Generative Artificial Intelligence in Tourism: The Roles of Perceived Usefulness, Trust in AI-Generated Content and Perceived AI Control among International Tourists
DOI:
https://doi.org/10.64534/ga10sh42Keywords:
AI adoption, GenAI usage behavior, generative AI, trust in AI-generated content, perceived AI control, ˙perceived usefulness.Abstract
The rapid rise of generative artificial intelligence (GenAI) is fundamentally changing the way tourists search for information, plan their trips, experience their trips, and share information after their trips. While the Technology Acceptance Model (TAM) considers perceived usefulness and perceived ease of use as central factors, this study extends the theoretical framework by incorporating two additional constructs: trust in AI-generated content and perceived AI control. This study examines GenAI usage behavior using survey data from 541 international tourists in Ho Chi Minh City. The PLS-SEM results show that perceived usefulness is the strongest driver of GenAI usage behavior and the main antecedent of trust in AI-generated content. Trust has a significant but secondary effect, functioning as a credibility mechanism that strengthens tourists’ confidence in useful AI-generated travel support. At the same time, perceived AI control has a direct and independent effect, highlighting the importance of tourists’ ability to guide, adjust, and redirect AI-generated recommendations. Notably, perceived ease of use does not directly influence usage behavior or trust, although it has a weak positive effect on perceived usefulness. This study refines TAM by showing that perceived usefulness remains the dominant driver of GenAI usage behavior, while trust serves as a complementary credibility mechanism and perceived AI control acts as an independent usage driver. Practically, it suggests that tourism GenAI applications should prioritize concrete travel value, controllability, and locally credible information for international tourists.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Ali, M. S. M., Wasel, K. Z. A., & Abdelhamid, A. M. M. (2024). Generative AI and Media Content Creation: Investigating the Factors Shaping User Acceptance in the Arab Gulf States. Journalism and Media, 5(4), 1624-1645. https://doi.org/10.3390/journalmedia5040101
Chin, C.-H., Wong, W. P.-M., & Ngian, E.-T. (2024). The Behavioral Intention of Young Travelers to Use Virtual Reality Technology in Cultural Tourism Destinations: An Application of Technology Acceptance Model. Pakistan Journal of Commerce and Social Sciences, 18(3), 552-570. https://doi.org/10.64534/Commer.2024.029
Christensen, J., Hansen, J. M., & Wilson, P. (2025). Understanding the role and impact of Generative Artificial Intelligence (AI) hallucination within consumers' tourism decision-making processes. Current Issues in Tourism, 28(4), 545-560. https://doi.org/10.1080/13683500.2023.2300032
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319-339. https://doi.org/10.2307/249008
Duong, C. D., Nguyen, T. H., Ngo, T. V. N., Pham, T. T. P., Vu, A. T., & Dang, N. S. (2025). Using generative artificial intelligence (ChatGPT) for travel purposes: parasocial interaction and tourists' continuance intention. Tourism Review, 80(4), 813-827. https://doi.org/10.1108/TR-01-2024-0027
Etlioglu, M. (2025). The Impact of Netflix's AI Powered Recommendation System on Consumers' Behavioral Intentions. Pakistan Journal of Commerce and Social Sciences, 19(4), 673-713. https://doi.org/10.64534/Commer.2025.662
Fakfare, P., Manosuthi, N., Lee, J. S., Han, H., & Jin, M. (2025). Customer word-of-mouth for generative AI: Innovation and adoption in hospitality and tourism. International Journal of Hospitality Management, 126, 104070. https://doi.org/10.1016/j.ijhm.2024.104070
Gursoy, D., Li, Y., & Song, H. (2023). ChatGPT and the hospitality and tourism industry: an overview of current trends and future research directions. Journal of Hospitality Marketing and Management, 32(5), 579-592. https://doi.org/10.1080/19368623.2023.2211993
Guttentag, D. A., Litvin, S. W., & Teixeira, R. (2024). Human vs. AI: can ChatGPT improve tourism product descriptions? Current Issues in Tourism, 28(22), 3601-3619. https://doi.org/10.1080/13683500.2024.2402563
Hair, J., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications. https://doi.org/10.1007/978-3-030-80519-7
Hu, L., Wang, H., & Xin, Y. (2025). Factors influencing Chinese pre-service teachers' adoption of generative AI in teaching: an empirical study based on UTAUT2 and PLS-SEM. Education and Information Technologies, 30, 12609-12631. https://doi.org/10.1007/s10639-025-13353-7
Hu, Y., & Min, H. K. (2025). Enhancing customer perceived control and trust through data privacy choices in interactions with service robots. Information Technology and Tourism, 27, 1111-1130. https://doi.org/10.1007/s40558-025-00335-1
Kang, S. E., Kim, M. J., Kim, J. S., & Olya, H. (2024). Can I Trust GenAI to Plan My Next Trip? A Multi-Method Approach to Optimizing Media Mix. Journal of Travel Research, 65(2), 335-353. https://doi.org/10.1177/00472875241305630
Kim, M. J., Hall, C. M., Kwon, O., Hwang, K., & Kim, J. S. (2024). Orbital and sub-orbital space tourism: motivation, constraint and artificial intelligence. Tourism Review, 79(2), 392-407. https://doi.org/10.1108/TR-01-2023-0017
