Investigating the effects of big data analytics-AI on sustainable performance through supply chain innovation capabilities and industry dynamism

Sustainability delivers the future aspirations of the textile and apparel industry, and the aim of transforming pollution into a clean industry. This study investigates the impact of Big Data Analytics and Artificial Intelligence (BDA-AI) on Sustainable Performance (SP) grounded in Dynamic Capability Theory. It further explores the mediating role of Supply Chain Innovation Capabilities (SCIC) and the moderating role of Industry Dynamism (ID) within the context of the textile organization. The work utilized PLS-SEM to analyze the data and assessed the measurement model and structural model for hypothesis testing. For data collection, a standardized survey questionnaire was circulated to important stakeholders, yielding 346 answers from industry leaders and followers. The findings revealed that the integration of artificial intelligence and big data analytics is enhancing the capacity of supply chains to innovate, leading to beneficial impacts on sustainable performance. Industry dynamism inversely affects the quality of the supply chain because an unsettled industry environment creates more chaos and less productive investment. Moreover, the supply chain's innovation capabilities mediate the correlation between BDA-AI and sustainable performance. The outcomes connect the dots between the adoption of BDA-AI in supply chain operations and the achievement of sustainable performance. It offers a detailed comprehension of how the capacities for innovation and the dynamic nature of the industry interact in this particular setting. To the author’s knowledge, there is no existing literature based on the combined effect of BDA-AI on sustainable performance by considering the value chain and industry dynamism. Thus, this study provides essential information for supply chain managers and policymakers who want to utilize big data analytics and artificial intelligence to achieve sustainability objectives.