Cited by 202AI in Service InteractionsDiverse Aspects of Tourism ResearchDigital Marketing and Social Media

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 access

Kim, Jeong Hyun, Kim, Jungkeun, Park, Jooyoung, Kim, Changju, Jhang, Jihoon, King, Brian·2025·Journal of Travel Research

DOI 10.1177/00472875231212996

Open this paper in CoChat — free account Open PDF

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1177/00472875231212996

Cites

90 of 90 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193146 · Cited by 65,971
Untitled work
(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
The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement
Park, D.-H., Lee, J., & Han, I. (2007) · International Journal of Electronic Commerce · doi:10.2753/jec1086-4415110405 · Cited by 2,318
Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective
Herr, P. M., Kardes, F. R., & Kim, J. (1991) · Journal of Consumer Research · doi:10.1086/208570 · Cited by 2,142
Self-citation
Algorithm 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
The impact of online reviews on hotel booking intentions and perception of trust
Sparks, B. A., & Browning, V. (2011) · Tourism Management · doi:10.1016/j.tourman.2010.12.011 · Cited by 1,688
The impact of electronic word‐of‐mouth
Cheung, C. M. K., Lee, M., & Rabjohn, N. (2008) · Internet Research · doi:10.1108/10662240810883290 · Cited by 1,663
Consumers acceptance of artificially intelligent (AI) device use in service delivery
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.03.008 · Cited by 1,437
Credibility of electronic word-of-mouth: informational and normative determinants of on-line consumer recommendations
Cheung, M. Y., Luo, C., Sia, C. L., & Chen, H. (2009) · International Journal of Electronic Commerce · doi:10.2753/jec1086-4415130402 · Cited by 1,286
Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services
Luo, X. R., Li, H., Zhang, J., & Shim, J. P. (2010) · Decision Support Systems · doi:10.1016/j.dss.2010.02.008 · Cited by 1,194
E-WOM and Accommodation
Filieri, R., & McLeay, F. (2013) · Journal of Travel Research · doi:10.1177/0047287513481274 · Cited by 1,187
Trust in a specific technology: An investigation of its components and measures
Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011) · ACM Transactions on Management Information Systems · doi:10.1145/1985347.1985353 · Cited by 1,185
Shifting attention to accuracy can reduce misinformation online
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021) · Nature · doi:10.1038/s41586-021-03344-2 · Cited by 1,155
Developing and validating a service robot integration willingness scale
Lu, L., Cai, R., & Gursoy, D. (2019) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2019.01.005 · Cited by 1,013 · cited 2×
“Similarly, considering the heterogeneity of travel contexts and the novel nature of AI devices, scholars recommend investigating both agent characteristics and environmental clues (Lu et al., 2019).”
Adoption of AI-based chatbots for hospitality and tourism
Pillai, R., & Sivathanu, B. (2020) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-04-2020-0259 · Cited by 986
Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth
Filieri, R., Alguezaui, S., & McLeay, F. (2015) · Tourism Management · doi:10.1016/j.tourman.2015.05.007 · Cited by 882 · cited 1×
“Filieri et al. (2015) argued that the more consumers view recommendations or word-of-mouth as trustworthy, the more likely they will follow.”
What ChatGPT and generative AI mean for science
Stokel-Walker, C., & Van Noorden, R. (2023) · Nature · doi:10.1038/d41586-023-00340-6 · Cited by 828
e-Tourism beyond COVID-19: a call for transformative research
Ulrike Gretzel, B., Fuchs, M., Baggio, R., Höpken, W., Law, R., Neidhardt, J., Pesonen, J. A., Zanker, M., & Xiang, Z. (2020) · Information Technology & Tourism · doi:10.1007/s40558-020-00181-3 · Cited by 670
