Knowledge and Process Management

Volume 13 Number 4 pp 264–269 (2006)

Published online in Wiley InterScience

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(www.interscience.wiley.com) DOI: 10.1002/kpm.2

& Research Article

Collaboration Between the TopKnowledge Management andIntellectual Capital Researchers

Ronald Dattero*

Department of Computer Information Systems, Missouri State University, Missouri, USA

*CorInforNatiE-ma

Cop

Recently, Serenko and Bontis published ameta-review of the knowledgemanagement (KM) andintellectual capital (IC) literature that identified the 64 most productive KM/IC researchers. Inthis paper, exploratory data analysis using graphical methods andmeasures is performed on thecollaboration patterns of these top 64 KM/IC researchers, and the resulting collaborationpatterns are discussed. Copyright # 2006 John Wiley & Sons, Ltd.

INTRODUCTION

Recently, Serenko and Bontis (2004) published ameta-review of the knowledge management (KM)and intellectual capital (IC) literature. The reviewfocused on research productivity and citationanalysis of individuals, institutions and countries.The 64 most productive KM/IC researchers wereidentified based on their number of publications inthe Journal of Intellectual Capital, Journal of KnowledgeManagement and Knowledge and Process Management.

In reviewing the research publications, Serenkoand Bontis (2004) found that almost half of thepapers were written by a single researcher. Theypointed out that this result is dramatically differentfrom the results of Bapna and Marsden’s (2002)study of quantitative and technical departments(such as management science, operations manage-ment and information systems) in colleges ofbusiness which found that only 25% of the papersin their study were single-authored. Serenko andBontis (2004) explain their surprising results withthe argument that ‘. . .KM/IC is a relatively young

respondence to: Ronald Dattero, Department of Computermation Systems, Missouri State University, 901 Southonal Avenue, Springfield, MO 65897, USA.il: RonDattero@MissouriState.edu

yright # 2006 John Wiley & Sons, Ltd.

field in which a single person may provide asubstantial contribution’ but add the caution that‘. . . future authors may find it more difficult toembark on challenging projects alone.’

Research collaboration between mathematicianshas been studied extensively (Grossmann 2002a,b).This study of research collaboration betweenmathematicians is usually referred to as the ErdosNumber Project (2006) (http://www.oakland.edu/enp/index.html). Paul Erdos was a great mathe-matician and an extremely prolific (publishingabout 1500 papers with over 500 different collab-orators) researcher. In fact, some consider Erdos thecentre of the mathematical universe. An ErdosNumber indicates the degrees of separationbetween a researcher and Erdos. Specifically, Erdosgets the number 0, all of Erdos’s collaborators getthe number 1, anyone who has collaborated withone of Erdos’s collaborators but not Erdos get thenumber 2, and so on (Watts, 2003). For example, theauthor of this paper has an Erdos Number of 5because he has collaborated with W.E. Stein (4) whohas collaborated with A. Rapoport (3) who hascollaborated with S.J. Brams (2) who has collabo-rated with P.C. Fishburn (1) who has collaboratedwith P. Erdos (0). To put the author’s Erdos Numberin perspective, it should be noted that about 90 000others have an Erdos Number of 5 and over 200 000

Knowledge and Process Management RESEARCH ARTICLE

have an Erdos Number of 5 or less (http://www.oakland.edu/enp/index.html).

The purpose of this exploratory study is theinvestigation of the collaboration patterns of the top64 KM/IC researchers. Methods andmeasures fromsocial network analysis and graph theory will beadapted to assess the collaboration patterns. Theorganization of the rest of the paper is as follows. Inthe next section, the graphical methods andmeasures will be discussed. Then, the data for thetop 64 KM/IC researchers will be presented and thecollaboration patterns will be discussed. The lastsection of this paper will provide some concludingremarks.

METHODS AND MEASURES

Social network analysis focuses on relationshipsbetween people, organizations etc. The goal is todescribe networks of relations as fully as possible,identify the prominent patterns in such networks,trace the flow of information (and other resources)through them, and discover what effects theserelations and networks have on people andorganizations (Scott, 2000).

