Abstract

Purpose: The increasing adoption of artificial intelligence (AI)–based decision support systems has transformed distribution planning processes in retail operations; however, empirical evidence explaining how human–AI collaboration influences distribution planning effectiveness remains limited. Drawing on Socio-Technical Systems theory, Human–AI Interaction theory, and Trust in Automation theory, this study examines how AI decision transparency, AI interpretability, and human–AI decision alignment relates to distribution planning effectiveness, considering the mediating role of planner trust in AI systems and the moderating role of task complexity. Methodology: Survey data were collected from 320 distribution planners and supply chain professionals working in retail firms in South Korea. The proposed mediation–moderation model was tested using partial least squares structural equation modeling (PLS-SEM). Results: The findings show that AI decision transparency and AI interpretability are positively associated with planner trust in AI systems, which in turn is linked to higher distribution planning effectiveness. Human–AI decision alignment exhibits a strong direct association with planning effectiveness. Task complexity significantly strengthens the relationship between planner trust and planning effectiveness. Conclusions: The study demonstrates that distribution planning effectiveness in retail operations depends not only on AI system capabilities but also on socio-technical alignment between AI systems and human planners. Transparent, interpretable, and trusted AI systems provide greater value in complex distribution planning environments.