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February 11, 2026Journal of Consumer Behaviour9 citations

Travel Recommendations of Tomorrow: Generative Artificial Intelligence and Travel Planning

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DMDušan MladenovićMasaryk UniversityEIElvira IsmagilovaSwansea UniversityEMEmmanuel MogajiJaipuria Institute of Management

Key Points

  • The aim is to understand how generative artificial intelligence affects travelers' decision-making and future usage intentions during travel planning.
  • Conducted semi-structured interviews in the UK.
  • Utilized scenario-based questionnaires in the USA.
  • Applied the stimulus–organism–response framework for data analysis.
  • GAI recommendations reduced information overload for travelers.
  • Decision-making was not significantly streamlined despite GAI usage.
  • Trust and information retrieval skills were identified as moderate influencers in the relationship between GAI and information overload.

Abstract

ABSTRACT This study aims to cultivate an initial understanding of travelers' engagement with generative artificial intelligence (GAI) during the travel planning phase. It focuses on its influence on decision‐making and intentions for continuous usage in planning tourism activities. Utilizing the stimulus–organism–response framework and domain literature, data were gathered through semi‐structured interviews (UK) and scenario‐based questionnaires (USA). The study reveals complex aspects of travelers' behavior, uncovering that while GAI recommendations mitigate the risk of information overload, their influence does not necessarily streamline decision‐making. Trust and information retrieval skills surfaced as moderate determinants of the relationship between recommendations and information overload. This work is a pioneer in empirically exploring and quantifying continuance intentions of generative artificial intelligence (GAI) usage, contributing novel insights to electronic Word of Mouth and decision‐making literature.

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Cite This Study

Mladenović et al. (2026) studied this question.

synapsesocial.com/papers/698c1bff267fb587c655e0dbhttps://doi.org/10.1002/cb.70126
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