Skip to main content
SpringerLink
Log in

Patterns and structures of intra-organizational learning networks within a knowledge-intensive organization

  • Research Article
  • Published:
Journal of Information Technology

Abstract

This paper employs the network perspective to study patterns and structures of intra-organizational learning networks. The theoretical background draws from cognitive theories, theories of homophily and proximity, theories of social exchange, the theory of generalized exchange, small-worlds theory, and social process theory. The levels of analysis applied are actor, dyadic, triadic, and global. Confirmatory social network analysis (exponential random graph modeling) was employed for data analysis. Findings suggest: (1) central actors in the learning network are experienced and hold senior positions in the organizational hierarchy; (2) evidence of homophily (in terms of gender, tenure, and hierarchical level relations) and proximity (in terms of geographical and departmental distances) in learning relationships; (3) learning relationships are non-reciprocal; and (4) transitivity and high local clustering with sparse inter-cluster ties are significant for intra-organizational learning networks.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. The set of structural properties of networks involves size, inclusiveness, component, connectivity, connectedness, density, centralization, symmetry, and transitivity (Brass, 1995). They are used to describe networks as a whole.

  2. Alternating k-triangles are a higher order measure of transitivity (Snijders et al., 2006), whereas alternating independent two-paths (Robins et al., 2009) represent a particular combination of k-independent-2-path counts into the one statistic and are used to examine clustering in networks. They are referred to as higher order because they include configurations with more than three nodes.

  3. Clustering coefficient of a vertex in a graph quantifies how close the vertex and its neighbors are from being a clique. Watts and Strogatz (1998) introduced the measure to determine whether a graph is a small-world network. Average clustering coefficient (CC) is calculated by summing up all the clustering coefficients and dividing them by the number of vertices. Average shortest path (L) is simply the sum of all the shortest paths between any pair of two vertices divided by the number of vertices. Comparison of CC and L for observed and random network allows for testing of the small-world phenomena. It is present when ratios CC for actual network divided by CC for random network exceed ratio L actual network divided by L random network.

References

  • Aldrich, H. (1972). Organizational Boundaries and Inter-Organizational Conflict, Human Relations 24 (4): 279–293.

    Article  Google Scholar 

  • Argote, L., Beckman, S.L. and Epple, D. (1990). The Persistence and Transfer of Learning in Industrial Settings, Management Science 36 (2): 140–154.

    Article  Google Scholar 

  • Argyris, C. and Schön, D.A. (1978). Organizational Learning: A theory of action perspective, Reading, MA: Addison-Wesley.

    Google Scholar 

  • Awazu, Y., Baloh, P., Desouza, K.C., Wecht, C.H., Kim, J.Y. and Jha, S. (2009). Information-Communication Technologies Open Up Innovation, Research-Technology Management 52 (1): 51–58.

    Google Scholar 

  • Batagelj, V. and Mrvar, A. (2007). Pajek – Program for large network analysis, [www document] http://vlado.fmf.uni-lj.si/pub/networks/pajek/ (accessed 6th January 2007).

  • Barabási, A.L. and Albert, R. (1999). Emergence of Scaling in Random Networks, Science 286 (5439): 509–512.

    Article  Google Scholar 

  • Baum, J.A.C. (1996). Organizational Ecology, in S. Clegg, C. Hardy and W. Nord (eds.) Handbook of Organization Studies, Thousand Oaks, CA: Sage, pp. 77–114.

    Google Scholar 

  • Blau, P.M. (1964). Exchange and Power in Social Life, New York: Wiley.

    Google Scholar 

  • Blau, P.M. (1955). The Dynamics of Bureaucracy: A Study of Interpersonal Relations in Two Government Agencies, Chicago, IL: University of Chicago.

  • Boh, W.F., Slaughter, A.A. and Espinosa, J.A. (2007). Learning from Experience in Software Development: A multilevel analysis, Management Science 53 (8): 1315–1332.

