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Research regarding the quality of e-tailers’ service during product returns is sparse and the little that has been performed treats returns as recovery from failure. However, that view is outdated. E-tailers' product return practices have substantially evolved and customers' return behavior has considerably increased, in turn, influencing expectations of customers. Thus, a need arises to revise the understanding of how customers evaluate the quality of e-tailers' service during product returns. This study conceptualizes customer-perceived e-return service quality, identifies e-return's current dimensions, and offers directions for measurement.

This study is conducted in two stages. The first stage follows an abductive approach, with a continuous back-and-forth movement between existing theory and two qualitative data sets to identify the dimensions of e-return service quality. Scale development process is started in the second stage to offer directions for measurement based on the empirically grounded dimensions.

The conceptualization of e-return service quality identifies six dimensions: (1) owning of responsibility, (2) return convenience, (3) return remedies, (4) service team support, (5) site's return friendliness, and (6) returns diligence. The factor analysis supports the six-factor solution that can be employed for developing a valid scale in future.

The study suggests that e-tailers who are looking to differentiate themselves through superior e-return service quality should focus on customizing their service through excellent performance on these dimensions.

The paper updates and refines the understanding of service quality in the context of product returns service provided by e-tailers and thus offers a novel contribution.

This study aims to investigate the conditions for the financial feasibility of an incentive-based model for self-drop or crowdsourced drop of the product to be returned at designated drop boxes (thereby ensuring a contactless process).

Constraint-based non-linear mathematical modeling was done for cost differential with and without crowdsourcing. This was analyzed against returns on investment for the installed infrastructure. Scenarios were looked into from the linear, iso-elastic and logarithmic demand functions to identify the optimal incentive policy. The results were further evaluated using “willingness to return” for customer willingness for product returns via drop boxes.

Crowdsourcing is viable when product returns are no more than 15%–20% of the overall products, with a logistics cost differential of 15%–25%. These were only viable when the product return incentive was within the range of 15%–20% of the product cost, as well as the penalty was in the range of 25 to 40% for wrong returns.

The findings are expected to aid the organizations in successfully designing product return policies while adhering to the post-COVID-19 norms, including contactless transactions and social distancing.

The study provides a look into the viability sensitivity of effective gains/profitability against the required level of service for returns, wrong returns, penalties and incentives for crowdsourcing in a developing country like India.

Halal frozen meat product returns are major challenges in the halal frozen meat supply chain because of the sheer volume and processing costs of returns. The purpose of this paper is to investigate the effect of integrated halal supply chain (IHSC) strategies on effective product returns with halal logistics (HL) as an interceding variable.

This paper used the cross-sectional technique to select samples from a population that revolved around the halal food industry in Malaysia. Data collected from halal service providers who handle halal frozen meat product returns provided insightful findings.

The findings of this paper indicate that the IHSC dimensions, such as interactive fairness, procedural fairness and service coverage, are positively associated with effective product returns. It also shows that HL plays a mediating role between the IHSC and effective product returns.

From a practical viewpoint, this paper suggests that an effective return service system can be designed to emphasise the category of interactive and flexible justice services through refunds or product replacement, depending on customer's demand.

The result of this paper provides insights into how logistics service provider managers effectively and efficiently handle the halal supply chain network when involving product returns.

With the booming of live commerce, sellers provide products through not only their traditional channels but also the anchors who show products by live broadcast, forming a live commerce supply chain. In fact, such selling mode generates two effects: the live broadcast service of the anchor affects the return rate of products sold live (live commerce effect) and related products of the manufacturer sold via its own channel (live commerce spillover effect). In this paper, the authors investigate the impacts of both live commerce and live commerce spillover effect on the price decisions as well as the anchor's service effort.

The authors establish a live commerce supply chain model where the manufacturer sells related products directly and by the anchor with a wholesale price contract. The manufacturer decides the price of product sold directly based on the anchor's broadcast effort since there exists the live commerce spillover effects. Backward induction is used to solve the Stackelberg game between the manufacturer and the anchor.

