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Academic ResearchJuly 31, 2026Journal of Hospitality and Tourism Technology

Understanding the acceptance and use of artificial intelligence (AI) as a travel planning tool: UTAUT2 extended model

This study extends the UTAUT2 model with perceived anthropomorphism and perceived intelligence to explain what drives travelers to adopt AI tools for trip planning. Based on 176 survey respondents, performance expectancy, hedonic motivation, perceived anthropomorphism, and perceived intelligence all positively predict behavioral intention, and habit turns out to be central to sustained use.

Authors

Ana Carolina Dias, Carlos Tam, Tiago Oliveira, Mijail Naranjo-Zolotov

Article content

What the paper studied

This investigation explores the factors that shape traveler acceptance of AI tools used for travel planning. The authors extend the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) framework with two additional constructs — perceived anthropomorphism and perceived intelligence — that speak to how human-like and how smart the AI feels. Data were collected via survey from 176 respondents and analyzed with structural modeling.

Key findings

  • Performance expectancy, hedonic motivation, perceived anthropomorphism, and perceived intelligence all positively correlate with behavioral intention to use AI for travel planning.
  • Habit emerged as a significant driver of continued, sustained AI tool adoption — not just first-time trial.
  • Extending UTAUT2 with perceived anthropomorphism and perceived intelligence improves the model's explanatory power for AI-specific travel contexts.

Why it matters for hospitality and tourism

AI travel planners (Gemini, ChatGPT, agentic OTA assistants) are becoming a serious discovery and planning channel. This paper tells hotels, OTAs, and destinations which levers actually move traveler adoption: perceived usefulness and enjoyment, plus how intelligent and human-like the assistant feels. And because habit predicts sustained use, the operators who get travelers into an AI-assisted planning loop early are likely to keep them.

Practical takeaways

  • When building or partnering on AI travel assistants, prioritize demonstrable usefulness (performance expectancy) alongside a pleasant, engaging interaction (hedonic motivation).
  • Invest in the assistant's perceived intelligence and appropriate anthropomorphism — bland, robotic responses depress adoption.
  • Design for habit formation: nudge repeat use, personalize across sessions, and treat first-time users as future recurring ones.

Tags

Artificial IntelligenceTechnology AdoptionTourismGenerative AIGuest ExperiencePersonalization

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