IDEAS home Printed from https://ideas.repec.org/a/taf/rcitxx/v29y2026i3p596-614.html

Comprehensive framework for understanding consumers’ intentions of artificial intelligence devices in the hospitality industry: a meta-analysis

Author

Listed:
  • Yanan Jia
  • Anshul Garg
  • Joaquim Dias Soeiro

Abstract

Researchers are increasingly interested in consumer acceptance and adoption of artificial intelligence devices, but an integrated approach remains underexplored. This study uses Stimulus-Organism-Response theory as a theoretical framework, builds an integrated model based on 81 empirical studies, and examines the role of macro-moderating variables. The results show that stimulus factors affect consumers’ intentions through central and peripheral pathways. Notably, the study identified differences in the impact of negative and positive factors on consumer intentions and pointed out that culture-related macro-factors play a limited moderating role. Finally, this study provides suggestions for future research by summarising the gaps in previous research and provides practical recommendations for stakeholders in the hospitality industry based on the study conclusions.

Suggested Citation

  • Yanan Jia & Anshul Garg & Joaquim Dias Soeiro, 2026. "Comprehensive framework for understanding consumers’ intentions of artificial intelligence devices in the hospitality industry: a meta-analysis," Current Issues in Tourism, Taylor & Francis Journals, vol. 29(3), pages 596-614, February.
  • Handle: RePEc:taf:rcitxx:v:29:y:2026:i:3:p:596-614
    DOI: 10.1080/13683500.2024.2429733
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13683500.2024.2429733
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/13683500.2024.2429733?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

    for a different version of it.

    More about this item

    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:taf:rcitxx:v:29:y:2026:i:3:p:596-614. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rcit .

    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.