Skip to main content
Log in

Knowledge heterogeneity and social network analysis – Towards conceptual and measurement clarifications

  • Article
  • Published:
Knowledge Management Research & Practice

Abstract

This literature review highlights some Social Network Analysis (SNA) concepts applicable to the study of organizational knowledge and, more particularly, to knowledge heterogeneity. Knowledge being all at the same time decentralized and distributed, knowing up to what point knowledge can be heterogeneous or homogeneous across organizational units becomes as important as the question of knowing how to structure the organization. SNA applied to knowledge management thus seems a stimulant for future research in the fields of management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  • Agresti A and Agresti B (1978) Statistical analysis of qualitative variation. In Sociological Methodology (SCHUESSLER KF, Ed), pp 204–237, Jossey-Bass, San Francisco.

    Google Scholar 

  • Ahuja G (2000) Collaboration networks, structural holes, and innovation: a longitudinal study. Administrative Science Quarterly 45 (3), 425–455.

    Article  Google Scholar 

  • Allan M (2004) A peek into the life of online learning discussion forums: implications for web-based distance learning. The International Review of Research in Open and Distance Learning 5 (2), http://www.irrodl.org/content/v5.2/allan.html (accessed 26 June 2007).

  • Allen TJ and Cohen SI (1969) Information flow in research and development laboratories. Administrative Science Quarterly 14 (1), 12–19.

    Article  Google Scholar 

  • Anand V, Manz CC and Glick WH (1998) An organizational memory approach to information management. The Academy of Management Review 23 (4), 796–809.

    Google Scholar 

  • Ancona DG and Caldwell DF (1992) Demography and design: predictors of new product team performance. Organization Science 3 (3), 321–341.

    Article  Google Scholar 

  • Argote L and Ingram P (2000) Knowledge transfer: a basis for competitive advantage in firms. Organizational Behavior and Human Decision Processes 82 (1), 150–169.

    Article  Google Scholar 

  • Argote L, McEvily B and Reagans R (2003) Managing knowledge in organizations: an integrative framework and review of emerging themes. Management Science 49 (4), 571–582.

    Article  Google Scholar 

  • Argyres NS (1996) Capabilities, technological diversification and divisionalization. Strategic Management Journal 17 (5), 395–410.

    Article  Google Scholar 

  • Baba ML, Gluesing J, Ratner H and Wagner KH (2004) The contexts of knowing: natural history of a globally distributed team. Journal of Organizational Behavior 25 (5), 547–587.

    Article  Google Scholar 

  • Barnes J (1954) Class and committees in a Norwegian Island Parish. Human Relations 7 (1), 39–58.

    Article  Google Scholar 

  • Batjargal B (2005) Software entrepreneurs in China and Russia: knowledge networks, product development, and venture performance. William Davidson Institute Working Paper No. 751 [WWW Document] http://www.people.hbs.edu/jsiegel/Batjargal_HBSFeb12007.doc(accessed 31 May 2007).

  • Beckman CM and Hauschild P (2002) Network learning: the effects of partners' heterogeneity of experience on corporate acquisitions. Administrative Science Quarterly 47 (1), 92–124.

    Article  Google Scholar 

  • Blackler F (1995) Knowledge, knowledge work and organizations: an overview and interpretation. Organization Studies 16 (6), 1021–1046.

    Article  Google Scholar 

  • Blau PM (1977) Inequality and Heterogeneity. The Free Press, New York.

    Google Scholar 

  • Boland R and Tenkasi R (1995) Perspective making and perspective taking in communities of knowing. Organization Science 6 (4), 350–372.

    Article  Google Scholar 

  • Bonifacio M, Bouquet P and Cuel R (2002) Knowledge nodes: the building blocks of a distributed approach to knowledge management. Journal of Universal Computer Science 8 (6), 652–661.

    Google Scholar 

  • Bonner JM and Walker Jr. OC (2004) Selecting influential business-to-business customers in new product development: relational embeddedness and knowledge heterogeneity considerations. Journal of Product Innovation Management 21 (3), 155–169.

    Article  Google Scholar 

  • Borgatti SP (2002) NetDraw: Graph Visualization Software. Analytic Technologies, Harvard.

    Google Scholar 

  • Borgatti SP and Everett MG (1997) Network analysis of 2-mode data. Social Networks 19 (3), 243–269.

    Article  Google Scholar 

  • Borgatti SP and Foster PC (2003) The network paradigm in organizational research: a review and typology. Journal of Management 29 (6), 991–1013.

