Predictive AI Crowd-Management Messaging and Its Influence on Perceived Control, Stress Reduction, Temporal Satisfaction, and Destination Loyalty

This study investigates the influence of predictive AI crowd-management messaging on tourists’ psychological and behavioral responses in crowded destinations. Drawing on the Stimulus–Organism–Response (S–O–R) framework, the study examines how predictive and data-driven crowd-management messages shape perceived control, stress reduction, temporal satisfaction, and destination loyalty. A quantitative cross-sectional design was employed using purposive sampling. Data were collected from 265 tourists who had recently visited destinations offering digital or smart tourism information services. The respondents were selected from tourism destinations utilizing AI-enabled crowdmanagement and informational systems. Established measurement scales adopted from prior studies were used, and data were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS. The findings reveal that predictive AI crowd-management messaging significantly enhances tourists’ perceived control, which subsequently reduces stress and improves temporal satisfaction. Temporal satisfaction emerged as a strong predictor of destination loyalty. Furthermore, the results support a serial mediation mechanism in which predictive AI crowd-management messaging enhances perceived control, leading to stress reduction, which subsequently increases temporal satisfaction and ultimately strengthens destination loyalty. The study extends smart tourism literature by integrating AI-based crowd-management communication with tourists’ cognitive, emotional, and evaluative processes. The findings suggest that destinations can leverage predictive AI communication to enhance tourists’ sense of control, reduce stress, improve time-related experiences, and foster long-term destination loyalty