    Article  Google Scholar 

  • Borgatti, S.P. and Cross, R. (2003). A Relational View of Information Seeking and Learning in Social Networks, Management Science 49 (4): 432–445.

    Article  Google Scholar 

  • Borgatti, S.P., Everett, M. and Freeman, L. (2006). UCINET for Windows, Analytic Technologies Inc. [www document] http://www.analytictech.com/ucinet/ucinet_5_description.htm (accessed 23rd August 2006).

  • Brass, D.J. (1985). Men's and Women's Networks: A study of interaction patterns and influence in organizations, Academy of Management Journal 28 (2): 327–343.

    Article  Google Scholar 

  • Brass, D.J. (1995). A Social Network Perspective on Human Resources Management, Research in Personnel and Human Resource Management 13: 39–79.

    Google Scholar 

  • Brass, D.J., Galaskiewicz, J., Greeve, H.R. and Tsai, W. (2004). Taking Stock of Networks and Organizations: A multilevel perspective, Academy of Management Journal 47 (6): 795–817.

    Article  Google Scholar 

  • Brown, J.S. 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 

  • Cohen, W.M. and Levinthal, D.A. (1990). Absorptive Capacity: A new perspective on learning and innovation, Administrative Science Quarterly 35: 128–158.

    Article  Google Scholar 

  • Contractor, N.S., Wasserman, S. and Faust, K. (2006). Testing Multitheoretical, Multilevel Hypotheses about Organizational Networks: An analytic framework and empirical example, Academy of Management Review 31 (3): 681–703.

    Article  Google Scholar 

  • Cook, K.S. and Emerson, R.M. (1978). Power, Equity, and Commitment in Exchange Networks, American Sociological Review 43 : 721–739.

    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: 67–76.

    Google Scholar 

  • Cross, R., Parker, A., Prusak, L. and Borgatti, S.P. (2001). Knowing What We Know – Supporting knowledge creation and sharing in social networks, Organizational Dynamics 30 (2): 100–120.

    Article  Google Scholar 

  • Crossan, M.M., Lane, H., White, R.E. and Djurfeldt, L. (1995). Organizational Learning: Dimensions for a theory, The International Journal of Organizational Analysis 3 (4): 337–360.

    Article  Google Scholar 

  • Cyert, R.M. and March, J.G. (1963). A Behavioral Theory of the Firm, Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Day, G.S. (1992). Continuous Learning about Markets, California Management Review 20 (5): 9–31.

    Google Scholar 

  • de Geus, A.P. (1988). Planning as Learning, Harvard Business Review 66 (2): 70–74.

    Google Scholar 

  • de Nooy, W., Mrvar, A. and Batagelj, V. (2005). Exploratory Social Network Analysis with Pajek, New York: Cambridge University Press.

    Book  Google Scholar 

  • Desouza, K.C. (2003). Barriers to Effective Use of Knowledge Management Systems in Software Engineering, Communications of the ACM 46 (1): 99–101.

    Article  Google Scholar 

  • Dimovski, V. and Škerlavaj, M. (2008). Organizational Learning as the Key towards Improved Organizational Performance, in A. Koohang, K. Harman and J. Britz (eds.) Knowledge Management: Research and application, Santa Rosa, CA: Informing Science Press, pp. 147–175.

    Google Scholar 

  • Dodgson, M. (1993). Organizational Learning – A review of some literatures, Organizational Studies 14 (3): 375–394.

    Article  Google Scholar 

  • Dussauge, P., Garette, B. and Mitchell, W. (2000). Learning from Competing Partners: Outcomes and durations of scale and link alliances in Europe, North America and Asia, Strategic Management Journal 21 (2): 99.

    Article  Google Scholar 

  • Easterby-Smith, M. (1997). Disciplines of Organizational Learning: Contributions and critiques, Human Relations 50 (9): 1085–1113.

    Google Scholar 

  • Emerson, R.M. (1976). Social Exchange Theory, Annual Review of Sociology 2: 335–362.

