ChatGPT adoption disposition and sustainable travel planning intention in a non-random international sample
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DOIhttp://dx.doi.org/10.21511/im.22(3).2026.10
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Article InfoVolume 22 2026, Issue #3, pp. 141-155
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Type of the article: Research Article
Abstract
Tourism decision-making increasingly relies on digital assistants, yet the mechanism through which conversational AI may support sustainable travel planning remains unclear. The purpose of this study was to examine whether ChatGPT adoption disposition is associated with sustainable travel planning intention and whether this association operates through ChatGPT relationship engagement and AI-assisted travel-planning experience. A quantitative cross-sectional survey was conducted online between January and March 2025 among adults from Tunisia, France, Italy, Germany, and other countries who had previously used ChatGPT to search for environmentally responsible travel information. From 234 questionnaires initially received, 48 were excluded after screening and quality control, leaving 186 valid responses. Partial Least Squares Structural Equation Modeling was used to test the measurement model, structural paths, mediation effects, and dummy-coded country controls, with France as the reference category. The results show that attitude (β = 0.421, p < 0.001), subjective norms (β = 0.353, p < 0.001), and perceived behavioral control (β = 0.297, p < 0.001) are positively associated with ChatGPT adoption disposition. The direct path from ChatGPT adoption disposition to sustainable travel planning intention is not significant (β = 0.080, p = 0.734). However, two indirect effects are significant: through ChatGPT relationship engagement (β = 0.250, p < 0.001) and through AI-assisted travel-planning experience (β = 0.124, p < 0.001). Country controls are non-significant. The study concludes that ChatGPT is more relevant as an experiential and relational facilitator than as a direct driver of sustainable travel intention, within the limits of a pooled non-probability international sample.
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JEL Classification (Paper profile tab)M31, L83, Q56, M30
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References40
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Tables6
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Figures2
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- Figure 1. Proposed research model
- Figure 2. Final structural model with country controls
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- Table 1. Respondent profile
- Table 2. Reliability and convergent validity assessment
- Table 3. Discriminant validity assessment – Fornell-Larcker criterion
- Table 4. Hypotheses testing results
- Table 5. Country dummy control effects on sustainable travel planning intention
- Table A1. Measurement scales and item descriptions
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- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
- Becker, J.-M., Cheah, J.-H., Gholamzade, R., Ringle, C. M., & Sarstedt, M. (2023). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 35(1), 321-346.
- Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252-271.
- Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet. Tourism Management, 29(4), 609-623.
- Chen, J. S., Tran-Thien-Y, L., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, 49(11), 1512-1531.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
- Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Sage.
- Dolnicar, S. (2020). Designing for more environmentally friendly tourism. Annals of Tourism Research, 84, 102933.
- Dwivedi, Y. K., Kshetri, N., Hughes, L., et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
- Gössling, S., Scott, D., & Hall, C. M. (2022). Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism, 29(1), 1-20.
- Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: Foundations and developments. Electronic Markets, 25(3), 179-188.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report PLS-SEM. European Business Review, 31(1), 2-24.
- Han, H., Hsu, L. T., & Sheu, C. (2010). Application of the Theory of Planned Behavior to green hotel choice: Testing the effect of environmental friendly activities. Tourism Management, 31(3), 325-334.
- Han, X., Rob, L., Jon, L., Jian, M. L., & Lu, L. (2024). Tourist acceptance of ChatGPT in travel services: The mediating role of parasocial interaction. Journal of Travel & Tourism Marketing, 41(7), 955-972.
- Harrigan, P., Evers, U., Miles, M. P., & Daly, T. (2017). Customer engagement with tourism social media brands. Tourism Management, 59, 597–609.
- Hollebeek, L. D., Srivastava, R. K., & Chen, T. (2019). S-D logic-informed customer engagement: Integrative framework, revised fundamental propositions, and application to CRM. Journal of the Academy of Marketing Science, 47, 161-185.
- Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50.
- Ivanov, S., & Webster, C. (2019). Conceptual framework of the use of robots, artificial intelligence and service automation in travel, tourism, and hospitality. In Robots, artificial intelligence and service automation in travel, tourism and hospitality (pp. 7-37). Emerald.
- Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models. Learning and Individual Differences, 103, 102274.
- Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227-261.
- Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96.
- Majid, G. M., Tussyadiah, I., & Kim, Y. R. (2024). Exploring the potential of chatbots in extending tourists’ sustainable travel practices. Journal of Travel Research, 63(6), 1292-1317.
- Pantano, E., Pizzi, G., Scarpi, D., & Dennis, C. (2021). Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. Journal of Business Research, 116, 209-213.
- Pappas, N., Kourouthanassis, P., Giannakos, M., & Chrissikopoulos, V. (2014). Shiny happy people buying: The role of emotions on personalized e-shopping. Tourism Management, 39, 183-195.
- Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.
- Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online customer experience in e-retailing: An empirical model of antecedents and outcomes. Journal of Retailing, 88(2), 308-322.
- Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.
- Sekaran, U., & Bougie, R. (2020). Research methods for business: A skill-building approach (8th ed.). Wiley.
- Sigala, M. (2016). Social media and the co-creation of tourism experiences. In M. Sotiriadis & D. Gursoy (Eds.), The handbook of managing and marketing tourism experiences (pp. 85-111). Emerald.
- So, K. K. F., King, C., Sparks, B. A., & Wang, Y. (2012). Customer engagement with tourism brands: Scale development and validation. Journal of Hospitality & Tourism Research, 38(3), 304-329.
- Steenkamp, J.-B. E. M., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25(1), 78-90.
- Tuomi, A., Tussyadiah, I., & Ascenção, M. P. (2025). Customized language models for tourism management: Implications and future research. Annals of Tourism Research, 110, 103863.
- UNWTO. (2023). Investing in a diverse, sustainable future for tourism. World Tourism Organization.
- Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204.
- Verma, V. K., Chandra, B., & Kumar, S. (2019). Values and ascribed responsibility to predict consumers’ attitude and concern towards green hotel visit intention. Journal of Cleaner Production, 229, 296-305.
- Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring customer relationships beyond purchase. Journal of Marketing Theory and Practice, 20(2), 122-146.
- Xiang, Z., Magnini, V. P., & Fesenmaier, D. R. (2015). Information technology and consumer behavior in travel and tourism: Insights from travel planning using the Internet. Journal of Retailing and Consumer Services, 22, 244-249.
- Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206.


