How network characteristics drive multi-layer innovation networks to achieve boundary-spanning convergence

Cross-domain knowledge integration and boundary-spanning collaboration among firms are increasingly recognized as key characteristics of vigorous development within the renewable energy sector. This paper, utilizing cooperative patent data from the new energy industry, employs Social Network Analysis (SNA) to analyze the evolutionary characteristics of multi-layer innovation networks and applies the Multilayer Exponential Random Graph Model (MERGM) to examine the driving effects of network characteristics on the boundary-spanning convergence of these innovation networks. The study's results indicate that closed triadic structures promote boundary-spanning convergence within innovation networks. Furthermore, knowledge diversity and heterogeneity facilitate boundary-spanning convergence in collaboration networks. The breadth of inter-firm cooperation also enhances boundary-spanning convergence in knowledge networks; however, the intensity of inter-firm cooperation does not necessarily promote boundary-spanning convergence in knowledge networks. This research broadens the perspective on multi-layer innovation networks and provides practical insights for fostering technological innovation and collaboration in the new energy industry.