Leveraging ChatGPT and other generative artificial intelligence (AI)-based applications in the hospitality and tourism industry: practices, challenges and research agenda
Dwivedi, Y. K., Pandey, N., Currie, W. L., & Micu, A. (2023) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-05-2023-0686 · Cited by 659
Artificial Intelligence and Marketing: Pitfalls and Opportunities
Bruyn, A. D., Viswanathan, V., Beh, Y. S., Brock, J. K., & von Wangenheim, F. (2020) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2020.04.007 · Cited by 647
ChatGPT for tourism: applications, benefits and risks
Carvalho, I., & Ivanov, S. H. (2023) · Tourism Review · doi:10.1108/tr-02-2023-0088 · Cited by 609
Attentional capture and inattentional blindness
Simons, D. J. (2000) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01455-8 · Cited by 592
Journal of Consumer Marketing
(2023) · Journal of Consumer Marketing · doi:10.1108/jcm · Cited by 522
A Comparison of Online Trust Building Factors between Potential Customers and Repeat Customers
National University of Singapore, S., Kim, H.-W., Xu, Y., National University of Singapore, S., Koh, J., & Electronics, S. (2004) · Journal of the Association for Information Systems · doi:10.17705/1jais.00056 · Cited by 452
ChatGPT and the hospitality and tourism industry: an overview of current trends and future research directions
Gürsoy, D., Li, Y., & Song, H. J. (2023) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2023.2211993 · Cited by 448
Thinking responsibly about responsible AI and ‘the dark side’ of AI
Mikalef, P., Conboy, K., Lundström, J. E., & Popovič, A. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2022.2026621 · Cited by 435
Generative Artificial Intelligence in the Hospitality and Tourism Industry: Developing a Framework for Future Research
Doğru, T., Line, N. D., Mody, M. A., Hanks, L., Abbott, J., Açikgöz, F., Assaf, A. G., Bakir, S., Berbekova, A., Bilgihan, A., et al. (2023) · Journal of Hospitality & Tourism Research · doi:10.1177/10963480231188663 · Cited by 415
Predicting the intentions to use chatbots for travel and tourism
Melián-González, S., Gutiérrez-Taño, D., & Bulchand-Gidumal, J. (2021) · Current Issues in Tourism · doi:10.1080/13683500.2019.1706457 · Cited by 406
Adapting to the Internet
Xiang, Z., Wang, D., O’Leary, J. T., & Fesenmaier, D. R. (2014) · Journal of Travel Research · doi:10.1177/0047287514522883 · Cited by 402
Marketing robot services in hospitality and tourism: the role of anthropomorphism
Murphy, J., Ulrike Gretzel, B., & Pesonen, J. A. (2019) · Journal of Travel & Tourism Marketing · doi:10.1080/10548408.2019.1571983 · Cited by 393
Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services
Lin, H., Chi, O. H., & Gürsoy, D. (2019) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2020.1685053 · Cited by 388
Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI
Wong, I. A., Lian, Q. L., & Sun, D. (2023) · Journal of Hospitality and Tourism Management · doi:10.1016/j.jhtm.2023.06.022 · Cited by 377
Do travelers trust intelligent service robots?
Tussyadiah, I. P., Zach, F., & Wang, J. (2020) · Annals of Tourism Research · doi:10.1016/j.annals.2020.102886 · Cited by 373
Inattentional Blindness
Mack, A. (2003) · Current Directions in Psychological Science · doi:10.1111/1467-8721.01256 · Cited by 364
Antecedents of Trust and Adoption Intention toward Artificially Intelligent Recommendation Systems in Travel Planning: A Heuristic–Systematic Model
Shi, S., Gong, Y., & Gürsoy, D. (2020) · Journal of Travel Research · doi:10.1177/0047287520966395 · Cited by 356
When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations
Kim, J., Giroux, M., & Lee, J. C. (2021) · Psychology & Marketing · doi:10.1002/mar.21498 · Cited by 353
