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Article

University–Industry Technology Transfer: Empirical Findings from Chinese Industrial Firms

1
Graduate School of Humanities and Social Science, Okayama University, 3-1-1 Tsushimanaka, Kitaku, Okayama 700-8530, Japan
2
School of Management, Department of Management, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Agota Giedrė Raišienė
Sustainability 2022, 14(15), 9582; https://doi.org/10.3390/su14159582
Received: 13 June 2022 / Revised: 26 July 2022 / Accepted: 2 August 2022 / Published: 4 August 2022
(This article belongs to the Special Issue Sustainable Organization through a Prism of Human Capital)
The knowledge and innovation generated by researchers at universities is transferred to industries through patent licensing, leading to the commercialization of academic output. In order to investigate the development of Chinese university–industry technology transfer and whether this kind of collaboration may affect a firm’s innovation output, we collected approximately 6400 license contracts made between more than 4000 Chinese firms and 300 Chinese universities for the period between 2009 and 2014. This is the first study on Chinese university–industry knowledge transfer using a bipartite social network analysis (SNA) method, which emphasizes centrality estimates. We are able to investigate empirically how patent license transfer behavior may affect each firm’s innovative output by allocating a centrality score to each firm in the university–firm technology transfer network. We elucidate the academic–industry knowledge by visualizing flow patterns for different regions with the SNA tool, Gephi. We find that innovation capabilities, R&D resources, and technology transfer performance all vary across China, and that patent licensing networks present clear small-world phenomena. We also highlight the Bipartite Graph Reinforcement Model (BGRM) and BiRank centrality in the bipartite network. Our empirical results reveal that firms with high BGRM and BiRank centrality scores, long history, and fewer employees have greater innovative output. View Full-Text
Keywords: collaborative networks; technology transfer; China; university–firm collaboration; social network analysis; economic policy; economic statistics collaborative networks; technology transfer; China; university–firm collaboration; social network analysis; economic policy; economic statistics
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MDPI and ACS Style

Jiang, J.; Zhao, Y.; Feng, J. University–Industry Technology Transfer: Empirical Findings from Chinese Industrial Firms. Sustainability 2022, 14, 9582. https://doi.org/10.3390/su14159582

AMA Style

Jiang J, Zhao Y, Feng J. University–Industry Technology Transfer: Empirical Findings from Chinese Industrial Firms. Sustainability. 2022; 14(15):9582. https://doi.org/10.3390/su14159582

Chicago/Turabian Style

Jiang, Jiaming, Yu Zhao, and Junshi Feng. 2022. "University–Industry Technology Transfer: Empirical Findings from Chinese Industrial Firms" Sustainability 14, no. 15: 9582. https://doi.org/10.3390/su14159582

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