Artificial intelligence for sustainable business through logistics: a multi-theory model of green transformation in the Gulf Cooperation Council (GCC)

This study examines how artificial intelligence (AI) adoption is associated with sustainable logistics outcomes in selected Gulf Cooperation Council (GCC) countries under varying organizational and institutional conditions. Using cross-sectional survey data from 384 professionals in logistics-facing sustainability, digital, compliance, and operational roles, the study applies partial least squares structural equation modeling (PLS-SEM) with indirect-path, interaction, and exploratory subgroup analyses. The results show statistically significant but modest associations: AI adoption is positively associated with logistics sustainability, smart distribution channels are associated with energy and emissions efficiency, and AI transparency is associated with stakeholder trust. Regulatory frameworks and infrastructure readiness show significant indirect statistical associations, while corporate strategy alignment and consumer awareness condition selected relationships. The model explains modest shares of variance across the main outcomes, and the subgroup results are interpreted cautiously because most path differences are nonsignificant and no formal measurement-invariance test is reported. The findings therefore suggest that AI-related sustainability outcomes in GCC logistics are conditional and should be interpreted as theory-consistent cross-sectional associations rather than causal effects.