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Network dynamics of Chinese university knowledge transfer

Yindan Ye (), Kevin De Moortel () and Thomas Crispeels ()
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Yindan Ye: Vrije Universiteit Brussel
Kevin De Moortel: Vrije Universiteit Brussel
Thomas Crispeels: Vrije Universiteit Brussel

The Journal of Technology Transfer, 2020, vol. 45, issue 4, No 10, 1228-1254

Abstract: Abstract Social networks are increasingly considered to be influential in explaining the knowledge transfer process. Despite scholarly efforts to integrate knowledge transfer and social network research, we lack understanding on how knowledge transfer networks emerge and evolve. We draw upon resource dependency theory and inter-organizational networks and collect patent data of 42 Double-First Class (DFC) universities to study structural properties of the Chinese university knowledge transfer network over time. Our results point to the existence of an increasingly complex yet remarkably efficient network. Universities and co-patent collaborations emerge in the network and act as knowledge bridges between other universities. The network moves from an early-stage single-centered network to a mature multi-centered network through a power-law pattern. Such movement allows for an aggregation phenomenon to appear as oligopolistic communities emerge and rule the network. While knowledge is more easily shared and accessible within communities, their existence also brings along control over knowledge bases embedded in those communities. Key universities take central positions within the expanding network, which allows them to gain control and easier access to knowledge. It also hints that it might be difficult for other DFC universities to become key players in the network. On an inter-regional level, our findings point to steadily increasing knowledge transfer activity, which is key to overcome the underdevelopment of some Chinese regions. Overall, this paper contributes to our understanding on the theoretical connection between knowledge transfer and social network dynamics, on how universities evolve through knowledge transfer networks, and on how their embeddedness translates into knowledge control, knowledge access, and knowledge bridges.

Keywords: Knowledge transfer; Network dynamics; University; China (search for similar items in EconPapers)
JEL-codes: O33 R11 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10961-019-09748-7

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