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Global structures and local network mechanisms of knowledge-flow networks

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...

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Autores principales: Cugmas, Marjan, Ferligoj, Anuška, Škerlavaj, Miha, Žiberna, Aleš
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886156/
https://www.ncbi.nlm.nih.gov/pubmed/33591999
http://dx.doi.org/10.1371/journal.pone.0246660
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author Cugmas, Marjan
Ferligoj, Anuška
Škerlavaj, Miha
Žiberna, Aleš
author_facet Cugmas, Marjan
Ferligoj, Anuška
Škerlavaj, Miha
Žiberna, Aleš
author_sort Cugmas, Marjan
collection PubMed
description 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.
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spelling pubmed-78861562021-02-23 Global structures and local network mechanisms of knowledge-flow networks Cugmas, Marjan Ferligoj, Anuška Škerlavaj, Miha Žiberna, Aleš PLoS One Research Article 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. Public Library of Science 2021-02-16 /pmc/articles/PMC7886156/ /pubmed/33591999 http://dx.doi.org/10.1371/journal.pone.0246660 Text en © 2021 Cugmas et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Cugmas, Marjan
Ferligoj, Anuška
Škerlavaj, Miha
Žiberna, Aleš
Global structures and local network mechanisms of knowledge-flow networks
title Global structures and local network mechanisms of knowledge-flow networks
title_full Global structures and local network mechanisms of knowledge-flow networks
title_fullStr Global structures and local network mechanisms of knowledge-flow networks
title_full_unstemmed Global structures and local network mechanisms of knowledge-flow networks
title_short Global structures and local network mechanisms of knowledge-flow networks
title_sort global structures and local network mechanisms of knowledge-flow networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886156/
https://www.ncbi.nlm.nih.gov/pubmed/33591999
http://dx.doi.org/10.1371/journal.pone.0246660
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