TBL sustainability, perceived value, and destination image as drivers of visit intention: a sequential mediation model in Thai tourism

Abstract

Tourism destinations must balance economic, social, and environmental sustainability, but how tourists translate these Triple Bottom Line (TBL) dimensions into visit intention remains unclear. Drawing on S-O-R theory and the hierarchical decision model, this study examines perceived value and destination image as sequential mediators between TBL sustainability and visit intention in Thai tourism. Survey data from 427 Thai tourists with recent tourism experience were analyzed using PLS-SEM in SmartPLS 4.0 with 5,000-resample bootstrapping. Economic, social, and environmental sustainability positively predicted perceived value, although environmental sustainability had only a weak effect, with social sustainability the strongest predictor. Economic sustainability was the strongest predictor of destination image, whereas social sustainability had no significant effect and environmental sustainability showed a negative effect contrary to the hypothesized direction. Perceived value positively predicted destination image and visit intention, and destination image was the strongest direct predictor of visit intention; perceived value mediated the economic and social sustainability pathways, while destination image mediated only the economic sustainability pathway and partially mediated the perceived value–visit intention relationship, confirming the hypothesized sequential pathway. These results reveal a selective sequential transmission mechanism, in which each TBL dimension is routed into one or both mediators and only the value-based portion proceeds through an ordered path into destination image before shaping visit intention, extending S-O-R theory by specifying how sustainability stimuli are selectively and sequentially processed into organism-level states. As the design is cross-sectional, this sequence should be read as the structural specification best supported by the data rather than a validated temporal ordering.