Determinants of travel intention on TikTok: A SEM-ANN approach.

Purpose: This study explores how eWOM, user-generated content trust (UGCT), economic perspective, and social perspective influence travel intention in TikTok users. Unlike previous research, we conceptually differentiate eWOM from UGCT and analyze their effect on travel decisions, providing empirical evidence from an emerging digital context. Design/methodology/approach We applied structural equation modeling (SEM) and artificial neural network (ANN) analysis to a sample of 470 Chilean TikTok users interested in travel. We validated the relationships using SPSS AMOS and used ANN to identify the relative importance of the predictors.
Findings: The results show that eWOM directly influences destination trust, economic perspective, and UGCT. In turn, user-generated content trust significantly impacts travel intention, being the most important predictor according to ANN. Economic and social perspectives strengthened trust in content, while the direct relationship between destination trust and travel intention was not significant, highlighting the mediating role of UGCT in tourism decisions.
Originality/Value The study contributes by differentiating eWOM from UGCT and demonstrating their specific influence on travel intention. Furthermore, it proposes an innovative approach by integrating SEM and ANN, offering theoretical and practical contributions to the design of digital tourism marketing strategies based on trust and pre-purchase interaction.