ESG-AI-MCDM: An AHP-TOPSIS Framework for Smart Tourism SMEs

This study addresses the underexplored financial side of smart tourism by validating an integrated ESG-AI-MCDM decision-support framework. The model links AI-driven predictive analytics with a hybrid multi-criteria loop combining AHP, TOPSIS, and DEA to evaluate strategic alternatives under seasonality and sustainability constraints. Quantitative validation via structural equation modelling (SEM) with 416 active participants proved that AI analytics significantly enhance managerial efficiency ( β = 0.49) and operational resilience ( β = 0.36). Empirically, the AI platform cut operational costs by 26% and boosted visitor satisfaction by 18%. When ESG criteria held a dominant 35% weight, hybrid optimisation identified sustainable energy investment as the optimal strategy, achieving a top TOPSIS score of 0.83 and a superior DEA efficiency score of 0.91. The framework eliminates subjective managerial bias, providing a scalable, resilient financial governance mechanism for tourism SMEs.