Why Do GenAI Platforms Matter for Tourists’ Decision Support Perception? Insights from Text Mining, PLS-SEM, NCA, and fsQCA

With the growing adoption of Generative Artificial Intelligence (GenAI) platforms in tourism services, their generative and interactive capabilities are reshaping how users evaluate decision support. However, the mechanisms underlying these evaluations remain unclear. Drawing on large-scale user review data, this study examines structural relationships and configurational patterns using text mining, Partial Least Squares Structural Equation Modeling (PLS-SEM), Necessary Condition Analysis (NCA), and fuzzy-set Qualitative Comparative Analysis (fsQCA). The results indicate that improvements in functionality and interaction do not necessarily enhance decision support perception. When outputs require additional user effort for filtering and adjustment, they may increase cognitive burden and reduce perceived effectiveness. No single condition is necessary for high-level outcomes; instead, evaluations emerge from the joint influence of multiple factors. High decision support perception can arise under both high-functionality and high-interaction conditions, as well as low-complexity and low-interaction contexts.