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Push–Pull–Mooring Effects on Travelers’ Switching Behavior from Online Travel Communities to Generative Artificial Intelligence Services: An Integrated Theoretical Framework

Guangyu Xiao et al.

Journal of Travel Research2026https://doi.org/10.1177/00472875261430749article
AJG 4ABDC A*
Weight
0.50

Abstract

The rise of generative artificial intelligence (GenAI) is transforming how travelers seek tourism information and challenging traditional online travel communities (OTCs). Integrating push–pull–mooring theory with the stressor–strain–outcome and information systems success models, we investigate why travelers intend to switch from OTCs to GenAI services. Using survey data from 445 travelers, we apply structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA). Results reveal that information overload and communication overload in OTCs drive fatigue and prompt travelers to adopt GenAI services, which offer superior system quality and information quality and thus increase travelers’ satisfaction. Inertia remains a significant barrier, whereas switching cost has minimal impact. Complementing the net effects, the fsQCA identifies three distinct configurational pathways to high switching intention. The findings suggest integrating human-generated content and GenAI into tourism information systems to reduce cognitive strain, improve relevance, and elevate satisfaction.

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https://doi.org/https://doi.org/10.1177/00472875261430749

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@article{guangyu2026,
  title        = {{Push–Pull–Mooring Effects on Travelers’ Switching Behavior from Online Travel Communities to Generative Artificial Intelligence Services: An Integrated Theoretical Framework}},
  author       = {Guangyu Xiao et al.},
  journal      = {Journal of Travel Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/00472875261430749},
}

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Push–Pull–Mooring Effects on Travelers’ Switching Behavior from Online Travel Communities to Generative Artificial Intelligence Services: An Integrated Theoretical Framework

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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.