Next-Gen Wellness: AI, MBCT, and Data-Driven Marketing Transforming Health Tourism

Evgenia Gkintoni (), Georgios Telonis, Constantinos Halkiopoulos and Basilis Boutsinas
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Evgenia Gkintoni: University of Patras
Georgios Telonis: University of Patras
Constantinos Halkiopoulos: University of Patras
Basilis Boutsinas: University of Patras

A chapter in Innovation and Creativity in Tourism, Business and Social Sciences, 2025, pp 819-855 from Springer

Abstract: Abstract The paper discusses how Artificial Intelligence, MBCT, and data-driven marketing can become an industry with completely new opportunities for health tourism. The PRISMA systematic review approach has been applied; then, 150 studies were filtered to identify 30 papers directly referring to the influence of those innovations. The findings bring out how AI-driven personalized wellness plans, continuous support through virtual buddies, and adaptive smart environments are here to stay. In the process, MBCT has been found to enhance mental health significantly by concerted mindfulness training. The present paper underlines data-driven marketing with a view to locating consumer preferences and optimizing strategies to enrich the whole wellness experience. The study reveals ethical issues about data privacy and the potential enhancement of well-being using technology. Recommendations from this study can be held as takeaways for industry stakeholders to understand how the evolving needs and preferences of health tourists may be met. It ultimately places integration around AI, MBCT, and data-driven marketing at the core of redefining the future of wellness tourism, ensuring better personalization, access, and mental health benefits.

Keywords: Health tourism; AI; MBCT; Data-driven marketing; Mental health; Holistic well-being; Personalized wellness plans; Virtual health assistants; Marketing strategies; Customer engagement (search for similar items in EconPapers)
JEL-codes: L86 M31 O32 O33 Z32 Z33 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-78471-2_37

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DOI: 10.1007/978-3-031-78471-2_37

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