    Article  Google Scholar 

  • Borgatti SP, Everett MG and Freeman LC (2002) UCINET for Windows: Software for Social Network Analysis. Analytic Technologies, Harvard.

    Google Scholar 

  • Brown JS and Duguid P (1991) Organizational learning and communities of practice: toward a unified view of working, learning and innovation. Organization Science 2 (1), 40–57.

    Article  Google Scholar 

  • Burkhardt ME and Brass DJ (1990) Changing patterns or patterns of change: the effects of a change in technology on social network structure and power. Administrative Science Quarterly 35 (1), 104–127.

    Article  Google Scholar 

  • Buskens V (1998) The social structure of trust. Social Networks 20 (3), 265–289.

    Article  Google Scholar 

  • Burt R (1992) Structural Holes. Harvard University Press, Cambridge, MA.

    Google Scholar 

  • Burt RS (2004) Structural holes and good ideas. American Journal of Sociology 110 (2), 349–399.

    Article  Google Scholar 

  • Carley KM and Reminga J (2004) ORA: Organization risk analyzer. Center for Computational Analysis of Social and Organizational Systems (CASOS) Technical Report, CMU-ISRI-04-106, Carnegie Mellon University, Pittsburgh http://reports-archive.adm.cs.cmu.edu/anon/isri2004/CMU-ISRI-04-106.pdf (accessed 12 February 2008).

  • Chan K and Liebowitz J (2006) The synergy of social network analysis and knowledge mapping: a case study. International Journal of Management and Decision Making 7 (1), 19–35.

    Article  Google Scholar 

  • Cohen W and Levinthal D (1990) Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly 35 (1), 128–152.

    Article  Google Scholar 

  • Conrath DW (1973) Communication environment and its relationship to organizational structure. Management Science 20 (4), 586–603.

    Article  Google Scholar 

  • Contractor N, Carley K, Levitt R, Monge P, Wasserman S, Bar F, Fulk J, Hollingshead A and Kunz J (2000) Co-evolution of knowledge networks and 21st century organizational forms: computational modeling and empirical testing. Working Paper TEC2000-01, University of Illinois at Urbana-Champaign.

  • Cooke NJ, Salas E, Cannon-Bowers JA and Stout RJ (2000) Measuring team knowledge. Human Factors 42 (1), 151–173.

    Article  Google Scholar 

  • Cross R and Parker A (2004) The Hidden Power of Social Networks: Understanding How Work Really Gets Done in Organizations. Harvard Business School Press, Boston.

    Google Scholar 

  • Cross R, Parker A, Prusak L and Borgatti SP (2001) Knowing what we know: supporting knowledge creation and sharing in social networks. Organizational Dynamics 30 (2), 100–120.

    Article  Google Scholar 

  • Cross R, Nohria N and Parker A (2002) Six myths about informal networks – and how to overcome them. Sloan Management Review 43 (3), 66–76.

    Google Scholar 

  • D'Adderio L (2003) Configuring software, reconfiguring memories: the influence of integrated systems on the reproduction of knowledge and routines. Industrial and Corporate Change 12 (2), 321–350.

    Article  Google Scholar 

  • DeSanctis G and Poole MS (1982) Capturing the complexity in advanced technology use: adaptive structuration theory. Organization Science 5 (2), 121–147.

    Article  Google Scholar 

  • Downs GW (1976) Bureaucracy, Innovation, and Public Policy. Lexington Books, Lexington.

    Google Scholar 

  • Earley PC and Mosakowski E (2000) Creating hybrid team cultures: an empirical test of transnational team functioning. Academy of Management Journal 43 (1), 26–49.

    Article  Google Scholar 

  • Espinosa JA, Carley KM, Kraut RE, Lerch FJ and Fussell SR (2002) The effect of task knowledge similarity and distribution on asynchronous team coordination and performance: empirical evidence from decision teams. In Second Information Systems Cognitive Research Exchange (IS CoRE) Workshop. Barcelona, Spain. http://www.cs.cmu.edu/~kraut/RKraut.site.files/articles/Espinosa02-KnowledgeSimilarityDistribCoordSubmitted.pdf (accessed on 27 January 2008).

    Google Scholar 

  • Everett MG and Borgatti SP (1999) The centrality of groups and classes. Journal of Mathematical Sociology 23 (3), 181–201.

