IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v90y2026ics1544612325025413.html

AI-driven, human–AI collaboration, and employee performance: The roles of digital finance

Author

Listed:
  • Wan, Siyi
  • Jiang, Yuhong

Abstract

Drawing on data from Chinese listed firms from 2004 to 2023, this paper examines the relationships among AI-driven, human–AI collaboration, and employee performance. The findings show that AI-driven initiatives significantly improve employee performance, with digital finance and capital allocation efficiency playing partial mediating roles in the relationship between AI-driven and employee performance. The moderating effect analysis indicates that human–AI collaboration positively moderates the impact of AI on employee performance. Heterogeneity analysis reveals that the effect of AI on improving employee performance is more pronounced in firms facing higher financing constraints. The results provide practical guidance for firms undergoing intelligent transformation to optimize human–AI relationships, leverage financial tools, and enhance resource allocation efficiency.

Suggested Citation

  • Wan, Siyi & Jiang, Yuhong, 2026. "AI-driven, human–AI collaboration, and employee performance: The roles of digital finance," Finance Research Letters, Elsevier, vol. 90(C).
  • Handle: RePEc:eee:finlet:v:90:y:2026:i:c:s1544612325025413
    DOI: 10.1016/j.frl.2025.109292
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612325025413
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2025.109292?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:finlet:v:90:y:2026:i:c:s1544612325025413. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.