The Evolution and Determinants of Interorganizational Coinvention Networks in New Energy Vehicles: Evidence from Shenzhen, China

Jia Liu,Zhaohui Chong,Shijian Lu,Wei Zhang

doi:10.1155/2021/6665945

Abstract

With the increasing attention to climate change, air pollution, and related public health issues, China’s new energy vehicles (NEVs) industry has developed rapidly. However, few studies investigated the evolution of interorganizational collaborative innovation networks in the sector domain of NEVs and the influence of different drivers on the establishment of innovation relationships. In this context, this paper uses the joint invention patent of Shenzhen, a low-carbon pilot city of China, to investigate the dynamics of network influencing factors. The social network analysis shows that the scale of coinvention network of NEVs is constantly increasing, which is featured with diversified cooperative entities, and collaboration depth (i.e., the intensity of the interactions with these partners) is also expanding. The empirical results from the Exponential Random Graph Model (ERGM) demonstrate that, with the deepening of collaborative innovation, technological upgrading caused by knowledge exchange makes organizations in the network more inclined to cognitive proximity and less dependent on geographical proximity. In addition, organizational proximity and triadic closure contribute positively to the collaborative network, with their relevance remaining nearly the same, while the impeding effect of cultural/language difference is slightly decreasing with time.

Highlights

With the increasing attention to climate change, air pollution, and related public health issues, China’s new energy vehicles (NEVs) industry has developed rapidly
This paper uses the joint invention patent of Shenzhen, a low-carbon pilot city of China, to investigate the dynamics of network influencing factors. e social network analysis shows that the scale of coinvention network of NEVs is constantly increasing, which is featured with diversified cooperative entities, and collaboration depth is expanding. e empirical results from the Exponential Random Graph Model (ERGM) demonstrate that, with the deepening of collaborative innovation, technological upgrading caused by knowledge exchange makes organizations in the network more inclined to cognitive proximity and less dependent on geographical proximity
Data. rough the patent information inquiry system of China National Intellectual Property Administration, the paper takes the applicant’s address and the new energy vehicle keywords as the screening conditions and uses custom Python scripts designed to crawl the specific coinvention patent data whose at least one of the coinventors is located at Shenzhen from 2006 to 2017. e new energy vehicle keywords include electric vehicles, new energy, new energy vehicles, charging piles, hybrid, intelligent vehicles, new energy batteries, electronic controls, automobile motors, and automobile safety. e paper has extracted 1351 NEVs coinvention patent data involving 2 or more inventors in the study period

Summary

Research Hypotheses

We mainly focus on four commonly identified types of geographical and nongeographical proximity in the development of NEVs networks, including geographical, cognitive, organizational, and cultural factors. Different linguistic areas are supposed to intensify the fragmentation of the research networks between Shenzhen and other cities As both the Chinese central government and the local government in Shenzhen have promulgated a set of policies aiming to strengthen the position of Shenzhen as a national scientific center and knowledge linkages to the globe, Shenzhen has established branches of many renowned URIs at the national and global scale, such as Peking University, Harbin Institute of Technology, and Moscow State University. It has launched its “Peacock Plan” in 2011 to attract overseas talent to work in the city. Hypothesis 4. e negative relationship of cultural/linguistic differences with the probability of a coinvention link between two organizations decreases over time

Data and Methodology

Methods

The Network Characteristic and Evolutionary Pattern

Evolution of Determinants of Coinvention Network

Conclusion and Discussion