Destination Evaluation and General Behavioural Intention in a Digitally Visible Destination: Evidence from Zibo, China

Digitally visible destinations can gain rapid online attention, yet their longer-term tourism relevance depends on how visitors evaluate the destination after travel. This study examines how online-information-informed destination image is associated with post-visit value, satisfaction, and future intentions in Zibo, China, a phenomenon-driven urban destination. Drawing on destination-image research, with Social Exchange Theory informing interpretations of the benefit-appraisal segment, we analysed questionnaire data from 405 visitors using covariance-based structural equation modelling and bootstrap-based sensitivity analyses. The prespecified model showed positive associations from cognitive image (CI) to affective image (AI), from CI and AI to perceived value (PV), from PV to satisfaction (SAT), and from SAT to general behavioural intention (GBI). Bootstrap analyses further indicated positive model-conditional indirect associations across the proposed evaluation sequence. Although the restricted model retained some residual misfit, the results consistently support the hypothesised relationships. The study shows how digitally informed destination images remain connected with experiential value, satisfaction, and loyalty-related intentions after travel, extending post-visit destination evaluation research to a highly visible, phenomenon-driven urban context.