AI-Assisted Travel Planning and Psychological Determinants of Wellness Tourism Value among Generation Z in Malaysia

The increasing adoption of artificial intelligence (AI) in tourism has transformed how travellers search for information, evaluate destinations, and plan travel experiences. Simultaneously, the growing popularity of wellness tourism among Generation Z has created a need to understand tourists’ value perceptions. This study examines theThe findings revealed that all four psychological factors significantly and positively influenced the effects of escapism, place attachment, memorable wellness tourism experience, and hedonic well-being on perceived value while investigating the moderating role of AI-assisted travel planning among Malaysian Generation Z wellness tourists. A quantitative, cross-sectional research design was employed, and data were collected from 414 respondents aged 18 to 28 using a structured online questionnaire distributed through convenience sampling. Data were analysed using descriptive statistics, Pearson correlation, multiple regression, and hierarchical regression analysis. Findings revealed that all four psychological factors significantly and positively influenced perceived value. Memorable wellness tourism experience emerged as the strongest predictor (β = 0.451, p < 0.001), followed by hedonic well-being (β = 0.273, p < 0.001), place attachment (β = 0.214, p < 0.001), and escapism (β = 0.118, p < 0.01). The regression model explained 77.3% of the variance in perceived value (R² = 0.773). Furthermore, AI-assisted travel planning significantly strengthened the relationships between all four psychological factors and perceived value, indicating that AI-enabled tools enhance tourists’ ability to identify, personalise, and maximise wellness tourism experiences. The originality of this study lies in integrating AI-assisted travel planning with psychological determinants of wellness tourism value within a single moderated framework. The findings contribute to the growing AI-driven consumer behaviour literature and offer practical insights for tourism marketers, destination managers, and wellness tourism operators seeking to design personalised, technology-enabled wellness experiences that resonate with Generation Z travellers.

AI-Assisted Travel Planning, Wellness Tourism, Generation Z

1. Balakrishnan, J., Dwivedi, Y. K., Malik, F. T., & Baabdullah, A. M. (2021). Role of smart tourism technology in heritage tourism development. Journal of Sustainable Tourism, 31(11), 2506–2525. https://doi.org/10.1080/09669582.2021.1995398 [Google Scholar] [Crossref]

2. Brochado, A., Veríssimo, J. M. C., & De Oliveira, J. C. L. (2022). Memorable tourism experiences, perceived value dimensions and behavioral intentions: a demographic segmentation approach. Tourism Review, 77(6), 1472–1486. https://doi.org/10.1108/tr-09-2021-0433 [Google Scholar] [Crossref]

3. Carvache-Franco, M., Carvache-Franco, O., Carvache-Franco, W., & Hernández-Lara, A. B. (2021). Segmentation of wellness tourists by their motivation and satisfaction. Sustainability, 13(3), 1170. https://doi.org/10.3390/su13031170 [Google Scholar] [Crossref]

4. Chu, M., & Liu, M. (2026). From perception to behavior: ChatGPT’s impact on tourists’ motivation and continuance intention in heritage tourism. Npj Heritage Science. https://doi.org/10.1038/s40494-026-02622-4 [Google Scholar] [Crossref]

5. Dandotiya, R., & Aggarwal, A. (2022). An examination of tourists’ national identity, place attachment and loyalty at a dark tourist destination. Kybernetes, 52(12), 6063–6077. https://doi.org/10.1108/k-08-2021-0756 [Google Scholar] [Crossref]

6. Dini, M., & Pencarelli, T. (2021). Wellness tourism and the components of its offer system: a holistic perspective. Tourism Review, 77(2), 394–412. https://doi.org/10.1108/tr-08-2020-0373 [Google Scholar] [Crossref]

7. Florido-Benítez, L., & del Alcázar Martínez, B. (2024). How Artificial Intelligence (AI) Is Powering New Tourism Marketing and the Future Agenda for Smart Tourist Destinations. Electronics, 13(21), 4151. https://doi.org/10.3390/electronics13214151 [Google Scholar] [Crossref]

8. Gan, T., Zheng, J., Li, W., Li, J., & Shen, J. (2023). Health and Wellness Tourists’ Motivation and Behavior Intention: The Role of Perceived Value. International Journal of Environmental Research and Public Health, 20(5), 4339. https://doi.org/10.3390/ijerph20054339 [Google Scholar] [Crossref]

9. García-Madurga, M., & Grilló-Méndez, A. (2023). Artificial intelligence in the tourism industry: An overview of reviews. Administrative Sciences, 13(8), 172. https://doi.org/10.3390/admsci13080172 [Google Scholar] [Crossref]

10. Global Wellness Institute. (2026, January 27). 2025 Global Wellness Economy Monitor - Global Wellness Institute. https://globalwellnessinstitute.org/industry-research/2025-global-wellness-economy-monitor/ [Google Scholar] [Crossref]

11. Hair, J., & Sabol, M. A. (2025). Overview of multivariate data analysis. In International Encyclopedia of Statistical Science (pp. 1853–1856). Springer Berlin Heidelberg. [Google Scholar] [Crossref]

12. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9 [Google Scholar] [Crossref]

13. Ivancsóné Horváth, Zs., Kupi, M., & Kundi, V. (2025). Digitalization and tourism: How X, Y, and Z generations make travel decisions in the online era. Geojournal of Tourism and Geosites, 60(2spl), 1302–1314. https://doi.org/10.30892/gtg.602spl26-1502 [Google Scholar] [Crossref]

