Exploring Functional and Psychological Barriers to Generative AI Adoption for Travel: A Cross-Cultural Study

Siamak Seyfi (), Myung Ja Kim, Changkyu Lee, Yunkyoung Jo and Mustafeed Zaman
Additional contact information
Siamak Seyfi: Taylor’s University, University of Oulu [Finland] = Oulun yliopisto [Suomi] = Université d'Oulu [Finlande]
Myung Ja Kim: Sejong University, PSU - Prince of Songkla University, Hanyang University, KHU - Kyung Hee University
Changkyu Lee: KHU - Kyung Hee University
Yunkyoung Jo: KHU - Kyung Hee University
Mustafeed Zaman: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

Post-Print from HAL

Abstract: The rapid evolution of generative artificial intelligence (GAI) is expected to transform the tourism industry; however, overcoming tourists' reluctance to adopt these tools remains a challenge. Grounded in innovation resistance theory, this study examines the barriers faced by South Korean and American tourists in adopting generative AI for travel purposes. Drawing on empirical data, this quantitative research highlights both functional barriers (related to usage, value, and risk) and psychological barriers (linked to image and tradition) that impede the adoption of AI for travel purposes. The study reveals distinct differences in how South Korean and American tourists engage in AI-generated travel recommendations. Considering the advancements in GAI in the travel industry, novel theoretical insights are offered into tourists' hesitation to adopt AI for travel planning and decision-making. Practical guidance is also offered to tourism stakeholders, to develop strategies that address diverse consumer attitudes and behaviors in the global market.

Date: 2026-05-01
References: Add references at CitEc
Citations:

Published in Journal of Travel Research, 2026, 65 (5), pp.1567-1587. ⟨10.1177/00472875251332955⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05637479

DOI: 10.1177/00472875251332955

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().