Satisficing Equilibrium and Multi-Actor Trust in Smart Tourism: Evidence from AI Governance

This study introduces the Three-Line Heuristic Framework (TLHF) as a descriptive, multi-actor lens for AI-enabled smart-tourism governance. It conceptualizes three recurring trajectories: (i) government credibility rises once transparency becomes visible, (ii) firm-side trust stabilizes as efficiency gains taper, and (iii) user confidence accumulates through familiar, low-friction use. Satisficing Equilibrium (SE) denotes a mid–high adequacy band in which participation and perceived trust cluster once minimum transparency and usability thresholds are perceived as “good enough,” without implying optimality, causality, or a game-theoretic solution. A mixed-method design integrates a cross-national online survey (N = 1,590; replication = 1,840) and 35 institutional interviews. Kernel-density and LOESS diagnostics visualize distributional concentration, while binary logit with Average Marginal Effects (AME) summarizes associative patterns. Information Control Level (ICL) is treated as a formative composite with low local VIFs (≈ 1.03–1.10). Results are consistent with the TLHF/SE interpretation: the Positive Index is positively associated with safe-platform preference (Q8) (p < 0.05; AME ≈ +3.2 pp), whereas privacy concern and AI-use breadth are not significantly negative once visibility cues are included. The mid–high adequacy concentration remains visually robust under ±25% smoothing and across both datasets. The dataset is Asia-anchored and China-dominant (≈84%); findings are therefore descriptive and bounded. Overall, the evidence suggests that sustainable AI governance in tourism may be supported less by maximal regulation than by credible transparency, dependable service quality, and low-friction usability, with implications for SDGs 8, 12, and 17.