IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i8p3562-d1635348.html
   My bibliography  Save this article

Research on the Mechanism of Intelligent Transformation of Enterprises Driven by Targeted Talent Introduction Policies: Taking New-Energy-Automobile Enterprises as an Example

Author

Listed:
  • Yawei Xue

    (The School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Yuchen Lu

    (The School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Chunqian Zhu

    (The School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

Abstract

The strategic goal of high-quality national development depends on intelligent manufacturing, where introducing and cultivating high-end technical talent is crucial. Although prior research has linked talent policies to technological innovation, few studies have examined how targeted talent policies promote intelligent transformation in enterprises. Methods: Focusing on industry fit, this study uses new-energy-vehicle companies to represent advanced manufacturing. Drawing on targeted talent policies issued by major Chinese cities from 2016 to 2022, we employ a multi-period difference-in-differences model to assess how these policies attract high-skilled talent related to the new-energy automotive sector and drive intelligent investment and technological upgrading. Results: Our findings indicate that targeted talent policies significantly boost intelligent investment, which holds for robustness tests. Mechanism analyses reveal that these policies optimize firms’ human capital by increasing the share of highly educated and technical employees, thereby enhancing technological innovation, patent output, production quality, and efficiency. Conclusions: This research extends the capital–skill complementarity theory by highlighting the importance of specialized talent for intelligent transformation. The results offer data-driven insights for refining talent policies to support the intelligent development of the new-energy-automobile industry.

Suggested Citation

  • Yawei Xue & Yuchen Lu & Chunqian Zhu, 2025. "Research on the Mechanism of Intelligent Transformation of Enterprises Driven by Targeted Talent Introduction Policies: Taking New-Energy-Automobile Enterprises as an Example," Sustainability, MDPI, vol. 17(8), pages 1-24, April.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3562-:d:1635348
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/8/3562/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/8/3562/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3562-:d:1635348. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.