Graphs are often used to depict social networks.A graph is a collection of nodes or vertices and linksor edges (between the nodes). In the case of researchcollaboration, the nodes represent researchers and alink represents collaboration between two authors.The collaboration relationship is undirected orsymmetric so the graph is undirected. Usually,the link gets the value of one (which is usuallysuppressed on the graph) if the authors havecollaborated on one or more publications. In thisexploratory study, we want to highlight the amountof collaboration so the link will be valued with acount of the collaborations (but with values of onesuppressed on the graph).

The degree of a node is the number of links thatare incident to it. A node with degree 0 is called anisolate. In the case of research collaboration, thedegree is the number of different researchers agiven researcher has collaborated with and anisolate is a researcher who has not collaborated withany other researcher.

The average degree is simply the average numberof links per node. The density is a normalizedaverage degree. The density is calculated bydividing the average degree by the maximumpossible degree (which is (n�1) in a graph with nnodes).

A component is a set of nodes that have aconnection path between each pair of nodes in theset but no connection path to nodes outside the set.

Collaboration Between KM/IC ResearchersDOI: 10.1002/kpm

The fragmentation of a graph can be assessed usingthe component ratio or the F measure of fragmenta-tion. The component ratio is simply the number ofcomponents divided by the number of nodes. The Fmeasure of fragmentation is a function of the size ofeach component and the total number of nodes inthe graph. The Fmeasure of fragmentation is relatedto a number of other measures of connectivity andfragmentation (Borgatti, 2006).

KM/IC COLLABORATION

Serenko and Bontis (2004) identified the 64 mostproductive KM and IC researchers based on aweighted publication count of research articlesappearing in the Journal of Intellectual Capital, Journalof Knowledge Management and Knowledge and ProcessManagement from the first issue for the first twojournals and starting in 1994 for the third journal.For each of the three journals, the last issue used hada publication date of 2003. Specifically, individualresearch productivity was based on the reciprocal ofthe number of authors on each research publication.For example, a research publication with threeauthors would earn each author 1/3 credit. Themost productive researcher, Ganesh D. Bhatt,earned a score of 5.33 which was based on fivesole authored publications and one publicationwhere he had two co-authors. In the original table inthe Serenko and Bontis (2004) paper, there were anumber of ties and each tied researcher was giventhe same rank. To uniquely identify each researcherfor this paper, tied researchers have been numberedfollowing the order of appearance in the table in theSerenko and Bontis (2004) paper. For example, LeifEdvinsson and Sven Voelpel were tied in 33rd placewith a score of 1.83 but Edvinsson was listed first inthe original table so Edvinsson was given theidentifier 33 while Voelpel was given the identifier34. These identifiers have no impact on the size ofany node or link in the collaboration graph. This listof the 64 researchers and their unique identifiers isgiven in Table 1.

Figure 1 shows the collaboration graph, whichwas drawn using the visone software (2006) forvisual analysis of social networks (see http://visone.info/). The numbers in the nodes correspondwith the top researcher’s identification numbersgiven in Table 1. The two lettered nodes (‘A’ and ‘B’)correspond to Alexander Kouzmin and ChrisEdwards respectively. These two researchers arethe only researchers that are not in the top 64 buthave two or more collaborations with at least one ofthe top 64 researchers. The solid nodes correspondto researchers not in the top 64 researchers who had

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Table 1 Top KM/IC researchers ranked by individual productivity

