关键词:

合作网络, 创新绩效, 网络结构, 机器学习, 决策路径

Abstract:

Collaborative network is an important way for enterprises to adapt to the external uncertainty environment and conduct open innovation. Based on the joint patent application data in the SoC chips industry from 2003 to 2021, this paper analyzes, in detail, the topology and characteristics of the collaborative innovation network by using the social network analysis method, and empirically interprets the interaction mechanism and promotion paths of heterogeneity characteristics in various collaboration contexts on the innovation performance of enterprises by using the CART decision tree, K-means, and other machine learning methods. The results show that in the simple binary relationship networks, the innovation performance is mainly affected by the cooperation depth, while in the complex collaborative relationship networks, it is affected by both structural and non-structural factors. Betweenness centrality is the main factor. Cooperation depth and eigenvector centrality positively affect the innovation performance in those firms with weak resource bases. Degree centrality positively affects the innovation performance in those platform firms with concentrated resources. The effects of other features differ less in this two collaborative networks and do not have significant impacts on the innovation performance. The findings provide a path reference for relevant technology research and development enterprises to make partner selection and rationalize network resources to further improve innovation performance, and expand the analysis perspective for paradigm exploration of social science research in the era of digital economy.

Key words:

collaborative network, innovation performance, network structure, machine learning; decision rules