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Network dynamics in university-industry collaboration: a collaboration-knowledge dual-layer network perspective

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Abstract

Collaborations between universities and firms provide a key pathway for innovation. In the big data era, however, the interactions between these two communities are being reshaped by information of much higher complexity and knowledge exchanges with more volume and pace. With this research, we put forward a methodology for comprehensively measuring both actor collaboration and produced knowledge in shaping network dynamics of university-industry collaboration. Using dual-layer networks consisting of organizations and topics, we mapped the longitudinal correlations between partnerships and knowledge in terms of both co-applications of patents and semantics. Network structures, individual characteristics, and knowledge proximity indicators were used to depict the longitudinal networks and then model the network dynamics. Further, a stochastic actor-oriented model was used to provide insights into the factors contributing to the network’s evolution. A case study on university-industry collaborations in the information and communications technology sector demonstrates the feasibility of the methodology. The result of this study can be used for future research into the mechanisms that underpin university-industry collaborations and opportunity discovery.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 72004009).

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Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, China

    Hongshu Chen, Xinna Song & Qianqian Jin

  2. Cyber Finance Department, Postal Savings Bank of China, Beijing, China

    Ximeng Wang

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  1. Hongshu Chen
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  2. Xinna Song
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  4. Ximeng Wang
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Correspondence to Hongshu Chen.

Appendix 1

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Table 4 Topics extracted from DII patents for ICT area in China
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Chen, H., Song, X., Jin, Q. et al. Network dynamics in university-industry collaboration: a collaboration-knowledge dual-layer network perspective. Scientometrics 127, 6637–6660 (2022). https://doi.org/10.1007/s11192-022-04330-9

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