This study aims to examine the effect of data-driven supply chain (DDSC) on artificial intelligence (AI) and supply chain sustainability (SCSU) including supplier sustainable development, environmental sustainability and social sustainability. The scanning, interpretation, action and performance (SIAP) model is used in this study to explain the research model.
Using a sample of 216 responses, collected from managers of manufacturing small and medium-sized enterprises (SMEs) via purposive sampling, this study applied partial least squares structural equation modeling (PLS-SEM), necessary condition analysis (NCA) and importance performance map analysis (IPMA) to test the impact of DDSC on AI and SCSU in the context of Malaysia.
The findings from multiple statistical techniques indicate that DDSC is positively associated with AI and SCSU. Besides, AI plays a mediating role between DDSC and SCSU. These findings argue that DDSC and AI are the important factors that enhance SCSU outcomes, thus prioritizing DDSC in achieving excellence in SCSU and reaching sustainable development goals.
The findings indicate that managers should strategically implement DDSC aligned with sustainability goals to improve real-time data utilization, reduce waste and optimize resource allocation. Integrating AI-driven analytics improves SC responsiveness and decision-making. Hence, firms should invest in digital capabilities to maximize these benefits and foster inclusive and effective SCSU outcomes.
To the best of the authors' knowledge, this article extends the sensing, interpretation, action and performance pillars of the SIAP model by integrating DDSC and AI to comprehend SCSU in manufacturing SMEs in the emerging economy of Malaysia. It conceptualizes AI as a mediator capability inherent within DDSC using PLS-SEM, NCA and IPMA to reveal both sufficiency and necessity conditions for SCSU outcomes.
