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Framework to Measure the Mobility of Technical Talents: Evidence from China’s Smart Logistics

Author

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  • Jun Guan

    (School of Economics and Management, Beijing University of Technology, Beijing 100124, China)

  • Chunxiu Liu

    (School of Economics and Management, Beijing University of Technology, Beijing 100124, China)

  • Guoqiang Liang

    (School of Economics and Management, Beijing University of Technology, Beijing 100124, China)

  • Lizhi Xing

    (School of Economics and Management, Beijing University of Technology, Beijing 100124, China)

Abstract

Talent mobility is the key driving force to accelerate innovation and economic development. Prior studies focused much attention on the mobility of scientific talents from the angle of bibliometrics. Still, the mobility of technical talents was not thoroughly analyzed through the lens of the complex network. In consideration of technical talents being the primary and direct labor force to foster innovation and economic growth, in this paper, we provide a framework to measure the mobility of technical talents based on patents from the perspective of the complex network. The Technical Talent Mobility Network (TTMN) model is constructed to measure the changes of network topology on the levels of network, node, and edge aspects, respectively, thus deepening our understanding of the important node and mobility channels of technical talents. We then take China’s smart logistics as an example to verify the framework promoted, and results show the framework can reveal the actual situation of technical talent mobility that was reported by the government gazette and related articles. The framework proposed in this paper points out a new method and perspective to measure technological talent mobility, which is essential to facilitate regional innovation and economic soar.

Suggested Citation

  • Jun Guan & Chunxiu Liu & Guoqiang Liang & Lizhi Xing, 2023. "Framework to Measure the Mobility of Technical Talents: Evidence from China’s Smart Logistics," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2481-:d:1051494
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