Implementation of an effective closed-loop supply chain (CLSC) requires knowledge of consumers' perceptions of returning end-of-life products. Consumers, as primary suppliers of end-of-life products, play a crucial role in recycling efforts. This study identifies the drivers behind the e-waste recycling intentions of consumers, bridging the gap in previous studies.
Extending the Theory of Planned Behavior, this research integrates other determinants applicable to e-waste return intentions, including eco-literacy, reverse logistics awareness, perceived risk and incentives. An online survey was conducted with 310 digitally literate Indian consumers. Structural equation modeling was used to analyze the relationships among the studied determinants. Fourteen hypotheses were tested using PLS-SEM to assess direct and moderating effects.
The results show significant relationships between the determinants and return intention. Moderation analysis indicates that incentives reinforce the positive impact of perceived behavioral (PB) control on return intention, while perceived risk undermines the attitude-return intention relationship, as well as the return intention–PB control relationship. Attitude, subjective norms, eco-literacy, collection method and incentives were significant positive predictors, while perceived risk acted as a key barrier.
Our study indicates that creating awareness programs on the importance of e-waste recycling, implementing incentive schemes and establishing more stringent e-waste management regulations can promote returning e-waste into the supply chain.
This study contributes to the literature on CLSCs by conceptualizing consumer e-waste return intention using an extended TPB applied in the Indian context – fast-emerging digitalized economy with a high percentage of informal recycling. Through the integration of cognitive (eco-literacy, reverse logistics awareness), contextual (collection mechanism, perceived risk) and motivational (rewards) determinants, the study empirically tests the moderating roles of rewards and risk through structural equation modeling, providing refined theoretical insights and practical implications for policymakers and industry stakeholders.
