Digital Trust in Tourism: From Information Quality to Willingness to Accept
Abstract
Many studies discuss artificial intelligence in the education sector using the IAM (Information Adoption Model) theory, but there are still very few studies discussing AI-based IAM for tourism activities. Therefore, usage behavior is assessed not only based on technology adoption but also on the information generated. The attitude in this study is employed to determine whether AI acceptance is emotional or rational, thereby comprehensively understanding AI acceptance based on information. The study involved 450 respondents, analyzed using PLS-SEM. PLS-SEM was employed because the study aims to predict relationships among latent constructs and assess a relatively complex structural model. PLS-SEM is particularly suitable for exploratory research, theory extension, and prediction-oriented studies, while accommodating data that may not meet multivariate normality assumptions (Hair et al., 2021). Among the six hypotheses tested, four were significant, while two were not. The conclusion is that in tourism activities, AI chatbots have inconsistent usage patterns because there are results that indicate trust but not readiness to use. This is an important point for developers because optimal performance is not enough to address users' concerns about using the technology, so additional psychological encouragement is needed to overcome these issues.
Full text article
References
Acosta-Enriquez, B. G., Farroñán, E. V. R., Zapata, L. V., García, F. S. M., Rabanal-León, H. C., Angaspilco, J. E. M., & Bocanegra, J. C. S. (2024). Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory. Heliyon, 10. https://doi.org/10.1016/j.heliyon.2024.e38315
Alagarsamy, S., & Mehrolia, S. (2023). Exploring chatbot trust: Antecedents and behavioural outcomes. Heliyon, 9(5).
Au, A., & Enderwick, P. (2000). A cognitive model on attitude towards technology adoption. Journal of Managerial Psychology, 15, 266–282. https://doi.org/10.1108/02683940010330957
Booyse, D., & Scheepers, C. (2023). Barriers to adopting automated organisational decision-making through the use of artificial intelligence. Management Research Review. https://doi.org/10.1108/mrr-09-2021-0701
Camilleri, M. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2024.123247
Chegg. (2025). Artificial intelligence (AI) use in travel and tourism - statistics & facts. Statista Research Department. https://www.statista.com/topics/10887/artificial-intelligence-ai-use-in-travel-and-tourism/#topicOverview
Chyung, S. Y., Roberts, K., Swanson, I., & Hankinson, A. (2017). Evidence‐based survey design: The use of a midpoint on the Likert scale. Performance Improvement, 56(10), 15–23.
Cornelissen, L., Egher, C., Van Beek, V., Williamson, L., & Hommes, D. (2022). The Drivers of Acceptance of Artificial Intelligence–Powered Care Pathways Among Medical Professionals: Web-Based Survey Study. JMIR Formative Research, 6. https://doi.org/10.2196/33368
Costa, A., Silva, F., & Moreira, J. J. (2024). Towards an AI-Driven User Interface Design for Web Applications. Procedia Computer Science. https://doi.org/10.1016/j.procs.2024.05.094
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9–30.
Ding, Y., & Najaf, M. (2024). Interactivity, humanness, and trust: a psychological approach to AI chatbot adoption in e-commerce. BMC Psychology, 12. https://doi.org/10.1186/s40359-024-02083-z
Dwivedi, Yogesh K, Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., & Krishen, A. S. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.
Dwivedi, Yogesh Kumar, Rana, N., Jeyaraj, A., Clement, M., & Williams, M. (2017). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21, 719–734. https://doi.org/10.1007/s10796-017-9774-y
Filieri, R., Alguezaui, S., & McLeay, F. (2015). Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth. Tourism Management, 51, 174–185. https://doi.org/10.1016/J.TOURMAN.2015.05.007
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Gao, C., Zhou, W., Xia, X., Lo, D., Xie, Q., & Lyu, M. (2020). Automating App Review Response Generation Based on Contextual Knowledge. ArXiv, abs/2010.0. https://doi.org/10.1145/3464969
García-Milon, A., Olarte-Pascual, C., Juaneda-Ayensa, E., & Pelegrín-Borondo, J. (2021). Tourist purchases in a destination: what leads them to seek information from digital sources? European Journal of Management and Business Economics, 30, 243–260. https://doi.org/10.1108/EJMBE-09-2019-0153
Getenet, S., Cantle, R., Redmond, P., & Albion, P. (2024). Students’ digital technology attitude, literacy and self-efficacy and their effect on online learning engagement. International Journal of Educational Technology in Higher Education, 21, 1–20. https://doi.org/10.1186/s41239-023-00437-y
Gkinko, L., & Elbanna, A. (2022). Hope, tolerance and empathy: employees’ emotions when using an AI-enabled chatbot in a digitalised workplace. Inf. Technol. People, 35, 1714–1743. https://doi.org/10.1108/itp-04-2021-0328
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587–632). Springer.
