AI-Driven Responsible Supply Chain Management and Ethical Issue Detection in the Tourism Industry

This study aims to develop and evaluate an AI- and big-data-based innovation system for proactively managing ESG (Environmental, Social, and Governance) risks within the tourism supply chain. Drawing on heterogeneous data sources including supply chain records, news articles, social media, and public databases, the research employs advanced methodologies such as network analysis, anomaly detection, natural language processing (including greenwashing detection), and predictive modeling. Through this comprehensive approach, the study demonstrates the feasibility and effectiveness of a dynamic AI-driven ESG risk management system that delivers reliable risk identification and quantitative performance evaluation. The theoretical contribution lies in bridging AI-driven ESG evaluation frameworks with sustainable tourism and hospitality literature, moving beyond static, indicator-based assessments toward a more systematic, replicable, and predictive methodology capable of capturing the dynamic, multiscalar, and networked nature of tourism supply chains. Ultimately, this research provides tourism and hospitality firms with a powerful tool to enhance transparency, mitigate ethical and reputational risks, and strengthen stakeholder trust, while offering actionable insights for managers and policymakers developing data-driven ESG integration strategies.