RESEARCH ARTICLE
AI-Powered Sustainable Tourism: Unlocking Circular Economies and Overcoming Resistance to Change
Hwang Bang-Ning,
Siriprapha Jitanugoon,
Pittinun Puntha,
Hwang Bang-Ning
Department and Graduate Institute of Business Administration, National Yunlin University of Science and Technology, Yunlin, Taiwan
Search for more papers by this authorSiriprapha Jitanugoon
Department and Graduate Institute of Business Administration, National Yunlin University of Science and Technology, Yunlin, Taiwan
Search for more papers by this authorCorresponding Author
Pittinun Puntha
Department and Graduate Institute of Business Administration, National Yunlin University of Science and Technology, Yunlin, Taiwan
Correspondence:
Pittinun Puntha ([email protected])
Search for more papers by this authorHwang Bang-Ning,
Siriprapha Jitanugoon,
Pittinun Puntha,
Hwang Bang-Ning
Department and Graduate Institute of Business Administration, National Yunlin University of Science and Technology, Yunlin, Taiwan
Search for more papers by this authorSiriprapha Jitanugoon
Department and Graduate Institute of Business Administration, National Yunlin University of Science and Technology, Yunlin, Taiwan
Search for more papers by this authorCorresponding Author
Pittinun Puntha
Department and Graduate Institute of Business Administration, National Yunlin University of Science and Technology, Yunlin, Taiwan
Correspondence:
Pittinun Puntha ([email protected])
Search for more papers by this authorFunding: The authors received no specific funding for this work.
ABSTRACT
This study examines the integration of artificial intelligence (AI) with circular economy (CE) principles in Thailand's tourism industry. It explores the interactions between AI-Enhanced Predictive Waste Analytics (AI-PWA), Regenerative Resource Integration (RRI), Dynamic Material Flow Optimization (DMFO), and AI-Induced Resistance to Change (AIRC). Using a mixed-methods approach, qualitative insights from industry stakeholders are combined with quantitative analysis via Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings reveal that AI-PWA improves real-time resource management, driving DMFO and supporting regenerative practices through RRI. However, AIRC moderates AI's effectiveness in sustainability transitions, with concerns such as job displacement, mistrust, and complexity hindering adoption. This study provides actionable strategies to mitigate resistance, enhance stakeholder collaboration, and scale AI adoption in resource-constrained settings, contributing to SDG 12 and SDG 13. The findings offer practical insights for aligning AI innovations with sustainable development in high-variability industries.
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