Heersmink, R., De Rooij, B., Vázquez, M. J. C., & Colombo, M. (2024). A phenomenology and epistemology of large language models: transparency, trust, and trustworthiness. Ethics Inf. Technol., 26, 41. https://doi.org/10.1007/s10676-024-09777-3
Hovland, C. I. (1953). Credibility on Communication.
Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172.
Jenneboer, L., Herrando, C., & Constantinides, E. (2022). The Impact of Chatbots on Customer Loyalty: A Systematic Literature Review. J. Theor. Appl. Electron. Commer. Res., 17, 212–229. https://doi.org/10.3390/jtaer17010011
Jeong, Y., Kim, E., & Kim, S.-K. (2020). Understanding Active Sport Tourist Behaviors in Small-Scale Sports Events: Stimulus-Organism-Response Approach. Sustainability. https://doi.org/10.3390/su12198192
Kelly, S., Kaye, S., & Oviedo-Trespalacios, O. (2022). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics Informatics, 77, 101925. https://doi.org/10.1016/j.tele.2022.101925
Khan, F. M., & Azam, M. K. (2023). Chatbots in hospitality and tourism: A bibliometric synthesis of evidence. Journal of the Academy of Business and Emerging Markets, 3(2), 29–40.
Khechine, H., Lakhal, S., & Ndjambou, P. (2016). A meta‐analysis of the UTAUT model: Eleven years later. Canadian Journal of Administrative Sciences-Revue canadienne des sciences de l’administration, 33, 138–152. https://doi.org/10.1002/CJAS.1381
Kim, J. H., Kim, J., Baek, T. H., & Kim, C. (2025). ChatGPT personalized and humorous recommendations. Annals of Tourism Research, 110, 103857.
Kim, T., Kim, M., & Promsivapallop, P. (2024). Investigating the influence of generative AI’s credibility and utility on travel consumer behaviour and recommendations through the lens of personal innovativeness. Current Issues in Tourism. https://doi.org/10.1080/13683500.2024.2364764
Lawshe, C. H. (1975). A quantitative approach to content validity. Personal Psychology, 28 (4), 563-575.
Lee, S.-W., Sung, H., & Jeon, H.-M. (2019). Determinants of Continuous Intention on Food Delivery Apps: Extending UTAUT2 with Information Quality. Sustainability. https://doi.org/10.3390/SU11113141
Liao, Y.-K., Wu, W., Truong, G. N. T., Binh, P. N. M., & Van Vu, V. (2021). A model of destination consumption, attitude, religious involvement, satisfaction, and revisit intention. Journal of Vacation Marketing, 27, 330–345. https://doi.org/10.1177/1356766721997516
Liesa-Orús, M., Latorre-Cosculluela, C., Sierra-Sánchez, V., & Vázquez-Toledo, S. (2022). Links between ease of use, perceived usefulness and attitudes towards technology in older people in university: A structural equation modelling approach. Education and Information Technologies, 28, 2419–2436. https://doi.org/10.1007/s10639-022-11292-1
Lin, C.-H., & Kuo, B. (2016). The Behavioral Consequences of Tourist Experience. Tourism Management Perspectives, 18, 84–91. https://doi.org/10.1016/J.TMP.2015.12.017
Liu, W. (2020). Summary of the Intelligent Social Management Function of Ideological from the Perspective of Information Theory with MOOC Platforms. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 486–489.
Magsamen-Conrad, K., Upadhyaya, S., Joa, C. Y., & Dowd, J. (2015). Bridging the divide: Using UTAUT to predict multigenerational tablet adoption practices. Computers in Human Behavior, 50, 186–196. https://doi.org/10.1016/J.CHB.2015.03.032
Majid, G. M., Tussyadiah, I., & Kim, Y. R. (2025). Exploring the potential of chatbots in extending tourists’ sustainable travel practices. Journal of Travel Research, 64(6), 1292–1317.
Marikyan, M., & Papagiannidis, P. (2021). Unified theory of acceptance and use of technology. TheoryHub Book.
Miah, M. S., Singh, J., & Rahman, M. (2023). Factors Influencing Technology Adoption in Online Learning among Private University Students in Bangladesh Post COVID-19 Pandemic. Sustainability. https://doi.org/10.3390/su15043543
Miller, H. (1996). The Multiple Dimensions of Information Quality. Inf. Syst. Manag., 13, 79–82. https://doi.org/10.1080/10580539608906992
Nguyen, D., Chiu, Y., & Le, H. (2021). Determinants of Continuance Intention towards Banks’ Chatbot Services in Vietnam: A Necessity for Sustainable Development. Sustainability. https://doi.org/10.3390/SU13147625
Nunnally, J. C., & Bernstein, I. H. (1978). Psychometric theory. New York: McGraw-Hill. Hill.
