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The return behavior of customers has a great impact on the e-retail industry and has resulted in the emergence of return-freight insurance (RI). Additionally, customer loss aversion arising from returns affects e-retailers' decisions and manufacturers' profits. Therefore, the main purpose of the authors' study is to determine how e-retailers and manufacturers choose their RI strategy and pricing according to customers' loss aversion.

The authors propose three scenarios: no RI, customer purchase RI and free e-retail RI (FRI). Meanwhile, the authors also model a Stackelberg game between e-retailers and manufacturers for analysis. Then, according to customer return behavior and loss aversion, the authors study the optimal pricing decision and RI premium allocation scheme for e-retailers and manufacturers under different scenarios.

It was found that the loss sensitivity reduces customers' willingness to buy RI, which is not conducive to the development of e-retailers and manufacturers. Additionally, with higher loss sensitivity, e-retailers and manufacturers offer FRI to gain higher profits, which supports the implementation of the FRI strategy.

The authors introduce customers' loss aversion into RI to analyze the optimal pricing decisions and profits of e-retailers and manufacturers, enriching the application of loss aversion theory. In addition, this study analyzes the two-way cost-sharing mechanism between manufacturers and e-retailers to provide FRI, which provides a theoretical basis for RI premium sharing.

This study investigates the impacts of return channel type on the relationships between return service quality (RSQ) and customer loyalty (CL) in an omnichannel retailing environment.

Data comes from Chinese customers having a return experience in omnichannel retailing that uses the channel type of both buy-online-return-in-store (BORIS) and buy-in-store-return-to-online warehouses (BSROW). The authors use the structural equation modeling to test the hypotheses and the bootstrapping method to test the mediation and moderation effect.

For BORIS channel, satisfaction of customer returns (CRS) partially mediates the relationship between convenience and CL, and fully mediates that between CL and responsiveness, transparency and competence, respectively. For BSROW channel, CRS partially mediates the relationship between responsiveness and CL, and fully mediates that between CL and convenience, transparency and competence, respectively. The mediation effects indicate that omnichannel customers may feel more satisfied due to higher omnichannel fulfillment (responsiveness and convenience) and omnichannel trust (transparency and competence) provided by retailers. Return channel type moderates the relationship between RSQ-convenience and CL. The results show the different expectations between BORIS and BSROW customers in the return process.

This paper serves as a pioneering study to apply cognition-affect-behavior paradigm into the field of return management in omnichannel retailing.

The findings suggest retailers develop their strategies on customer returns and post-sales service quality improvement in the omnichannel. Also, retailers should develop an integrated return system across channels to provide convenient service to BORIS customers and quick response to BSROW customers.

Studying return service management in the omnichannel from customer's cognition appraisal, this study contributes to the literature of the reverse service management by bringing in the effect of omnichannel type to explore the relationship between RSQ and CL.

As e-commerce has expanded rapidly, online shopping platforms have become widespread in India and throughout the world. Product return, which has a negative effect on the E-Commerce Industry's economic and ecological sustainability, is one of the E-Commerce Industry's greatest challenges in light of the substantial increase in online transactions. The authors have analyzed the purchasing patterns of the customers to better comprehend their product purchase and return patterns.

The authors utilized digital transformation techniques-based recency, frequency and monetary models to better understand and segment potential customers in order to address personalized strategies to increase sales, and the authors performed seller clustering using k-means and hierarchical clustering to determine why some sellers have the most sales and what products they offer that entice customers to purchase.

The authors discovered, through the application of digital transformation models to customer segmentation, that over 61.15% of consumers are likely to purchase, loyal customers and utilize firm service, whereas approximately 35% of customers have either stopped purchasing or have relatively low spending. To retain these consumer segments, special consideration and an enticing offer are required. As the authors dug deeper into the seller clustering, we discovered that the maximum number of clusters is six, while certain clusters indicate that prompt delivery of the goods plays a crucial role in customer feedback and high sales volume.

This is one of the rare study that develops a seller segmentation strategy by utilizing digital transformation-based methods in order to achieve seller group division.

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.

Pro-consumer refund system (PCRS) should be understood as a process in customer service, within which activities are related to the handling of goods that do not meet customer expectations, both in business-to-consumer (B2C) and business-to-business (B2B) relations. The research monitored three groups of variables – the importance of shopping decision factors, customer ID characteristics and PCRS characteristics. The authors explore relationships between these three groups of variables, and the authors tried to understand better the role of the PCRS system in customer perception of service quality.

An electronic survey was used to collect data in the planned structure. Research questions were developed into variables, and these were then the basis for creating questionnaires. Data were collected through electronic questionnaires. The sample consisted of 327 respondents from Poland (confidence level = 95%, confidence interval = 5.42).

The results of the research show that the PCRS is very important and appreciated from the customer's perspective. Most of the respondents are aware of their consumer rights and correctly understand the meaning of the term “pro-consumer refund system”. Respondents require complex service in terms of returns; their satisfaction does not depend on any individual factor. This also means that the return system must be lenient in each aspect.

A detailed analysis of aspects of PCRS has not yet been carried out in the literature. Although previous studies have focused, at least in part, on defining the essential attributes of PCRS, in most cases, it was an organization-driven view of this topic. The research examined the interrelationships between aspects of PCRS based on empirical data and offered a new perspective on this evolving concept.

