The DOI matches its Crossref record, with a small difference in title or year; 90 of the 90 references it cites were found in a registry or an index.
When ChatGPT Gives Incorrect Answers: The Impact of Inaccurate Information by Generative AI on Tourism Decision-Making
Open accessAbstract
This study investigates how inaccurate information provided by ChatGPT impacts travelers’ acceptance of recommendations. Six experiments were conducted based on the accessibility-diagnosticity framework. These examined the moderating role of the prominence and type of incorrect information and their effects on decision-making. The results show that participants perceived more accuracy and trustworthiness, leading to stronger intentions to visit when incorrect information was absent. However, there was a decline in their intentions to visit when incorrect information was present and more prominent or in the same domain. This effect diminished when multiple domains were involved or when participants were focused on the initial task. The research highlights that both the prominence and type of incorrect information are boundary conditions and provides insights into AI applications in tourism. Furthermore, it offers practical implications for online travel agencies in terms of user interface and user experience design planning.
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“Finally, AI-generated misinformation influences attitudes and decisions (Kim et al. 2023; Spitale et al. 2023).”
From Generative AI and misinformation: a scoping review of the role of generative AI in the generation, detection, mitigation, and impact of misinformation (2026) · cites this paper as Kim et al. 2023
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“Kim et al. (2025) found that when ChatGPT provided incorrect recommendations, users were less likely to rely on the GenAI tools and less inclined to act on its advice.”
From Perceived Search Overload, Generative AI Credibility, and Comparative Usefulness: A Channel Complementarity Approach to Health Information Seeking (2025) · cites this paper as Kim et al., 2025
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Cites
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193146 · Cited by 65,971
(2013) · Cited by 44,563
Developing and validating trust measures for e-commerce: an integrative typology
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002) · Information Systems Research · doi:10.1287/isre.13.3.334.81 · Cited by 5,550
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232 · cited 2×
“For example, Dietvorst et al. (2015) suggested that people prefer to make use of humans (vs. algorithms) when they observe forecasting errors.”
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
Park, D.-H., Lee, J., & Han, I. (2007) · International Journal of Electronic Commerce · doi:10.2753/jec1086-4415110405 · Cited by 2,318
Herr, P. M., Kardes, F. R., & Kim, J. (1991) · Journal of Consumer Research · doi:10.1086/208570 · Cited by 2,142
Self-citationAlgorithm appreciation: People prefer algorithmic to human judgment
Logg, J. M., Minson, J. A., & Moore, D. A. (2019) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2018.12.005 · Cited by 2,061
ChatGPT: five priorities for research
van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023) · Nature · doi:10.1038/d41586-023-00288-7 · Cited by 1,842 · cited 1×
“This medium has been increasingly adopted in the travel and tourism fields (Stokel-Walker & Van Noorden, 2023; van Dis et al., 2023).”
Task-Dependent Algorithm Aversion
Castelo, N., Bos, M. W., & Lehmann, D. R. (2019) · Springer eBooks · doi:10.1007/978-3-031-88067-4_21 · Cited by 1,723
Research works printed but not linked: 3
- 10.1177/0022243719851788 · DOI · Paper page
- Fuchs M., 2022, Transitioning towards the future of tourism destinations: Resilient, smart, and green development, 45
- Obadă R., 2019, Argumentum, 17, 144
Cited by
Sorted by citations
Li, Y., & Lee, S. O. (2024) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-11-2023-1767 · Cited by 106
Chen, H., Wang, P., & Hao, S. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108448 · Cited by 92
Using generative artificial intelligence (GenAI) in marketing: Development and practices
Chan, H., & Choi, T. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115276 · Cited by 100
Wandelt, S., Zheng, C., Wang, S., Liu, Y., & Sun, X. (2024) · Applied Sciences · doi:10.3390/app14177455 · Cited by 86
Li, H., Xi, J., Hsu, C. H. C., Yu, B. X. B., & Zheng, X. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105179 · Cited by 95
Metadata
- Authors
- Kim, J. H., Kim, J., Park, J., Kim, C., Jhang, J., & King, B.
- Affiliations
- Kyung Hee University (KR)
- Peking University (CN)
- Ritsumeikan University (JP)
- University of Central Arkansas (US)
- Texas A&M University (US)
- Corresponding author
- Kim, Jeong Hyun
- Venue
- Journal of Travel Research
- Publisher
- SAGE Publications
- Published
- 2023-11-29
- Volume
- 64
- Issue
- 1
- Pages
- 51-73
- ISSN
- 0047-2875 (ISSN-L)
- 1552-6763
- OpenAlex
- W4389201667
- Cited by
- 202
- References
- 90
- Citations by year
- 2026: 82 · 2025: 91 · 2024: 29
- FWCI
- 19.04
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Travel Research (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Tourism, Leisure and Hospitality Management › Diverse Aspects of Tourism Research
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- ChatGPT, generative AI, incorrect information, tourist decision-making, recommendation acceptance, trustworthiness, visit intention, online travel agencies, user interface design, user experience design
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