14. Ivasciuc, I.-S., Candrea, A. N., & Ispas, A. (2025). Exploring tourism experiences: The vision of Generation Z versus artificial intelligence. Administrative Sciences, 15(5), 186. https://doi.org/10.3390/admsci15050186 [Google Scholar] [Crossref]

15. Joshanloo, M. (2025). A person-centered exploration of happiness conceptions and their relation to hedonic and eudaimonic well-being. Journal of Happiness Studies, 26(7). https://doi.org/10.1007/s10902-025-00953-w [Google Scholar] [Crossref]

16. Karagianni, V., Kalantonis, P., Tsartas, P., & Sdrali, D. (2025). Wellness tourism experiences and tourists’ satisfaction: A multicriteria analysis approach. Tourism and Hospitality, 6(4), 179. https://doi.org/10.3390/tourhosp6040179 [Google Scholar] [Crossref]

17. Kim, J. H. (2018). The impact of memorable tourism experiences on loyalty behaviours. Journal of Travel Research, 57(7), 856–870. https://doi.org/10.1177/0047287517721369 [Google Scholar] [Crossref]

18. Kim, J. H., Ritchie, J. R. B., & McCormick, B. (2012). Development of a scale to measure memorable tourism experiences. Journal of Travel Research, 51(1), 12–25. https://doi.org/10.1177/0047287510385467 [Google Scholar] [Crossref]

19. Koo, C., Shin, S., Gretzel, U., & Xiang, Z. (2025). AI-powered smart tourism 2.0: A 10-year retrospective and updated model. Electronic Markets, 35(1). https://doi.org/10.1007/s12525-025-00847-y [Google Scholar] [Crossref]

20. Liu, L., Zhou, Y., & Sun, X. (2023). The impact of the wellness tourism experience on tourist well-being: The mediating role of tourist satisfaction. Sustainability, 15(3), 1872. https://doi.org/10.3390/su15031872 [Google Scholar] [Crossref]

21. Lu, W., Pongsakornrungsilp, P., Klamsaengsai, S., Ketkaew, K., Tonsakunthaweeteam, S., & Li, L. (2025). Influence of creative tourist experiences and engagement on Gen Z’s environmentally responsible behavior: A moderated mediation model. Sustainability, 17(11), 4992. https://doi.org/10.3390/su17114992 [Google Scholar] [Crossref]

22. Marciszewska, B., & Wanagos, M. (2024). Development of tourism products and the concept of experience economy. European Research Studies Journal, 0(2), 868–883. [Google Scholar] [Crossref]

23. Mariani, M., & Baggio, R. (2022). Big data and analytics in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 34(1), 231–278. https://doi.org/10.1108/ijchm-03-2021-0301 [Google Scholar] [Crossref]

24. Marino-Romero, J. A., Robina-Ramírez, R., Pavón-Pérez, J. A., & Roets, A. O. (2025). Enhancing happiness and well-being: AI-driven solutions for accessible, inclusive travel experiences for people with disabilities. Disability and Rehabilitation Assistive Technology, 20(7), 2173–2184. https://doi.org/10.1080/17483107.2025.2488391 [Google Scholar] [Crossref]

25. Oh, H., Fiore, A. M., & Jeoung, M. (2007). Measuring experience economy concepts: Tourism applications. Journal of Travel Research, 46(2), 119–132. https://doi.org/10.1177/0047287507304039 [Google Scholar] [Crossref]

26. Pine, B. J., & Gilmore, J. H. (1998). Welcome to the experience economy. Harvard Business Review, 76(4), 97–105. https://pubmed.ncbi.nlm.nih.gov/10181589 [Google Scholar] [Crossref]

27. Pratminingsih, S. A., Johan, A., & Salsabil, I. (2022). The effect of destination attractiveness on destination attachment and its impact on tourist sustainable behavior. Jurnal Siasat Bisnis, 26(2), 222–234. https://doi.org/10.20885/jsb.vol26.iss2.art8 [Google Scholar] [Crossref]

28. Rahman, M. K., Gazi, M. A. I., Bhuiyan, M. A., & Rahaman, M. A. (2023). Digital transformation in tourism and hospitality. Sustainability, 15(7), 6024. [Google Scholar] [Crossref]

29. Ramkissoon, H., Van Der Veen, R., Salaripour, A., Reihani, Z. S., & Aflaki, A. (2024). The impact of sensory experiences on place attachment, place loyalty and civic participation: Evidence from Rasht, Iran. City, Culture and Society, 38, 100592. https://doi.org/10.1016/j.ccs.2024.100592 [Google Scholar] [Crossref]

30. Sthapit, E., Björk, P., & Coudounaris, D. N. (2022). Towards a better understanding of memorable wellness tourism experience. International Journal of Spa and Wellness, 6(1), 1–27. https://doi.org/10.1080/24721735.2022.2136120 [Google Scholar] [Crossref]

31. Vada, S., & Prentice, C. (2022). Tourist well-being, experience and behaviours: a positive psychological perspective. In Edward Elgar Publishing eBooks (pp. 176–194). https://doi.org/10.4337/9781839109393.00020 [Google Scholar] [Crossref]

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