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A Role-Configuration Perspective on Collaboration Networks and Breakthrough Innovation in the New Energy Vehicle Industry
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DOI：10.1177/21582440261454130.png)
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Abstract
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This study investigates how inventors' collaboration network structures are associated with breakthrough innovation performance in the new energy vehicle industry from a role-configuration perspective. Drawing on a dataset of 2,372 inventors derived from granted invention patents, the research integrates social network analysis with machine-learning techniques to uncover heterogeneous innovation patterns at the individual level. First, a K-means clustering approach based on inventors' patent output and number of collaborators identifies three distinct inventor roles: peripheral inventors, collaborator inventors, and star inventors. Second, classification and regression tree models are constructed separately for each group to explore how different configurations of network features including degree centrality, closeness centrality, structural holes, clustering coefficient, and collaboration intensity, are associated with high breakthrough innovation performance. The results reveal substantial role heterogeneity in the network-innovation relationship. For collaborator inventors, closeness centrality plays a dominant role, indicating the importance of efficient knowledge access within moderately connected structures. Peripheral inventors rely primarily on the joint conditions of closeness centrality and structural holes, suggesting that selective brokerage positions compensate for limited collaboration breadth. In contrast, star inventors achieve superior breakthrough performance under a configuration characterized by cohesive local structures combined with relatively low collaboration intensity, highlighting the value of focused and less redundant teamwork. By linking role differentiation with network configurations, this study provides new micro-level evidence on the heterogeneous pathways through which collaboration networks are associated with breakthrough innovation and demonstrates the effectiveness of machine-learning methods for uncovering complex innovation mechanisms.
Keywords:
breakthrough innovation
inventor collaboration network
decision tree
configurational perspective
social network analysis
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