IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v180y2024ics0148296324002030.html
   My bibliography  Save this article

Navigating a paradigm shift: Technology and user acceptance of big data and artificial intelligence among advertising and marketing practitioners

Author

Listed:
  • Iyer, Pooja
  • Bright, Laura F.

Abstract

The advertising and marketing industry is witnessing a paradigm shift with the inclusion of big data and artificial intelligence, expecting practitioners to adapt to this rapidly transforming environment. Utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) model, a mixed methods approach examines how performance and effort expectancy mediate the psychological factors of anxiety and self-efficacy on behavioral intention to accept and engage with big data and AI systems amongst practitioners. To examine the psychological factors of UTAUT in relation to behavior, this research surveyed 100 mid-level advertising and marketing practitioners and found performance expectancy fully mediated anxiety, and effort expectancy partially mediated self-efficacy on behavioral intention. Qualitative insights identified psychological, social, and organizational factors, including fear of losing jobs, collaboration, motivation, training, social influence, and facilitating factors are critical to technology acceptance. Theoretical and managerial implications are discussed as they relate to this ongoing paradigm shift.

Suggested Citation

  • Iyer, Pooja & Bright, Laura F., 2024. "Navigating a paradigm shift: Technology and user acceptance of big data and artificial intelligence among advertising and marketing practitioners," Journal of Business Research, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:jbrese:v:180:y:2024:i:c:s0148296324002030
    DOI: 10.1016/j.jbusres.2024.114699
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296324002030
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2024.114699?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 search for a different version of it.

    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:180:y:2024:i:c:s0148296324002030. 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.