Big data analytics-artificial intelligence and sustainable performance through green supply chain practices in manufacturing firms of a developing country

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Abstract
Purpose: This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI). Design/methodology/approach: Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model. Findings: This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP. Originality/value: This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain.

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