sector superior-performer organizationsin Cluster B (Fig. 4g), superior-performer organiza-tions in the public and private sectors follow thebasic shape relating to their respective geographicCluster as depicted in Fig. 3. Furthermore, eventhough no specic pattern is found for public supe-

rior-performers of Cluster B, none is located in whatcorresponds to region 1 of Fig. 2.Contrasting the three clusters of public organiza-

tions in Fig. 4, Cluster C organizations provide thebest results both in terms of concentration of supe-rior-performers and differentiation between superiorand lower performers. More specically, 80% of superior-performer organizations are located inCluster C whereas only 52% of lower performersare located in the same area. The next best resultsare obtained from Cluster A organizations (65%superior-performers and 53% of lower-performers),while Cluster B locates 60% of superior-performersand 50% of lower-performers.

Comparing the three clusters of private organiza-tions of Fig. 4, organizations in Cluster B providethe clearest differentiation between superior andlower-performer organizations, with 76% of supe-rior-performers compared to 52% of lower-perform-ers. Cluster C located 84% of superior-performersand 72% of lower-performers, while Cluster Alocated 71% of superior-performers and 60% of lower-performers. The country specic analysisrevealed that the largest difference between superior

Fig. 2. The whole-sample SOM and the U -matrix visualization.

Fig. 3. Three different HR Clusters among EU superior-per-former organizations. The bottom of the map, which correspondsto region 3 of Fig. 2, is covered by all models and includes themajority of superior-performer organizations, while the top of themap, which corresponds to region 1 of Fig. 2, includes the fewestsuperior-performer organizations.



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and lower-performer organizations for a given coun-try corresponds to the former W. Germany. Fig. 4(1)illustrates the specic visualization of the W. Ger-man organizations position on the U -matrix, with(a) showing superior-performing private organiza-tions, and (b) showing private lower-performers.

Fig. 4(1)(a) shows that 74% of superior-per-former organizations in the former W. Germanyare located in Cluster B (i.e. at the bottom of theFig. 2 map, mainly in region 3), but only 34% of lower-performers are located in the same area. Themajority of lower-performers are located in region1 of Fig. 2, where only a very small percentage of superior-performers are located in that area. Theseresults lead us to conclude that organizations of Cluster B in the former W. Germany that followthe HR practices used in this study as competitivetools, have 74% probability of being superior-per-formers as against 34% of being lower-performers.

Overall, the concentration of private and publicsuperior-performer organizations in their respective

clusters is high, especially for private organizationsof Clusters B and C and for public organizationsof Cluster C. The results for the public organiza-tions compared to the private ones were less pro-nounced, both in terms of concentration of superior-performers in their corresponding clusterand of differentiation between superior and lower-performers. The fact that lower-performers werefound in all three clusters, reminds us that HR prac-tices are by no means the only factors that denesuperior/inferior organizational performance.

The above visualizations lead us to the followingconclusions regarding our rst research question:(a) three different HRM models of competitiveadvantage exist which correspond to the three spec-ied clusters of EU countries; (b) HRM models aresimilar in the private and public sectors. However,the probability of becoming superior organizationfollowing one of the three different HRM modelsdiffers. Those organizations classied in Cluster B(UK and W. Germany) appear to have a greater

Fig. 4. Visualization of the position of organizations by cluster, type and performance. Organizations are represented in the gures asdots. The size of the dots is analogous to the number of organizations located in a specic area of the map. (a), (c), (e), (g), (i) and (k) showthe position of top-performer private and public organizations; (b), (d), (f), (h), (j) and (l) show the position of lower-performerorganizations.



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chance (compared to organizations in the other twoclusters) of becoming superior performers by fol-lowing their clusters HRM model. Also private sec-tor organizations appear to have a greater chancethan public sector bodies of becoming superior per-formers by following their clusters HRM model.

The clustering pattern of SOM and the three clus-ters created, differentiating superior from lower-per-former organizations, are strongly affected by certainHR functions. Therefore, to answer the second

research question, we explored which of the ve cat-egories of HR functions contribute most to the crea-tion of the cluster structure of the SOMs (see Fig. 5).

According to this gure, Training & Develop-ment and Communication & Participation are themost important functions among superior EU orga-nizations, while Planning is less important. Whenthe above gure is examined in light of the threeclusters, it becomes clear that Training & Develop-ment is a very important function in contributingto clusters B and C and the only signicant functionfor Cluster C. The Communication & Participationfunction is most important for Cluster A and of sec-ondary importance to Cluster B. Planning, thoughless signicant, seems to contribute most to ClustersB and A.

