AI‐Powered Sustainable Tourism: Unlocking Circular Economies and Overcoming Resistance to Change DOI Open Access

Hwang Bang‐Ning,

Siriprapha Jitanugoon, Pittinun Puntha

et al.

Business Strategy and the Environment, Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

ABSTRACT This study examines the integration of artificial intelligence (AI) with circular economy (CE) principles in Thailand's tourism industry. It explores 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 quantitative analysis via Partial Least Squares Structural Equation Modeling (PLS‐SEM). Findings reveal that AI‐PWA improves real‐time resource management, driving DMFO supporting regenerative practices through RRI. However, AIRC moderates AI's effectiveness sustainability transitions, concerns such as job displacement, mistrust, complexity hindering adoption. provides actionable strategies mitigate resistance, enhance stakeholder collaboration, scale AI adoption resource‐constrained settings, contributing SDG 12 13. The findings offer practical for aligning innovations sustainable development high‐variability industries.

Language: Английский

AI‐Powered Sustainable Tourism: Unlocking Circular Economies and Overcoming Resistance to Change DOI Open Access

Hwang Bang‐Ning,

Siriprapha Jitanugoon, Pittinun Puntha

et al.

Business Strategy and the Environment, Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

ABSTRACT This study examines the integration of artificial intelligence (AI) with circular economy (CE) principles in Thailand's tourism industry. It explores 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 quantitative analysis via Partial Least Squares Structural Equation Modeling (PLS‐SEM). Findings reveal that AI‐PWA improves real‐time resource management, driving DMFO supporting regenerative practices through RRI. However, AIRC moderates AI's effectiveness sustainability transitions, concerns such as job displacement, mistrust, complexity hindering adoption. provides actionable strategies mitigate resistance, enhance stakeholder collaboration, scale AI adoption resource‐constrained settings, contributing SDG 12 13. The findings offer practical for aligning innovations sustainable development high‐variability industries.

Language: Английский

Citations

0