    Article  Google Scholar 

  • Festinger L, Schachter S and Back K (1950) Social Pressures in Informal Groups: A Study of a Housing Project. Harper & Row, New York.

    Google Scholar 

  • Freeman LC (1979) Centrality in social networks: conceptual clarification. Social Networks 1 (3), 215–239.

    Article  Google Scholar 

  • Freeman LC (2000) Social network analysis: definition and history. In Encyclopedia of Psychology (KAZDAN AE, Ed), pp 350–351, Oxford University Press, New York.

    Google Scholar 

  • Galunic DC and Rodan SA (1998) Resource recombinations in the firm: knowledge structures and the potential for Schumpeterian innovation. Strategic Management Journal 19 (12), 1193–1201.

    Article  Google Scholar 

  • Gibbons M, Limoges C, Nowotny H, Schwartzman S, Scott P and Trow M (1994) The New Production Knowldege: The Dynamics of Science and Research in Contemporary Societies. Sage, London.

    Google Scholar 

  • Goodman PS and Darr ED (1998) Computer-aided systems and communities: mechanisms for organizational learning in distributed environments. MIS Quarterly 22 (4), 417–440.

    Article  Google Scholar 

  • Granovetter M (1973) The strength of weak ties. The American Journal of Sociology 78 (6), 1360–1380.

    Article  Google Scholar 

  • Hamilton MA (2002) Heterogeneous organizational learning: overcoming the paradox. In Managing the Complex IV: Conference on Complex Systems and the Management of Organization (LISSACK M and STEELE D, Eds) Institute for the study of Coherent Emergence, Fort Myers, FL. http://isce.edu/ISCE_Group_Site/web-content/ISCE_Events/Naples_2002/Naples_2002_Papers/Hamilton.pdf (accessed 28 January 2008).

    Google Scholar 

  • Hanneman RA and Riddle M (2005) Introduction to Social Network Methods, Riverside. University of California, Riverside, CA.

    Google Scholar 

  • Hansen MT (1999) The search-transfer problem: the role of weak ties in sharing knowledge across organizational subunits. Administrative Science Quarterly 44 (1), 82–111.

    Article  Google Scholar 

  • Hansen MT (2002) Knowledge networks: explaining effective knowledge sharing in multiunit companies. Organization Science 13 (3), 232–248.

    Article  Google Scholar 

  • Herfindahl OC (1950) Concentration in the U.S. steel industry. PhD Thesis, Columbia University, New York.

  • Hood GM (2005) PopTools version 2.7.1. [WWW Document] http://www.cse.csiro.au/poptools (accessed 11 September 2006).

  • Hutt MD, Reingen PH and Ronchetto Jr JR (1988) Tracing emergent processes in marketing strategy formation. Journal of Marketing 52 (1), 4–19.

    Article  Google Scholar 

  • Jaccard P (1901) Distribution de la florine alpine dans la Bassin de Dranses et dans quelques régions voisines. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 241–272.

    Google Scholar 

  • Jensen MC and Meckling WH (1992) Specific and general knowledge and organizational structure. In Contract Economics (WERIN L and WIJKANDER H, Eds), p 251. Blackwell, Oxford.

    Google Scholar 

  • Kao J (1996) Jamming: The Art and Discipline of Business Creativity. Harper Collins, New York.

    Google Scholar 

  • Kitaygorodskaya N (2006) Measurement of team knowledge: transactive memory system and team mental models. In Proceedings of the Research Forum to Understand Business in Knowledge Society (MAULA M, Ed), pp 1–6, ICEB+eBRF, Tampere, Finland. http://www.ebrc.fi/kuvat/Kitaygorodskaya_paper.pdf (accessed 28 November 2007).

    Google Scholar 

  • Klein KJ, Dansereau F and Hall RJ (1994) Levels issues in theory development, data collection, and analysis. The Academy of Management Review 19 (2), 195–229.

    Google Scholar 

  • Klimoski R and Mohammed S (1994) Team mental model: construct or metaphor? Journal of Management 20 (2), 403–437.

    Article  Google Scholar 

  • Kogut B and Zander U (1996) What firms do? Coordination, identity, and learning. Organizational Science 7 (5), 502–518.

    Article  Google Scholar 

  • Krackhardt D (1990) Assessing the political landscape: structure, cognition, and power in organizations. Administrative Science Quarterly 35 (2), 342–369.

    Article  Google Scholar 

  • Krackhardt D and Hanson J (1993) Informal networks: the company behind the chart. Harvard Business Review 71 (4), 104–111.

