Formation Drivers and Evolutionary Dynamics of Industry-University-Research Collaborative Networks in China’s Clean Energy Industry - Qiezeng Yuan; Heng Chen; Chang Liu (Systems)
Robust industry-university-research (I-U-R) collaborative networks are essential for accelerating innovation in the clean energy industry (CEI). This study employs the exponential random graph model to investigate how the network structural, node, and edge attributes drive the formation of I-U-R collaborative networks in China’s CEI, using a dataset of 5276 I-U-R collaborative patents. Key findings reveal the following: (1) convergence structures and transitive closure differentially drive network formation across the growth and maturity phases; (2) the nodes with strong R&D capabilities consistently drive network formation, though enterprises increasingly exert a negative effect and universities show a weakening positive effect; (3) multi-dimensional proximity displays temporal dynamics—geographic proximity follows an inverted U-curve, social proximity is U-shaped, and technological proximity gradually weakens; (4) node types exhibit heterogeneous moderating effects. Enterprises negatively moderate R&D capability during growth and maturity periods, weakening the technological proximity across all periods, and social and geographic proximity in maturity. Universities positively moderate the R&D capability but show period-specific effects on proximity: weakening social proximity in the sprouting stage, geographic proximity in the growth stage, and shifting their moderation of technological proximity from positive (growth) to negative (maturity). These findings deepen the understanding of how the I-U-R collaborative networks in China’s CEI format, contributing to the collaborative innovation theory through insights into the dynamic roles of node types.