(untitled) (Lex localis - Journal of Local Self-Government, 2025)
This study aims to investigate the impact of AI on reverse logistics in the construction sector, and focuses specifically on how sustainability performance plays a mediating role. The research is based on the RBV and DCT theories, to investigate how AI-powered processes are associated with sustainability practices; as well as reverse logistics optimization. This study collects primary data through structured questionnaires from key professionals in the construction industry. To evaluate AI, sustainability performance and reverse logistics concepts of this relation a Partial Least Squares Structural Equation Modeling (PLS-SEM) technique is used in the study. The findings reveal that AI is used to improve reverse logistics processes, while an improvement in sustainability performance is this process’s most important intermediate consequence. The relationship between AI and sustainability magnifies the need to embed sustainable behaviors in AI-driven logistics strategies for better business productivity and environmental performance. The study emphasized that AI technologies must be adaptable to sustainability outcomes which can offer firms unique advantages in an increasingly complex and environmentally aware industry. This research contributes to the literature; this research seeks to contribute by providing empirical evidence concerning sustainability performance as a mediating variable explaining how AI influences reverse logistics. The content provided in this article will help industry practitioners and policymakers to benefit from AI for sustainable logistics management of construction practices.