Benchmarking best practices: integrating big data analytics and green supply chain management for enhanced sustainable performance in food processing SMEs

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

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Publication Details

Journal
Benchmarking An International Journal
Published
2026-01-14
DOI
https://doi.org/10.1108/bij-08-2024-0650
Primary Topic
Food Waste Reduction and Sustainability
Type
article
Field-Weighted Citation Impact
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article

Benchmarking best practices: integrating big data analytics and green supply chain management for enhanced sustainable performance in food processing SMEs

Syed Mir Muhammad Shah, Hongyun Tian, Ghalib Ahmed, Quswah Makhdoom et al.
Benchmarking An International Journal
Food Waste Reduction and Sustainability
article

Benchmarking best practices: integrating big data analytics and green supply chain management for enhanced sustainable performance in food processing SMEs

Syed Mir Muhammad Shah, Hongyun Tian, Ghalib Ahmed, Quswah Makhdoom, Jan Muhammad Sohu, Faiz Muhammad Sohu, Mehtab Hussain Talpur
article en

Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

Benchmarking An International Journal
Jiangsu University (CN), Shenzhen University (CN), Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (PK), Sichuan Agricultural University (CN), Xiangtan University (CN), Sukkur IBA University (PK)
Responsible consumption and production
Openalex Percentile: Top 89%
Food Waste Reduction and Sustainability
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