IDEAS home Printed from https://ideas.repec.org/a/eee/bushor/v69y2026i2p241-252.html

AI as a talent management tool: An organizational justice perspective

Author

Listed:
  • Bennett, Nathan
  • Martin, Christopher L.

Abstract

This article presents a discussion of the challenges that organizational decision-makers face due to the emerging role of artificial intelligence (AI) as a tool for talent management. Already, AI has become a critical element in customer decision-making, as witnessed via customer-facing platforms like Netflix and Amazon. Companies are rapidly adopting AI to enhance talent management practices, including recruitment, selection, and performance reviews. As the use of AI becomes more widespread, managers will need to be prepared to address significant concerns voiced by job applicants and employees whose livelihoods could be impacted. Anecdotal evidence suggests that employees are concerned about issues of transparency, fairness, and the potential for bias in AI algorithms, and as a result, they may perceive its use as unfair. Decades of research have shown that employees who experience or witness unfair treatment tend to exhibit undesirable attitudinal and behavioral characteristics. To assist managers in deploying AI in a manner that employees will embrace rather than resist, organizational justice theory is utilized to develop recommendations for anticipating, understanding, and addressing employee reactions to talent management decisions informed by AI.

Suggested Citation

  • Bennett, Nathan & Martin, Christopher L., 2026. "AI as a talent management tool: An organizational justice perspective," Business Horizons, Elsevier, vol. 69(2), pages 241-252.
  • Handle: RePEc:eee:bushor:v:69:y:2026:i:2:p:241-252
    DOI: 10.1016/j.bushor.2025.03.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0007681325000497
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.bushor.2025.03.005?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:bushor:v:69:y:2026:i:2:p:241-252. 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/bushor .

    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.