This study investigates the main barriers affecting return management (RM) in e-commerce, where the rapid growth in product returns, further accelerated by the COVID-19 pandemic, has made reverse logistics an increasingly complex managerial challenge. The study aims to develop a decision-support framework that promotes a strategic and innovation-driven approach to RM, enabling firms to mitigate the negative impacts of product. The empirical context refers to a multi-category e-commerce company handling a broad assortment of products. The research also seeks to prioritize innovative managerial strategies that balance two key objectives: maximizing customer satisfaction and reducing return-related costs.
The study adopts a multi-criteria decision-making (MCDM) framework to support RM decisions in a multi-category e-commerce context. The framework consists of three main steps. First, an integrated AHP-TOPSIS procedure is used to identify and prioritize the barriers and sub-barriers affecting reverse logistics, and to rank alternative RM strategies. Second, Pareto analysis is used to identify non-dominated strategies, namely those representing efficient trade-offs between customer satisfaction and return-cost reduction. Finally, the VIKOR method is applied to assess the robustness of the resulting strategy rankings under different decision-making preferences.
The results reveal the relative importance of operational, organizational, and customer-related barriers in e-commerce reverse logistics, highlighting key areas for innovation. The study also provides a prioritized set of managerial strategies capable of improving RM performance while balancing cost reduction and customer satisfaction. Furthermore, Pareto analysis identifies efficient trade-offs among alternative strategies, while VIKOR confirms the robustness of the strategy rankings under different decision-making logics. These findings offer decision-makers reliable insights for enhancing reverse logistics and developing more effective, and innovation-oriented RM practices.
This study contributes to the RM literature by proposing an integrated and structured framework, that combines behavioural and operational barriers within a unified and innovation-oriented decision model. It advances prior research by explicitly modelling RM as a dual-objective problem, distinguishing between customer satisfaction and return cost reduction. The use of a multi-category e-commerce context further illustrates the applicability of the framework across different product categories and e-commerce companies. The integration of AHP-TOPSIS with Pareto analysis and VIKOR extends existing MCDM applications by enabling the identification of efficient trade-offs and the assessment of strategy ranking stability under different decision-making preferences.