    Article  Google Scholar 

  • Erat, P., Desouza, K.C., Schäfer-Jugel, A. and Kurzawa, M. (2006). Business Customer Communities and Knowledge Sharing: Exploratory study of critical issues, European Journal of Information Systems 15 (5): 511–524.

    Article  Google Scholar 

  • Festinger, L., Schachter, S. and Back, K. (1950). Social Pressures in Informal Groups: A study of human factors in housing, Palo Alto, CA: Stanford University Press.

    Google Scholar 

  • Fiol, C.M. and Lyles, M.A. (1985). Organizational Learning, Academy of Management Review 10 (4): 803–813.

    Google Scholar 

  • Frank, O. and Strauss, D. (1986). Markov Graphs, Journal of the American Statistical Association 81 : 832–834.

    Article  Google Scholar 

  • Gioia, D.A. and Manz, C.C. (1985). Linking Cognition and Behavior: A script processing interpretation of vicarious learning, Academy of Management Review 10 (3): 527–540.

    Google Scholar 

  • Granovetter, M. (1985). Economic Action and Social Structure: The problem of embeddedness, American Journal of Sociology 91 (3): 481–510.

    Article  Google Scholar 

  • Heald, M.R., Contractor, N.S., Koehly, L. and Wasserman, S. (1998). Formal and Emergent Predictors of co-Workers’ Perceptual Congruence on an Organization's Social Structure, Human Communication Research 24: 536–563.

    Article  Google Scholar 

  • Hollingshead, A.B. (2000). Perceptions of Expertise and Transactive Memory in Work Relationships, Group Processes and Intergroup Relations 3 (3): 257–267.

    Article  Google Scholar 

  • Holmquist, M. (2004). Experimental Learning Processes of Exploitation and Exploration Within and Between Organizations: An empirical study of product development, Organization Science 15 (1): 15–32.

    Google Scholar 

  • Homans, G.C. (1950). The Human Group, New York: Harcourt Brace.

    Google Scholar 

  • Homans, G.C. (1974). Social Behaviour: Its elementary forms (Rev Ed). New York: Harcourt Brace.

  • Huber, G.P. (1991). Organizational Learning: The contributing processes and the literature, Organization Science 2 (1): 88–115.

    Article  Google Scholar 

  • Ibarra, H. (1992). Homophily and Differential Returns: Sex differences in network structure in an advertising firm, Administrative Science Quarterly 37 (3): 422–447.

    Article  Google Scholar 

  • Ibarra, H. (1993). Personal Networks of Women and Minorities in Management: A conceptual framework, Academy of Management Review 18 (1): 56–87.

    Google Scholar 

  • Ibarra, H., Kilduff, M. and Tsai, W. (2005). Zooming In and Out: Connecting individuals and collectivities at the frontiers of organizational network research, Organization Science 16 (4): 359–374.

    Article  Google Scholar 

  • Kang, S.-C., Morris, S.S. and Snell, S.A. (2007). Relational Archetypes, Organizational Learning, and Value Creation: Extending the human resource architecture, Academy of Management Review 32 (1): 236–257.

    Article  Google Scholar 

  • Kaše, R., Paauwe, J. and Zupan, N. (2009). HR Practices, Interpersonal Relations, and Intrafirm Knowledge Transfer in Knowledge-Intensive Firms: A social network perspective, Human Resource Management 48 (4): 615–639.

    Article  Google Scholar 

  • Kim, D.H. (1993). The Link Between Individual and Organizational Learning, Sloan Management Review, Fall: 37–50.

  • Kogut, B. and Walker, G. (2001). The Small World of Germany and the Durability of National Ownership Networks, American Sociological Review 66 : 317–335.

    Article  Google Scholar 

  • Kogut, B., Shan, W. and Walker, G. (1993). Knowledge in the Network and the Network as Knowledge: Structuring of new industries, in G. Grabher (ed.) The Embedded Firm: On the socioeconomics of industrial network, New York: Routledge, pp. 67–94.