    Google Scholar 

  • Krackhardt D and Porter LW (1985) When friends leave: a structural analysis of the relationship between turnover and stayers' attitudes. Administrative Science Quarterly 30 (2), 242–261.

    Article  Google Scholar 

  • Krebs V (1998) Knowledge networks-mapping and measuring knowledge creation. [WWW Document] http://www.knetmap.com/knowledge-networks-mapping.html (accessed 26 June 2007).

  • Krebs V (2007) Social network analysis: a brief introduction, orgnet.com [WWW Document] http://www.orgnet.com/sna.html (accessed 23 June 2007).

  • Kuhn T and Corman SR (2003) The emergence of homogeneity and heterogeneity in knowledge structures during a planned organizational change. Communication Monographs 70 (3), 198–229.

    Article  Google Scholar 

  • Laseter T and Cross R (2007) The craft of connection. enews, January 31 http://www.strategy-business.com/media/file/enews-01-31-07.pdf (accessed 26 June 2007).

  • Levesque LL, Wilson JM and Wholey DR (2001) Cognitive divergence and shared mental models in software development project teams. Journal of Organizational Behavior 22 (2), 135–144.

    Article  Google Scholar 

  • Leydesdorff L (2007) Betweeness centrality as an indicator of the “Interdisciplinary” of scientific journals. Journal of the American Society for Information Science and Technology 58 (9), 1303–1319.

    Article  Google Scholar 

  • Liang DW (1994) The effects of top management team formation on firm performance and organizational effectiveness. PhD Thesis, Carnegie Mellon University, Pittsburgh, PA.

  • Liebowitz J (2007) Social Networking: The Essence of Innovation. Press/Rowman & Littlefield, Scarecrow.

    Google Scholar 

  • Marengo L (1998) Knowledge distribution and coordination in organisations. In Trust and Economic Learning (LAZARIC N and LORENZ E, Eds), pp 227–246, Edward Elgar, Cheltenham.

    Google Scholar 

  • Marsden PV (1990) Network data and measurement. Annual Review of Sociology 16, 435–463.

    Article  Google Scholar 

  • Mayo M and Pastor JC (2005) Networks and effectiveness in work teams: the impact of diversity. Working Paper No. WP05-10, Instituto de Empresa Business School, Madrid, Spain, http://latienda.ie.edu/working_papers_economia/WP05-10.pdf (accessed 23 May 2007).

  • Mello AS and Ruckes ME (2006) Team composition. Journal of Business 79 (3), 1019–1039.

    Article  Google Scholar 

  • Mohammed S and Dumville BC (2001) Team mental models in a team knowledge framework: expanding theory and measurement across disciplinary boundaries. Journal of Organizational Behavior 22 (2), 89–106.

    Article  Google Scholar 

  • Monge PR and Contractor NS (2000) Emergence of communication networks. In The New Handbook of Organizational Communication (JABLIN FM and PUTNAM LL, Eds), pp 440–502, Sage, Thousand Oaks.

    Google Scholar 

  • Moorman C and Miner AS (1997) The impact of organizational memory on new product performance and creativity. Journal of Marketing Research 34 (1), 91–106.

    Article  Google Scholar 

  • Moreno JL (1934) Who Shall Survive? Foundations of Sociometry, Group Psychotherapy, and Sociodrama Nervous and Mental Disease Monograph 58. Nervous and Mental Disease Publishing Company, Washington, DC.

    Google Scholar 

  • Morrison A and Rabellotti R (2005) Knowledge dissemination and informal contacts in an Italian wine local system. In Proceedings of The Danish Research Unit for Industrial Dynamics (DRUID) Tenth Anniversary Summer Conference on Dynamics of Industry and Innovation: Organizations, Networks and Systems (MASKELL P, Ed), pp 1–24, June 27–29, Copenhagen, Denmark. http://www2.druid.dk/conferences/viewabstract.php?id=2638&cf=18 (accessed 01 December 2007).

    Google Scholar 

  • Mulgan G (1998) Connexity: Responsibility, Freedom, Business and Power in the New Century. Vintage, London.

    Google Scholar 

  • Müller-Prothmann T (2005) Leveraging knowledge communication for innovation – framework, methods and applications of social network analysis in research and development. PhD Thesis, Freien Universität Berlin, Germany.

  • Obstfeld D (2005) Social networks, the Tertius Lungens orientation, and involvement in innovation. Administrative Science Quarterly 50 (1), 100–130.

