IDEAS home Printed from https://ideas.repec.org/a/gam/jadmsc/v16y2026i2p87-d1860263.html

AI-Driven Personalization in Marketing Administration: Qualitative Insights from European Professionals

Author

Listed:
  • Marcos Komodromos

    (Department of Management, School of Business, University of Nicosia, 46 Makedonitissas Avenue, 2417 Nicosia, Cyprus)

Abstract

This qualitative study employs interpretive phenomenology and Actor–Network Theory (ANT) to examine the evolving role of AI as an agent within European marketing contexts. Drawing on semi-structured interviews with 36 senior executives from the tourism, fintech, professional services, and digital media sectors, the study identifies four interconnected themes: (1) ambivalent human–AI co-agency, where AI operates as a “co-strategist” influencing budgets and decisions; (2) infrastructural and regulatory challenges arising from legacy systems and GDPR/EU AI Act constraints; (3) ethical issues concerning opacity, bias, and exclusion in hyper-personalization; and (4) the redefinition of professional identities towards hybrid socio-technical roles. The findings underscore AI’s role as a co-creator of strategy, governance, and power, highlighting the necessity of balanced co-agency, robust infrastructure, ethical safeguards, and adaptable skill sets. The AI-MARC framework (Agency, Infrastructure, Responsibility, Capability) provides a practical framework for governance of sustainable AI integration. This work addresses gaps in qualitative AI marketing research by emphasising reflexive practices amid evolving regulations, with the aim of fostering equitable networks that align innovation, fairness, and accountability.

Suggested Citation

  • Marcos Komodromos, 2026. "AI-Driven Personalization in Marketing Administration: Qualitative Insights from European Professionals," Administrative Sciences, MDPI, vol. 16(2), pages 1-19, February.
  • Handle: RePEc:gam:jadmsc:v:16:y:2026:i:2:p:87-:d:1860263
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-3387/16/2/87/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2076-3387/16/2/87/
    Download Restriction: no
    ---><---

    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:gam:jadmsc:v:16:y:2026:i:2:p:87-:d:1860263. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.