The results show that (1) the existence of the live commerce spillover effect brings more profit to the manufacturer while it reduces the anchor's profit. Moreover, the total profit of the live commerce supply chain first decreases and then increases as the intensity of the live commerce spillover effect improves. (2) The pricing of products sold directly by the manufacturer and sold through the anchor is nonmonotonic with respect to the live commerce spillover effect. (3) The increase in return cost always leads to an increase in the profit of the anchor, whether it is borne by the anchor or by the consumer. (4) If the baseline return probability is high, the anchor should increase her effort, thus securing more profit. However, the spillover effect of live commerce and the horizontal differences between products will discourage the anchor from increasing the live streaming service level.

The study proposes the live commerce supply chain model where the anchor balances the cost and benefit of her live broadcast effort, which lowers the consumers expected return possibility. In addition the live commerce spillover effect is introduced, reducing the expected return rate for the related products without live broadcast (in the direct channel). With the inter-influence of live commerce, the price competition between the live anchor and the manufacturer becomes more complex. By solving the typical live commerce game model, managerial insights are given for the decision makers among the live commerce supply chain.

Product returns information gives firms an opportunity for continuous strategic adaptation by allowing them to understand the reasons for product returns, learning from them and improving their products and processes accordingly. By applying the Dynamic Capabilities (DCs) view in the context of closed-loop supply chains (CLSC), this study explores how firms can continuously learn from product returns information.

This study adopts a qualitative Delphi study-inspired approach. Experts from industry and academia are interviewed in two interview rounds. First round of interviews are based on extant research, while the second round allows the experts to elaborate and correct the results.

This study culminates into a conceptual model for incremental learning from product returns information. The results indicate incremental learning from product returns can potentially lead to a competitive advantage. Additionally, the authors identify the sources of information, capabilities along with their microfoundations and the manifestations of product return information. Three propositions are formulated embedding the findings in DC theory.

This study supports extant literature in confirming the value of product returns information and opens concrete avenues for research by providing several propositions.

This research elucidates the practices, processes and resources required for firms to utilize product returns information for continuous strategic adaptation. Practitioners can use these results while implementing continuous learning practices in their organizations.

This study presents the first systematic framework for incremental learning from product returns information. The authors apply the DC framework to a new functional domain, namely CLSC management and product returns management. Furthermore, the authors offer a concrete example of how organizational learning and DC intersect, thus advancing DC theoretical knowledge.

In this paper, the authors aim to investigate the relationship between buyback policy and the potential number of used products that could be collected by developing a robust fuzzy reverse logistics network.

In this approach, the authors seek to determine the amount of buyback based on the condition of used products at the time of return. In this process, the authors also take into account that apart from the condition of used products, other factors exist that the actual return rate could be dependent on them. This matter propelled us to make a novel distinction between the probability of return estimated from appropriate buybacks offered to consumers, and the actual return rate of used products using fuzzy mathematical methods. Besides that, a compatible robust fuzzy optimization method has been implemented on the model to deal with uncertain properties of it and simultaneously fortifying its responses against any possible effect of return rate fluctuation.

To analyze and evaluate the model performance, the authors decided to apply a series of exhaustive randomly generated experiments onto it. Also, the authors introduced a Lagrangian relaxation solution methodology to facilitate and improve the solving process of the model. Then, the evaluation of the results enabled us to demonstrate the model validity, and underscore its utility to deal with problems with more sophisticated used product collection process that practitioners tend to encounter in the real-world circumstances.

This study suggests a novel way to design the return rate of used products in a reverse logistics network with buyback offers through a complete set of factors affecting it. Furthermore, the procedure of developing the model encompasses several important aspects that significantly decrease its complexity and improve its applicability.

This paper investigate how fit uncertainty impacts product return costs in online retailing and how digital product fitting, a pre-sales fitting practice, can reduce fit uncertainty.

The paper analyzes the current performance of a retailer's e-commerce and return operations by estimating costs generated by product returns, including product handling costs, tied-up capital, inventory holding costs, transportation costs, and order-picking costs. The estimated costs were built on 2,229 return transactions from a Scandinavian fashion footwear retailer. A digital product fitting technology was tested with the retailer’s products and resulted in estimations on how such technology could affect product returns.