    Google Scholar 

  • Korzenny, F. and Bauer, C. (1981). Testing the Theory of Electronic Propinquity: Organizational teleconferencing, Communication Research 8 (4): 479–498.

    Google Scholar 

  • Krackhardt, D. (1987). Cognitive Social Structures, Social Networks 9: 109–134.

    Article  Google Scholar 

  • Lave, J. and Wenger, E. (1991). Situated Learning: Legitimate peripheral participation, Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Lazega, E. (1992). The Micropolitics of Knowledge, New York: A. de Gruyter.

    Google Scholar 

  • Lazega, E. (2001). The Collegial Phenomenon, Oxford, UK: Oxford University Press.

    Book  Google Scholar 

  • Lazega, E., Lemercier, C. and Mounier, L. (2006). A Spinning Top Model of Formal Organization and Informal Behavior: Dynamics of Advice Networks Among Judges in a Commercial Court, European Management Review 3 (2): 113–122.

    Article  Google Scholar 

  • Lazega, E. and Pattison, P. (2001). Social Capital and Social Mechanisms and Collective Assets: The example of status auctions among colleagues, in N. Lin, K. Cook and R. Burt (eds.) Social Capital: Theory and research, New York: Aldine-de Gruyter, pp. 185–208.

    Google Scholar 

  • Leavitt, H.J. (1951). Some Effects of Certain Communication Patterns on Group Performance, Journal of Abnormal and Social Psychology 46 : 38–50.

    Article  Google Scholar 

  • Lewis, D. (1969). Convention: A philosophical study, Oxford: Blackburn.

    Google Scholar 

  • Lewis, K., Lange, D. and Gillis, L. (2005). Transactive Memory Systems, Learning, and Learning Transfer, Organization Science 16 (6): 581–600.

    Article  Google Scholar 

  • Liang, D.W., Moreland, R. and Argote, L. (1995). Group Versus Individual Training and Group Performance: The mediating role of transactive memory, Personality and Social Psychology Bulletin 21 (4): 384–393.

    Article  Google Scholar 

  • Liebeskind, J.P., Oliver, A.L., Zucker, L. and Brewer, M. (1996). Social Networks, Learning, and Flexibility: Sourcing scientific knowledge in new biotechnology firms, Organization Science 7 (4): 428–444.

    Article  Google Scholar 

  • March, J.G. and Simon, H.A. (1958). Organizations, New York: Wiley.

    Google Scholar 

  • McPherson, J.M. and Smith-Lovin, L. (1987). Homophily in Voluntary Organizations, American Sociological Review 52 (3): 370–379.

    Article  Google Scholar 

  • McPherson, J.M., Smith-Lovin, L. and Cook, J.M. (2001). Birds of the Feather: Homophily in social networks, Annual Review of Sociology 27: 417–444.

    Article  Google Scholar 

  • Milgram, S. (1967). The Small World Problem, Psychology Today 1 (1): 60–67.

    Google Scholar 

  • Mitchell, J.C. (1969). The Concept and Use of Social Networks, in J.C. Mitchell (ed.) Social Networks in Urban Situations, Manchester, UK: University of Manchester Press, pp. 1–44.

    Google Scholar 

  • Monge, P.R. and Contractor, N.S. (2003). Theories of Communication Network, Oxford, UK: Oxford University Press.

    Google Scholar 

  • Moreland, R.L. (1999). Transactive Memory: Learning who knows what in work groups and organizations, in L. Thompson, D. Messick and J. Levine (eds.) Sharing Knowledge in Organizations, Mahwah, NJ: Erlbaum, pp. 327–346.

    Google Scholar 

  • Moreno, J.L. (1953). Who Shall Survive? Beacon, NY: Beacon House Inc.

  • Muthusamy, S.K. and White, M.A. (2005). Learning and Knowledge Transfer in Strategic Alliances: A social exchange view, Organization Studies 26 (3): 415–441.

