Growing concerns about sustainability in food supply chains have prompted food processing small and medium-sized enterprises (SMEs) to adopt new technologies to improve their ecological performance. This study explores the integration of big data analytics capabilities powered by artificial intelligence (BDAC-AI) within green supply chain management (GSCM), examining the mediating roles of waste management (WM) and managerial environmental concern (MEC) in enhancing sustainable performance (SP). The research applies the technology acceptance model (TAM) and dynamic capability theory (DCT) as an integrated framework to understand these relationships.
The study employs a quantitative approach using partial least squares structural equation modeling (PLS-SEM) to examine direct and indirect pathways among the variables. The sample consists of 489 managers and directors from food processing SMEs, a sector vital for food security that faces significant waste management challenges. Data were collected via a structured survey and analyzed with SPSS and SmartPLS.
GSCM, BDAC-AI, WM and MEC each have a significant positive effect on SP. BDAC-AI and WM partially mediate the relationship between GSCM and SP, and MEC significantly moderates the impact of BDAC-AI on SP. These results provide a deeper understanding of how TAM and DCT together explain sustainable performance in food SMEs.
The findings offer guidance for managers and policymakers, showing that integrating big data analytics into GSCM strategies can yield improved sustainability outcomes and suggesting that supportive policies (e.g. incentives for green technology adoption) can amplify these benefits.
This study is novel in combining TAM and DCT to examine sustainability practices in food supply chains. It aligns GSCM with SP through the mediating roles of BDAC-AI and WM and the moderating role of MEC, thus filling a significant gap in literature. By benchmarking green supply chain best practices and leveraging BDAC-AI, food processing SMEs can enhance their sustainable performance.