The cost of a return is approximately 17% of the prime cost. The major cost elements are product handling costs and transportation costs, which together amount to 72% of the total costs. If well calibrated, the fitting technology can cut fit-related return costs by up to 80%. The findings show how customers reacted to the fitting technology: it was unable to verify fit every time, but it serves as a useful and effective support tool for customers when placing orders.

Virtual fit verification using digital product fitting is key to retailers to reduce fit-related returns. Digital product fitting using three-dimensional scanning is more appropriate for some products, but it is unsuitable for products that are difficult to measure and scan.

The paper contributes an empirical estimate of retail supply chain costs associated with fit uncertainty, as well as theoretical understanding of the role of pre-sales fit verification in avoiding product returns.

This study conducts a systematic literature review of e-tail product returns research. E-tail product returns are essentially acquisition of products that have been sold through purely online or brick-and-click channels and then returned by consumer to business.

Using a systematic literature review protocol, we identified 75 peer-reviewed articles on e-tail product returns, conducted bibliometric analysis and content analysis of the articles and summarised our findings.

The findings reveal that the subject of e-tail returns is a new research area; academics have started to investigate several aspects of e-tail returns through different research methodologies and theoretical foundations. Further research is required in leading e-commerce countries and on key areas such as omni-channel returns management, customer satisfaction and service, the impact of resources such as people skills, the benefits of technology and IT systems in managing e-tail returns.

The study offers a summative account of current e-tail knowledge areas, which can serve as a reference guide for e-tailers to develop strategies for more efficient and competitive product returns.

This study contributes theoretically by developing clusters of key themes or knowledge areas about e-tail returns. It also provides a conceptual framework for e-tail returns management, which can be used as a springboard for further empirical research.

The purpose of this paper is to investigate the dynamic acquisition pricing strategy for collecting used products (also known as cores or returns) in a finite planning horizon. In particular, this paper studies a cost-minimization model in which a firm offers acquisition price that impacts the quantity of the returns, and remanufactures the used product to satisfy the customer demand.

This paper uses multi-period stochastic dynamic programming theory to model a remanufacturing system that faces the random demand for remanufactured products. The number of the returns at each period is uncertain and increases linearly with the acquisition price offered.

The study shows that when the uncertainty of demand for remanufactured products increases, the remanufacturer should hold a higher core stock level to minimize the expected total cost and thus a higher acquisition price is needed to attract returns. However, given demand uncertainty, the optimal price decreases in the initial core stock level in each period. It also indicates that the optimal acquisition price increases in the variance of the returns, but decreases in the mean of the returns.

The findings suggest that a remanufacturer could reduce the expected total cost by adjusting the acquisition price according to the number of returns periodically.

Introducing the impact of supply uncertainty on the acquisition price of used products, this paper uses a multi-period dynamic model, instead of single period model in previous studies, to examine the remanufacturer’s dynamic acquisition pricing policy.

Forecasting product returns plays an important role in the operations of reverse logistics (RL). However, their contribution to sustainability performance is yet to be explored. The purpose of this paper is to explore the product returns in Indian electronics industry and examine the relationship of forecasting product returns with triple bottom line performance of RL.

In this study, based on past literature review, four hypotheses, relating to forecasting of product returns and its association with performance, were developed. A questionnaire was sent to 700 respondents from the Indian electronics industry. Overall, 208 received responses were found suitable for the research. The necessary statistical analysis was carried out to ensure the reliability and validity of the questionnaire. In order to test different hypotheses, partial least square path modelling (PLSPM) technique of structural equation modeling was utilized.

Measurement model had shown sufficient data fit for the modeling. PLSPM results reveal that the accuracy in forecasting product returns is positively associated with operational performance of RL. It also plays an important role in the sustainability efforts of an organization.

Managers can utilize results of study for exploring and emphasizing issues of product returns for improving RL performance. One of the limitations is that data are collected only from Indian electronics industry. Another limitation is that only product returns are considered for the operational and TBL performance of RL. In future, study may be carried out considering different factors in other sectors and countries.

The intent of forecasting product returns is considered to be operational efficiency. It can make significant contributions to the sustainability efforts of an organization. Review of the past literature indicates that research in the field of RL is in developing stage, and issues related to forecasting product returns are under-represented. The paper adds value to the few available articles on product returns.