    Article  Google Scholar 

  • Pahor, M., Škerlavaj, M. and Dimovski, V. (2008). Evidence for the Network Perspective on Organizational Learning, Journal of the American Society for Information Science and Technology 59 (12): 1985–1994.

    Article  Google Scholar 

  • Pool, I.S. and Kochen, M. (1978). Contacts and Influence, Social Networks 1: 1–48.

    Article  Google Scholar 

  • Powell, W.W. (1990). Neither Market nor Hierarchy: Network forms of organization, Research in Organizational Behavior 12: 295–336.

    Google Scholar 

  • Powell, W.W., Koput, K.W. and Smith-Doerr, L. (1996). Interorganizational Collaboration and the Locus of Innovation: Networks of learning in biotechnology, Administrative Science Quarterly 41 (1): 116–146.

    Article  Google Scholar 

  • Reagans, R., Argote, L. and Brooks, D. (2005). Individual Experience and Experience Working Together: Predicting learning rates from knowing who knows what and knowing how to work together, Management Science 51 (6): 869–881.

    Article  Google Scholar 

  • Ren, Y., Carley, K.M. and Argote, L. (2006). The Contingent Effects of Transactive Memory: When is it more beneficial to know what others know? Management Science 52 (5): 671–682.

    Article  Google Scholar 

  • Rhee, M. (2004). Network Updating and Exploratory Learning Environment, Journal of Management Studies 41 (6): 933–949.

    Article  Google Scholar 

  • Robins, G., Pattison, P. and Wang, P. (2009). Closure, Connectivity and Degrees: New Specifications for Exponential Random Graph (p*) Models for Directed Social Networks, Social Networks 31 (2): 105–117.

    Article  Google Scholar 

  • Sanchez, R. (2001). Knowledge Management and Organizational Competence, Oxford, UK: Oxford University Press.

    Google Scholar 

  • Schwandt, D. and Marquardt, M. (2000). Organizational Learning: From world-class theories to global best practices, Boca Raton, FA: St Lucie.

    Google Scholar 

  • Sherif, M. (1958). Superordinate Goals in the Reduction of Inter-group Conflicts, American Journal of Sociology 63 (4): 349–356.

    Article  Google Scholar 

  • Shrivastava, P.A. (1983). Typology of Organizational Learning Systems, Journal of Management Studies 20 (1): 1–28.

    Article  Google Scholar 

  • Škerlavaj, M. and Dimovski, V. (2007). Towards Network Perspective of Intra-Organizational Learning: Bridging the gap between acquisition and participation perspective, Interdisciplinary journal of information, knowledge, and management 2: 43–58.

    Google Scholar 

  • Škerlavaj, M., Dimovski, V., Mrvar, A. and Pahor, M. (2010). Intra-Organizational Learning Networks within Knowledge-Intensive Learning Environments, Interactive Learning Environments 18 (1): 39–63.

    Article  Google Scholar 

  • Snijders, T.A.B. (2001). The Statistical Evaluation of Social Network Dynamics, in M.E. Sobel and M.P. Becker (eds.) Sociological Methodology, Boston: Blackwell, pp. 361–395.

    Google Scholar 

  • Snijders, T.A.B. (2002). Markov Chain Monte Carlo Estimation of Exponential Random Graph Models, Journal of Social Structure 3: 2–40.

    Google Scholar 

  • Snijders, T.A.B., Pattison, P.E., Robins, G.L. and Handcock, M.S. (2006). New Specifications for Exponential Random Graph Models, Sociological Methodology 36: 95–153.

    Article  Google Scholar 

  • Sorenson, O. (2003). Interdependence and Adaptability: Organizational learning and the long-term effect of integration, Management Science 49 (4): 446–463.

    Article  Google Scholar 

  • Sorenson, O., Rivkin, J. and Fleming, L. (2006). Complexity, Networks and Knowledge Flow, Research Policy 35 (7): 994–1017.

