AI-driven supply chain innovation: The roles of supply chain ambidexterity and complexity

Although prior research has identified artificial intelligence (AI) as a key driver of supply chain innovation (SCI), further investigation is required into whether and how AI influences SCI, and the contingencies that may affect such an impact. Integrating socio-technical systems theory (STST) and contingency theory (CT), we develop a conceptual model that links AI to SCI through the mediating role of supply chain ambidexterity (SCA) and incorporates the moderating effects of supply chain complexity (SCC). Drawing on survey data from 245 Chinese manufacturing firms, we first ensure measurement quality through tests of common method bias, non-response bias, reliability, and validity. We then proceed to test our research hypotheses using structural equation modeling and hierarchical regression analysis. The results show that AI enhances SCI both directly and indirectly through the mediating role of the two dimensions of SCA, namely SC exploration and exploitation. Additionally, the findings indicate that SCC has a positive moderating effect on the relationships between AI and SCI and between AI and SC exploration/exploitation. Besides deepening our theoretical understanding of the drivers of SCI, and specifically of the processes of AI-driven SCI, the study offers practical insights for advancing innovation in the SC.