5. Discussion, implications and conclusions

The contribution of this study is twofold: itshows the utility of Kohonens SOM to HRMresearch and it advances the extant knowledge andresearch regarding the HR-performance relation-ship. First, we have demonstrated how Kohonen

maps can aid in understanding the overall patternof human resource practice in the EU and creatingnew possibilities of exploration within the eld of human resource management. The concept of SOM is one of the most elegant examples of unsu-pervised learning, where an articial neural networkattempts to extract stable features or prototypes

Fig. 5. (a) The Fig. 2 U -matrix visualization using all the

prototype vectors. (b)(f) visualize the U -matrix using prototypesvectors calculations of the components corresponding to indi-vidual key HR functions. The contribution of a variable or a setof variables to the SOM cluster structure is found by comparing(a) U -matrix and the U -matrices of (b)(e).

Fig. 4(1). Visualization of West German private sector organi-zations on the U -matrix (a) the position of superior-performerprivate organizations on the U -matrix; (b) the position of lower-performer private organizations on the U -matrix.



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from a database without an outside teacher(Kohonen, 1995 ). This procedure is superior toother methods, not in only reducing multi-dimen-sional data through clustering, but also projectingthem non-linearly in a two-dimensional map

(Vesanto, 1999 ). Furthermore, it detects clustersexisting in the original data while avoiding creatingarticial ones, thus providing a true representationof the original datas characteristics. SOMs maybe viewed as a combination of principal compo-nents and cluster analyses with both proceduresinuencing each other in the algorithm ( Deichmannet al., 2003). More specically, SOMs provide anintuitively useful method of visualizing a datasetthat is otherwise too amorphous and complex toconceptualize.

Perhaps the most outstanding feature of thismethod for this study is the nature of clustering of the specic countries on the map, clearly reectingspatial patterns in the vectors of non-spatial vari-ables. Following the work of Kaski et al. (1998),no geographical data were entered into the model,but remarkably, the countries organized themselvesin a manner that mimicked their relative culturaland geographical locations, with geographical neigh-bors and culturally similar pairs being closely locatedin the U -matrix, revealing very similar HR practices.

Through this analysis we were also able to gain

insights also on the positions of individual countriesin clusters that did not match our geographic expec-tations. Thus, geographically, Ireland should fall ina cluster with the UK, but a closer examination of Irish culture reveals closer relations with Cluster Crather than Cluster B ( Hofstede, 2001; Ignjatovicand Sveltic, 2003). Similarly Denmark wouldappear to fall culturally and geographically intoCluster A, yet organizations in Denmark appearedto form no clustering. Finally, while superior W.German organizations fall into the same clusterwith those of the UK, they differentiate themselvesas a model to follow within the EU in utilizingHR as a competitive tool. First they have a muchhigher percentage of superior organizations usingthese key HR practices as functions of competitiveadvantage, and second, these superior organizationsare better differentiated from the lower-performers.

Second, in relation to furthering HR knowledge,this study shows that geographic context doesappear to make a difference to the HR-performancerelationship, but that organizational context doesnot: at the organizational level, these results clarifythat EU public sector organizations can utilize HR

as a competitive tool in a manner not inherently dif-ferent from their private counterparts. In turn,human resource management techniques may beexported successfully from one sector to anotherwithin the context of superior performance. For

the EU, which depends heavily on national publicsectors and strives to become the most competitiveand dynamic knowledge-based economy in theworld, capable of sustainable economic growth withmore and better jobs and greater social cohesion,these results are very encouraging.

At the EU level, three clear yet overlapping clus-ters are formed on the basis of the HR-performancerelationship. Specically, the set of Training &Development is of primary importance among supe-rior organizations in the clusters. Overall, this is con-sistent with existing research suggesting that trainingand development add value to an organization bymaximizing productivity; enriching employee skills;and helping it confront external pressures moreeffectively (Peteraf, 1993). In addition, superiororganizations in Clusters A and B emphasize Com-munication & Participation . This does not hold true,however, for Cluster C, perhaps because in organiza-tions in these countries hierarchies may prevail overatter structures, and thus place less emphasis on thepotential contribution of communication as a com-petitive tool. As Pfeffer and Veiga (1999) note, shar-

ing information is a very important component of high performance work systems and consequentlyof achieving competitive advantage.