Orden-Mejía, M., Carvache-Franco, M., Huertas, A., Carvache-Franco, O., & Carvache-Franco, W. (2025). Analysing how AI-powered chatbots influence destination decisions. PLOS One, 20. https://doi.org/10.1371/journal.pone.0319463
Pei, X.-L., Guo, J.-N., Wu, T.-J., Zhou, W.-X., & Yeh, S.-P. (2020). Does the effect of customer experience on customer satisfaction create a sustainable competitive advantage? A comparative study of different shopping situations. Sustainability, 12(18), 7436.
Prasanna, A., Pushparaj, P., & Kushwaha, B. P. (2025). Conversational AI in Tourism: A systematic literature review using TCM and ADO framework. Journal of Hospitality and Tourism Management, 101310.
Sbaffi, L., & Rowley, J. (2017). Trust and Credibility in Web-Based Health Information: A Review and Agenda for Future Research. Journal of Medical Internet Research, 19. https://doi.org/10.2196/jmir.7579
Shankar, R. S. (2020). Impact of cognitive and affective image on tourists’ travel motivation.
Shi, J., Lee, M., Girish, V. G., Xiao, G., & Lee, C.-K. (2024). Embracing the ChatGPT revolution: unlocking new horizons for tourism. Journal of Hospitality and Tourism Technology, 15(3), 433–448.
Sigala, M., Ooi, K.-B., Tan, G. W.-H., Aw, E. C.-X., Buhalis, D., Cham, T.-H., Chen, M.-M., Dwivedi, Y. K., Gretzel, U., & Inversini, A. (2024). Understanding the impact of ChatGPT on tourism and hospitality: Trends, prospects and research agenda. Journal of Hospitality and Tourism Management, 60, 384–390.
Solomovich, L., & Abraham, V. (2024). Exploring the influence of ChatGPT on tourism behavior using the technology acceptance model. Tourism Review.
Stawowy, M., Duer, S., Paś, J., & Wawrzyński, W. (2021). Determining Information Quality in ICT Systems. Energies. https://doi.org/10.3390/en14175549
Su, J., Wang, Y., Liu, H., Zhang, Z., Wang, Z., & Li, Z. (2025). Investigating the factors influencing users’ adoption of artificial intelligence health assistants based on an extended UTAUT model. Scientific Reports, 15(1), 18215.
Suanpang, P., & Pothipassa, P. (2024). Integrating Generative AI and IoT for Sustainable Smart Tourism Destinations. Sustainability. https://doi.org/10.3390/su16177435
Sussman, S. W., & Siegal, W. S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65.
Taherdoost, H. (2016). Validity and reliability of the research instrument: How to test the validation of a questionnaire/survey in research. International Journal of Academic Research in Management (IJARM), 5.
To, W. M., & Yu, B. T. W. (2025). Artificial Intelligence Research in Tourism and Hospitality Journals: Trends, Emerging Themes, and the Rise of Generative AI. Tourism and Hospitality, 6(2), 63.
Topsakal, Y., & Çuhadar, M. (2025). Usage intention of tourists regarding the acceptance of artificial intelligence-enhanced tour guides apps. Current Issues in Tourism, 28(15), 2415–2431.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
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, 157–178.
Wong, I., Lian, Q. L., & Sun, D. (2023). Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI. Journal of Hospitality and Tourism Management. https://doi.org/10.1016/j.jhtm.2023.06.022
Yi, M., Yoon, J., Davis, J., & Lee, T. (2013). Untangling the antecedents of initial trust in Web-based health information: The roles of argument quality, source expertise, and user perceptions of information quality and risk. Decis. Support Syst., 55, 284–295. https://doi.org/10.1016/j.dss.2013.01.029
Zhang, J., Chen, Q., Lu, J., Wang, X., Liu, L., & Feng, Y. (2023). Emotional expression by artificial intelligence chatbots to improve customer satisfaction: Underlying mechanism and boundary conditions. Tourism Management. https://doi.org/10.1016/j.tourman.2023.104835
Zhang, Y., Li, X., & Fan, W. (2020). User adoption of physician’s replies in an online health community: An empirical study. Journal of the Association for Information Science and Technology, 71, 1179–1191. https://doi.org/10.1002/asi.24319
Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons Attribution 4.0 License or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution: other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
With the understanding that the above condition can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher's final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher's request, the Author agrees to furnish promptly to Publisher, at the Author's own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- The Work is the Author's original work;
- The Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- The Work is not pending review or under consideration by another publisher;
- The Work has not previously been published;
- The Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- The Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author's breach of the representations and warranties contained in Paragraph 7 above, as well as any claim or proceeding relating to Publisher's use and publication of any content contained in the Work, including third-party content.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Article Details
Accepted 2026-07-26
Published 2026-07-31