IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v164y2023ics0148296323003569.html
   My bibliography  Save this article

Artificial intelligence (AI) competencies for organizational performance: A B2B marketing capabilities perspective

Author

Listed:
  • Mikalef, Patrick
  • Islam, Najmul
  • Parida, Vinit
  • Singh, Harkamaljit
  • Altwaijry, Najwa

Abstract

The deployment of Artificial Intelligence (AI) has been accelerating in several fields over the past few years, with much focus placed on its potential in Business-to-Business (B2B) marketing. Early reports highlight promising benefits of AI in B2B marketing such as offering important insights into customer behaviors, identifying critical market insight, and streamlining operational inefficiencies. Nevertheless, there is a lack of understanding concerning how organizations should structure their AI competencies for B2B marketing, and how these ultimately influence organizational performance. Drawing on AI competencies and B2B marketing literature, this study develops a conceptual research model that explores the effect that AI competencies have on B2B marketing capabilities, and in turn on organizational performance. The proposed research model is tested using 155 survey responses from European companies and analyzed using partial least squares structural equation modeling. The results highlight the mechanisms through which AI competencies influence B2B marketing capabilities, as well as how the later impact organizational performance.

Suggested Citation

  • Mikalef, Patrick & Islam, Najmul & Parida, Vinit & Singh, Harkamaljit & Altwaijry, Najwa, 2023. "Artificial intelligence (AI) competencies for organizational performance: A B2B marketing capabilities perspective," Journal of Business Research, Elsevier, vol. 164(C).
    • Handle: RePEc:eee:jbrese:v:164:y:2023:i:c:s0148296323003569
    DOI: 10.1016/j.jbusres.2023.113998
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296323003569
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2023.113998?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Zongrun & Zhang, Taiyu & Ren, Xiaohang & Shi, Yukun, 2024. "AI adoption rate and corporate green innovation efficiency: Evidence from Chinese energy companies," Energy Economics, Elsevier, vol. 132(C).
    2. Shen, Lei & Shi, Qingyue & Parida, Vinit & Jovanovic, Marin, 2024. "Ecosystem orchestration practices for industrial firms: A qualitative meta-analysis, framework development and research agenda," Journal of Business Research, Elsevier, vol. 173(C).

    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:164:y:2023:i:c:s0148296323003569. 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.