Finally, in contrast to existing research ndings,Planning appeared not to be a very strong consider-ation among superior EU organizations of any clus-ter. Maybe HRM does not yet hold a clearlystrategic role among EU organizations, leavingHR somewhat disconnected from organizationalstrategy and performance ( Truss, 2001 ). Or, maybethe key here is responsiveness to planning, as theeffects of planning on performance will be evident(and measurable) later than when it actually takesplace (Brewster et al., 2004). In addition to Plan-ning , Compensation & Benets and Staffing practicesappear to have weak relationships with superiorperformance in EU organizations. It is quite possi-ble that the former are too contingent on the socio-economic and regulatory structures in Europe tohave any signicant impact on performance, whilethe latter is contingent on the generally high unem-ployment in Europe ( Brewster et al., 2004).

Based on the above, further research should beconducted in order to investigate specic parts of



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the maps and develop greater understanding of thespecic differences among clusters and among coun-tries within clusters. Also, SOMs can be utilized toexamine how organizations have changed theirHR practices over the years and their effect in orga-

nizational performance. Finally, while SOM clustersof HR practices in the EU closely mimic their cul-tural and geographic proximity, some exceptionswere noted in this study. This raises the possibilitythat other types of HR-related distances such aspolitical, administrative and industrial effects may inuence country classications. Therefore,future research should include measures in additionto HR practices per se as explanatory variables inthe SOM algorithm.

Such research may facilitate methodologicaladvancements and conceptual as well as practicalbreakthroughs in the study of HRM. Beyond theresearch sphere, the hope must be that increasedunderstanding will lead to a better utilization of HRM as a tool for increasing competitiveness inthe private sector and service quality as well as pro-ductivity in the public sector, as well as improving

the working conditions of work-forces of all grades,both in the EU and around the globe.

Acknowledgements

This research was supported in part by a Univer-sity of Cyprus grant on HRM and Competitiveness.We would like to express our appreciation to ourcolleagues at the EU member countries part of Cra-net who gave us permission to use their data. Wewould also like to thank the two anonymous review-ers and Roman Slowinski the editor of the Euro-pean Journal of Operational Research as well asour copy editor Jonathon Morgan, for their invalu-able contributions towards improving the qualityand rigor of this manuscript.

Appendix. Technical demonstration of the SOMprocedure

We would like to demonstrate the SOM proce-dure with an example. In the example, the numberof sample units (organizations) is 300 and the num-

Fig. 6. The application of SOM algorithm and its visualizations on a sample of 300 organizations by the use of 25 neurons. (a) Theoriginal data set of 300 organizations, (b) two-dimensional map grid neuron space, and (c) the training procedure.



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ber of variables is two. This data set forms threenatural clusters (see Fig. 6a). For demonstrationpurposes, we chose a two-dimensional example,where the clusters in the original data are visiblewithout visualization though SOM. When the origi-

nal data set has more than two dimensions, espe-cially multi-dimensional data sets, it is impossibleto see the clusters without further analysis. Usingthe U -matrix, multi-dimensional information isreduced visually into two dimensions, making thedata easy to assess.

When visualizing these data through SOM, atwo-dimensional hexagonal map grid with 25 neu-

rons (M = 25) is created (see Fig. 6b). Each neuronon this map has a two-dimensional prototype vectorin the original data set. At each training step, onesample unit x from the original data set is chosenrandomly and its Best Matching Unit (BMU) is

found. Next, the BMU and its topological neigh-bors are moved closer to the sample unit in the ori-ginal data set through the procedure demonstratedin Fig. 6(c). The position of prototype vectors (blackdots) of the neurons after training the SOM isshown in Fig. 7(a). One can easily see that prototypevectors have been concentrated in the area of thethree clusters that existed in the original data.

Fig. 7. The output of the SOM algorithm and the clustering of data via the U -matrix. (a) The original data set with the prototype vectors(after training), (b) the two-dimensional map grid neuron space, and (c) the U -matrix, showing the clustering structure of the data set.



