<![CDATA[This study examines the associations among public participation, policy transparency, public trust, and community satisfaction in local tourism governance in South Sulawesi, Indonesia. The study employed a cross-sectional, one-time, same-respondent self-report survey involving 450 residents recruited through non-probability procedures at purposively selected tourism destinations. The final sample comprised 150 respondents from each of three contextual tourism clusters: natural, marine and island, and cultural tourism destinations. Partial least squares structural equation modelling was used to estimate the proposed associations. The retained statistical output indicates positive associations between public participation and community satisfaction, policy transparency and community satisfaction, public participation and public trust, policy transparency and public trust, and public trust and community satisfaction. The corrected standardized coefficients were 0.165, 0.414, 0.462, 0.498, and 0.395, respectively. The corresponding indirect associations through public trust were 0.182 for public participation and 0.197 for policy transparency. However, the reported inter-construct correlations ranged from 0.905 to 0.928 and exceeded the corresponding square roots of average variance extracted. The Fornell–Larcker criterion was therefore not satisfied, indicating that the constructs were not demonstrated to be empirically distinct in the available analysis. Because the respondent-level dataset, collection-mode identifiers, data-cleaning log, and complete SmartPLS project could not be recovered, the measurement model could not be re-specified or independently re-estimated. The findings must consequently be interpreted as exploratory associations within the non-probability sample rather than as causal effects or population-level estimates. The study highlights both the potential interrelationship of participatory governance perceptions and the need for future research using independently validated measures, probability-based sampling, and longitudinal or multi-source designs.]]>
