An improving of green supply chain performance using green digital learning and artificial intelligence integration - Qahman, Ashraf Ibrahim Abdallah; Al-Zaqeba, Murad Ali Ahmad; Jarah, Baker Akram Falah; Al-Kharbsheh, Abdelruhman; Assaf, Nasser (International Journal of Innovative Research and Scientific Studies, 2025)
The recognition of sustainability as a top supply chain management issue has led to the exploration of cutting-edge digital technologies such as Big Data Analytics and Artificial Intelligence (BDA-AI), along with the Artificial Intelligence of Things (AIoT), for environmental and operational enhancements. Guided by the research gap regarding the effectiveness of these technologies in green supply chain frameworks, this study aims to explore the direct and indirect impacts of using BDA-AI, AIoT integration, and Green Digital Learning on GSCP. The quantitative survey relied on data compiled from 184 industry professionals and was analyzed using Structural Equation Modeling – Partial Least Squares.4 (SEM-PLS-4). Industrial business functions indicate that BDA-AI positively affects GSCP in two pathways: a direct impact and an indirect pathway to Green Digital Learning, which has a strong moderating effect. AIoT integration was also a strong predictor of GSCP, implying that AIoT integration in the supply chain can improve resource allocation and process efficiencies. These results indicate that Green Digital Learning is vital for augmenting the sustainability effect of digitalization technologies by offering employees the ability to effectively apply data-based insights. The study makes a twofold contribution to academic literature and industry practice by proposing a reference model for energy sustainability while enabling digital transformation and providing managerial insights that, in turn, permit organizations aspiring to achieve a green supply chain through operational excellence.