Name Score Affiliation Country

Ganesh D.Bhatt 5.33 Morgan State University USANick Bontis 3.67 McMaster University CanadaSyed Z. Shariq 3.58 Stanford University USALuiz Antonio Joia 3.00 Brazilian School of Public Admin. BrazilPatricia Ordonez de Pablos 3.00 University of Oviedo SpainJennifer Rowley 3.00 Edge Hill College of Higher Education UKKarl M. Wiig 3.00 Knowledge Research Institute USARodney McAdam 2.83 University of Ulster UKJay Liebowitz 2.81 University of Maryland–Baltimore County USAMarcus Blosch 2.50 Model Resource Group UKAndrew Korac-Kakabadse 2.50 Cranfield University UKNada Korac-Kakabadse 2.50 Cranfield University UKVictor Newman 2.50 Cranfield University UKWalter Skok 2.50 Kingston University UKIan Caddy 2.33 University of Western Sydney AustraliaJavier Carrillo 2.33 ITESM MexicoJames Guthrie 2.33 Macquarie University AustraliaVerna Allee 2.00 Integral Performance Group USARoelof P. uit Beijerse 2.00 EIM The NetherlandsJohn Van Beveren 2.00 University of Ballarat AustraliaAlberto Carneiro 2.00 Lusofona University of Human and Technologies PortugalRory L. Chase 2.00 Teleos UKPetter Gottschalk 2.00 Norwegian School of Management NorwayJosephine Chinying Lang 2.00 Nanyang Technological University SingaporePeter Matthews 2.00 Anglian Water UKMarjatta Maula 2.00 Helsinki School of Economics and B. A. FinlandMark W. McElroy 2.00 Macroinnovation Associates USAInaki Pena 2.00 ESTE SpainKenneth Preiss 2.00 University of Technology AustraliaPatrick H. Sullivan Sr 2.00 The ICM Group USAMark N. Wexler 2.00 Simon Fraser University CanadaAshley Braganza 1.92 Cranfield University UKLeif Edvinsson 1.83 UNIC SwedenSven Voelpel 1.83 University of St Gallen SwitzerlandGregoris Mentzas 1.75 National Technical University of Athens GreeceHarry Scarbrough 1.75 University of Warwick UKMajed Al-Mashari 1.50 King Saud University Saudi ArabiaDebra Amidon 1.50 Entovation International USAWendi R. Bukowitz 1.50 PricewaterhouseCoopers USAThomas H. Davenport 1.50 Babson College USAFaren Foster 1.50 IBM USANigel Holden 1.50 Kassel International Management School GermanyDavis Klaila 1.50 Celemi USANed Kock 1.50 Temple University USARado Kotorov 1.50 Bowling Green State University USADaryl Morey 1.50 The Parthenon Group USAJoy Palmer 1.50 Interknectives UKFawzy Soliman 1.50 University of Technology AustraliaKarl-Erik Sveiby 1.50 Swedish School of Economics and B. A. FinlandAmrit Tiwana 1.50 Emory University USAKurt A. April 1.33 University of Cape Town South AfricaColin Armistead 1.33 Bournemouth University UKWilliam Keogh 1.33 Heriot-Watt University UKDavid Paper 1.33 Utah State University USARichard Petty 1.33 University of Hong Kong Hong KongJames A. Rodger 1.33 University of Pittsburgh at Johnstown USAJonas Roth 1.33 Chalmers UniversityUniversity of Technology SwedenAlexander Styhre 1.33 Chalmers University of Technology SwedenP. N. SubbaNarasimha 1.33 St Cloud State University USADimitris Apostolou 1.25 Planet Greece

(Continues)

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RESEARCH ARTICLE Knowledge and Process Management

Table 1 (Continued)

Name Score Affiliation Country

Amar Gupta 1.25 MIT USAKuan-Tsae Huang 1.20 IBM USARichard T. Herschel 1.17 St Joseph’s University USARob Lambert 1.17 Cranfield University UK

Knowledge and Process Management RESEARCH ARTICLE

only a single collaboration with one of the top 64researchers.

The size of the node indicates the raw publicationcount by the researcher. For example, node 2 (NickBontis) is the largest because Nick Bontis has a rawcount of seven publications. The next largest nodesare node 1 (Ganesh D. Bhatt), node 11 (AndrewKorac-Kakabadse), and node 12 (Nada Korac-Kakabadse) because each of these researchers hasa raw publication count of six publications. Recall,the Serenko and Bontis (2004) rankings used thereciprocal of the number of authors on each researchpublication. So the rank order in Table 1 does notcorrespond to the node size in Figure 1.

Each link is in Figure 1 corresponds to acollaboration effort. For example, node 1 (GaneshD. Bhatt), node 54 (David Paper), and node 56

Figure 1 KM/IC

Collaboration Between KM/IC ResearchersDOI: 10.1002/kpm

(James A. Rodger) collaborated on a paper. Sincethere are three authors collaborating on this onepaper, there are three links for this collaboration: alink between node 1 and node 54, a link betweennode 1 and node 56, and a link between node 54 andnode 56. For the general case of n collaborators on agiven paper, the number of binary links for thiscollaboration is ‘n choose 2’ – that is, n�(n�1)/2. Forexample, there are 21 links to the left of node 9 (JayLiebowitz) because this one paper was a collabor-ation between seven researchers.

