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Intention to Use Generative Artificial Intelligence for Hotel Selection Among Consumers: An Explanatory Sequential Investigation

Ruizhe Fang et al.

Journal of Tourism and Services2025https://doi.org/10.29036/tafksg14article
ABDC C
Weight
0.50

What the paper says

Hotel choice is highly information-intensive, and generative AI (GAI) increasingly aggregates dispersed hotel data into personalized, conversational recommendations that reduce search and verification effort. Against this backdrop, this study examines consumers' intention to use such tools, adopting an explanatory sequential mixed-methods design and an integrated Technology Acceptance Model-Theory of Planned Behavior (TAM-TPB) framework. The quantitative phase comprises an online survey in China (N = 529; recruited and randomly distributed via a national panel) analyzed with maximum-likelihood structural equation modeling (SEM). The qualitative phase involves six follow-up interviews to interpret results. Findings show that perceived ease of use (PEU) increases perceived usefulness (PU) and attitude (ATT), while ATT and subjective norm (SN) strongly predict intention to use GAI; by contrast, perceived behavioral control (PBC) is non-significant. Interviews clarify a boundary condition: because conversational GAI is perceived as low in complexity, users prioritize information credibility and effort savings over feelings of control, which attenuates PBC's role-especially among experienced travelers who rely on effective existing routines. The study contributes to tourism research by specifying how classic adoption beliefs operate in GAI-assisted hotel choice and by delineating when PBC contributes little to intention. Practical implications are stakeholder-specific: for OTAs/AI vendors (product/data owners), design for verification-effort reduction and provenance/credibility transparency; for hotel managers/marketers, ensure accurate structured property data and activate credible social proof to reinforce ATT and SN. These insights inform the design and deployment of AI-assisted decision tools that help travelers choose hotels faster, with warranted confidence, in real tourism settings.

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https://doi.org/https://doi.org/10.29036/tafksg14

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@article{ruizhe2025,
  title        = {{Intention to Use Generative Artificial Intelligence for Hotel Selection Among Consumers: An Explanatory Sequential Investigation}},
  author       = {Ruizhe Fang et al.},
  journal      = {Journal of Tourism and Services},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.29036/tafksg14},
}

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Intention to Use Generative Artificial Intelligence for Hotel Selection Among Consumers: An Explanatory Sequential Investigation

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.