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.

According to the Organization for Economic Co-operation and Development (OECD) report, small and medium-sized enterprises (SMEs) are responsible for approximately 64 percent of the total industrial environmental pollution worldwide. This is mainly a dangerous concern for developing countries. This study aims to determine the impact of green organizational culture on the sustainable logistics performance of SME manufacturing in Pakistan, with the mediating role of a big data-driven supply chain and green transformational leadership. Furthermore, employee age and work experience are control variables to measure sustainable logistics performance. The hypotheses were formulated using the resource-based view (RBV) and the theory of transformational leadership. To test the proposed hypothesis, primary data were gathered from 370 employees of SME manufacturing in Pakistan. A survey method was adopted, and the concerned firm’s HR department requested data collection. Data was collected and analyzed with the help of confirmatory analysis and structural equation modeling in SmartPLS version 3. The findings revealed the direct effect of green organizational culture (GOC), big data-driven supply chain (BDDSC), green transformational leadership (GTL), and work experience on sustainable logistics performance (SLP). However, the direct effect of employee age does not impact the sustainable logistics performance of SME manufacturing in Pakistan. Moreover, a partial mediation effect was confirmed, indicating a BDDSC and GTL between GOC and SLP in SME manufacturing in Pakistan. Furthermore, the study extends the current theoretical framework by integrating transformational leadership theory into the process of integration from the resource-based view (RBV) perspective. In this light, the current research is relevant to green transformational leadership, as the mediating link appeals to the leadership behaviour that determines the association between GOC and SLP. A theoretical resource-based view incorporating leadership theories provides a broader framework to explain organizational resources, leadership, and sustainable outcomes that are likely to occur in organizations.