    Google Scholar 

  • Parise S, Cross R and Davenport TH (2006) Strategies for preventing a knowledge-loss crisis. Sloan Management Review 47 (4), 31–38.

    Google Scholar 

  • Postrel S (2002) Islands of shared knowledge: specialization and mutual understanding in problem-solving teams. Organization Science 13 (3), 303–320.

    Article  Google Scholar 

  • Prat A (1996) Shared knowledge vs diversified knowledge in teams. Journal of the Japanese and International Economies 10 (2), 181–195.

    Article  Google Scholar 

  • Radcliffe-Brown AR (1940) On social structure. Journal of the Royal Anthropological Society of Great Britain and Ireland 70, 1–12.

    Article  Google Scholar 

  • Reagans R (2005) Preferences, identity, and competition: predicting tie strength from demographic data. Management Science 51 (9), 1374–1383.

    Article  Google Scholar 

  • Reagans R and McEvily B (2003) Network structure and knowledge transfer: the effects of cohesion and range. Administrative Science Quarterly 48 (2), 240–267.

    Article  Google Scholar 

  • Reagans R and Zuckerman EW (2001) Networks, diversity, and productivity: the social capital of corporate R&D teams. Organization Science 12 (4), 502–517.

    Article  Google Scholar 

  • Rice RE and Aydin C (1991) Attitudes toward new organizational technology: network proximity as a mechanism for social information processing. Administrative Science Quarterly 36 (2), 219–244.

    Article  Google Scholar 

  • Rodan S and Galunic C (2004) More than network structure: how knowledge heterogeneity influences managerial performance and innovativeness. Strategic Management Journal 25 (6), 541–562.

    Article  Google Scholar 

  • Rulke DL and Galaskiewicz J (2000) Distribution of knowledge, group network structure, and group performance. Management Science 46 (5), 612–625.

    Article  Google Scholar 

  • Sandström A (2004) Innovative policy networks – the relation between structure and performance. PhD Thesis, Lulea University of Technology, Sweden.

  • Sarbaugh-Thompson M and Feldman MS (1998) Electronic mail and organizational communication: does saying “Hi” really matter? Organization Science 9 (6), 685–698.

    Article  Google Scholar 

  • Scholten VE (2006) The early growth of academic spin-offs: factors influencing the early growth of Dutch spin-offs in the Life Sciences, ICT and Consulting. PhD Thesis, Wageningen University and Researchcentrum, The Netherlands.

  • Shaw ME (1954) Group structure and the behavior of individuals in small groups. Journal of Psychology 38, 139–149.

    Article  Google Scholar 

  • Simmel G (1950) The Sociology of Georg Simmel. Translated by KH Wolff. Glencoe, Free Press, Illinois.

    Google Scholar 

  • Sperling BK (2005) Information distribution in complex systems to improve team performance. PhD Thesis, Georgia Institute of Technology.

  • Stewart AM, Mullarkey GW and Craig JL (2003) Innovation or multiple copies of the same lottery ticket: the effect of widely shared knowledge on organizational adaptability. Journal of Marketing Theory and Practice 11 (3), 25–44.

    Article  Google Scholar 

  • Tortoriello M (2005) The social underpinnings of absorptive capacity: external knowledge, social networks, and individual innovativeness. PhD Thesis, Tepper School of Business, Carnegie Mellon University.

  • Turner VW (1957) Schism and Continuity in an African Society: A Study of Ndembu Village Life. Manchester University Press, Manchester.

    Google Scholar 

  • Tushman ML and Katz R (1980) External communication and project performance: an investigation into the role of gatekeeper. Management Science 26 (11), 1071–1085.

    Article  Google Scholar 

  • Uzzi B (1997) Social structure and competition in interfirm networks: the paradox of embeddedness. Administrative Science Quarterly 42 (1), 35–67.

    Article  Google Scholar 

  • Van Wijk R (2003) Organizing knowledge in internal networks – a multilevel study. PhD Thesis, Erasmus University, Rotterdam.

  • von Hayek FA (1937) Economics and knowledge. Economica 4 (13), 33–54.

    Article  Google Scholar 

  • Walsh J (1995) Managerial and organizational cognition. Organization Science 6 (3), 280–321.

    Article  Google Scholar 

  • Wasserman S and Faust K (1994) Social Network Analysis: Methods and Applications. Cambridge University Press, New York.

