Integrating ESG Perceptions and the Technology Acceptance Model to Explain Destination Image Formation: A PLS-SEM Approach

Abstract

NThe convergence of sustainability governance and artificial intelligence is reshaping how tourists form perceptions of tourism destinations, yet the mechanisms through which ESG destination practices and AI travel technology acceptance jointly influence destination image remain empirically unexplored. This study develops and validates an integrated ESG–TAM model in which ESG destination perceptions (environmental, social, and governance) and TAM constructs (perceived usefulness, perceived ease of use, and online experience) predict cognitive and affective destination image, with trust proposed as a shared mediating mechanism. A quantitative, cross-sectional design was adopted, collecting data from 227 tourists who had used AI travel planning applications. The hypothesized relationships were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0 and 5,000 bootstrap resamples. Results confirm that ESG perceptions directly and significantly enhance destination image (β = 0.232, p = 0.033), with significant indirect effects on both its cognitive (β = 0.225, p = 0.034) and affective (β = 0.206, p = 0.035) sub-dimensions. Perceived ease of use emerged as the dominant TAM driver (β = 0.600, p < 0.001), significantly predicting online experience, which in turn exerted a significant total effect on destination image (β = 0.246, p = 0.018). The trust mediation hypothesis was not confirmed: although trust was strongly predicted by both ESG perceptions and online experience, it did not significantly transmit to destination image, suggesting that trust operates as a terminal outcome of sustainability and technology acceptance processes rather than as a bridge toward image formation. This study is the first to empirically integrate ESG and TAM within a unified destination image model, directly responding to the research agenda of Huang et al. (2026) and advancing theoretical understanding of how institutional sustainability signals and AI-mediated digital experiences jointly shape tourists' perceptions in the pre-trip planning stage.