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In addition, and most importantly, neuronswhose prototype vectors belong to the same clusterin Fig. 7(a) form a cluster in the neuron space inFig. 7(b). A way of identifying the three clusters inthe neuron space (and hence in the original dataset) is by using the U -matrix. The interpretation of the U -matrix is done as follows: High values onthe U -matrix indicate cluster borders and uniformareas of low values indicate clusters themselves.High values are represented by darker shades andlow values by lighter ones. The U -matrix visualiza-tion is shown in Fig. 7(c). This map grid consistsof a 9 by 9 grid of hexagonal cells. The shade of these cells shows the distance as demonstrated bythe scale on the right side denoting the value of eachshade. In the specic example, the U -matrix showsthree clusters in the data. In the U -matrix, the shadeof the numbered hexagons represents the averagedistance between the prototype vector of the corre-sponding neuron and the prototype vectors of the

neighboring neurons. To illustrate, the shade of the hexagon denoted with the number 7 inFig. 7(c) represents the average distance betweenthe prototype vector of neuron 7 in Fig. 7(b) andthe prototype vectors of neighboring neurons,namely 6, 2, 8, 13, 12, and 11. Finally, the shadeof the hexagons that are not numbered representsthe distance between the prototype vectors of thetwo adjacent neurons on the U -matrix. For exam-ple, in Fig. 7(c), the dark shade of the hexagonbetween hexagons denoted with numbers 11 and16 shows that the distance of prototype vectorsbetween neurons 11 and 16 is large. In turn, 11and 16 belong to two different clusters.

An interesting question then is where on the mapa specic data sample is located. The simplestanswer is to nd the BMU of the data sample. FromFig. 7(a), we selected a sample unit from Cluster 1and named it Company A. As we can see fromFig. 7(a), Neuron 6 is the closest to Company A.

Fig. 8. The U -matrix and the contribution of a variable to the cluster structure of SOM (a) the U -matrix, showing the clustering structureof the data set, (b) the U -matrix by using in calculations variable 1, and (c) the U -matrix by using in calculations variable 2.



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Next, the position of Company A is visualized inFig. 7(c) and again is located in the area of cluster 1.

Finally, the contribution of a variable to the clus-ter structure of data is also very important. Toachieve this, the U -matrix of each variable should

be visualized and compared to the U

-matrix of allvariables together, as shown in Fig. 8. By comparingFig. 8(a) and (b), it can be concluded that variable 1contributes to the creation of the three clusters. Bycomparing Fig. 8(a) and (c), it can be concludedthat variable 2 contributes to the creation of cluster2.

References

Anderson, E.W., Fornell, C., Rust, R.T., 1997. Customersatisfaction, productivity, and protability: Differences

between goods and services. Marketing Science 16 (2), 129 145.Baltes, B.B., Briggs, E.T., Huff, W.J., Wright, A.J., Neuman,

A.G., 1999. Flexible and compressed workweek schedules: Ameta-analysis of their effects on work related criteria. Journalof Applied Psychology 84, 496514.

Becker, B., Gerhart, B., 1996. The impact of human resourcemanagement on organizational performance: Progress andprospects. Academy of Management Journal 39 (4), 779802.

Blum, E., Clegg, R., 2003. Percent plans: Admissions of failure.The Chronicle of Higher Education 49 (28), B10B11.

Boyne, G.A., 2002. Public and private management: Whats thedifference? Journal of Management Studies 39 (1), 97122.

Boxall, P., 1996. The strategic HRM debate and the resource-

based view of the rm. Human Resource ManagementJournal 2, 6079.

Boxall, P., Steeneveld, M., 1999. Human resource strategy andcompetitive advantage: A longitudinal study of engineeringconsultancies. Journal of Management Studies 36, 443463.

Braddon, D., Foster, D., 1996. Privatization: Social ScienceThemes and Perspectives. Dartmouth Publishing, Aldershot.

Brewster, C., 1995. Towards a European model of humanresource management. Journal of International BusinessStudies 26 (1), 121.

Brewster, C., Hegewisch, A., Lockhart, J.T., Holden, L., 1993.The European Human Resource Management Guide. Aca-demic Press, London.

Brewster, C., Mayrhofer, W., Morley, M., 2004. Trends inHuman Resource Management in Europe: Convergence orDivergence. Butterworth, Heinemann, UK.

Brown, K., Ryan, N., Parker, R., 2000. New modes of servicedelivery in the public sector: Commercializing governmentservices. The International Journal of Public Sector Manage-ment 13 (3), 206221.

Cascio, W.C., 1992. Managing Human Resources: Productivity,Quality of Work Life, Prots. McGraw-Hill, New York.

Cunha, R.C., Cunha, M.P., Morgado, A., Brewster, C., 2002.Market impacts on strategy, HRM practices and organiza-tional performance: Toward a European model. ManagementResearch 1 (1), 7991.

