Antecedents of big data analytics adoption and its impact on decision quality and environmental performance of SMEs in recycling sector

Big data analytics is a novel technique of extracting patterns from structured or unstructured information for improved decision accuracy, operational efficiency and higher environmental performance. It is a critical resource to generate significant insights enabling firm's operational and strategic needs in dynamic environments. The study explores the BDA adoption and the mechanism through which it affects processes, operations, and decisions to achieve higher environmental performance of SMEs in the scrape and recycling industries. The study integrates the factors from the technology-organization-environment theory, resource-based view model, and ecological modernization theory to examine the antecedents of big data analytics adoption and its effect on supply chain capabilities, sustainable operations, decision quality, and environmental performance. The study draws results by collecting data from 317 SMEs in China. The findings validate the proposed model where green economic incentives remain the most significant stimuli for big data analytics. Sustainable operations and decision quality explain environmental performance, and big data analytics affect SMEs' capabilities, operations, and sustainable performance. The study validated an extended holistic model that helps to comprehend the antecedents of big data analytics adoption and the consequences of big data analytics on processes, operations, and environmental performance. It also emphasizes policymakers to devise incentive-based policies to encourage adoption and managers to update their tangible and intangible resources to nurture BDA benefits.