The objective of this study was to examine the impact of green supply chain management (GSCM) on sustainable performance (SP) and the mediating role of green knowledge sharing (GKS), green innovation (GI), and big data-driven supply chain (BDDSC) in SMEs of Pakistan as a developing country. Primary data was gathered through adopted questionnaires from SME employees. Four hundred sixty-nine cases were considered for data analysis after data clearing in SPSS version 25. Furthermore, the proposed hypotheses were tested with the help of SmartPLS version 3 through structural equation modeling SEM. Findings revealed all seven direct hypotheses, including GSCM on SP, GKS, GI, and BDDSC and GKS, GI, and BDDSC on SP in SMEs of Pakistan. Moreover, the partial mediation effect of GKS, GI, and BDDSC was also confirmed between GSCM and SP. This study contributes to the context of SMEs in developing countries and recommends findings for future policies and implications at the firm and government levels for better results. Policymakers, SME owners, and managers must support innovation culture, engage their stakeholders, and invest in new products, processes, and business models relevant to addressing environmental and sustainability concerns. Moreover, Pakistan’s government policymakers recognize SMEs’ power to effectively integrate GSCM knowledge sharing, green innovation, GSCM practices, and big data technology into supply chain management.

Background: This study aims to empirically test a comprehensive interrelationship between green supply chain management (GSCM), green technology innovation (GTI), waste management (WM), big data analytics capability powered by artificial intelligence (BDAC-AI), and their collective impact on sustainable performance (SP) in organizational contexts. Methods: This study was conducted in Pakistan’s food processing sector. The respondents included 495 managers working in the food processing industry. A structural equation modelling (SEM) approach is used to examine direct and indirect relationships between the variables. The originality of this study lies in integration of the technology acceptance model (TAM) and dynamic capability theory (DCT) to understand sustainable practices in the context of the provided model. Results: This study highlights that GSCM, GTI, WM, and BDAC-AI have positive, strong, and direct impacts on SP. Furthermore, GTI and WM only partially mediate the link between GSCM and SP, whereas the two moderate the link. In addition, BDAC-AI had a moderating effect on the relationship between GTI and SP. This study has managerial implications, including strategies that involve the use of theoretical frameworks for technological acceptance and dynamic capabilities to support sustainable initiatives. However, it is worth noting that the findings provide a practical contingency for managers and businesses interested in implementing green studies effectively, improving technologies, and strengthening sustainable performance capabilities. Conclusions: The study extends the literature by establishing a model for operationalizing GSCM in the food processing sector. Furthermore, it adds value in that it first integrates TAM and DCT to explain sustainable operations and their impact on organizations. Furthermore, it extends the existing literature by establishing a relationship between GSCM and SC. It offers a model through which GSCM can be operationalized in the context of the FS sector.