<![CDATA[Generative artificial intelligence (AI) offers potential assistance with hotel managerial work, but prospective use may depend on expected benefits, perceived risks, and workplace support. This study examined these three factors jointly in relation to hotel managers’ generative AI adoption intentions in Semarang, Indonesia. A cross-sectional, purposively specified survey dataset contained 220 managers and 16 five-point questionnaire items, with recorded survey dates from December 2025 to February 2026. Reflective Mode A partial least squares structural equation modeling was estimated, with 10,000 bootstrap resamples and repeated ten-fold holdout assessment. Performance expectancy was positively associated with intention (β = 0.456, 95% CI [0.358, 0.552]), perceived risk was negatively associated (β = −0.225, 95% CI [−0.326, −0.127]), and organizational support was positively associated (β = 0.263, 95% CI [0.161, 0.361]); all p values were below .001. The model explained 43.1% of intention variance, and item-level holdout Q²_predict values ranged from 0.250 to 0.350. Performance expectancy had the largest conditional effect size. The findings support examining expected work benefits alongside risk appraisal and technology-specific organizational support. They concern stated intentions rather than observed adoption or performance. Hotels may consider task-focused demonstrations, learning support, and guidance for reviewing AI outputs. Cross-sectional self-report measurement, nonprobability sampling, and unavailable verification of hotel membership limit causal and organizational inference.]]>
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