Self-citation
Herd behavior in purchasing books online
Chen, Y. (2008) · Computers in Human Behavior · doi:10.1016/j.chb.2007.08.004 · Cited by 305
People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error
Dietvorst, B. J., & Bharti, S. (2020) · Psychological Science · doi:10.1177/0956797620948841 · Cited by 294
Game of algorithms: ChatGPT implications for the future of tourism education and research
Ivanov, S. H., & Soliman, M. S. A. (2023) · Journal of Tourism Futures · doi:10.1108/jtf-02-2023-0038 · Cited by 263
Holy or Unholy? Interview with Open AI’s ChatGPT
Iskender, A. (2023) · European Journal of Tourism Research · doi:10.54055/ejtr.v34i.3169 · Cited by 260
Information and Communication Technologies and Society
Fuchs, C. (2009) · European Journal of Communication · doi:10.1177/0267323108098947 · Cited by 253
Multidimensional Facets of Perceived Risk in Mobile Travel Booking
Park, S., & Tussyadiah, I. P. (2016) · Journal of Travel Research · doi:10.1177/0047287516675062 · Cited by 241
Behavioral Frequency Judgments: An Accessibility-Diagnosticity Framework
Menon, G., Raghubir, P., & Schwarz, N. (1995) · Journal of Consumer Research · doi:10.1086/209446 · Cited by 228
“How may i help you?” Says a robot: Examining language styles in the service encounter
Choi, S., Liu, S. Q., & Mattila, A. S. (2019) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2019.03.026 · Cited by 216
When does technology anthropomorphism help alleviate customer dissatisfaction after a service failure? – The moderating role of consumer technology self-efficacy and interdependent self-construal
Fan, A., Wu, L., Miao, L., & Mattila, A. S. (2019) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2019.1639095 · Cited by 214
Trust as indicator of robot functional and social acceptance. An experimental study on user conformation to iCub answers
Gaudiello, I., Zibetti, E., Lefort, S., Chetouani, M., & Ivaldi, S. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2016.03.057 · Cited by 203
Impact of cultural values on technology acceptance and technology readiness
Sunny, S., Patrick, L., & Rob, L. (2019) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2018.06.017 · Cited by 197
An elaboration likelihood model of consumer respond action to facebook second-hand marketplace: Impulsiveness as a moderator
Chang, H. H., Lu, Y.-Y., & Lin, S. C. (2019) · Information & Management · doi:10.1016/j.im.2019.103171 · Cited by 187
Antecedents and consequences of travelers’ trust towards personalized travel recommendations offered by ChatGPT
Ali, F., Yasar, B., Ali, L., & Dogan, S. (2023) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2023.103588 · Cited by 185
ChatGPT for e-Tourism: a technological perspective
Mich, L., & Garigliano, R. (2023) · Information Technology & Tourism · doi:10.1007/s40558-023-00248-x · Cited by 177
Not such smart tourism? The concept of e-lienation
Tribe, J., & Mkono, M. (2017) · Annals of Tourism Research · doi:10.1016/j.annals.2017.07.001 · Cited by 174
Who or What to Believe: Trust and the Differential Persuasiveness of Human and Anthropomorphized Messengers
Touré-Tillery, M., & McGill, A. L. (2015) · Journal of Marketing · doi:10.1509/jm.12.0166 · Cited by 167
Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations
Kim, J., Kim, J. H., Kim, C., & Park, J. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103494 · Cited by 157
Self-citation
Why trust an algorithm? Performance, cognition, and neurophysiology
Alexander, V., Blinder, C., & Zak, P. J. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.07.026 · Cited by 148
Houston, we have a problem!: The use of ChatGPT in responding to customer complaints
Koç, E., HATİPOĞLU, S., Kıvrak, O., ÇELİK, C., & Koc, K. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102333 · Cited by 136
