Big Data Analytics and Innovation in the Sustainable Performance of Electronic Waste Reverse Logistics: An Empirical Study in India

Abstract Electronic waste (e-waste), encompassing discarded electrical and electronic devices, demands effective reverse logistics management to ensure optimal resource use and environmental preservation. Despite growing interest in Big Data Analytics within the scientific community, its slow practical implementation in e-waste management and the absence of validated measurement models hinder both industry adoption and empirical studies. To address these challenges and drawing upon the Resource-Capability-Advantage (RCA) theory, this study aims to investigate the interplay between Big Data Analytics (BDA) management capabilities, BDA talent capabilities, Reverse Logistics (RL) innovation, and sustainable RL performance. A conceptual model was tested using primary data from practitioners and managers in India’s e-waste reverse logistics network, with Structural Equation Modeling (SEM) as the primary analytical method. The results highlight the multifaceted contributions of Big Data Analytics Management and Talent Capabilities to Reverse Logistics Innovation and Sustainable Reverse Logistics Performance.