IDEAS home Printed from https://ideas.repec.org/a/eee/bushor/v69y2026i4p463-475.html

AI as an emerging trend for managing employee efficiency in the retail and services industries

Author

Listed:
  • Mattingly, E. Shaunn
  • Kroes, James R.
  • Manikas, Andrew S.

Abstract

Generative artificial intelligence (generative AI) is transforming the nature of work, offering clear benefits in efficiency and innovation. Yet, for practitioners, adoption remains complex because of limited resources and current operational demands. This study examines how firms manage these challenges by analyzing AI adoption through the lens of the exploration-exploitation trade-off. Using data from public company filings, we analyze the drivers and barriers of adoption, links to R&D investment, and industry-specific patterns. We find that larger firms with low employee efficiency are eager to adopt AI. Adoption rates also correlate with changes in the scale and focus of R&D spending. Notably, retail and services firms—among the least efficient in terms of labor productivity—are leading in AI uptake. However, their focus remains on automating routine tasks and expanding product offerings, rather than investing in workforce development. These results provide useful guidance for leaders seeking to utilize AI in industries struggling with employee productivity.

Suggested Citation

  • Mattingly, E. Shaunn & Kroes, James R. & Manikas, Andrew S., 2026. "AI as an emerging trend for managing employee efficiency in the retail and services industries," Business Horizons, Elsevier, vol. 69(4), pages 463-475.
  • Handle: RePEc:eee:bushor:v:69:y:2026:i:4:p:463-475
    DOI: 10.1016/j.bushor.2025.06.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0007681325001089
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.bushor.2025.06.005?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:4:p:463-475. 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.