A study on the multidimensional driving mechanism of cross-regional scientific collaboration network in China

Multidimensional proximity has been considered the important factor affecting regional scientific collaboration network (RSCN) and has received extensive attention from scholars. This paper constructs a cross-RSCN based on co-authorship across 31 regions in China, incorporates traditional multidimensional proximity and network embedding proximity into a unified framework and proposes a five-factor driving model for dissecting the mechanism of cross-regional scientific collaborative innovation (CI). The results show that the scientific collaboration network is gradually becoming saturated with connections, and the polarisation of cross-regional collaboration is increasingly serious. The collaboration intensity is affected by the co-movement of several proximity factors, among which geographic proximity (GP) is the main but not the sole one that positively affects the cross-regional scientific collaboration. Based on the decision tree model, we conclude that structure proximity may compensate for low GP and low economic proximity (EP) on cross-regional CI performance. The positive effect of network location proximity on regional scientific collaboration is more sensitive when the EP is not high. The findings provide significant guidance for scientific research institutions, universities and regional policy-makers to select appropriate partners as well as network embedding strategies, and further contribute to long-term improvement of scientific innovation performance and stable regional economic growth.