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Research Article

How virtual tourism influences travel intention: a study combined with eye movement and scenario experiment

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Pages 1241-1260 | Received 01 Jul 2023, Accepted 05 Dec 2023, Published online: 27 Dec 2023
 

ABSTRACT

To explore the impact of virtual tourism on travel intention, this study developed the model incorporating virtual tourism content and form, visual appeal, presence, virtual attachment, and travel intention. Eye movement experiments, scenario experiments, and questionnaire surveys were integrated. Analysis of 48 eye movement datasets and 210 survey responses indicated that virtual tourism affected travel intention mostly through visual appeal: (1) Regarding visual appeal, videos of real scenes were stronger than videos of virtual scenes, and videos of virtual scenes were stronger than pictures of virtual scenes. (2) Visual appeal had a direct positive effect on travel intention.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Conceptualization: Jian Xu, Xing Liu, Hua Pang, Kaizhong Cao, Shuyi Du; Methodology: Jian Xu, Xing Liu, Shuyi Du, Xiaolan Zhuo; Formal analysis and investigation: Jian Xu, Xing Liu, Hua Pang, Kaizhong Cao, Shuyi Du, Xiaolan Zhuo, Xinyi Zheng, Fangqi Zhou, Yuting Huang; Writing – original draft preparation: Jian Xu, Xing Liu; Writing – review and editing: Jian Xu, Xing Liu, Hua Pang, Kaizhong Cao; Funding acquisition: Jian Xu; Resources: Xinyi Zheng, Xing Liu, Shuyi Du, Xiaolan Zhuo, Fangqi Zhou, Yuting Huang; Supervision: Jian Xu, Hua Pang, Kaizhong Cao.

Additional information

Funding

This work was supported by the [Guangdong Provincial Natural Science Foundation – General Project: Research on Visual Landscape Assessment Method of Guangdong Traditional Villages Based on Artificial Intelligence] under [grant number 2023A1515011191]; National Outstanding Youth Science Fund Project of National Natural Science Foundation of China projects “Research on visual landscape evaluation method of traditional villages in Guangdong based on deep learning” under [grant number 52208057].

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