How Do Network Embeddedness and Node Attributes Identify Key Inventors? A Dynamic fsQCA Analysis
基于纳米能源领域数据，用动态模糊集定性比较分析发现，知识组合新颖性始终是识别关键发明人的核心条件，而关系强度从未出现；知识深度与广度在同一时期相互促进。
Network embeddedness and node attributes are widely regarded as important dimensions for the formation of key inventors, but few studies have clarified the mechanism of identifying key inventors from a dynamic perspective. Based on a sample of nano-energy and dynamic fuzzy-set qualitative comparative analysis, results show that multiple causal configurations equally explain the conditions for identifying key inventors, in which novelty of knowledge combination is always the core condition in the four time periods, but tie strength is never present. In terms of interconditional relationship patterns, novelty of knowledge combination and knowledge depth remain relatively stable across all time periods, with knowledge depth and knowledge scope mutually reinforcing each other within the same period. The association between structural holes and conditions within node attributes has been progressively diminishing since the high efficiency R&D period. Theoretically, this study provides a dynamic research framework for identifying key inventors, but also challenges the paradoxical view of network embeddedness and the opposing view of knowledge recombination. Practical insights for managers and policymakers to identify and cultivate key inventors are provided by offering conditions and configurations evolution trajectory.