Personalized multi-period tour recommendations
Kotiloglu, S., Lappas, T., Pelechrinis, K., & Repoussis, P. P. (2017) · Tourism Management · doi:10.1016/j.tourman.2017.03.005 · Cited by 133
Interactive design of personalised tourism routes
Díaz, B. R., Molina, J., García, F. P., & Caballero, R. (2011) · Tourism Management · doi:10.1016/j.tourman.2011.09.014 · Cited by 124
Is ChatGPT the right technology for service individualization and value co-creation? evidence from the travel industry
Demir, M., & Demir, Ş. Ş. (2023) · Journal of Travel & Tourism Marketing · doi:10.1080/10548408.2023.2255884 · Cited by 122
Algorithm Overdependence: How the Use of Algorithmic Recommendation Systems Can Increase Risks to Consumer Well-Being
Banker, S., & Khetani, S. (2019) · Journal of Public Policy & Marketing · doi:10.1177/0743915619858057 · Cited by 120
eTourism: critical information and communication technologies for tourism destinations.
Buhalis, D., Leung, D., & Law, R. (2011) · CABI eBooks · doi:10.1079/9781845937621.0205 · Cited by 117
Effects of AI ChatGPT on travelers’ travel decision-making
Kim, J. H., Kim, J., Kim, S., & Hailu, T. B. (2023) · Tourism Review · doi:10.1108/tr-07-2023-0489 · Cited by 109
Self-citation
ChatGPT in practice: Increasing event planning efficiency through artificial intelligence
Keiper, M. C. (2023) · Journal of Hospitality Leisure Sport & Tourism Education · doi:10.1016/j.jhlste.2023.100454 · Cited by 94
Using a heuristic algorithm to design a personalized day tour route in a time-dependent stochastic environment
Liao, Z., & Zheng, W. (2018) · Tourism Management · doi:10.1016/j.tourman.2018.03.012 · Cited by 91
Towards Safe and Trustworthy Social Robots: Ethical Challenges and Practical Issues
Salem, M., Lakatos, G., Amirabdollahian, F., & Dautenhahn, K. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25554-5_58 · Cited by 84
Online users' attitudes toward fake news: Implications for brand management
Tiago, M. T. B., Tiago, F., Silva, O., Martínez, J. M. G., & Botella-Carrubí, D. (2020) · Psychology and Marketing · doi:10.1002/mar.21349 · Cited by 82
ICT and the future of tourist management
Hughes, K., & Moscardo, G. M. (2019) · Journal of Tourism Futures · doi:10.1108/jtf-12-2018-0072 · Cited by 77
Bridging the gap of bibliometric analysis: The evolution, current state, and future directions of tourism research using ChatGPT
Shin, H., & Kang, J. (2023) · Journal of Hospitality and Tourism Management · doi:10.1016/j.jhtm.2023.09.001 · Cited by 76
Genetic Counseling and the Fiction of Choice: Taught Self‐Determination as a New Technique of Social Engineering
Samerski, S. (2009) · Signs · doi:10.1086/597142 · Cited by 72
Free but fake speech: When giving primacy to the source decreases misinformation sharing on social media
Domenico, G. D., Nunan, D., Sit, J., & Pitardi, V. (2021) · Psychology and Marketing · doi:10.1002/mar.21479 · Cited by 69
Tools for degrowth? Ivan Illich's critique of technology revisited
Samerski, S. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.10.039 · Cited by 62
The effect of fake news in marketing halal food: a moderating role of religiosity
Wisker, Z. L. (2020) · Journal of Islamic marketing · doi:10.1108/jima-09-2020-0276 · Cited by 62
Tourism and the COVID-(Mis)infodemic
Williams, N. L., Wassler, P., & Ferdinand, N. (2020) · Journal of Travel Research · doi:10.1177/0047287520981135 · Cited by 59
ChatGPT and tourism academia
Nautiyal, R., Albrecht, J. N., & Nautiyal, A. (2023) · Annals of Tourism Research · doi:10.1016/j.annals.2023.103544 · Cited by 56
Owning Mistakes Sincerely: Strategies for Mitigating AI Errors
Mahmood, A., Fung, J. W., Won, I., & Huang, C.-M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517565 · Cited by 52 · cited 1×