    Article  Google Scholar 

  • Sorenson, O.J. and Sorenson, J.B. (2001). Finding the Right Mix: Franchising, organizational learning, and chain performance, Strategic Management Journal 22: 713–724.

    Article  Google Scholar 

  • Stata, R. (1989). Organizational Learning: The key to management innovation, Sloan Management Review 30: 63–74.

    Google Scholar 

  • Steglich, C.E.G., Snijders, T.A.B. and West, P. (2006). Applying SIENA: An illustrative analysis of the co-evolution of adolescents’ friendship networks, taste in music, and alcohol consumption, Methodology 2: 48–56.

    Article  Google Scholar 

  • Tichy, N.M., Tushman, M.L. and Fombrun, C. (1979). Social Network Analysis for Organizations, Academy of Management Review 4 (4): 507–519.

    Google Scholar 

  • Tucker, A.L., Nembhard, I.M. and Edmondson, A.C. (2007). Implementing New Practices: An empirical study of organizational learning in hospital intensive care units, Management Science 53 (6): 894–907.

    Article  Google Scholar 

  • Wasserman, S. and Faust, K. (1994). Social Network Analysis, Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Watts, D.J. (1999). Networks, Dynamics, and the Small-World Phenomenon, American Journal of Sociology 105 (2): 493–527.

    Article  Google Scholar 

  • Watts, D.J. and Strogatz, S. (1998). Collective Dynamics of ‘Small-World’ Networks, Nature 393: 440–442.

    Article  Google Scholar 

  • Wegner, D.M. (1987). Transactive Memory: A contemporary analysis of the group mind, in B. Mullen and G.R. Goethals (eds.) Theories of Group Behaviour, New York: Springer, pp. 185–208.

    Chapter  Google Scholar 

  • Wegner, D.M. (1995). A Computer Network model of Human Transactive Memory, Social Cognition 13 (3): 319–339.

    Article  Google Scholar 

  • Wellman, B. (1979). The Community Question, American Journal of Sociology 84 : 1201–1231.

    Article  Google Scholar 

  • Wegner, D.M., Erber, R. and Raymond, P. (1991). Transactive Memory in Close Relationships, Journal of Personality and Social Psychology 61: 923–929.

    Article  Google Scholar 

  • Wellman, B. (1988). Structural Analysis: From method and metaphor to theory and substance, in B. Wellman and S.D. Berkowitz (eds.) Social Structures: A Network Approach, New York: Cambridge University Press.

    Google Scholar 

  • Wellman, B. and Berkowitz, S.D . (eds.) (1988). Social Structures: A Network Approach, New York: Cambridge University Press.

Download references

Acknowledgements

The authors express their gratitude to Daniel J. Brass and Emmanuel Lazega for their comments on the early version of the paper as well as Tom Snijders, Andrej Mrvar, and Anuška Ferligoj for suggestions on research methodology and data analysis. Our appreciation also goes to the European Science Foundation and the seminar participants at the Quantitative Methods in Social Sciences seminars at Faculty of Social Sciences, University of Ljubljana and University of Groningen. We are also grateful to the managers and employees of the organization who provided access to their network data. Last but not least, we thank the special issue editors, Elaine Ferneley and Remko Helms, as well as two anonymous reviewers, who provided valuable feedback on the manuscript. All errors and omissions are solely our responsibility.

Author information

Authors and Affiliations

  1. Department for Management and Organization, Faculty of Economics, University of Ljubljana, Ljubljana, Slovenia

    Miha Škerlavaj & Vlado Dimovski

  2. The Information School, University of Washington, Seattle, WA, USA

    Kevin C Desouza

Authors
  1. Miha Škerlavaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vlado Dimovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kevin C Desouza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miha Škerlavaj.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Škerlavaj, M., Dimovski, V. & Desouza, K. Patterns and structures of intra-organizational learning networks within a knowledge-intensive organization. J Inf Technol 25, 189–204 (2010). https://doi.org/10.1057/jit.2010.3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jit.2010.3

Keywords

Search

Navigation