Advancing destination image research through a Bayesian approach: evidence from China

Purpose This study aims to examine the evolving perceptions of US residents toward China as a travel destination and to assess how these perceptions influence travel intentions. It further seeks to contribute methodologically by examining how the application of a Bayesian regression approach can enhance the modeling and interpretation of destination image within hospitality and tourism research. Design/methodology/approach The study sample consisted of 521 US residents, with data collected through purposive sampling via an online survey. To examine how perceptions influence travel intentions, a Bayesian regression analysis with informative priors was conducted using R (version 4.3.3). This approach allowed the incorporation of prior information into parameter estimation, providing an alternative perspective to conventional inference methods. Findings The findings indicate that US residents’ perceptions of China’s destination image improved overall, with culture remaining a robust factor, while safety and security emerged as the most important predictors of travel intentions. Practical implications Destination managers should prioritize communicating credible safety measures while using cultural assets as supporting elements and managing geopolitical perceptions to strengthen destination image and influence travel intentions. Originality/value This study advances destination image research by adopting a Bayesian regression approach, an underutilized methodology in hospitality and tourism research. It offers a rigorous analytical framework to address uncertainty and incorporate prior empirical information. It further provides methodological insights that can guide doctoral students and emerging scholars in understanding and applying Bayesian techniques in empirical investigations within the field.