This study investigates how tourists adopt AI-Generated Content (AIGC) in travel planning by combining two theoretical frameworks: the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model and the Source Credibility Model (SCM). While AIGC has recently become a popular research topic, existing studies have primarily focused on its technical design, content quality, or broad applications in tourism. However, limited attention has been given to why and how tourists develop behavioral intentions toward AIGC in planning their trips. Addressing this gap, the present research emphasizes the joint influence of technology acceptance factors and source credibility cues, offering a comprehensive perspective on the adoption mechanisms.
The study collected data through an online survey distributed via social media platforms, using simple random sampling. Of the 394 responses received, 363 valid responses were analyzed using partial least squares structural equation modeling (PLS-SEM). The research evaluated model fit using multiple indicators including SRMR, NFI, and VIF values. Reliability and validity were assessed through Cronbach's alpha, composite reliability, and average variance extracted (AVE), while discriminant validity was confirmed using the HTMT ratio.
The study found that most factors from both the UTAUT2 and SCM significantly influence tourists' adoption of AIGC in travel planning. While performance expectancy showed no significant effect, other technological factors (facilitating conditions, hedonic motivation, and habit) and communication elements (expertise, trustworthiness, and homophily) all positively influenced behavioral intention to use AIGC. The integrated theoretical framework demonstrated strong explanatory power for understanding AIGC adoption in tourism planning.
This study makes three key contributions: First, it successfully integrates UTAUT2 and the SCM in the AIGC tourism context, thereby addressing the overlooked intersection between technological affordances and communication credibility. Second, it extends the SCM by reconceptualizing credibility in relation to AI rather than human or peer sources. Third, by highlighting the non-significant role of performance expectancy, the study challenges conventional assumptions of the UTAUT2, suggesting that in experiential contexts like tourism planning, hedonic, credibility, and contextual factors outweigh utilitarian expectations.