Kim, Y., Blazquez, V., & Oh, T. (2024). Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model. Behavioral Sciences, 14(11), 1035. https://doi.org/10.3390/bs14111035
Kock, N. (2015). Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach. International Journal of E-Collaboration, 11(4), 1-10. https://doi.org/10.4018/ijec.2015100101
Li, M., & Zhang, H. (2024). A Study on the Behavioral Intention of Primary and Secondary School Teachers Using Generative Artificial Intelligence in Teaching. ICDEL '24: Proceedings of the 2024 9th International Conference on Distance Education and Learning, 35-41. https://doi.org/10.1145/3675812.3675870
Lin, W., & Jiang, P. (2025). Factors Influencing College Students' Generative Artificial Intelligence Usage Behavior in Mathematics Learning: A Case from China. Behavioral Sciences, 15(3), 295. https://doi.org/10.3390/bs15030295
Luo, X., Xu, D., Li, Y. (William), & Wan, L. C. (2025). Advancing information search through GenAI: the roles of search type, travel motive and GenAI customization level. International Journal of Contemporary Hospitality Management, 37(5), 1725-1743. https://doi.org/10.1108/IJCHM-06-2024-0941
Ma, H., & Li, N. (2024). Exploring User Behavioral Intentions and Their Relationship with AI Design Tools: A Future Outlook on Intelligent Design. IEEE Access, 12, 149192- 149205. https://doi.org/10.1109/ACCESS.2024.3441088
Mladenović, D., Ismagilova, E., Mogaji, E., & Dwivedi, Y. K. (2026). Travel Recommendations of Tomorrow: Generative Artificial Intelligence and Travel Planning. Journal of Consumer Behaviour, 25(3), 1365-1384. https://doi.org/10.1002/cb.70126
Mogaji, E., Viglia, G., Srivastava, P., & Dwivedi, Y. K. (2024). Is it the end of the technology acceptance model in the era of generative artificial intelligence? International Journal of Contemporary Hospitality Management, 36(10), 3324-3339. https://doi.org/10.1108/IJCHM-08-2023-1271
Nicolau, J. L. (2025). When ChatGPT Designs Your Trip: How GenAI Adds a Cognitive Layer to Smart Tourism. Journal of Smart Tourism, 5(4), 137-151. https://doi.org/10.1177/27652157251371101
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539-569. https://doi.org/10.1146/annurev-psych-120710-100452
Seyfi, S., Kim, M. J., Lee, C., Jo, Y., & Zaman, M. (2025). Exploring Functional and Psychological Barriers to Generative AI Adoption for Travel: A Cross-Cultural Study. Journal of Travel Research, 65(5), 1567-1587. https://doi.org/10.1177/00472875251332955
Seyfi, S., Kim, M. J., Nazifi, A., Murdy, S., & Vo-Thanh, T. (2025). Understanding tourist barriers and personality influences in embracing generative AI for travel planning and decision-making. International Journal of Hospitality Management, 126, 104105. https://doi.org/10.1016/j.ijhm.2025.104105
Tavitiyaman, P., Zhang, X., Xu, J., & Tsui, B. (2024). Impact of Smart Tourism Technology Attributes on Perceived Usefulness, Service Experience Evaluation, and Business Performance: A Perspective of Hotel Employees. Journal of Quality Assurance in Hospitality & Tourism, 27(1), 184-209. https://doi.org/10.1080/1528008X.2024.2313194
Topsakal, Y. (2025). How Familiarity, Ease of Use, Usefulness, and Trust Influence the Acceptance of Generative Artificial Intelligence (AI)-Assisted Travel Planning. International Journal of Human-Computer Interaction, 41(15), 9478-9491. https://doi.org/10.1080/10447318.2024.2426044
Tuyen, T., & Hanh, N. T. Van. (2025). Technology Readiness and Technology Acceptance in Virtual Reality Tourism: An Integration of TOE and TAM Frameworks. Pakistan Journal of Commerce and Social Sciences, 19(1), 102-125. https://doi.org/10.64534/Comm.2025.005
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157-178. https://doi.org/10.2307/41410412
Wong, I. A., Lian, Q. L., & Sun, D. (2023). Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI. Journal of Hospitality and Tourism Management, 56, 253-263. https://doi.org/10.1016/j.jhtm.2023.06.022
Wong, J. W. C., Lai, I. K. W., & Lin, Y. (2025). The perceived reliability and adoption intention towards human-generated content vs. AI-generated content for travel planning: a moderating role of travel persona. Journal of Travel & Tourism Marketing, 42(4), 461-478. https://doi.org/10.1080/10548408.2025.2468468
Wu, Q., Tian, J., & Liu, Z. (2025). Exploring the usage behavior of generative artificial intelligence: a case study of ChatGPT with insights into the moderating effects of habit and personal innovativeness. Current Psychology, 44(9), 8190 - 8203. https://doi.org/10.1007/s12144-024-07193-w
Yang, H., Song, H., Zhang, Y., Ma, E., & Yang, A. (2026). Communication modality, authenticity, and continuance usage intention of GenAI chatbots: A media richness theory perspective. Tourism Management, 112, 105273. https://doi.org/10.1016/j.tourman.2025.105273
Yu, J., & Meng, T. (2025). Image Generative AI in Tourism: Trends, Impacts, and Future Research Directions. Journal of Hospitality and Tourism Research, 50(2), 288-301. https://doi.org/10.1177/10963480251324676
Zhou, T., Song, Y., & Zhou, P. (2022). Continued use intention of travel apps: from the perspective of control and motivation. Technology Analysis and Strategic Management, 34(6), 703-716. https://doi.org/10.1080/09537325.2021.1916457
