How do prolific inventors impact firm innovation in ICT: implications from patent co-inventing network

Abstract

Prolific inventors not only own higher innovation productivity, but also impact other inventors through innovation networks. This paper contributes to existing literatures by differentiating prolific inventors from non-prolific inventors in the network context, and making an empirical analysis of the effect of prolific inventors. We use the patent filing data from the State Intellectual Property Office of China (SIPO) in investigating the effect of prolific inventors on firm innovation. We use the patents filed by 10 largest Information & Communication Technology firms during 1995–2010 and establish the innovation network with patent co-inventing data. The empirical result shows that prolific inventors positively impact their partners who used to co-invent at least one patent with them. Furthermore, prolific inventors positively impact inventors who do not have a close contact with them. The closer the inventors are to prolific inventors, the more patents they produce. Members are thereby more likely to gather around prolific inventors and formulate intensive clusters. In networks centered by prolific inventors, useful knowledge outweighs redundant knowledge, and high clustering that facilitates knowledge flow is proved to be beneficial; while in networks without prolific inventors, high clustering may not be beneficial as there are less inventors holding advanced knowledge. Policy implications are discussed at the end of this study.

Keywords:

• prolific inventors
• patent
• innovation network
• knowledge flow

Acknowledgements

This study is supported by 2014 National Natural Science Foundation of China “Comparative Study on Small World Innovation Networks of Oriental and Occidental Firms Based on Patent Co-Authorship Data”, Beijing Natural Science Foundation (9144036), Ministry of Education, Humanities and Social Sciences Youth Fund Project (13YJC630219).

Notes on contributors

Gupeng Zhang received his Ph.D. in Management from Beihang University in 2012 for a thesis focusing on patent value of China. He is assistant professor in the College of Technology Management, University of Chinese Academy of Science. His current research interest is in innovation network, the private value of patent right in China, intellectual property protection, innovation network analysis, etc.

Xiaofeng Lv is interested in economic issues of China, such as income inequality, patent value, international trade, and rural financial market in china. He has published several papers about patent value in China with econometric theory in journals such as China Economic Review, Statistical Methodology, and AStA Advances in Statistical Analysis.

Hongbo Duan is currently an assistant professor in School of Management, in University of Chinese Academy of Sciences. His research interests include innovation diffusion and energy–economy–environmental modelling.

Notes

1. Here we define the direct and indirect contact with the patent co-inventing data. For example, if Inventors A and B co-invented at least one patent, we claim that A and B have a direct contact; if Inventors A and B co-invented at least one patent, B and C co-invented at least one patent, we claim that A and C have an indirect contact.

2. Or other terminologies with the similar meanings.

3. We tested other thresholds, e.g. 5%, 15%, and 20%, the empirical result does not make any differences, which confirms the robustness of our empirical result.

4. Lower than 10%.

5. The patent co-inventing network may not be totally connected, e.g. there are two isolated components in a firm, where inventors within each component co-invent with each other, while inventors between these two components never co-invent any patents.

6. Different lags and window sizes did not demonstrate substantively different results.

7. If the parameter estimates of is significantly negative and is zero, clustering will negatively impact patent output, since the clustering coefficient never takes negative value, and only the right-hand side of the parabola will function on the relationship between clustering and patent output.