    Book  Google Scholar 

  • Wegner DM (1986) Transactive memory: a contemporary analysis of the group mind. In Theories of Group Behavior (MULLEN B and GOETHALS GR, Eds), pp 185–208, Springer-Verlag, New York.

    Google Scholar 

  • Wellman B (1983) Network analysis: some basic principles. In Social Structure and Network Analysis (MARSDEN PV and LIN N, Eds) Sage, Beverly Hills.

    Google Scholar 

  • Wellman B, Koku E and Hunsinger J (2006) Chapter 57: networked scholarship. In The International Handbook of Virtual Learning Environments (WEISS J, NOLAN J and HUNSINGER J, Eds), Vol. 14, pp 1429–1447, Springer International Handbooks of Education, Holland.

    Chapter  Google Scholar 

  • Wenger E and Snyder W (2000) Communities of practice: the organizational frontier. Harvard Business Review 78 (1), 139–145.

    Google Scholar 

  • Wexler MN (2002) Organizational memory and intellectual capital. Journal of Intellectual Capital 3 (4), 393–414.

    Article  Google Scholar 

  • Wholey DR, Wilson AR, Riley W and Knoke D (2007) Work and talk: information provision by informal consulting in medical clinics. Strategic Management Research Center, University of Minnesota http://www.csom.umn.edu/assets/83536.pdf (accessed 01 June 2006).

  • Xerox (2002) Sharing knowledge through documents. Digital Perspectives 1 (5), Xerox Corporation, Corporate Integrated Marketing, Stamford, http://www.xerox.com/downloads/kstreet/sharing_english_.pdf (accessed 29 July 2002).

  • Zack MH (2000) Researching organizational systems using social network analysis. In Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS) (SPRAGUE RH, Ed), p 7, 4–7 January, Maui, Hawaii. http://web.cba.neu.edu/~mzack/articles/socnet/socnet.htm (accessed 23 June 2007).

    Chapter  Google Scholar 

  • Zack MH and McKenney JL (1995) Social context and interaction in ongoing computer-supported management groups. Organization Science 6 (4), 394–422.

    Article  Google Scholar 

  • Zuboff S (1988) In the Age of the Smart Machine. Basic Books, New York.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed El Louadi.

Appendix

Appendix

An illustrative example:

We derived the knowledge heterogeneity measure from the knowledge distance matrix (D) obtained from the knowledge similarity matrix (S, Table 2). Like Rodan & Galunic (2004), we started by calculating the uniqueness of knowledge for each member.

The uniqueness of knowledge of a member i is some function of the uniqueness of each of his or her contacts. This is obtained by calculating, for each network member, the value u i given by formula (1):

Eq. (2) is the characteristic equation for extracting eigenvectors and eigenvalues. The uniqueness values at the level of the entire network was obtained by solving for Eq. (2) in which U is the m × 1 eigenvector corresponding to the eigenvalue of the first principal component, λ, obtained on the basis of D (the largest eigenvalue of D) using the UCINET command, Network>Centrality>Eigenvector.

We solved this equation using the PopTools Excel addin (version 2.7) (Hood, 2005). We should point out that the derivation of U and λ was based on the assumption of a symmetric matrix in which all entries are comprised between 0 and 1.

The vector U is the eigenvector composed of the uniqueness measures for each member. Thus, at the member's level, heterogeneity hi is given by formula (3):

The 1/m factor compensates for network size since the eigenvalue, λ, increases linearly with m (Rodan & Galunic, 2004). At the network level, knowledge heterogeneity is obtained by summing the values given for each of its members. As an example, the network in Figure 2 and the similarity matrix of Table 2 where a number in row i and column j indicates the proportion of knowledge common to members i and j (obviously, the amount of knowledge common to a member and herself is 100%, or 1):

and

Since λ=3.71, it follows that:

Thus, for the first member (i=1), h1 is equal to: 0.79/6=0.13. Applying the same formula to each and every member of the network and computing the average of h1, h2, h3, h4, h5, and h6, yields a network knowledge heterogeneity index of 0.295.

Using Rulke & Galaskiewicz's (2000, p. 616) formula, the knowledge heterogeneity index is equal to 0.7373 and using UCINET's average of the cohesion densities, it is equal to 0.7373 as well.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Louadi, M. Knowledge heterogeneity and social network analysis – Towards conceptual and measurement clarifications. Knowl Manage Res Pract 6, 199–213 (2008). https://doi.org/10.1057/kmrp.2008.9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/kmrp.2008.9

Keywords

Navigation