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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.

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.

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.

The value of online reviews has been well documented by academics and practitioners. However, to maximise the benefits of consumer reviews, online sellers must avoid the negative consequences associated with customer feedback, such as reputation loss, or product returns after purchase. In developing a better understanding of the relationships between online reviews and their potential for negative impacts, this research aims to explore product returns. Through a quantitative model, this research demonstrates why online reviews can result in product return behaviours.

The hypotheses were tested via two studies. In Study 1, the authors examine the direct effects of review valence and review volume on product returns by analysing secondary data on 4,995 stores on China's Taobao.com. Study 2 further extends and validates the findings of Study 1 with a survey sample of 795 participants across several online shopping platforms. This analysis examines the mechanics and conditions that influence the relationships between online reviews and product returns through partial least squares-structural equation modelling (PLS-SEM).

The results show that both review valence (i.e. average star ratings) and the number of reviews can increase the probability of product returns due to the high expectations that result from positive online reviews. Further, the effect of review valence on product returns is stronger for first-time purchasers at a store. In terms of mitigation, the analysis shows that bilateral communications between sellers and buyers can temper the unrealistic expectations set by positive reviews, leading to fewer product returns.

This research adds to the literature on online reviews by exploring the negative consequences of online reviews and the role they play in online purchasing decisions. The findings also provide direct evidence as to why online reviews can result in more product returns, adding clarity to extant research which contains conflicting conclusions as to how online reviews affect product return behaviours.

This paper aims to investigate whether the consumers who return a product and those who end up keeping a product after experiencing post-purchase dissonance (PPD) possess distinct underlying characteristics.

Field survey study consisting of two separate surveys conducted with consumers of New York City and neighboring areas of New York and New Jersey.

Product returners and keepers exhibited disparate demographic profiles regarding gender and household income, along with ethnicity to some extent. The two groups also exhibited different predispositions with regard to confidence in the purchase decision and expectations about their purchase. Finally, returners and keepers were engaged in divergent thoughts, feelings and activities to cope with PPD.

The findings of this study offer marketing practitioners new knowledge and insight into understanding product returners and keepers and will assist them in developing strategies to reduce and manage increasing product returns by consumers more effectively.

This study is the first to present empirical evidence that product returners and keepers have distinct profiles of demographic characteristics and predispositions toward purchase. The study also has found divergent PPD coping strategies used by the two types of consumers, which exposes an obsolete understanding of PPD in the marketing literature.

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.

While remanufactured products represent an increasingly researched phenomenon in the literature, not much is known about consumers' understanding and acceptance of such products. This study explores this issue in the context of the theory of perceived risk (TPR), investigating return policy leniency and distribution channel choice as potential factors to foster remanufactured products' sales.

This research utilizes an experimental design composed of a pre-test and a scenario-based main experiment to explore how return policy leniency might mitigate consumers' perceived risk and how their related purchase intention differs across two types of retail distribution channel structures (i.e. brick-and-mortar vs. online).

The investigation into the efficacy of return policy leniency within two retail distribution channel settings (i.e. brick-and-mortar vs. online) illustrates that providing a lenient return policy is an effective “cue” in increasing consumer purchase intention for remanufactured products. While prior literature has established that consumers value return policy leniency for new products, the authors provide empirical evidence that this preference also applies to remanufactured products. Notably, that return policy preference holds true in both channel settings (i.e. brick-and-mortar vs. online) under consideration. Additionally, and contrary to the authors’ predictions, consumers perceived remanufactured products sold via both channel settings as equally risky, thus highlighting that both are appropriate distribution channels for remanufactured products. Finally, while research on new products provides some initial guidance on consumer perceptions of quality and risk, the study provides empirical evidence into the difference of perceived risk with regard to new versus remanufactured products.

By employing the TPR, this research explored the role played by two supply chain management related factors (returns policy and channel structure) in reducing consumer's perceived risk and increasing purchase intention. In doing so, this study answers the call for more consumer-based supply chain management research in a controlled experimental research setting.

The authors explore complex adaptive systems (CAS) theory as an updated theoretical perspective for managing product returns that better matches the chaotic nature of recent consumer behaviors. CAS theory highlights the importance of agents who create and self-organize to help systems adapt in unpredictable environments.

This research utilizes data collected from return managers in an online survey and applies regression analyses to estimate the influence of the focal variables.

Empirical evidence of the firm flexibility–firm adaptability link is established, and return processor creativity positively relates to this link. The firm flexibility–firm adaptability link fully mediates the relationship between return processor creativity and returns management performance and partially mediates the relationship between return processor creativity and relationship quality. Nonmediated effects were observed for turnover and revenue size.

Managers of returns who embrace an adaptability approach become facilitators of returns by supporting processor creativity. Enhancing the autonomy of processors in their day-to-day work increases the knowledge-creation capabilities of the firm, which helps the firm move forward and adapt in an uncertain environment.

This research presents empirical evidence of the underlying mechanisms of CAS theory in the product returns context by studying processor agents and argues that CAS theory better fits the current dynamics of the product returns environment. Further, this paper extends work by Espinosa et al. (2019) and Nilsson (2019) by studying how a specific human characteristic – creativity – impacts product returns management.

In order to achieve competitive advantage over the physical marketplace, the e-retailers are insisted on endowing with lenient return policies. The piece-wise returns-and-reordering process incurs excessive buffering and unwanted logistics costs which raises overall fulfillment charges. The objective of this study is to re-design e-retail distribution policy by providing temporal storage at logistics service provides' (LSP) location. The impact of recurrent returns on pricing and profit margins are also investigated over time continuum.

A framework is developed to reduce the non-value added (NVA) storage and distribution efforts by providing collaborative buffering between LSP and e-retailer. The knapsack based buffering approach is tested and compared with traditional e-retail distribution practices. The revenue sharing concept is mathematically modelled and implemented in GAMS, which finally validated through multiple return scenarios.

The proposed model outperforms the existing one under all scenarios with different configuration settings of re-ordering, profit margins, and buffer time windows. The distribution cost is found, linearly related to the necessary product buffering space. The findings help to re-design sustainable return policies for individual products so that maximum customer value can be yield with minimum costs.

This study helps to determine the NVA efforts incurred while storing and delivering multi-time returned products to ensure desired service levels. The revenue sharing model provides pricing strategies for e-retail practitioners deciding which product should store in what quantity for how much time at the shipping agency location so that it fulfils the re-ordering at least waiting and sufficient buffering.

The proposed model extends the role of LPSs as temporary buffer providers to reduce returns-and-reordering fulfilment efforts in the e-retail network. This Collaborative framework offers an opportunity to amend the distribution contracts and policies time by time that enhances e-retailer's performance and customer satisfaction.

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.