Satisficing Equilibrium and Multi-Actor Trust in AI-Enabled Smart Tourism: Nonlinear Evidence from Digital Governance Dynamics

Artificial intelligence (AI) is reshaping digital tourism, where sustainable governance hinges on how governments, firms, and users build trust. This study develops a Three-Line Heuristic Framework (TLHF)—a nonparametric model describing three trust trajectories: governments gain credibility through visible transparency, firms face diminishing returns once efficiency stabilizes, and users develop confidence via familiar, low-friction interaction. Using 1,590 survey responses and 35 interviews, and robustness checks with an expanded 1,840-sample dataset, Kernel Density Estimation (KDE), LOESS, and Binary Logit with Average Marginal Effects (AME) reveal nonlinear trust patterns. Trust converges within a mid–high Satisficing Equilibrium (SE) band (X≈2, Y≈0.4–0.6), where transparency and usability reinforce confidence. The Positive Index significantly increases safe-platform preference (p < 0.05; AME ≈ +3.2 pp), while privacy concern and AI use lose significance once visibility is achieved. TLHF and SE together show that adequacy—not maximization—anchors multi-actor trust, promoting balanced, human-centered AI governance aligned with SDGs 8, 12, and 17.