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This paper explores how talent flow network and the firm life cycle affect the innovative performances of firms. We first established an interorganizational talent flow network with the occupational mobility data available from the public resumes on LinkedIn China. Thereafter, this information was combined with the financial data of China’s listed companies to develop a unique dataset for the time period between 2000 and 2015. The empirical results indicate the following: (1) The breadth and depth of firms’ embedding in the talent flow network positively impact their innovative performances; (2) Younger firms’ innovations are mostly promoted by the breadth of network embedding, but this positive effect weakens as firms increase in age; (3) Mature firms’ innovations are primarily driven by the depth of network embedding, and this positive effect strengthens as firms increase in age. This paper enriches and deepens the studies of talent flow networks, and it provides practical implications for innovation management based on talent flow for various types of firms at different development stages.

This paper examines the complex relationship between different types of talent flow networks and firms’ innovation. Based on the social network theory and human capital theory, we divide the talent flow networks into “management talent flow networks” and “technical talent flow networks”. The paper then investigates the potential interacting effect and matching effect between the two types of networks when they influence the innovation of firms. The empirical results, which draw from LinkedIn (China) resume data show that: (1) in both management talent flow networks and technical talent flow networks, higher degree of centrality and larger structural hole indexes can enhance firms’ innovation performance; (2) there is significant interacting effect between management talent flow networks and technical talent flow networks in their influence on firms’ innovation. That is, the interaction between firms’ centrality in management talent flow networks and technical talent flow networks, and the interaction between firms’ structural hole indexes in the two networks can both enhance their innovation performance; (3) there is also noteworthy matching effect between the two network types. That is, firms with balanced degree centrality (high-high, or low-low) and balanced structural hole indexes (high-high, or low-low) in management talent flow networks and technical talent flow networks exhibit better innovation performance than those with imbalanced degree centrality (high-low, or low-high) and structural hole indexes (high-low, or low-high) in the two networks. This paper contributes to the classification research on talent flow networks, and deepens our understanding of the complex influencing mechanism between talent flow networks and firms’ innovation. Moreover, it provides managerial implications for firms to improve innovation performance via talent flow management.