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Data Analysis and Knowledge Discovery
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Research on R&D Team Identification and Innovation Outcome based on Patent Collaboration Network
Guan Peng,Wang Yuefen,Fu Zhu,Jin Jialin
(School of Economics and Law, Chaohu University, Hefei 238000, China) (School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094, China) (School of Economics and Management, Jiangsu University of Science & Technology, Zhenjiang 212003, China)
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Abstract  

[Objective] We use the patent inventor collaboration network to identify technology R&D teams, and analyze the factors influencing team innovation outcome statistically.

[Methods] Firstly, the detection algorithm of core R&D personnel is designed, and the identification algorithm of R&D team based on core R&D personnel is proposed. Secondly, the quantity of patent output is taken as the quantity index of innovation output, and the number of patent citations and patent claims is taken as the quality index of innovation output. The negative binomial regression model is used to analyze the impact of R&D team characteristics on team innovation output.

[Results] An empirical study in the field of speech recognition technology shows that the proposed algorithm can effectively identify 566 evolutionary sequences of R&D teams, including 1827 R&D teams in each snapshot, with an average size of 16.670 R&D teams. The R & D team, as a sub-network, has an average clustering coefficient of 0.856 and an average shortest path length of 1.646, showing obvious small-world characteristics.

[Limitations] R&D team identification algorithm for some small scale and lack of well-known experts in the field of technology R&D team identification effect is not good. Further expansion of the empirical research samples is needed to verify the universality of the research results.

[Conclusions] Based on the sample data of voice recognition technology domain, the impact of R&D team characteristics on team innovation outcome is analyzed. The results of negative binomial regression model show that team size and average shortest path length of team network have significant positive effects on the quantity and quality of innovation output.Team persistence, team stability and team network density have significant negative effects on the quantity and quality of innovation output. The team clustering coefficient has a significant negative effect on the quantity of innovation output, but no significant effect on the quality of innovation output.


Key words Patent Cooperation Network      R&      D Team       Innovation Outcome      
Published: 14 December 2021
ZTFLH:  C931  

Cite this article:

Guan Peng, Wang Yuefen, Fu Zhu, Jin Jialin. Research on R&D Team Identification and Innovation Outcome based on Patent Collaboration Network . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467. 2021.0772     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y0/V/I/1

[1] Guan Peng,Wang Yuefen,Jin Jialin,Fu Zhu. Developments of Tech-Innovation Network for Patent Cooperation: Case Study of Speech Recognition in China[J]. 数据分析与知识发现, 2021, 5(1): 112-127.
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