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From Adoption to Continuance: Explaining AI Use in Travel Planning

  • Jul 1, 2026
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Abstract Book of the 11th International Conference on Management, Economics and Finance

Year: 2026

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From Adoption to Continuance: Explaining AI Use in Travel Planning

Dr. Sharon Teitler Regev, Shlomit Hon Snir

ABSTRACT:

Artificial intelligence (AI) tools are increasingly integrated into travel planning, offering travelers interactive and personalized decision support across multiple planning stages. Despite their growing availability, limited research has examined the factors that drive not only adoption but also continuous usage and recommendation behavior of AI-based travel planning tools. This study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the determinants of AI use in tourism, with a particular focus on performance expectancy, continuous usage intention, and word of mouth. Data were collected through a self-administered online questionnaire distributed by a polling company in December 2025, yielding a sample of 501 respondents. Structural Equation Modeling (SEM) was employed to test the proposed relationships among the constructs. The results indicate that effort expectancy and facilitating conditions, as well as social influence, significantly increase performance expectancy. Performance expectancy emerged as the strongest predictor of both continuous usage intention and word of mouth recommendation. Social influence also had a direct positive effect on continuous usage and recommendation behavior. In contrast, effort expectancy and facilitating conditions did not directly predict continuous usage or word of mouth, suggesting that their influence operates primarily through performance expectancy. Additionally, trip self-planning showed a small but significant negative effect on continuous usage intention. Overall, the findings highlight the central role of perceived performance benefits and social endorsement in shaping sustained engagement with AI travel planning tools. The study contributes to the growing literature on AI adoption in tourism by extending the UTAUT framework to generative AI applications and offering insights into the mechanisms driving long-term use and diffusion.

Keywords: Generative AI; performance expectancy; UTAUT model; Continuous Usage; Word of Mouth