Deboeck, G., Kohonen, T., 1998. Visual Explorations in Finance

with Self-Organizing Maps. Springer, London.

Deichmann, J., Eshghi, A., Haughton, D., Sayek, S., Teebagy,H., Topi, H., 2003. Geography matters: Kohonen classica-tion of determinants of foreign direct investment in transitioneconomies. Journal of Business Strategies 20 (1), 2325.

Delaney, T.J., Huselid, A.M., 1996. The impact of humanresource management practices on perceptions of organiza-tional performance. Academy of Management Journal 39,949969.

Delery, J.E., Doty, D.H., 1996. Modes of theorizing in strategichuman resource management: Tests of universalistic, contin-gency, and congurational performance predictions. Acad-emy of Management Journal 39 (4), 802835.

Duffey, J., 1988. Competitiveness and human resources. Califor-nia Management Review 30 (3), 92101.

Ferris, G.R., Hochwarter, W.A., Buckley, M.N., Harrell-Cook,D., Frink, D., 1999. Human resources management: Somenew direction. Journal of Management 25 (3), 385418.

Filella, J., 1991. Is there a Latin model in the management of human resources? Personnel Review 23 (20), 1423.

Flanagan, D.J., Deshpande, S.P., 1996. Top managements

perceptions of changes in HRM practices after union electionsin small rms: Implications for building competitive advan-tage. Journal of Small Business Management 34 (4), 2335.

Fox, D., Byrne, V., Rouault, F., 1999. Performance improve-ment: What to keep in mind. Training & Development 53 (8),3849.

Gerhart, B., Wright, P.M., McMahan, G.C., 2000a. Measure-ment error in research on the human resources and rmperformance relationship: Further evidence and analysis.Personnel Psychology 53 (4), 855872.

Gerhart, B., Wright, P.M., McMahan, G.C., Snell, S.A., 2000b.Measurement error in research on human resources andperformance: How much error is there and how does itinuence effect size estimates? Personnel Psychology 53 (4),803834.

Guest, D.E., 1990. Human resource management and theAmerican dream. Journal of Management Studies 27 (4),377397.

Guest, D.E., 2001. Human resource management: When researchconfronts theory. International Journal of Human ResourceManagement 12 (7), 10921106.

Guest, D.E., Michie, J., Conway, N., Sheehan, M., 2003. Humanresource management and corporate performance in the UK.British Journal of Industrial Relations 41 (2), 291314.

Hax, A.C., Wilde II, D.L., 1999. The delta model: Adaptivemanagement for a changing world. Sloan ManagementReview 40 (2), 11.

Hofstede, G., 2001. Cultures Consequences: Comparing Values,Behaviors, Institutions and Organizations across Nations.Sage Publications, London.

Huselid, M.A., 1995. The impact of human resource managementpractices on turnover, productivity, and corporate nancialperformance. Academy of Management Journal 38 (3),635672.

Huselid, M.A., Becker, B.E., 2000. Comment on measurementerror in research on human resources and rm performance:How much error is there and how does it inuence effect sizeestimates? Personnel Psychology 53 (4), 835854.

Ichniowski, C., Shaw, K., Prennushi, G., 1997. The effects of human resource management practices on productivity: Astudy of steel nishing lines. American Economic Review 87

(3), 291312.



15/15

Ignjatovic, M., Sveltic, I., 2003. European HRM clusters. ESBReview 17, 2539.

Jap, S.D., 2001. Pie Sharing in complex collaboration contexts.Journal of Marketing Research 38 (1), 86100.

Kaski, S., Nikkila, J., Kohonen, T., 1998. Method for interpret-ing a self-organizing map in data analysis. In: Verleysen, M.,Proceedings of ESANN98, 6th European Symposium onArticial Neural Networks, D-facto, Brussels, pp. 185190.

Kerr, D., Beaujot, R., 2002. Family relations, low income andchild outcomes: A comparison of Canadian children inintact-, step-, and lone-parent families. International Journalof Comparative Sociology 43 (2), 134153.

Klingner, D.E., Lynn, D.B., 1997. Beyond civil service: Thechanging force face of public personnel management. PublicPersonnel Management 26 (2), 157174.

