IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v18y2026i2p1073-d1845240.html

Brand Trust in AI-Driven E-Commerce Personalization: The Well-Being–Privacy Trade-Off

Author

Listed:
  • Samet Aydin

    (Department of International Trade and Logistics, Maltepe University, Istanbul 34857, Türkiye)

Abstract

The rapid advancement of artificial intelligence (AI) in e-commerce has intensified data-driven personalization, raising important questions about its psychological implications for consumers and its role in shaping sustainable and responsible digital business practices. This study examines how AI-driven personalization affects consumer psychological well-being in the Turkish e-commerce market and investigates the roles of privacy concerns and brand trust in shaping this relationship from a social sustainability and responsible AI perspective. The research develops and empirically tests an integrated model comprising perceived personalization, privacy concerns, psychological well-being, and brand trust. Survey data from 400 active e-commerce customers were analyzed using partial least squares structural equation modeling (PLS-SEM). Findings show that both perceived relevance and perceived specificity significantly enhance psychological well-being by reducing cognitive overload and increasing perceived value. However, these personalization dimensions also increase privacy concerns, with perceived specificity exerting a notably stronger effect. Privacy concerns negatively affect psychological well-being and competitively mediate the relationship between personalization and well-being, reflecting the Personalization–Privacy Paradox in AI-driven e-commerce contexts. Moreover, brand trust significantly moderates this dynamic by weakening the harmful impact of privacy concerns on psychological well-being. Overall, the findings indicate that privacy concerns represent a latent social cost that can undermine the long-term sustainability of data-intensive business models when not governed by trust-based mechanisms.

Suggested Citation

  • Samet Aydin, 2026. "Brand Trust in AI-Driven E-Commerce Personalization: The Well-Being–Privacy Trade-Off," Sustainability, MDPI, vol. 18(2), pages 1-27, January.
  • Handle: RePEc:gam:jsusta:v:18:y:2026:i:2:p:1073-:d:1845240
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/18/2/1073/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/18/2/1073/
    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:jsusta:v:18:y:2026:i:2:p:1073-:d:1845240. 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.