IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v26y2024i3d10.1007_s10796-022-10294-1.html
   My bibliography  Save this article

Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective

Author

Listed:
  • Brendan James Keegan

    (Maynooth University)

  • Denis Dennehy

    (Swansea University)

  • Peter Naudé

    (Manchester Metropolitan University)

Abstract

Anecdotal evidence suggests that artificial intelligence (AI) technologies are highly effective in digital marketing and rapidly growing in popularity in the context of business-to-business (B2B) marketing. Yet empirical research on AI-powered B2B marketing, and particularly on the socio-technical aspects of its use, is sparse. This study uses Activity Theory (AT) as a theoretical lens to examine AI-powered B2B marketing as a collective activity system, and to illuminate the contradictions that emerge when adopting and implementing AI into traditional B2B marketing practices. AT is appropriate in the context of this study, as it shows how contradictions act as a motor for change and lead to transformational changes, rather than viewing tensions as a threat to prematurely abandon the adoption and implementation of AI in B2B marketing. Based on eighteen interviews with industry and academic experts, the study identifies contradictions with which marketing researchers and practitioners must contend. We show that these contradictions can be culturally or politically challenging to confront, and even when resolved, can have both intended and unintended consequences.

Suggested Citation

  • Brendan James Keegan & Denis Dennehy & Peter Naudé, 2024. "Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective," Information Systems Frontiers, Springer, vol. 26(3), pages 1025-1039, June.
  • Handle: RePEc:spr:infosf:v:26:y:2024:i:3:d:10.1007_s10796-022-10294-1
    DOI: 10.1007/s10796-022-10294-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-022-10294-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10796-022-10294-1?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.

    More about this item

    Keywords

    Artificial Intelligence; Activity Theory; B2B Marketing;
    All these keywords.

    JEL classification:

    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:spr:infosf:v:26:y:2024:i:3:d:10.1007_s10796-022-10294-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.