This paper examines how sophisticated and better-informed investors, such as short sellers, trade on information along the supply chain. Given the economic linkages between suppliers and customers, one would expect short sellers to trade on such information and to capitalize on investors' inattention to such economic links.

This paper uses both multivariate regression analysis and portfolio analysis where the time series averages of equally weighted monthly portfolio returns are reported to explore the abnormal returns of long-short trading strategies.

Results indicate that short interest predicts unexpected earnings news, consistent with short sellers extracting information from economic relationships. There is a strong negative relationship between short interest in the supplier firm and the one-month future stock return of the customer firm. This negative relation significantly persists for at least 12 months. One plausible channel explaining the information content of supplier (customer) firm's short interest for the customer (supplier) firms is the short sale constraints on the customer (supplier) firms.

The paper addresses a gap in the literature by examining whether short selling in a firm in the months leading up to a customer's (supplier's) negative shock is negatively correlated to the customer's (supplier's) future performance. Overall, the findings suggest that short sellers play an important role in the price discovery of related firms in the supply chain, which is beyond the direct effects documented in prior literature.

The purpose of this paper is to introduce qualitative comparative analysis (QCA) to the field of supply chain management and provide a detailed roadmap that supply chain researchers can utilize when applying this methodology.

Data collection focused on the evaluation of product returns management practices as perceived by business customers who operate in a supplier–customer context. In order to analyze the data using the QCA approach, a multi-step analysis was developed.

The results indicate five solutions that lead to high levels of customer satisfaction. The existence of multiple sufficient configurations for customer satisfaction indicates equifinality because multiple alternative solutions can lead to the same outcome.

The authors make a methodological contribution by applying the QCA method to the field of supply chain management and providing a detailed roadmap that supply chain researchers can utilize.

The authors provide managers five different and novel combinations of antecedents that lead to higher levels of customer satisfaction.

This study offers supply chain researchers a better understanding of when it is appropriate to use QCA and how to apply this methodology. From a theoretical perspective, past studies focused exclusively on the “net effects” of these antecedents, thus, did not capture the complexity of the relationships between these various antecedents and customer satisfaction. This is a noteworthy contribution as it highlights the complexity of the amalgam of relationships and factors that impact customer satisfaction within the context of reverse supply chain.

Marketing/finance interface and application of its new insights in marketing decisions have recently found great interest among marketing researchers and practitioners. There is a relatively large body of marketing literature about incorporating modern portfolio theory (MPT) into customer portfolio context and taking advantage of it in marketing resource allocation decisions. Previous studies have modelled customer portfolio risk in the form of historical return/profitability volatility of customer base. However, the risk is a future-oriented measure, and deals with future volatility associated with return stream. This study aims to address this research problem.

The well-known Pareto/non-binomial distribution (NBD) approach is used to model customer purchases in a non-contractual setting of research practice. Then, the results were used to simulate the customers’ future buying behaviour and associated returns via the Monte Carlo simulation approach. Subsequently, the mean-variance portfolio optimization model was applied to find the optimal customer portfolio mix.

The results illustrated the better performance of the proposed efficient portfolio versus the current customer portfolio. These results are applicable in analyzing customer portfolio composition, and can be used as a guidance to make decisions about marketing resource allocation in different segments.

This study proposes a new approach to analyze customer portfolio by using the customers’ future buying behaviour. Taking advantage of rich marketing literature about statistical assumptions describing the customers’ buying behaviour, this study tries to take some steps forward in the application of the MPT theory in customer portfolio management context.

The purpose of this paper is to examine the potential impacts of various variables on product return activities after online shopping. Previous studies on customer behaviour have been predominantly concerned with return on used products and other product-quality-related constructs in the model. This study aims to specially examine the logistics service-related and customer intention–related variables for general products under the e-commerce circumstance.

Structured questionnaire data for this study were collected in the two southeast cities of China (162 useable responses). Structural equation modelling was used to examine the latent variables.

The results confirmed that product return intention has the greatest impact on online shopping returns with a direct effect of 0.63, followed by the flexibility in return (logistics service) with a direct effect of 0.49.

Such a model not only enriches the theoretical understanding of customer behaviour studies but also offers online shopping stores and platforms a quantitative benchmark and new perspective on the design of online shopping supply chains by considering product returns so as to improve the customer satisfaction.

The purpose of this paper is to investigate the relationship between the repurchase intention of a customer and his/her perception of various aspects of an e-tailer’s product return process, such as leniency, fairness and quality of the return process.

This research focuses on these aspects through the expectation disconfirmation theoretical lens, looking at the relationship between expectations shaped by the product return policy and the repurchase intention. This research collects data using a survey approach and analyzes it using structural equation modeling.

It was found that perceived return policy leniency, perceived fairness of the return experience and perceived quality of the return experience are important and supporting factors that influence a customer’s intention to be a return customer to e-tailers. Perceived leniency was found to not only be the most influential factor for return purchase intention but it also significantly impacted the perceived fairness and the quality of the return process. As a result, perceived leniency of the return policy had a “halo” effect on the other factors.

This suggests that the majority of an e-tailer’s effort should be expended determining a return policy and experience that is widely perceived as lenient as this will overall improve customer perception of the return process and increase repurchase intention.

This research extends research on lenient policy within the growing e-tailer sector by examining the return experience of the customer and subsequent repurchase intention based on multiple factors.