IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v201y2025ics0148296325005193.html

A serial mediation effect of digital transformation on employee performance: An ability-motivation-opportunity framework and employee unlearning

Author

Listed:
  • Zhou, Qiwei
  • Huang, Ran
  • Mao, Jih-Yu

Abstract

Digital transformation has become a critical driver for organizational success in an increasingly technology-driven and competitive world. While prior research has identified various individual-level factors that enable employee adaptation, these insights remain fragmented and lack a systematic framework. Drawing on the theory of work adjustment and the ability-motivation-opportunity framework, this research proposes and tests a serial mediation model through which organizational digital transformation enhances employee competence, motivation to learn, and job autonomy and then employee unlearning, leading to enhanced employee job performance. Results based on a time-lagged field study involving 265 employees and their respective supervisors from six companies in southern China support all hypotheses. By integrating previously disparate perspectives under a cohesive framework, this research advances theory on employee adaptation in the digital era and offers a few meaningful takeaways for practitioners.

Suggested Citation

  • Zhou, Qiwei & Huang, Ran & Mao, Jih-Yu, 2025. "A serial mediation effect of digital transformation on employee performance: An ability-motivation-opportunity framework and employee unlearning," Journal of Business Research, Elsevier, vol. 201(C).
  • Handle: RePEc:eee:jbrese:v:201:y:2025:i:c:s0148296325005193
    DOI: 10.1016/j.jbusres.2025.115696
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296325005193
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2025.115696?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:jbrese:v:201:y:2025:i:c:s0148296325005193. 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/jbusres .

    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.