Global structures and local network mechanisms of knowledge-flow networks

Marjan Cugmas; Anuška Ferligoj; Miha Škerlavaj; Aleš Žiberna

doi:10.1371/journal.pone.0246660

Global structures and local network mechanisms of knowledge-flow networks

PLoS One. 2021 Feb 16;16(2):e0246660. doi: 10.1371/journal.pone.0246660. eCollection 2021.

Authors

Marjan Cugmas¹, Anuška Ferligoj^{1

2}, Miha Škerlavaj^{3

4}, Aleš Žiberna¹

Affiliations

¹ Faculty of Social Sciences, University of Ljubljana, Ljubljana, Slovenia.
² National Research University Higher School of Economics, Moscow, Russia.
³ School of Economics and Business, University of Ljubljana, Ljubljana, Slovenia.
⁴ Department of Leadership and Organizational Behavior, BI Norwegian Business School, Oslo, Norway.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Humans
Knowledge
Knowledge Management / statistics & numerical data*
Models, Theoretical
Work Performance / statistics & numerical data*
Work Performance / trends

Grants and funding

This research was financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research programs P5–0168 (M.C., A.F., A.Ž.) and P5-0410 (M.Š.) and the research project J7-8279 (Blockmodeling multilevel and temporal networks) (A.Ž.). M. C. was included into the “Young Researchers” programme which is financed by the Slovenian Research Agency (www.arrs.gov.si). The work of A.F. was partly supported by the Russian Academic Excellence Project 5–100 (https://www.5top100.ru/en/). This research was also supported by COSTNET (CA15109) (https://www.cost.eu/actions/CA15109). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.