Most links do not have a number above them.This indicates that there is only one collaborativeeffort between the two researchers (nodes). If twoauthors have collaborated on more than one paper,the number on the link indicates the number ofcollaborations. For example, node 11 (Andrew

Collaboration

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Korac-Kakabadse) and node 12 (Nada Korac-Kakabadse) collaborated six times.

In Figure 1, the placement of the numbered nodeswas based on the amount of collaboration. In the leftcorner, there are 17 isolates (nodes with no links).For the four nodes (4, 5, 6 and 7) in the first group(larger nodes) of isolates, each have three soleauthored papers while for the 13 nodes in thesecond group (smaller nodes) of isolates each havetwo sole authored papers. In the middle, topresearchers who collaborate but not with othertop researchers are placed (numbered nodes linkedonly to solid nodes). In the right corner, collabor-ation between researchers in the top 64 (numberednodes linked to other numbered nodes) is illus-trated.

Some of the more interesting collaborationpatterns are with the top researchers. As mentionedpreviously, the top researcher (Ganesh D. Bhatt)collaborated on a paper with David Paper (node 54)and James A. Rodger (node 56). The second rankedresearcher, Nick Bontis, engaged in a number ofdifferent collaborations with researchers not listedin the top 64. Bontis earned his score of 3.67 throughone sole authored paper, four two-authored papers,and two three-authored papers. The third rankedresearcher, Syed Z. Shariq, earned his score of 3.58through three sole authored papers, one two-authored paper, and one three-authored paper;Shariq’s co-authors were all different and nonewerein the top 64. The eighth ranked researcher, RodneyMcAdam, earned his score of 2.83 through one soleauthored paper, three two-authored papers, andone three-authored paper; McAdam’s co-authorswere all different and none were in the top 64. Theninth ranked researcher, Jay Liebowitz, earned hisscore of 2.81 through one sole authored paper, onetwo-authored papers, one six-authored paper, andone seven-authored; Liebowitz’s co-authors wereall different and none were in the top 64. The tenthranked researcher, Marcus Blosch, earned his scoreof 2.50 through two sole authored papers and onetwo-authored papers. The greatest collaborationefforts were between the eleventh and twelfthranked researchers, Andrew and Nada Korac-Kakabadse. They collaborated six times and inthree of these efforts, they were joined by AlexanderKouzmin (node A).

From the graph, it is clear that the collaborationefforts are rather fragmented. As indicated earlier,this graph has 17 isolates among the top 64. Thereare 153 nodes in the graph so 89 other researcherscollaborated with the top 64 researchers. Thelinkage between nodes is slight as there are only162 links. Almost all of the links have a value of one(the number one is suppressed on links with a value

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of one). The average degree for the graph is only2.118 and the density is only 0.014 for this graph.Because of the limited collaboration between the top64 researchers, there are 55 components in thisgraph. Hence, the component ratio is 0.359 for thisgraph. As expected, the F measure of fragmentationis almost one (0.978).

CONCLUDING REMARKS

The results of the exploratory study vividly showthe lack of collaboration in the KM/IC literature. Asreported by Serenko and Bontis (2004), almost halfof all publications are sole authored. The reason forthis may be caused by the fact that in manyuniversities and organizations there is a singleperson who leads the KM/IC research efforts.

In addition, there was a high degree of fragmen-tation on the collaboration graph. This may haveoccurred because of the limited number of research-ers and papers considered in this exploratory study.In addition, KM and IC are emerging and evolvingdisciplines. As time goes by, it is likely that thecollaboration graph will become less fragmented. Itwill be also interesting to see if one or moreresearchers become major focus points of thecollaboration graph and become the ‘Erdos’ ofKM/IC.

In terms of future research, it would be interestingto study the evolution of the collaboration graph ofall (not just the top 64) KM/IC research efforts inorder to see how both the number of researchers andnumber of collaborations have increased over time.By studying the dynamics of the collaborationgraph, the rate of growth of the KM/IC field can becharacterized (possibly, the adoption of KM/IC as aworthwhile research area could be viewed analo-gously to the technology adoption life cycle).

ACKNOWLEDGEMENTS

The author thanks Alexander Serenko andNick Bontis fortheir comments and encouragement.

REFERENCES

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Borgatti SP. 2006. Identifying sets of key players in a socialnetwork. Computational and Mathematical OrganizationalTheory, 12(1): 21–34.

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