"本地-超本地"双重合作网络嵌入、知识凝聚性与企业双元创新OACHSSCD

Embeddedness theory posits that firms' innovation activities are embedded within social relationships.Given the difficulty for firms to internally possess all the resources needed for innovation,inter-organizational R&D collaboration has become a crucial means of innovation,gradually evolving into complex R&D collaboration networks in which firms are embedded as network nodes.Prior studies have primarily focused on how different dimensions of network embeddedness relate to firm innovation performance,but the findings have been inconsistent.Some have further explored potential nonlinear effects and moderating factors such as structural holes in knowledge networks and digital transformation.This study differentiates between types of collaborative networks based on administrative boundaries and investigates how network embeddedness affects firms' ambidextrous innovation performance.Specifically,we define and distinguish between local and extralocal collaborative networks,theorize the mechanisms through which different types of network embeddedness influence exploratory and exploitative innovation,and examine how knowledge network cohesion moderates these relationships.This study makes three key contributions.First,it introduces administrative boundary considerations into embeddedness theory,enriching the literature on network embeddedness.Second,it promotes the integration of embeddedness theory with ambidextrous innovation theory.Third,by incorporating knowledge combination theory,it enhances the explanatory power of embeddedness theory in the context of ambidextrous innovation,particularly under the influence of administrative boundaries. Using unbalanced panel data of Chinese embedded firms from 2013 to 2021,we measure exploratory and exploitative innovation performance based on IPC classification codes.We construct weighted collaboration networks—distinguishing between local and extralocal networks—using firms as nodes and collaboration frequency as edge weights.Structural embeddedness is calculated as two minus the structural constraint index,and relational embeddedness is measured using weighted network centrality,which accounts for both node count and tie strength.Knowledge networks are built through IPC code co-occurrence;global cohesion is measured by weighted network density,while local cohesion is measured by the weighted average clustering coefficient.We then apply negative binomial regression models with interaction terms to systematically examine the effects of local and extralocal network embeddedness on firms' ambidextrous innovation performance.Furthermore,we explore the moderating roles of global and local cohesion within knowledge networks and validate our findings with a case study of Jiangsu Sobute New Materials Co.,Ltd. The results show that dual structural embeddedness positively affects ambidextrous innovation performance.Extralocal structural embeddedness is more conducive to exploratory innovation,while local structural embeddedness better supports exploitative innovation.Dual relational embeddedness negatively affects exploratory innovation but positively influences exploitative innovation,with the positive effect of extralocal relational embeddedness on exploitative innovation being stronger than that of local relational embeddedness.Additionally,global cohesion in the knowledge network weakens the positive impact of structural embeddedness on ambidextrous innovation,while local cohesion weakens the relationship between structural embeddedness and exploitative innovation.Both global and local cohesion negatively moderate the effects of relational embeddedness on ambidextrous innovation performance. This study provides several practical implications.First,firms should tailor their local and extralocal collaboration strategies according to their innovation goals to ensure alignment between innovation strategy and network configuration.They should also strengthen knowledge management practices to enhance the effectiveness of R&D collaborations and adjust their internal knowledge structures to support innovation objectives.Second,administrative barriers to inter-regional collaboration should be dismantled to facilitate cross-boundary partnerships and foster a cooperative network ecosystem that enhances regional competitiveness.Local governments should leverage the advantages of both local and extralocal collaborations to develop region-specific industrial technologies,guide resources toward strategic emerging industries,and cultivate future-oriented sectors through context-specific industrial coordination and specialization.