Artificial intelligence (AI) is increasingly embedded in tourism platforms and services, yet continued participation often coexists with residual privacy concerns, accountability uncertainty, and uneven governance visibility. This study examines multi-actor trust in AI-enabled smart tourism governance as a problem of governance adequacy rather than trust maximization. Using the Three-Line Heuristic Framework (TLHF) and Satisficing Equilibrium (SE) as descriptive organizing tools, it asks whether trust-related outcomes can be organized around a mid-to-high adequacy region when visibility, accountability, usability, and platform choice conditions are perceived as sufficiently acceptable. TLHF organizes government-related visibility, firm-side operational adequacy, and user-side familiarity, while SE provides a descriptive governance heuristic for interpreting adequacy-oriented concentration under bounded concern. Empirically, the study uses a multi-context but unevenly distributed adult online survey sample of 1568 respondents, together with 35 semi-structured interviews as contextual qualitative material. Kernel density estimation, LOESS diagnostics, internal visual checks, and binary logit with Average Marginal Effects are used to summarize concentration patterns and marginal associations with safe platform preference. The results show that platform benefit evaluation and higher residual privacy concern after reverse scoring have the clearest positive associations with safe platform preference, while AI use breadth shows a more modest positive association and travel frequency is negatively associated. The density profiles show a recognizable mid-to-upper concentration zone, with similar visual patterns under limited resampling and split half comparisons. Within the retained adult sample, the findings highlight the relevance of visible transparency, maintainable service conditions, residual privacy concern, and low-friction usability for sustainable AI tourism governance and destination management.
Su et al. (Mon,) studied this question.