Kohonen, T., 1995. Self-organizing Maps. Springer, Berlin.Ma, H., 1999. Creation and preemption for competitive advan-

tage. Management Decision 37 (3), 259267.MacDuffie, P.J., 1995. Human resource bundles and manufac-

turing performance: Organizational logic and exible pro-

duction systems in the world auto industry. Industrial andLabor Relations Review 48, 197221.Mazanec, J.A., 1995. Positioning analysis with self-organizing

maps: An exploratory study on luxury hotels. Cornell Hotel &Restaurant Administration Quarterly 36 (6), 8096.

McHugh, M., OBrien, G., Ramondt, I., 1999. Organizationalmetamorphosis led by front line staff. Employee Relations 21(6), 556576.

Moreno, D., Marco, P., Olmeda, I., 2006. Self-organizing mapscould improve the classication of Spanish mutual funds.European Journal of Operational Research 174 (2), 1039 1054.

Pearce, J.A., Robbins, D.K., Robinson, R.B., 1987. The impactof grand strategy and planning formality on nancial perfor-mance. Strategic Management Journal 8, 2534.

Perry-Smith, J.E., Blum, T.C., 2000. Work-family humanresource bundles and perceived organizational performance.Academy of Management Journal 43, 11071117.

Peteraf, M.A., 1993. The cornerstones of competitive advantage:A resource-based view. Strategic Management Journal 14 (3),179192.

Pfeffer, J., Veiga, J.F., 1999. Putting people rst for organiza-tional success. The Academy of Management Executive 13(2), 3738.

Podsakoff, P.M., Mackenzie, S.B., Lee, J.Y., Podsakoff, N.P.,2003. Common method biases in behavioural research: Acritical review of the literature and recommended remedies.Journal of Applied Psychology 88, 879903.

Rank, R.M., Hirschl, A.T., 2001. Rags or riches? Estimating theprobabilities of property and affluence across the adultAmerican life span. Social Science Quarterly 82 (4), 651670.

Savery, L.K., 1998. Management and productivity increases.Journal of Management Development 17 (1), 6874.

Sherman, D., Young, D., Collingwood, H., 2003. Prots YouCan Trust: Spotting and Surviving Accounting Landmines.Prentice Hall, New York.

Singh, H., Motwanis, J., Komar, A., 2000. A review and analysisof the state-of-the-art research on productivity measurement.Industrial Management & Data Systems 100 (5), 234241.

Smith, K.A., 1999. Neural networks for combinatorial optimi-zation: A review of more than a decade of research.INFORMS Journal on Computing 11 (1), 1534.

Smith, J.B., Barclay, D.W., 1997. The effects of organizationaldifferences and trust on the effectiveness of selling partnerrelationships. Journal of Marketing 61 (1), 321.

Sparrow, P.R., Hiltrop, J.M., 1997. Redening the eld of European human resource management: A battle betweennational mindsets and forces of business transition? HumanResource Management 36 (2), 201219.

Sparrow, P.R., Schuler, R.S., Jackson, S.E., 1994. Convergenceand divergence: Human resource practices and policies for

competitive advantage worldwide. International Journal of Human Resource Management 5 (2), 267299.Stavrou, E., Brewster, C., 2005. The congurational approach to

linking strategic human resource management bundles withbusiness performance: Myth or reality? Management Review16 (2), 186201.

Truss, C., 2001. Complexities and controversies in linking HRMwith organizational outcomes. Journal of Management Stud-ies 38 (8), 11221149.

Ultsch, A., Siemon, H.P., 1990. Kohonens self organizingfeature maps for explanatory data analysis. In: Proceedingsof INNC90, International Neural Networks Conference.Kluwer, Dordrecht, pp. 305308.

Veiga, F.J., Lubatkin, M., Calori, R., Very, P., Tung, Y.A., 2000.Using neural network analysis to uncover the trace effects of national culture. Journal of International Business Studies 31(2), 223245.

Vesanto, J., 1999. SOM-based data visualization methods.Intelligence Data Analysis 3 (2), 111126.

Welbourne, T.M., Andrews, A.O., 1996. Predicting performanceof initial public offerings: Should human resource manage-ment be in the equation?. Academy of Management Journal39 (4) 8911101.

Wood, S., 1999. Human resource management and performance.International Journal of Management Reviews 1 (4), 367413.

Wright, P.M., Dunford, B.B., Snell, S.A., 2001. Human resourcesand the resource based view of the rm. Journal of Manage-ment 27 (6), 701722.

Zeithaml, V., Berry, L.L., Parasuraman, A., 1996. The behavioralconsequences of service quality. Journal of Marketing 60 (2),3147.


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