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Global structures and local network mechanisms of knowledge-flow networks

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  • Marjan Cugmas
  • Anuška Ferligoj
  • Miha Škerlavaj
  • Aleš Žiberna

Abstract

Understanding the patterns and underlying mechanisms that come into play when employees exchange their knowledge is crucial for their work performance and professional development. Although much is known about the relationship between certain global network properties of knowledge-flow networks and work performance, less is known about the emergence of specific global network structures of knowledge flow. The paper therefore aims to identify a global network structure in blockmodel terms within an empirical knowledge-flow network and discuss whether the selected local network mechanisms are able to drive the network towards the chosen global network structure. Existing studies of knowledge-flow networks are relied on to determine the local network mechanisms. Agent-based modelling shows the selected local network mechanisms are able to drive the network towards the assumed hierarchical global structure.

Suggested Citation

  • Marjan Cugmas & Anuška Ferligoj & Miha Škerlavaj & Aleš Žiberna, 2021. "Global structures and local network mechanisms of knowledge-flow networks," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-23, February.
    • Handle: RePEc:plo:pone00:0246660
    DOI: 10.1371/journal.pone.0246660
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    References listed on IDEAS

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    1. Marko Pahor & Miha Škerlavaj & Vlado Dimovski, 2007. "The Network Perspective To Organizational Learning — A Comparison Of Two Companies," World Scientific Book Chapters, in: Christian Stary & Franz Barachini & Suliman Hawamdeh (ed.), Knowledge Management Innovation, Technology and Cultures, chapter 6, pages 65-79, World Scientific Publishing Co. Pte. Ltd..
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    4. Deborah Dougherty, 1992. "Interpretive Barriers to Successful Product Innovation in Large Firms," Organization Science, INFORMS, vol. 3(2), pages 179-202, May.
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    1. Marjan Cugmas & Aleš Žiberna & Anuška Ferligoj, 2021. "The Relative Fit measure for evaluating a blockmodel," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1315-1335, December.
    2. Yunfang Ma & Jose M. Sallan & Oriol Lordan, 2024. "Rail Transit Networks and Network Motifs: A Review and Research Agenda," Sustainability, MDPI, vol. 16(9), pages 1-21, April.

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