2021
DOI: 10.1080/13662716.2020.1860738
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Cast apart by the elites: how status influences assortative matching in industrial clusters

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Cited by 9 publications
(3 citation statements)
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References 48 publications
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“…This pattern of behavior can encourage the practice of selecting prestigious organizational partners that are often the dominant choice of others in the selection of collaborative partners. Maghssudipour et al (2021) consider the role of status in inter-firm knowledge transfer networks in an industrial cluster. They found that status was the key driver of assortative matching in the formation of knowledge transfer ties.…”
Section: Geography and Collaborationmentioning
confidence: 99%
“…This pattern of behavior can encourage the practice of selecting prestigious organizational partners that are often the dominant choice of others in the selection of collaborative partners. Maghssudipour et al (2021) consider the role of status in inter-firm knowledge transfer networks in an industrial cluster. They found that status was the key driver of assortative matching in the formation of knowledge transfer ties.…”
Section: Geography and Collaborationmentioning
confidence: 99%
“…The second paper in the special issue (Maghssudipour, Balland, and Giuliani 2021) concentrates namely on to what extent local knowledge networks are shaped by the heterogeneity of organisations in clusters. The empirical investigation draws attention towards one dimension that is impacting firms' embeddedness into local knowledge networks, which however, has received comparatively little attention so far, namely status.…”
Section: Articles In the Special Issuementioning
confidence: 99%
“…Hence, the paper addresses a timely issue, what happens to (knowledge) networks when for instance a crisis leads to a (regional) core organisation to go out of business. Similarly, like Maghssudipour, Balland, and Giuliani (2021), the authors track the evolution of a knowledge network over time, in this case that of the (publicly funded) R&D collaboration network of the ICT industry in Trentino in Italy. With the use of node-level centrality measures, actors are ranked according to their prominence in the network.…”
Section: Articles In the Special Issuementioning
confidence: 99%