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Innovation Capability and Innovation Talents: Evidence from China Based on a Quantile Regression Approach

Author

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  • Fenfen Wei

    (School of Management, Hefei University of Technology, Hefei 230009, China)

  • Nanping Feng

    (School of Management, Hefei University of Technology, Hefei 230009, China)

  • Kevin H. Zhang

    (Department of Economics, Illinois State University, Normal, IL 61790-4200, USA)

Abstract

Innovation talents, as a most active and important resource in innovation activities, are receiving increasing attention in the enhancement of innovation capability. It seems that areas with strong innovation capability are more attractive to innovation talents. To explore the impact of innovation capability—measured by innovation environment input efficiency—on the distribution of innovation talent, and given the heavy-tailed distribution of talents, a quantile regression approach is adopted for Chinese data covering 2001–2015. The results show that: (a) at the country level, the innovation environment and innovation talents are surprisingly negatively related due to pre-reform special regional strategies and the immature innovation environment in China, while both innovation input and efficiency facilitates the agglomeration of innovation talent; and (b) at the regional level, some different influences on talents appear: the strongest negative impact of the innovation environment is in the areas with a low level of talents, moderate positive effects of innovation input and efficiency can be seen in areas with a medium level of talents, and significantly positive contributions from innovation input and efficiency can be seen in the areas that already have a high level of talents. The results offer some suggestions for managers and the government, which are beneficial for the guidance of the ordered flow of innovation talents and the enhancement of regional innovation capability and sustainability.

Suggested Citation

  • Fenfen Wei & Nanping Feng & Kevin H. Zhang, 2017. "Innovation Capability and Innovation Talents: Evidence from China Based on a Quantile Regression Approach," Sustainability, MDPI, vol. 9(7), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:7:p:1218-:d:104281
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    2. Runyuan Wang & Weiguang Cai & Hong Ren & Xianrui Ma, 2023. "Heterogeneous Effects of the Talent Competition on Urban Innovation in China: Evidence from Prefecture-Level Cities," Land, MDPI, vol. 12(3), pages 1-15, March.
    3. Jiaoping Yang & Shujun Wang & Shan Sun & Jianhua Zhu, 2022. "Influence Mechanism of High-Tech Industrial Agglomeration on Green Innovation Performance: Evidence from China," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    4. Ming-liang Yue & Rui-nan Li & Gui-yan Ou & Xia Wu & Ting-can Ma, 2020. "An exploration on the flow of leading research talents in China: from the perspective of distinguished young scholars," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1559-1574, November.
    5. Ahmed Hassanein & Jamal Ali Al-Khasawneh & Hany Elzahar, 2022. "R&D expenditure and managerial ownership: evidence from firms of high-vs-low R&D intensity," Journal of Financial Reporting and Accounting, Emerald Group Publishing Limited, vol. 21(3), pages 654-672, January.
    6. Mingfeng Tang & Peng Xu & Patrick Llerena & Asghar Afshar Jahanshahi, 2019. "The Impact of the Openness of Firms’ External Search Strategies on Exploratory Innovation and Exploitative Innovation," Sustainability, MDPI, vol. 11(18), pages 1-20, September.
    7. Shuya Wang & Xinjia Tian & Hui Wang & Chang Liu & Zhilin Wang & Qiuhua Song, 2023. "Forecasting and Coupled Coordination Analysis of Supply and Demand for Sustainable Talent in Chinese Agriculture," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    8. Chung-Chu Chuang & Chung-Min Tsai & Hsiao-Chen Chang & Yi-Hsien Wang, 2021. "Applying Quantile Regression to Assess the Relationship between R&D, Technology Import and Patent Performance in Taiwan," JRFM, MDPI, vol. 14(8), pages 1-14, August.

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