“Prior research on consumer adoption of AI has focused on two areas: (1) how consumer attitudes change following AI errors (e.g., Dietvorst & Bharti, 2020) and (2) what procedures can mitigate the negative effects of AI errors (e.g., Mahmood et al., 2022).”
Encountering misinformation online: antecedents of trust and distrust and their impact on the intensity of Facebook use
Cheng, Y., & Chen, Z. F. (2021) · Online Information Review · doi:10.1108/oir-04-2020-0130 · Cited by 45
The role of emotions in shaping purchase intentions for innovations using emerging technologies: A scenario‐based investigation in the context of nanotechnology
Seegebarth, B., Backhaus, C., & Woisetschläger, D. M. (2019) · Psychology and Marketing · doi:10.1002/mar.21228 · Cited by 40
Tailoring heuristics and timing AI interventions for supporting news veracity assessments
Horne, B. D., Nevo, D., Adalı, S., Manikonda, L., & Arrington, C. (2020) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2020.100043 · Cited by 34
A digital humanism view on e-tourism
Werthner, H. (2022) · Information Technology & Tourism · doi:10.1007/s40558-022-00237-6 · Cited by 33
AI, ChatGPT and the university
Fesenmaier, D. R., & Wöber, K. W. (2023) · Annals of Tourism Research · doi:10.1016/j.annals.2023.103578 · Cited by 30
Search for the underlying mechanism of framing effects in multi-alternative and multi-attribute decision situations
Kim, J., Kim, J., & Marshall, R. (2013) · Journal of Business Research · doi:10.1016/j.jbusres.2012.12.024 · Cited by 28
Self-citation
ChatGPT: Cross cultural tourism research imperative
Chen, S., Zhang, K., Li, X., Ye, H., Lin, K. J., & Law, R. (2023) · Journal of Economics and Management · doi:10.22367/jem.2023.45.07 · Cited by 19
Strategic Use of Information Technologies in Tourism: A Review and Critique
Fuchs, M., & Σιγάλα, Μ. (2021) · Springer eBooks · doi:10.1007/978-3-030-05324-6_67-1 · Cited by 13
On Technology and the Prospects for Good Practice in the Human Services: Donald Schön, Martin Heidegger, and the Case forPhronesisandPraxis
Emslie, M., & Watts, R. (2017) · Social Service Review · doi:10.1086/692117 · Cited by 12
Buddhist Philosophy and Scientific Naturalism
Thompson, E. T. (2021) · Sophia · doi:10.1007/s11841-021-00880-2 · Cited by 5
Probing for Relevance: Information Theories and Translation
Dejica, D. (2009) · ELOPE English Language Overseas Perspectives and Enquiries · doi:10.4312/elope.6.1-2.81-99 · Cited by 3

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

Sorted by citations

Navigating the generative AI travel landscape: the influence of ChatGPT on the evolution from new users to loyal adopters
Li, Y., & Lee, S. O. (2024) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-11-2023-1767 · Cited by 106
AI in the spotlight: The impact of artificial intelligence disclosure on user engagement in short-form videos
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
Large Language Models for Intelligent Transportation: A Review of the State of the Art and Challenges
Wandelt, S., Zheng, C., Wang, S., Liu, Y., & Sun, X. (2024) · Applied Sciences · doi:10.3390/app14177455 · Cited by 86
Generative artificial intelligence in tourism management: An integrative review and roadmap for future research
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

See all 202

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
ORCID: Kim, Jeong Hyun
Kim, Jungkeun
Park, Jooyoung
Kim, Changju
Jhang, Jihoon
King, Brian
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
DOI: 10.1177/00472875231212996
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
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

When ChatGPT Gives Incorrect Answers: The Impact of Inaccurate Information by Generative AI on Tourism Decision-Making

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks