Investigating Returns Management across E-Commerce Sectors and Countries: Trends, Perspectives, and Future Research

Abstract

Background: The systematic literature review with additional descriptive analysis at hand focuses on analysing returns management in e-commerce, which is an increasingly critical issue as the volume of online shopping is rising. Methods: Drawing from a comprehensive search of academic databases and a manual review of Google Scholar, 54 articles dating from 2007 onwards were collected and fully read. Results: The review reveals a main research effort emerging mainly from Germany and other countries, with a notable focus on fashion retail. The bulk of these studies aim to understand and reduce the frequency of customer returns, addressing a substantial operational challenge for online retailers. The findings provide multiple research streams extracted from the collected literature and combined to an overview. Conclusions: Through this, there are tendencies which can be interpreted to derive the evolution of the research field. The illustrated results in this review paint a detailed picture of the existing research landscape. This highlights the importance of ongoing research, which, e.g., holds potential benefits for customer satisfaction and environmental sustainability. The review also lists future research directions, recommending the continued investigation of areas such as predictive analytics and customer behaviour to further refine returns management practices.

1. Introduction

As a result of growing e-commerce, many companies have to deal with customers returning goods that they have purchased [1]. In the area of returns management, companies must then ensure that the returned items are registered and, if necessary, refurbished or repaired and stored back in the warehouse [2]. The decision on whether an item is kept and resold or destroyed depends on the condition and quality. Returns management plans, controls, and monitors all returned goods and provides an environmentally friendly return process for a customer as well as a minimal loss of value for the company [3]. In [4], e.g., the authors analysed real shop data from an apparel e-tailer to examine the drivers of consumer returns and thus study returns management hypotheses based on real consumer returns. They also provide an example of e-tailers use sales stimuli to impact returns rates.

The article at hand faces the challenges of current literature reviews, including a lack of analysis on the up-to-date aspects of returns management. Additionally, there is a deficiency in country-specific studies to understand global and local dynamics in this field. Such aspects can be found as possible research proposals in [5], which underline the necessity of the present review. Lastly, some articles may be limited or biased due to their selection of literature based on specific database choices in previous reviews. This study therefore intends to present a comprehensive picture of the current state of research in the field of returns management (RM) in e-commerce, formulating exploratory research questions that ensure a broad and inclusive perspective. The picture consists of a classification of relevant articles, a categorisation of the literature, an overview of the research, answers to the research questions in this study, the formulation of future research, and a final conclusion.

In this article, the structure is organised as follows. The methodology is discussed in Section 2, providing an overview of the research approach used. Section 3 delves into former literature reviews on returns management in e-commerce, covering topics such as reverse logistics (Section 3.1), closed-loop supply chains (Section 3.2), returns management in online retail (Section 3.3), and the necessity for conducting this literature analysis. Section 4 focuses on returns management tasks, followed by Section 5 which presents a descriptive analysis. Section 6 offers a literature analysis and overview, leading to Section 7, where a discussion takes place, with subsections on practical implications (Section 7.1) and future research in returns management in e-commerce (Section 7.2). The article concludes in Section 8, summarising the key findings and insights obtained throughout the study.

2. Methodology

As part of the research, an extensive systematic literature review with additional descriptive analysis was carried out in the fourth quarter of 2023. In order to outline and restrict the object investigation and to set a research focus, the following research questions (RQs) are formulated, which are to be answered through this review. The RQs are as follows:

RQ1: 

How intensively is research being conducted in the area of returns management in e-commerce? Since when are there articles and surveys on returns management in the literature?

RQ2: 

Which RM-tasks are differentiated and which tasks are primarily dealt with? Which solution approaches are proposed?

RQ3: 

Which countries are performing the most research on returns management?

RQ4: 

In which sectors of e-commerce is research being conducted?

RQ5: 

What future research needs have been identified in the literature? What are therefore the future research perspectives in returns management?

By answering these RQs, we obtain precise information about the state of research on returns management in e-commerce and at the same time can discover research gaps within this discipline.

Drawing upon the recommendations of [6], we thoroughly integrated the essential components of their steps (summarise, synthesise, conceptualise, and energise) into our research. During the “summarise step”, we differentiated our review by focusing on areas not covered in previous surveys and justified the scope and publication range selection. At the “synthesise step”, we interlinked findings from various studies to highlight the trends and contradictions, providing a coherent overview of the field. In the “conceptualize step”, we ensured that our thematic and conceptual framework was evident, manifesting in a structured approach to categorise existing knowledge and identify research gaps. To “energise”, we proposed future research agendas with specific inquiries and potential approaches, connecting the dots between current knowledge and future possibilities.

3. Former Literature Reviews on Returns Management in E-Commerce

In order to avoid overlaps with other studies and to emphasise the relevance of this work, it is first verified whether such a review already exists. We determined this by searching for comprehensive literature reviews as well as for articles that partially contain relevant literature reviews. Several noteworthy articles delve into the broader topic of returns and e-commerce, such as [7], which emphasised the significance of lenient return policies and cash-on-delivery options in e-commerce contexts, especially in developing countries like Jordan. Additionally, [8] investigated how lenient return policies, seller reputation, and customer-based signals like positive/negative comments on social networking sites influence perceived seller and product uncertainty in retail. These insights into the broader scope of returns now pave the way for a focused study of returns management specifically within e-commerce, offering a thorough analysis of the distinct challenges and tactics relevant to returns in online retail operations.

The procedure at this point can be assigned to the “summarise” and “synthesise steps”, as we initially examined previous literature reviews to acknowledge the necessity of our own, followed by the synthesis of our findings. By categorising the found reviews, we presented the key topics within the body of research we analysed. This strategy enabled us to not only highlight areas that had been previously neglected but also to interweave the results of various studies, thereby shedding light on trends and contrasts. Consequently, we created a coherent and comprehensive overview of the field, ensuring that both the summarising and synthesising steps were effectively undertaken.

For this purpose, a first systematic literature review was conducted to identify existing surveys. Based on the search terms from [9], the terms “returns management” and “literature*” were searched for in combination (the wildcard “*” enables terms such as “literature research”, “literature analysis”, or “literature review”). The research carried out takes into account the English descriptions as well as the German translations (see

Table 1. Search results for literature reviews for RM in e-commerce.

Search Term	SpringerLink	ProQuest	Science Direct	Web of Science	Scopus	EBSCO	EconBiz	JSTOR	Google Scholar
“Returns Management” AND “Literature*”	0	0	0	4	3	0	0	0	21

).

Due to the low number of hits regarding reviews when using “e-commerce” in combination with the terms “returns management” and “literature*”, we concentrated on finding reviews on the topic of RM in general (regardless of the specification of e-commerce). This enabled us to find relevant research that we could then analyse with regard to the status quo. However, when using the general search terms in the standard search of the databases, many irrelevant hits were found. Thus, the terms were only searched for in the title, in the keywords, or in the abstract of an article (apart from in Google Scholar, where the differentiation is not possible).

Only publications that are available in full text and have been peer-reviewed were included. The search was performed on the basis of the following electronic databases: SpringerLink, ProQuest, ScienceDirect, Web Of Science, Scopus, EBSCO, EconBiz, JSTOR, and Google Scholar. An obstacle of the search was the use of the ScienceDirect database, which does not support the used wildcard, so the search terms were written out as listed above. The results of the reviews found are shown in Table 1.

Although Google Scholar could expand the search area and the unrestricted free access is another advantage in favour of its consideration, Google Scholar also has disadvantages. From the point of view of objectivity, replicability, and verifiability, it is problematic that Google Scholar users cannot replicate the finding of the indexed source references and that Google does not publicly document the search algorithm [10].

The search resulted in a total of 28 surveys, which were analysed in detail. Articles that were not yet in print but already available online were also included. After a more detailed analysis of the search hits, nine literature reviews were removed that were not considered relevant for the present work, such as the empirical study by [11]. The remaining 19 reviews were then investigated further. The surveys found focused on the different aspects of RM. For example, some articles deal with the topic of reverse logistics, some with closed-loop supply chains, while others look at RM from a systematic perspective. In Section 3.1, Section 3.2 and Section 3.3, the 19 reviews are grouped and described with regard to their focus. Section 3.4 subsequently demonstrates why a separate literature review is necessary.

3.1. Reverse Logistics

In consideration of increased environmental awareness and heightened customer sensitivity, reverse logistics (RL) activities have received increased attention from academics and practitioners [12]. In the area of RL, the following sub-areas, in particular, are considered: product retrieval (collection, transport), product inspection, product recovery (direct, recycling, reprocessing, repair), inventory management, waste management, and reintegration into the forward supply chain. Thus, RM, which is covered in this review and specifically considers the registration, inspection, and refurbishment or disposal of returned items (see Section 1), can be seen as a component of RL. Sasikumar and Kannan [13] describes the different sub-areas of the reverse supply chain and name the corresponding contributions with the tools and techniques for analysis and modelling. In [14], a research framework for the field of reverse logistics is developed by categorising the found articles into six categories, i.e., recycling, remanufacturing, reuse, return policy, outsourcing, and others. For the articles, the methods used, the key issues, and the key findings are shown. Chan et al. [15] dealt with the relationship and effect of just-in-time (JIT) and RL on each other. This study aims to analyse and understand the influences of JIT on RL. Overall, adapting RL to JIT principles can lead to more efficient processes and better cost control. Hazen et al. [16] identified factors that should be considered when creating a strategic knowledge and decision support system for RL. The authors theorised that a company’s decision to engage RL is influenced by potential costs, potential profits, market conditions, customer behaviour, existing supply chain and production capacities, regulatory factors, and environmental effects. Given the increasing importance of RL as an essential component of supply chain management, a comprehensive bibliometric analysis of the academic literature on RL for the period from 1992 to 2015 is conducted in [12]. The study identifies the most influential publications with the respective number of citations. In addition, the contributions are summarised in five different periods to show the growing interest in RL. By taking co-citation analysis into account, the authors are able to present important research topics, knowledge groups, and future research opportunities. In [17], a literature review on RL is provided for the years 2007–2016. The results indicate that the benefits achieved through RL management are primarily economic and environmental in nature. The article emphasises the importance of RL as a critical factor in modern retailing, particularly with regard to product and packaging returns and the promotion of social and environmental responsibility. The integration of RL into existing supply chains has led to complex optimisation problems, e.g., network design, location-allocation, vehicle routing, production or assembly/disassembly problems. Due to their difficulty, these problems often cannot be solved using exact methods or simulations. Instead, metaheuristics are utilised in the field of RL, which are given in [18]. The various papers are reviewed and organised based on the metaheuristic approaches used and in the context of RL problems.

3.2. Closed-Loop Supply Chains

A supply chain (SC) is traditionally defined by a forward flow of products and materials. By taking back products or their components, materials flow forwards and backwards in an SC [19]. A closed-loop supply chain utilises reverse logistics to reintroduce goods or materials that have fulfilled their purpose back into the logistics process. This can be seen in [20], where RFID helps track products and provides information about their usability, increasing the collection rate of used products. Returned products then become part of the company’s product range through repair, reuse, or resale. In a closed-loop SC, all processes and actions of the forward and backward material flow are considered holistically. In [21], the aspect of the profitable utilisation of returns in closed-loop supply chain management (SCM) is pursued. By dividing the process into five development phases, an overview of the progress made and future potential in operational research is provided. Borade and Bansod [22] present in a comprehensive literature review the various aspects that need to be considered when managing a supply chain. They also include the aspects of RL. The authors highlight the necessity of SCM from an organisational point of view. The interaction between SCM and the Internet is the subject of [23]. In the study, the authors focus on defining, analysing, and describing e-SCM as well as identifying relevant factors that help practitioners achieve IT-enabled SCM. Furthermore, they explain how research in this area developed during the period 1995–2005 and point out some avenues for further research. Krapp and Kraus [24] focused on coordination in closed-loop supply chains. In particular, the control of the reverse flow is considered, whereby three approaches are distinguished: (i) two or more independent supply chains exist, each optimising its own objective function; (ii) there is at least one reverse flow from a lower tier of the SC to a higher tier; and (iii) a mechanism to coordinate the flow along either the forward and/or the backward SC is investigated. The findings are used to derive which research gaps result from the respective state of research. Ritola et al. [25] deals with the use of information associated with returns. The article conducts a systematic literature review and combines relevant research from different research streams related to information systems, knowledge management, organisational learning, and information sharing. Furthermore, Ritola et al. [26] looked at SCM and returns from an information science perspective. The review identifies three important types of information: operational, product-related, and customer-related. In [27], articles on returns acquisition, sorting, and disposition are analysed and recommendations are made for future research priorities that support the implementation of a circular economy.

3.3. Returns Management in Online Retail

In this subsection, we look specifically at returns management in online retail. Here, returns are no exception, but a recurring part of day-to-day business. Walsh et al. [28] showed that a product returns management system (PRMS) is important for companies to both reduce the costs associated with returns and create a better shopping experience for customers. The authors combined insights from the literature with interviews with managers of large online retailers. The resulting framework considers three types of preventive instruments (i.e., monetary, procedure, and customer-based) that online retailers use to reduce return rates. Moreover, factors are identified that influence the relationship between the decision to implement a PRMS and the type of instruments chosen. In [29], consumer behaviour in online retail is analysed in the context of order fulfilment. The aim of the study is three-fold: (i) To identify elements of order fulfilment that are relevant to online consumer behaviour (purchase, repurchase, product return); (ii) to establish an understanding of the relationship between order fulfilment, inventory management, last-mile delivery, returns management, and consumer behaviour; and (iii) to develop customer service strategies that take into account the link between behavioural responses and order fulfilment outcomes. The work in [30] aimed to identify the key logistics research areas related to the adoption of e-commerce. For each area, key performance indicators that should be considered are then analysed with a particular focus on sustainable aspects. The presented methodological framework for e-commerce implementation summarizes five main research areas from a logistics perspective: supply chain network design, outbound logistics, reverse logistics, warehousing, as well as IT and data management. For each research area, input and output variables are identified and it is shown how they influence each other. The study by [31] conducts a systematic literature review on returns in e-commerce. It is found that returns in e-commerce is a relatively new area of research, with various aspects being investigated. Further research is needed, in particular, on omni-channel returns management, customer satisfaction and service, as well as resources and technology in dealing with returns. The study offers a theoretical contribution by providing a conceptual framework for e-commerce returns management that can serve as a reference guide for efficient and competitive returns processes. The review by [32] provides a summary of current developments in the field of returns and explores future research directions. The results show that research can be categorised into three main clusters: (i) operations management of product returns; (ii) retailer and (re-)manufacturer issues; and (iii) customer’s psychology, experience, and perception regarding marketing and sales. Potential future research directions discussed in the context of returns include digitalisation, globalisation versus localisation, more complex returns policies, understanding and predicting customer returns behaviour, and customer perception.

3.4. Necessity for Own Literature Analysis

By reviewing the existing literature reviews on returns management, the need for a further literature review becomes clear, despite some overlaps with previous works such as [5,31]. The previous survey papers and its results do not fully cover the specific aspects of the exploratory research questions that shall ensure a broad and inclusive perspective of the field. In particular, the following aspects should be mentioned: (i) some of the existing studies relate to periods far in the past, meaning that the most recent trends are not covered; (ii) the areas of returns management and e-commerce are sometimes insufficiently analysed or only analysed in a different context (e.g., not on the basis of tasks); (iii) country-specific analyses, which are important for understanding global or local dynamics, are often missing; (iv) previous research has different methodological limitations that affect compatibility (e.g., only qualitative methods are taken into account); and (v) the selection of databases in previous searches may have resulted in a limited or biased selection of literature.

To synthesise the existing reviews and to illustrate the necessity of this study, the existing reviews, which have previously been categorised, were sorted and visualised tabularly for a better overview. Towards this,

Table 2. Overview of existing reviews.

Author(s)	Year	Focus Area	Research Synthesis	RQ1	RQ2	RQ3	RQ4	RQ5
Sasikumar and Kannan [13]	2009	Reverse logistics	The review of 543 contributions reveals a long-standing and growing academic interest in returns management, with research spanning diverse content issues, solutions, and a global scope, necessitating further exploration to refine and guide future research directions. The classification schemes proposed by the authors emphasise the complexity and potential within this field, highlighting the essential need for a detailed investigation into task differentiation, sector-specific studies, and country-level contributions to the literature.		1
Chan et al. [15]	2010	Reverse logistics	By categorising reverse logistics into six distinct research areas and conducting an extensive literature review, the article sets out to fulfil the needs of both academia and industry, guiding academics to concentrate their research efforts effectively and enabling practitioners to derive nuanced managerial guidelines for reverse logistics. This methodological organisation aims to answer key questions regarding the intensity, focus, and direction of research in reverse logistics, as well as to provide a clearer understanding of the field’s scope and the specific decisions faced by practitioners.	1
Setaputra and Mukhopadhyay [14]	2010	Reverse logistics	The article addresses the interplay between just-in-time principles and reverse logistics, identifying their environmental benefits and potential conflicts, such as JIT’s demand for stable supply and the unpredictability of returned product volumes in reverse logistics. Through an extensive literature review and a developed model, the study highlights key areas including reverse logistics structure, process models, product life cycle, information systems, and JIT performance, demonstrating the potential of integrating JIT into reverse logistics to enhance the cost efficiency and effectiveness, supported by details outlined in the paper.					1
Hazen et al. [16]	2010	Reverse logistics	The study seeks to enhance strategic decision making in reverse logistics by identifying variables critical to the development of a knowledge and decision support system, filling a research gap in integrating returns management with a supply chain firm’s overarching strategy, particularly regarding reverse logistics activities. Through examining reverse logistics frameworks and conducting a content analysis, the authors discover seven influential factors for the disposition of returned products and propose directions for future research in optimising reverse logistics decisions.	1				1
Wang et al. [12]	2017	Reverse logistics	The paper provides a comprehensive bibliometric analysis of reverse logistics literature from 1992 to 2015, identifying key publications and thematic research contributions through co-citation and burst detection analyses using CiteSpace software to guide future research opportunities in the increasingly strategic field of RL. Utilising the impact factor as a novel article selection criterion, the study offers a valuable representation of RL core literature and insights into the RL knowledge domain for both academic and practical advancements.	1				1
Dias et al. [17]	2019	Reverse Logistics	The study conducts a systematic literature review on reverse logistics in retail, specifically focusing on return management from 2007 to 2016, and finds that the topic is still emerging, with only 10 out of 116 references being significantly relevant and most research having an exploratory nature, highlighting the economic and environmental benefits of reverse logistics. The recent uptick in publications, especially in 2016, indicates growing attention in this area and underscores the critical of retail in advancing reverse logistics practices.	1				1
Rachih et al. [18]	2019	Reverse Logistics	This contribution reviews literature on reverse logistics, specifically examining how meta-heuristic approaches have been employed to address complex optimisation problems within the reverse supply chain that are otherwise difficult to solve using exact methods or simulations. The review categorises previous studies by the meta-heuristic methods used and the context within the RL issues they tackle, discussing the effectiveness of these methods and suggesting future research directions and practical applications for the field.		1			1
Guide and Wassenhove [21]	2001	Closed Loop Supply Chains	The article develops a framework to analyse the profitability of environmentally friendly reuse activities, identifying the acquisition of used products as a key factor in the profitable management of product returns, affecting overall firm strategies and operations. This suggests that reuse activities must create value to be viable and that product returns management plays a crucial in the profitability of remanufactured products, calling for future research to quantify the relationship between the acquisition price and the quality of returned products.		1			1
Borade and Bansod [22]	2007	Closed Loop Supply Chains	A systematic literature review of supply chain management is presented, offering a comprehensive view by categorising the principal activities within the supply chain and providing a detailed framework for understanding SCM’s complexity and scope across different industries and companies. The review’s intent is to capture the state of the art in SCM and propose a methodological approach for the in-depth exploration of the field to benefit manufacturers, professionals, and researchers.	1
Giménez and Lourenço [23]	2008	Closed-loop supply chains	The purpose of this review is to scrutinise the convergence of supply chain management and the Internet, emphasising how the Internet reinforces SCM through improved real-time information sharing and enhanced collaboration among trading partners. Through a literature review in prominent Operations Management and Logistics journals from 1995 to 2005, the paper distils the influence of the Internet on SCM processes, pinpoints the emergence of e-SCM as a significant topic post-2000 focusing on e-procurement, e-fulfilment, and information flows, and outlines potential trajectories for future research.	1				1
Krapp and Kraus [24]	2019	Close-loop supply chains	This review addresses the growth of supply chain management with a focus on returns, driven by legal requirements and economic factors, highlighting the inconsistency in terms and definitions related to returns handling. Through a state-of-the-art literature review, content, and cluster analysis, it offers a classification that views approaches from a returns perspective, identifying research gaps and providing a road map for future work, while giving practitioners a comprehensive overview of current methodologies in returns management and coordination.	1	1			1
Ritola et al. [25]	2019	Closed-loop supply chains	This study explores the informational value derived from product returns, an area where firms have struggled to capitalise effectively, by undertaking a systematic literature review to outline the current state and future research directions. This study identifies three categories of information—operational, product-related, and customer-related—and four value-creating factors, namely strategic IS decisions, organisational learning, information sharing, and technological solutions, offering insights for practitioners and presenting limitations with recommendations for advancing this field of study.	1	1			1
Ritola et al. [26]	2020	Closed Loop Supply Chains	A systematic literature review is presented to consolidate research on the informational value of product returns, an underutilised resource in many firms for enhancing products, services, and decision making. It distinguishes three types of informational value (operational, product-related, and customer-related) and four factors that create value (strategic information systems decisions, organisational learning, information sharing, and technological solutions), discusses implications for practitioners, and points out the current research limitations, providing a trajectory for future scholarly work in this domain.		1			1
Gunasekara et al. [27]	2023	Closed-loop supply chains	The article critically evaluates the research progression on circular supply chains, crucial for achieving a circular economy, by examining 131 high-impact articles focused on acquisition, sorting, and disposition decisions from the past decade (2012–2021). The review reveals that, while areas like closed-loop supply chain coordination and remanufacturing are well-represented, gaps persist due to the scarcity of empirical studies, limited validation of mathematical models, economic-centric objectives, and oversimplified behavioural and uncertainty assumptions. The authors advocate for comprehensive research, incorporating joint decision making, cross-sector collaborations, and product-service systems, to bolster the transition towards a circular economy.	1	1			1
Walsh et al. [28]	2014	Returns management in online retail	This article investigates the challenge online retailers face with product returns, offering a grounded theory framework derived from the literature insights and interviews with managers, to understand the factors leading to the implementation of a product returns management system (PRMS). The framework also outlines three types of preventive tools online retailers use to lower return rates, along with moderating factors influencing the relationship between the PRMS decision and the efficacy of these instruments, concluding with considerations for future research.				1	1
Nguyen et al. [29]	2016	Returns management in online retail	A systematic review is conducted to explore the intersection of consumer behaviour and order fulfilment in online retailing, aiming to identify relevant order-fulfilment elements, understand their impact on consumer behaviour, and motivate research on consumer service strategies that consider these interactions. Covering literature from 2000 to September 2015 in marketing and operations, the study reveals a gap in understanding how consumer service instruments can influence consumer behaviour, culminating in a unique framework that aligns the elements of order-fulfilment operations with online consumer behaviour, bridging perspectives from both marketing and operations.				1	1
Zennaro et al. [30]	2022	Returns management in online retail	This review investigates the transformation of logistics in the supply chain due to the proliferation of e-commerce, further propelled by the COVID-19 pandemic, with a focus on identifying key logistics research areas as well as relevant factors and performance indicators, particularly sustainability aspects. Through a structured literature analysis, it pinpoints five primary research domains: supply chain network design, outbound logistics, reverse logistics, warehousing, and IT and data management, offering a comprehensive methodological framework and a consolidated set of inputs, outputs, and their interrelationships to managers in implementing or enhancing their e-commerce operations.		1			1
Ahsan and Rahman [31]	2022	Returns management in online retail	The study performs a systematic review of existing literature on e-tail product returns, an emerging research field investigating the return of products sold through online or hybrid channels. Through bibliometric and content analysis of 75 articles, the study maps the academic landscape, identifying the need for further research in areas including omni-channel returns, customer satisfaction, skill development, and technology utilisation. The findings offer e-tailers insights for refining their returns strategies, while the study itself expands our theoretical understanding by clustering key themes and proposing a conceptual framework for future research in e-tail returns management.	1				1
Duong et al. [32]	2022	Returns management in online retail	The review synthesises the body of work in the product returns (PR) domain, employing a rigorous six-step research framework that combines machine learning topic modelling with bibliometric analysis to cluster and identify key themes from a large dataset of academic publications. It discerns that PR research falls into three categories: operations management of PR, retailer and (re-)manufacturer challenges, and customer psychology, proposing five avenues for future study including digitalisation, globalisation vs. localisation of PR processes, multi-layer/multi-channel return policies, customer return behaviour prediction through online data, and customer perceptions at the marketing–operations interface.	1	1			1
Karl [5]	2024	Returns Management in Online Retail	The study delves into the predication of consumer returns in the e-commerce sector, highlighting the challenges faced by online retailers due to the high return rates and the importance of effective returns management. By examining previous meta-research and exploring the methodology, data sources, predictors, and techniques used in return forecasting models, the article identifies critical research gaps and proposes future research directions, such as investigating returns timing, developing real-time forecasting systems, conducting cross-industry studies, and analysing the implementation and effectiveness of forecasting systems in e-commerce. Ultimately, this study offers valuable insights to guide future research in returns management in e-commerce, especially regarding the forecasting of consumer returns.	1			1	1

has a column “Research Synthesis”, where the essence of each review is captured. Furthermore, the table includes columns labelled RQ1–RQ5, each evaluating whether the article in question could potentially provide an answer to the corresponding research question. A value of 1 in any column, such as RQ2 = 1, indicates that the article may indeed address that research question, independently of the publication year. This approach makes it possible to clarify the degree to which the studies under consideration can potentially answer the five research questions (see Table 2).

When assessing whether the articles have the potential to answer the research questions, we need to consider their currency, approach, focus, and consistency. The field of RM is characterised by rapid change and studies can quickly become outdated, e.g., through the recent history of disruptive changes in online consumer behaviour triggered by the COVID-19 pandemic. Methodological approaches in the literature vary: some rely on bibliometric analyses, while others use theoretical frameworks, each offering different types of insights and differing in their empirical basis. The focus of individual articles also has its limitations. For example, an article that focuses on the environmental aspects of reverse logistics may not address the economic impact or consumer behaviour aspects of returns management, thus only partially answering the research questions. In terms of consistency, the rigour with which the methods are applied and discussed can significantly affect the reliability and applicability of the results to RQs. Furthermore, answering the five questions on the basis of different studies is not desirable. Attempting to answer research questions using different studies may lead to inconsistent findings due to varied methodologies and conceptual definitions. Such an approach risks data fragmentation, making it challenging to draw reliable and coherent conclusions. Due to these limitations, there is no study that conclusively answers all five RQ, which underscores the need for a new, comprehensive review.

In the following a new, second search for articles (not literature reviews) was carried out. This can be seen in Figure 1, which visualises the two-phased literature review approach (first searching for existing reviews, then for relevant articles). It can be seen that the search follows the same basic procedure: determine and apply search terms, examine electronic databases for relevant literature, filter found literature from irrelevant studies, duplicates, or literature with restricted access, and finally processing the remaining reviews or articles.

Searching for relevant articles on RM, we initially found 74 articles exclusive to our search in Google Scholar. To guarantee a broader spectrum, we also integrated Google Scholar where we manually discovered 30 more articles by hand. After that, we removed 3 articles that were reviews and we removed 47 articles that were either duplicates or not available in full length. Finally, we received 54 relevant articles. These were read carefully, analysed, and categorised. The findings are presented in an overview in Section 5.

4. Returns Management Tasks

Returns are cost-intensive for companies, as the processing, handling, and transport costs are incurred and the returned products have lost value due to ageing, use, and technical changes. The tasks of returns management relate on the one hand to the prevention and avoidance of returns (before a return occurs) and on the other hand, to returns processing (when returns are received by the company). According to [3,9], who are widely acknowledged in the field of RM and whose categorisation is used in this study, returns management can therefore be summarised into two overall categories. The categories are preventive RM and curative RM. In addition, RM is defined by four RM-tasks, which are assigned to the categories. Curative RM includes only one task, whereas preventive RM includes three. In preventive RM, (1) returns prevention, (2) returns avoidance, and (3) returns promotion are taken into account. In curative RM, (4) effective returns processing is carried out (see Figure 2).

The tasks (1)–(4) can be defined as follows: (1) Implement measures that make returning items more difficult or prevent it altogether, such as return fees or requirements that discourage customers from sending products back. Incentives may also be offered to refrain from returning items. (2) Take proactive steps to address return causes, such as quality improvements and campaigns to encourage thoughtful purchasing, aiming to eliminate the root causes and reduce the incidence of returns. (3) Encourage the return of products with a positive net return value by informing customers of collection and recycling systems and offering incentives like credits or donation options to motivate product returns and enhance customer loyalty. (4) Focus on handling returned products to create value through a designed returns network and optimally repurpose the goods through resale, refurbishment, parts or materials recovery, donation or disposal.

Sorting the 54 publications found in our article search (see Section 3.4) according to the tasks of RM, a focus of part research areas becomes apparent. From the category of preventive RM, 24 articles deal with the task of returns avoidance, 13 with returns prevention, 5 with returns promotion. In the category of curative RM, only five articles address the returns processing. There are seven articles in the evaluation that could not be assigned to any task. The sorting shows a clear focus on the category of preventive RM; 77.8% of the studies can be assigned to this category. In contrast, only 9.3% of the studies are in the curative RM category (see Figure 3).

5. Descriptive Analysis

As part of our subsequent descriptive analysis, the 54 relevant articles (see Section 6) on the topic of returns management in e-commerce are analysed with regard to the aspects, which are adapted from [31]: (I) the development of the topic; (II) leading journals; (III) country affiliation of the authors; and (IV) research methodology. The aspects make the scientifical embedding of the topic clear, provide a more detailed insight, and help us answer our RQ1–RQ5. Our review starts by outlining the key characteristics of the studies to establish a clear context and ensure a transparent approach, setting the stage for a thorough analysis of the literature. Due to the fact that the articles cannot always be assigned precisely to one RM-task and one methodology, there may be slight overlaps in the studies with regard to categorisation. When categorising, we proceeded in such a way that we assigned the categories that are most likely to be represented in the work. This provides a clear picture of the subject area.

(I) 

Development of the topic

The analysed articles cover the period from 2007 to 2023, although the year 2023 had not yet ended at the time of this analysis. The topic of “returns management” as such was addressed in 1995, [9], the combination of “returns management” and “e-commerce” can only be found from 2007 onwards [33]. Since that year, at least one contribution on the topic is published almost every year, with an upward trend (see Figure 4).

(II) 

Leading journals

All 54 articles found were published in a total of 42 different journals. The number of publications in the various journals is analysed in order to determine which of these journals is the leading journal. For this purpose, clusters are initially formed from 1 to 6, because at least 1 and a maximum of 6 articles were published in a journal. The clusters are thus formed by combining the journals with the same number of publications into one cluster, e.g., 36 journals each had 1 contribution on the topic (cluster 1), 4 journals each had 2 publications on the topic (cluster 2), and so on. The results of the distribution of the analysed publications and journals can be seen in

Table 3. Relation of journals and publications in quantities.

Cluster	Amount of Individual Journals	Amount of Contributions in Journals	Percentage of Publications to Total
1	36	36	66.7%
2	4	8	14.8%
3	0	0	0.0%
4	1	4	7.4%
5	0	0	0.0%
6	1	6	11.1%
Total	42	54	100.0%

.

The table shows that 66.7% of the articles were published in different journals. Only four journals have two different contributions each. The remaining two journals comprised a total of 10 different publications on the topic and are therefore identified as the leading journals. Four publications were published in The International Journal of Logistics Management, and six publications were published in HMD Praxis der Wirtschaftsinformatik.

Both journals are therefore the leading journals. It should be mentioned at this point that the contributions in both journals have no linguistic overlap. In “The International Journal of Logistics Management”, only English-language articles were published, whereas in “HMD Praxis der Wirtschaftsinformatik” only German-language articles were published. Moreover, the authors of the articles are different.

(III) 

Country affiliation of the authors

Adapted from the work of [31], we visualise the authors’ countries and their interrelationships using the VOSviewer software. Due to the utilisation of databases not supported by the software, but which promise a considerable gain in knowledge, the geographical data must be extracted in another way. For this reason, the authors’ affiliations were read from the first page of the available publications. This procedure makes it possible to see where most of the publications come from and thus to draw conclusions about the countries in which the most research is being conducted on the topic.

Table 4. Authors’ and country affiliations.

Country	Authors with Affiliations
Germany	25
USA	14
UK	7
China	4

lists the four countries from which the most contributions (at least four) originate.

It can be seen that the majority of articles come from authors with affiliations to Germany (25), followed by authors with affiliations to the USA (14).

(IV) 

Research methodologies

Adapting the categorisation of articles from [31,34], our 54 publications are divided into the methodologies “Empirical”, “Conceptual”, and “Modelling”. In this way, we obtain 32 empirical articles, 11 conceptual, and 11 modelling. Empirical studies in the field of returns management in e-commerce account for 59.3%, and conceptual and modelling studies for 20.4% each (see Figure 5).

The empirical articles can be further subdivided into the categories “case study”, “secondary data analysis” (i.e., derived or processed data from a previous data collection), and “survey interview”. With 20 publications containing a survey interview, this is the leading category in the empirical context. The other two sub-categories each have 6 articles. This means that most empirical work is in the form of a survey interview (62.5%), followed by the other categories at 18.8% each.

6. Literature Analysis and Overview

In this section, the 54 evaluated articles on returns management are assigned to the RM-tasks and differentiated in terms of research methods. Also, the problem and solution methodology used in the studies are briefly described. The structure of the overview is such that all publications that have been categorised with the RM-task (1) returns prevention are listed first. This is followed by all studies with (2) returns avoidance, (3) returns promotion, and (4) effective returns processing. Finally, the articles that were not assigned to any task are mentioned. We distinguished between the methodologies conceptual (C), modelling (M), and empirical (E). In conceptional studies, concepts for the solution are described and discussed and no statistical analyses are conducted. Articles that perform basic calculations based on hypothetical data are also categorised here. Studies classified as “modelling” form conclusions based on a model (e.g., descriptive model, explanatory model, or decision model). Empirical studies (qualitative or quantitative) use one or more analytical methods, ranging from simple descriptive analysis to correlation, factor, cluster, regression or conjoint analysis and multivariate analysis. Lastly, the literature of each combination (e.g., (1) and (C)) is sorted by publication year in ascending order (see

Table 5. Literature overview.

ID	RM-Task	Methodology	Authors	Title	Problem	Solution
1	(1)	(C)	Möhring et al. [1]	Preventive returns management in eCommerce (in German: Präventives Retourenmanagement im eCommerce)	The growth of business-to-consumer (B2C) e-commerce is increasing the issue of consumer returns for online retailers, often due to customers returning items simply because they do not like them. This generates costs for online retailers and has a negative impact on profitability.	The use of big data can decrease the return rates by identifying patterns that predict returns through the analysis of structured and unstructured data within e-commerce transactions. The results of the analysis can be used to initiate proactive measures to prevent returns.
2	(1)	(C)	Walsh et al. [35]	Big data: New opportunities in e-commerce (in German: Big Data: neue Möglichkeiten im E-Commerce)	Identifying customers with a high tendency to return goods and recognising possible triggers for a return are important tasks in RM. Conventional data analysis methods quickly reach their limits if all available information needs to be included in analysis.	Big data technology enables the faster processing of large, varied datasets, creating new opportunities in e-commerce such as identifying customers likely to return items and recognising triggers for returns.
3	(1)	(C)	Yang [36]	Returns reverse logistics management strategy in e-commerce B2C market	Consumers make their purchasing decisions primarily on the basis of graphic and textual information provided by the seller. They return goods if they are not of the desired quality or size, if parts are lost or if numbers were entered incorrectly when ordering. The resulting reverse logistics is very costly and should be improved.	The article proposes management strategies for the B2C market’s return process and analyses implementation barriers. Correct and complete information should be provided to customers, and an extension of the cancellation period is also a possible action. Sellers need to fulfil customers’ requirements while reducing losses as much as possible.
4	(1)	(C)	Walsh and Möhring [37]	Avoiding returns in e-commerce: Can big data help? (in German: Retourenvermeidung im E-Commerce: Kann Big Data helfen?)	In e-commerce, customers cannot physically inspect products before purchase. This increases the returns and drives up processing costs.	The article suggests using “big data” and “text mining” to develop preventative strategies to avoid returns, aiming to reduce handling costs without impacting customer satisfaction.
5	(1)	(C)	Möhring and Schmidt [38]	Data-driven company architectures in e-commerce for preventive returns management (in German: Daten-getriebene Unternehmensarchitekturen im E-Commerce für das präventive Retourenmanagement)	Online consumers face high pre-purchase risks and post-purchase dissonance as they cannot physically examine or try products before buying, leading to high return rates. This is intensified by the fact that European consumers have the right to return items without reason within a 14-day period, often free of charge.	In the article, it is proposed that separate decision services are implemented into business processes. With the help of “big data” and “text mining”, the system should ideally be able to carry out historical analysis in real time in order to identify returns behaviour during the purchase process. Predictive and recommendation analyses should also be performed to develop preventive strategies to prevent returns, which would maintain customer satisfaction.
6	(1)	(C)	Lockhauserbäumer andMayr [39]	Returns processing in B2C-e-commerce (in German: Retourenabwicklung im B2C-E-Commerce)	Return management in B2C e-commerce is a critical success factor, particularly considering new consumer regulations and the assignment of shipping costs.	Based on practical experiences, the article introduces methods to prevent returns before orders are placed and suggests that customer-friendly return policies can reduce return-related costs.
7	(1)	(C)	Deges [40]	Returns controlling in online retail (in German: Retourencontrolling im Online-Handel)	Online retailers must not tolerate returns, as a high return rate causes costs and affects revenue recognition and profitability through delayed transactions. The reasons for returns must be identified and the return behaviour of customers analysed.	Options for action are presented that enable online retailers to influence customer behaviour through preventive and reactive measures, minimise the number of returns and reduce return costs. In addition, key performance indicators are presented in the context of returns controlling, which can measure the success of a company-specific returns strategy.
8	(1)	(M)	Asdecker [41]	Returning mail-order goods: Analysing the relationship between the rate of returns and the associated costs	The growth of online retailing is accompanied by liberalised return policies that build consumer trust but also incur significant costs for retailers due to a disproportional relationship between return rates and associated costs.	A circular model for the sales and returns process is proposed to help decision makers evaluate the effectiveness of preventive returns management measures.
9	(1)	(M)	Urbanke et al. [42]	Predicting product returns in e-commerce: The contribution of Mahalanobis feature extraction	There is a lack of strategies in the literature for limiting returns that are tailored to individual consumer behaviour. For this purpose, forecasting models that predict product returns in e-commerce are required.	The article introduces a decision support system capable of predicting product returns using a novel approach for large-scale feature extraction. This enables online retailers to proactively address transactions likely to result in returns.
10	(1)	(M)	Heilig et al. [43]	Data-driven product returns prediction: A cloud-based ensemble selection approach	The e-commerce apparel sector suffers significant costs due to product returns and there is a lack of data-driven models and approaches for predicting these returns.	The article presents an ensemble selection approach for forecasting product returns in the apparel sector. To manage the computational demands, a scalable cloud-based framework is proposed to streamline the ensemble selection process, offering the potential to reduce product returns, and enhance profit margins for retailers.
11	(1)	(M)	Asdecker et al. [4]	Examining drivers of consumer returns in e-tailing using real shop data	There is a research gap in empirical research on the drivers of consumer returns in online retail, which is crucial for making informed decisions regarding return flows.	The study uses linear and logistic regression models to analyse an extensive dataset from an online apparel retailer.
12	(1)	(E)	Griffis et al. [44]	The customer consequences of returns in online retailing: An empirical analysis	From a business perspective, product returns are often only seen as a cost driver. However, the operations taken to manage returns have the potential to influence customers’ future purchasing behaviour.	As part of an empirical analysis and using a database with a purchase and returns history, the relationship between operations and repurchase behaviour is analysed. The study suggests that smooth processing can positively impact repurchase behaviour. To this end, the returns management process should be considered an important part of customer service.
13	(1)	(E)	Walsh et al. [28]	Preventive product returns management systems: A review and model	Online retailers face tough competition and high customer expectations that lead to high product returns. They must therefore find ways to reduce return rates without losing profits.	The authors propose a framework based on a grounded theory approach that combines literature-based insights with qualitative interviews with managers. The framework outlines why online retailers should implement a PRMS, highlights preventative instruments to reduce returns, and examines factors influencing the relationship between PRMS decisions and the type of instruments used.
14	(1)	(E)	Jeszka [45]	Returns management in the supply chain	It is necessary to analyse whether the degree of cooperation in the area of RM between selected retail chains, logistics operators, and suppliers has an impact on customer relations, costs, value recovery, inventory reduction, and profitability.	An empirical study using a questionnaire distributed to sales personnel in the clothing retail industry in Poland evaluated various aspects of reverse logistics cooperation. It highlights the importance of return policies in strengthening customer relationships and identifies the need for retail chains to enhance their return handling processes for better efficiency and increased profitability.
15	(1)	(E)	Möhring et al. [46]	Fashion consumer returns in German online retail: An empirical study (in German: Moderetouren im Deutschen Onlinehandel: Eine empirische Untersuchung)	Online retail, particularly in the fashion sector, is a low-margin business overall. For that reason, cost drivers such as product returns need to be identified and mitigated.	The study presents a literature-based model identifying four key influences on product returns. The model was tested using customer data. The findings provide online retailers with starting points for preventive returns management.
16	(1)	(E)	Bernon et al. [47]	Online retail returns management: Integration within an omni-channel distribution context	The growth of omni-channel retailing has led to increased levels of consumer returns from online sales. Retailers are faced with challenges in configuring their networks and managing the returns effectively.	The authors use a mixed-method approach, in which both qualitative and quantitative data are processed. The results highlight the need for retailers to improve their returns processes and network design to offer a seamless solution.
17	(1)	(E)	Xu and Jackson [48]	Investigating the influential factors of return channel loyalty in omni-channel retailing	In omni-channel retail, there is a lack of empirical studies on customer perceptions of the returns process.	The article uses empirical analysis and structural equation modelling to identify factors affecting customer loyalty to return channels. It also explores internal and external factors affecting perceived risk.
18	(1)	(E)	Lin et al. [49]	Analysis of customers’ return behaviour after online shopping in China using SEM	There is a need to understand the impact of various variables on product return activities after an online purchase. In particular, effects on variables related to logistics service and customer intention for general products within e-commerce environment need to be considered.	The authors provide valuable insights for e-commerce platforms to design supply chains that consider product returns and aim to enhance customer satisfaction. Using structured questionnaire data and structural equation modelling, the study finds that return intentions have the most significant impact on product returns, followed by the flexibility of the returns process.
19	(1)	(E)	Stöcker et al. [50]	New insights in online fashion retail returns from a customers’ perspective and their dynamics	High return rates in the fashion sector lead to costs for remanufacturing and restocking as well as to inconvenience for customers who have to ship the items. In addition, the environment is negatively impacted by repeated shipping.	The study examines measures to prevent or reduce returns throughout the pre-purchase, purchase and post-purchase phases, alongside technological developments in RM. In an online survey, the customer satisfaction is assessed, taking into account Kano’s “theory of attractive quality” and various customer segments.
20	(1)	(E)	Rintamäki et al. [51]	Customers’ perceptions of returning items purchased online: Planned versus unplanned product returners	Managing product returns in online fashion retailing is complex. The challenges lie in how customers perceive the returns process and how it affects their satisfaction, loyalty, and word-of-mouth, especially considering whether returns were planned or unplanned.	Through a combination of interviews and a survey study, the research shows that customers’ perceptions of returns are influenced by monetary costs, convenience, stress, and guilt. Retailers should focus on the customer return experience and adapt their returns strategies to enhance customer outcomes socially, environmentally, and in terms of company performance.
21	(1)	(E)	Gaidarzhy et al. [52]	Returns management practices in Swiss online apparel retailing: A multiple case study approach	Increasing product returns cause significant costs for online retailers. Returns are often handled without active coordination or thorough investigation; this needs to be changed.	The findings from multiple case studies and interviews suggest implementing returns management practices that incorporate return policies, product categories, preventive actions, and avoidance practices. Additionally, the management of returns in omni-channel retail (e.g., shops as returns collection/pickup points), the use of artificial intelligence, and the consideration of sustainability in consumer behaviour can reduce product returns and improve company performance.
22	(1)	(E)	El Kihal and Shehu [53]	It’s not only what they buy, it’s also what they keep: Linking marketing instruments to product returns	Online retailers use various marketing instruments to increase sales, but often overlook the influence these instruments may have on product returns.	The authors empirically examine whether and how a comprehensive set of marketing instruments (e.g., newsletters, catalogues, affiliate advertising) influence product returns. Data from two large online retailers are used, which shows that return effects vary greatly depending on the instruments.
23	(1)	(E)	Asdecker and Karl [54]	Shedding some light on the reverse part of e-commerce: A systematic look into the black box of consumer returns in Germany	The growth of e-commerce poses economic and ecological challenges for the e-commerce industry. Data on the extent and impact of returns over a lower period of time are not available.	The article presents findings from a comprehensive long-term study among German online retailers. The research contributes valuable data that can be used for benchmarking and enhancing the decision-making systems of e-commerce businesses.
24	(1)	(E)	Frei et al. [55]	The impact of COVID-19 on product returns management in multichannel retail	The COVID-19 pandemic altered customer shopping behaviours, leading to increased product return rates, returns fraud, and forced retailers to modify their returns processes due to public health measures.	As part of the study, semi-structured interviews were conducted with multichannel retailers. The results recommend that retailers analyse the pandemic-induced changes in returns processes and apply these observations to develop strategies to mitigate the effects of heightened returns and fraud. These strategies should also be effective outside of pandemic circumstances.
25	(2)	(C)	Gelbrich et al. [56]	Rewarding customers who keep a product: How reinforcement affects customers’ product return decision in online retailing	Online retailers experience high costs due to frequent product returns under generous return policies that encourage customers to order more.	As part of the concept, it is proposed to incentivise customers to keep purchased items. This promotional strategy can supplement generous return policies. Evidence from experimental studies indicates that giving rewards can significantly boost customers’ intention to keep items.
26	(2)	(M)	Asdecker and Karl [57]	Big data analytics in returns management – Are complex techniques necessary to forecast consumer returns properly?	Small- and medium-sized e-tailers, in particular, struggle with forecasting returns due to the growing volume of online shopping and often lack the resources to utilise complex big data analytics methods for planning returns management capacities.	The article analyses the effectiveness of various data analysis methods with differing complexity using real data from an apparel retailer. The findings suggest that while complex methods perform better, a simple model such as binary logistic regression can also provide satisfactory results.
27	(2)	(M)	Karl [58]	Data mining in returns management: Evaluation of returns volume forecasts based on the transaction data of a shoe and clothing retailer (in German: Data Mining im Retourenmanagement: Evaluation von Retourenmengenprognosen anhand der Transaktionsdaten eines Schuh- und Bekleidungsversandhändlers)	The question is investigated as to whether the prediction of future returns of the time of ordering with the help of data mining models is promising.	The contribution utilises historical transaction data from a German shoe and clothing retailer, and derives future returns at the time of order applying data mining methods. Various models (e.g., binary linear regression, neural networks, decision trees, etc.) are used and practical recommendations are given on which methods are most suitable for predicting return volumes.
28	(2)	(M)	Difrancesco and Huchzermeier [59]	Multichannel retail competition with product returns: Effects of restocking fee legislation	Product returns present challenges for retailers competing across different sales channels, including brick-and-mortar, click-and-mortar, and strictly online.	The study develops a model to understand the competitive dynamics among sales channels concerning product returns, specifically focusing on the existence of Nash equilibrium conditions given different restocking fee policies and the customer perceptions of channel value. It explores the profitability of refurbishing and reintroducing returned items multiple times into the forward supply chain and evaluates the impact of legislation on free returns.
29	(2)	(M)	Shang et al. [60]	Using transactions data to improve consumer returns forecasting	While generous return policies to customers boost marketing metrics like willingness to pay and purchase frequency, they also increase the return rate. Return rates are very valuable as input for strategic and tactical decision-making tools. Improving the forecasting accuracy of return rates can lead to considerable savings in the practice of RM.	A forecasting approach is presented that uses transaction-level data (such as purchase and return timestamps) and predicts future return volumes using a two-step “predict–aggregate” process. The developed prediction model is tested on real data from an electronics and a jewellery retailer and has the advantage that the prediction error is relatively small.
30	(2)	(M)	Russo et al. [61]	Examining the impact of service recovery resilience in the context of product replacement: The roles of perceived procedural and interactional justice	Retail supply chains face increasing customer expectations and disruptions in product returns management, which can impact customer satisfaction and loyalty.	The authors introduced the concept of service recovery resilience as a supply chain capability that enables firms to meet customer requirements during disruptions, particularly in product replacement scenarios. Utilising procedural justice theory, the research demonstrates that both the justice of the recovery process (procedural justice) and the quality of retailer–customer interactions (interactional justice) significantly improve customer satisfaction and loyalty.
31	(2)	(E)	Brusch [62]	Developments and classifications of online shopping behaviour in Germany	Online shopping is evolving with trends like managing product returns and offering same-day delivery. However, understanding and effectively targeting customer segments based on their purchasing behaviour is challenging.	The study offers an overview of new developments in online shopping, particularly in the German e-commerce market. An empirical investigation is conducted to describe and classify German online buyers into groups with similar behaviour, aiming to select and address appropriate customer segments.
32	(2)	(E)	De Araújo et al. [63]	An exploratory study on the returns management process in an online retailer	Despite the strategic importance of returns management for reducing costs and providing competitive advantage, there is limited research on the subject. The main weaknesses in the RM process need to be highlighted.	In this contribution, the returns management process of the largest Brazilian online retailer is described and analysed in a case study. The development and performance of the process is evaluated and key areas for improvement in RM are identified. Finally, strategies for creating a more efficient returns management system are derived.
33	(2)	(E)	Dobroselskyi et al. [64]	Analysis of return logistics in e-commerce companies on the example of the Slovak Republic	The growth of e-commerce means that returns logistics must be addressed with great attention. Returns logistics in Slovakia should be compared with returns logistics worldwide.	The article studies the current state of returns logistics in global e-commerce and compares it with data from a survey of e-commerce businesses in Slovakia. Based on the responses, statistical indicators are derived. Moreover, best practices can be identified in the face of increasing returns.
34	(2)	(E)	Cullinane and Cullinane [11]	The logistics of online clothing returns in Sweden and how to reduce its environmental impact	The increase in online fashion retail is accompanied by a rise in returns, which have negative environmental consequences due to the involved logistics flow. There is a lack of detailed understanding of the returns process, hindering a thorough analysis of its environmental impact.	The article combines case-study interviews and a qualitative expert survey to map out the complexities of the returns process. It emphasises the shared responsibility of consumers, retailers, and carriers in reducing the environmental effects of the returns process.
35	(2)	(E)	Overstreet et al. [65]	Stemming the tide of increasing retail returns: Implications of targeted returns policies	Limited research exists on the impact of changes to return policies on customer behaviour, specifically regarding negative word-of-mouth, switching to other retail channels, and switching retailers.	The study employs psychological contract theory and organisational justice theory to assess how changes in return policies affect customer intentions. Through a two-phase mixed-method approach, the research finds that the type of policy change influences negative word-of-mouth. Furthermore, the intensity of a retailer’s communication about these changes moderates and mediates customer reactions.
36	(2)	(E)	Karlsson et al. [66]	Retail returns management strategy: An alignment perspective	E-commerce retailers lack strategic alignment in their approaches to managing product returns, which is essential for formulating effective returns management strategies.	The research uses case studies and interviews with managers to reveal alignment as a key factor in proficiently managing returns, along with identifying seven strategic misalignments that weaken this process. It offers a conceptual framework and empirical insights into strategic formation and potential conflicts within returns management.
37	(2)	(E)	Zhang et al. [67]	Strategies and practices to reduce the ecological impact of product returns: An environmental sustainability framework for multichannel retail	Retail product returns are not only costly, but also create negative environmental impacts through the transport, packaging, processing, and waste processes. There is a need for strategies and practices that retailers can adopt to manage their returns in an environmentally friendly way.	Multichannel retailers, retail experts, and returns service providers were interviewed. The findings were used to identify barriers for implementing environmental sustainability in returns management, outlining strategies to minimise the environmental impact of returns and creating a framework for sustainable returns management.
38	(2)	(E)	Martínez-López et al. [68]	Reducing e-commerce returns with return credits	The most common returns in e-commerce are satisfaction-related returns (due to colour, style, and material). It should be analysed whether these returns can be reduced, e.g., through the use of return credits (a maximum free returns amount).	The authors conducted an experiment to evaluate the use of return credits to reduce satisfaction-related e-commerce returns. The experiment testing found that return credits can effectively prevent returns, with higher credit amounts causing fewer negative side effects.
39	(3)	(C)	Guide and Wassenhove [21]	Managing product returns for remanufacturing	Companies are encouraged to offer environmentally friendly and reused products. However, taking back the products for reuse only makes sense if the activities are profitable and contribute to shareholder wealth.	The article develops a framework for analysing the profitability of product reuse activities and suggests that the management of product returns, specifically the acquisition of used products, can be optimised to ensure value creation and profitability.
40	(3)	(M)	Difrancesco et al. [69]	Optimising the return window for online fashion retailers with closed-loop refurbishment	To attract customers, online retailer must address their needs, i.e., offer free returns and the option to return goods as late as possible. The key challenge for online fashion retailers is therefore to find a compromise between customer expectations for generous return policies and the high costs associated with re-transportation and product devaluation.	The contribution models closed-loop supply chains, employing a queueing system to optimise the performance of forward and reverse logistics. The model provides insights into strategically setting return policies and deciding between refurbishing returned items or selling them on a secondary market. Among other things, the economic effects of multiple loops through the supply chain and controlled delays in reprocessing are analysed.
41	(3)	(M)	Tanai [70]	Framework for stochastic returns management in a closed-loop supply chain	In reality, some of the products originally sold are always returned by customers for a full refund. Due to the stochastic value of returns, it is not easy to identify the profit for a company that maintains a closed-loop supply chain and works with a third-party reverse logistics provider (3PRLP).	In the article, the forward flow between the supplier, the retailer, and the 3PRLP is modelled by a lot-size-order-point inventory policy. A queueing network is used for the return flow activities of the 3PRLP. Numerical studies show that the profit of both companies from handling returns increases as the return rate increases.
42	(3)	(E)	Mollenkopf et al. [33]	Managing internet product returns: A focus on effective service operations	An effective returns management system reduces the costs associated with returns. It can also make a significant contribution to improving customer relationships and customer loyalty.	The authors use data from a survey of 464 customers across 5 online retailers to show how effective returns management systems can positively influence customer loyalty intentions. They present a structural equation model illustrating the impact of returns service quality and perceived value on loyalty.
43	(3)	(E)	Hjort et al. [71]	Customer segmentation based on buying and returning behaviour	Fashion e-commerce businesses traditionally use a one-size-fits-all strategy, not leveraging consumer returns data for service differentiation, which could impact profitability and resource allocation.	The article empirically examines whether a one-size-fits-all strategy is appropriate for fashion e-commerce and whether returns have the potential to be profitable. Transactional sales and returns data are analysed to categorise customers based on their purchasing and returning behaviours. The results argue for a differentiated service system that saves resources and links the SC to customer purchase and returns behaviour to avoid over- or under-servicing customers. It turns out that the most profitable customers may be those who frequently return goods.
44	(4)	(M)	Stevenson and Rieck [72]	Digital transformation for incoming returns: Classification model for pre-sorting returns parcels (in German: Digitale Transformation im Retoureneingang: Klassifikationsmodell zur Vorsortierung von Retourenpaketen)	In order to avoid inefficiencies in the sorting of packages returned by customers, there is a need for good classification in the returns process.	A classification model is developed that uses real-world data from a German B2C online retailer and a three-stage calculation scheme to predict the number of products in a return package. In this way, less complex and labour-intensive returns can be separated and processed accordingly. The system has been cost-effectively integrated into the existing IT-infrastructure, resulting in a significant improvement in the digitalisation and efficiency of the returns warehouse, as confirmed by a case study.
45	(4)	(E)	Weinfurtner et al. [73]	Effects of digitalisation in retail using the example of the returns process	E-commerce retailers seek ways to optimise and reduce the expenses associated with returns. The techniques and elements of advancing digitalisation can be used to support returns processing.	The article explores how technologies can transform the returns process. It introduces digital elements that could significantly change how this process is managed in the future, such as software systems for tracking returns, big data for forecasting return volumes to improve staffing support systems for returns registration (RFID, smart glasses, voice input), and robotic systems. A survey of experts led to the conclusion that data collection and returns processing must be more automated and standardised in the future.
46	(4)	(E)	Russo and Marsogo [2]	Searching for the right operations strategy to manage the repair process across the reverse supply chain	Retailers need to strategically manage product returns and repair processes, deciding whether to outsource or insource these operations efficiently.	The contribution uses action-based research on a case study of an Italian online retailer to examine different repair process strategies. Through analysing return rates and the cost-benefit of each strategy in relation to outsourcing-insourcing decisions, it helps determine the best practice for managing the repair process.
47	(4)	(E)	Stevenson and Rieck [74]	Optimisation of inbound returns processes: E-commerce case study for the B2C sector (in German: Optimierung der Prozesse im Retoureneingang: E-Commerce Case Study für den B2C-Bereich)	B2C online retailer with non-digitalised processes face the challenge of processing returns. During the COVID-19 pandemic, these challenges were further aggravated, as the existing logistical processes could not keep up with the rise in orders and returns. Incoming returns can be categorised into classes and then processed according to the classes.	A case study analyses and optimises the returns process in the returns warehouse of a German online retailer for furniture and home accessories. Using Frequent Itemset Mining, common return characteristics are identified and, based on these findings, classes are created for categorising incoming returns (e.g., returns with one product and low/high weight and returns with several products). It is recommended that returns with several products are processed by experienced employees.
48	-	(M)	Muir et al. [75]	A simulation model of multi-echelon retail inventory with cross-channel product returns	Retailers face challenges in managing inventory effectively in cross-channel retail environments, particularly when processing product returns with non-stationary demand.	The study examines the impact of returns processing structures on multi-echelon inventory system performance under different product returns policies. Using a contingency framework and a discrete-event simulation model with data from a large U.S. retailer, it is found that aligning the logistical structure with a cross-channel returns policy enhances inventory effectiveness.
49	-	(M)	Jiang et al. [76]	Integrating order delivery and return operations for order fulfillment in an online retail environment	Online retailers must manage both delivery and return orders cost-effectively while sharing and coordinating fulfilment resources to meet promised customer time windows.	The article proposes a flow-based model to optimise the allocation of inventory, selection of fulfilment centres for returns, vehicle routing and scheduling, and delivery and return itineraries. A hybrid algorithm combining variable neighbourhood search and adaptive large neighbourhood search is developed to tackle this complex problem.
50	-	(E)	Brusch and Stüber [77]	Trends in logistics in the German e-commerce and the particular relevance of managing product returns	Online retailers are challenged by the major trends in e-commerce. They need to adapt to multiple sales channels, integrate new payment methods, offer same-day delivery, and manage product returns effectively.	The study provides an overview of major trends in e-commerce and conducts an empirical analysis of German online buyers to identify key factors influencing their behaviours. In this way, four significant factors are identified, which are then used to differentiate buyers into four distinct groups, aiding retailers in addressing customer requirements.
51	-	(E)	Chen et al. [78]	Ensuring returns management software effectiveness through joint development orientation	There is a need to assess the impact of customisable software on improving the returns management.	The contribution focuses on the effectiveness of customisable returns management software, grounded in service-dominant logic and using empirical survey data. It confirms that effective returns management software can enhance a company’s market performance and highlights the importance of a joint development orientation between companies and software providers to achieve effective software solutions for returns management.
52	-	(E)	Chen et al. [79]	Measuring returns management orientation	The concept of returns management orientation (RMO) reflects the recognition of the returns process from a managerial perspective. Management orientation is an important factor for improving returns management that is largely ignored.	In the article, a conceptualisation of RMO is developed through expert interviews and literature review, followed by empirical survey data to validate a new RMO measurement scale. This RMO measurement scale can be a useful tool for companies to assess the level of emphasis placed on returns management within their organisation.
53	-	(E)	Chen et al. [80]	Returns management employee development: antecedents and outcomes	The importance of employee development within the area of returns management is largely unrecognised, leading to challenges in effectively handling product returns.	The study empirically examines the impacts of employee development in returns management. The findings suggest that supply chain learning, returns management orientation, and information support significantly contribute to employee development in this domain.
54	-	(E)	Patale and Zohair [81]	A theoretical framework for evaluating returns management performance of online retailers using fuzzy analytic hierarchy process	Online retailers lack a comprehensive framework to measure returns management performance, making it challenging to identify areas for improvement.	A framework is proposed that identifies the key components of returns management and uses the fuzzy analytic hierarchy process to prioritise criteria and assess performance, taking into account the subjective importance of 16 professionals for each criterion. The study provides a structured approach for online retailers to evaluate and enhance their returns management performance, with a focus on general and reverse logistics capabilities.

). This procedure ensured that the “conceptualise step” was also taken into account and applied.

7. Discussion

This systematic literature review addresses the field of returns management in e-commerce. As part of this systematic literature review, 54 relevant articles were identified, which are presented and analysed. The research is based on five exploratory research questions that ensure a broad and inclusive perspective, which shed light on the subsequent aspects: Development of research within the field, distribution of research within RM-tasks, geographical focus of research, industry focus of practice-orientated research, and future research. By answering these research questions, we can develop a thorough and comprehensive understanding of the research landscape on returns management in e-commerce, encompassing development trends, task-specific distinctions, and geographic and industry nuances, ultimately serving as a guide for future academic and practitioner pursuits. In what follows, research questions RQ1–RQ5 posed at the beginning are answered individually and a final conclusion is drawn.

RQ1: 

How intensively is research being conducted in the area of returns management in e-commerce? Since when are there articles and surveys on returns management in the literature?

Exploring RQ1, we observe a growing interest in returns management in e-commerce, starting from 2007 and gaining momentum (see Figure 4). This illustrates the growing importance of the field alongside with the rise of online shopping. If we also take a look at the number of articles on the different RM-tasks (see Figure 6), there is a recognisable uprising of the field in the scientific community, both in terms of number and the various RM-tasks.

RQ2: 

Which RM-tasks are differentiated and which tasks are primarily dealt with? Which solution approaches are proposed?

If we look at the spread of articles in relation to all RM-tasks (see Figure 3), we can see a clear focus on empirical studies, after which conceptual studies and then modelling methods are conducted. The most addressed task is (1) returns prevention (44.4%), followed by (2) returns avoidance (24.1%). This is followed by the contributions that fall thematically into the area “returns management in e-commerce” but cannot be assigned to any of the available tasks by definition (“no task” with 13.0%). The task of curative returns management, namely (4) effective returns processing and the task (3) returns promotion, have received the least attention to date with 9.3%.

RQ3: 

Which countries are performing the most research on returns management?

Looking at RQ3, the review reveals that most of the articles come from authors who have affiliations in Germany, in the US, UK, and then China (see Table 4). The result is also supported by the fact that the leading journals for this field are “International Journal of Logistics Management” and “HMD Praxis der Wirtschaftsinformatik”.

RQ4: 

In which sectors of e-commerce is research being conducted?

In order to answer the present research question, the sectors from the 54 studies were summarised and evaluated. The industries were first viewed individually and then summarised into the following five groups: fashion, electronics, home and living, media, and other. This approach shows that fashion is the most considered sector with 56.5%. The next largest sector is the electronics with 19.6%. This is then followed by the “other group” with 13.0%, which includes many small sectors such as animal products, office supplies, and motorbike equipment. Finally, home and living with 6.5% and media with 4.3% are the least considered sectors in the research area of returns management in e-commerce (see Figure 7).

RQ5: 

What future research needs have been identified in the literature? What are therefore the future research perspectives in returns management?

Finally, addressing RQ5, our review outlines potential areas for specific future studies and directions, emphasising, among others, the need for better data analysis and environmentally sustainable practices in returns management to reduce and avoid the amount of returns. Future directions also include developing intelligent systems for analysing returns, finding ways to encourage more eco-friendly return practices, and to understand the impact of these practices on both profits and the planet.

After answering the research questions, this section will also focus on the practical implications of the study (cf. Section 7.1) and identify future research directions in the area of RM in e-commerce (cf. Section 7.2).

7.1. Practical Implications

In this subsection, we explore the practical implications gathered from a synthesis of the 54 articles focusing on returns management in e-commerce. Through stringent criteria and methodological rigour, a comprehensive set of practical implications was identified, reflecting the complex nature of returns processing in online retail. Our goal is to extract actionable recommendations and strategic considerations from a variety of research studies to help online retailers efficiently handle returns, boost customer loyalty, and drive profitability. The practical implications discussed here cover a range of approaches, from utilising advanced data analysis for return trend prediction to promoting sustainable practices and strengthening customer relationships. After identifying individual practical implications, the next crucial step was to group these insights into coherent clusters. By establishing connections between related implications and identifying common themes, strategies, and implications, we aimed to create meaningful clusters. This restructuring process not only improved the presentation but also deepened the understanding of the principles governing returns management in e-commerce. Presenting these synthesised practical implications in a consolidated manner was intended to offer readers a comprehensive yet easy-to-understand summary of the key insights from various research articles. This approach enhances the accessibility and readability of the implications and equips stakeholders in the e-commerce sector with practical guidance for navigating returns processing effectively. It serves as a valuable resource for stakeholders, enabling informed decision making and strategic planning in the digital marketplace.

By implementing big data analytics and text mining for predictive returns modelling, online retailers can anticipate return patterns, enabling them to proactively address potential returns before they occur, thereby reducing return rates and associated costs. Developing preventive strategies using data analysis techniques allows e-commerce businesses to lower their handling costs without compromising customer satisfaction, ultimately leading to improved operational efficiency and financial savings. Additionally, utilising separate decision services and analysing transaction data helps in preventing return behaviour during the purchase process, enabling retailers to make informed decisions based on consumer insights and leading to a reduction in return incidents and enhanced forecasting accuracy. Providing correct information, extending cancellation periods, and meeting customer requirements contribute to improved reverse logistics and customer satisfaction, fostering trust and loyalty which can result in repeat business and positive word-of-mouth referrals. Strengthening customer relationships through effective returns management practices not only increases profitability but also enhances brand loyalty and customer lifetime value through personalised experiences and exceptional service. Furthermore, rewarding customers and studying their satisfaction levels enhances customer retention by incentivising them to keep purchased items, reducing return rates, and creating positive customer experiences that drive long-term engagement. Influencing customer behaviour through preventive and reactive measures helps in minimising returns and optimising revenue recognition by effectively managing return costs and maintaining a loyal customer base. Analysing the drivers of consumer returns, implementing circular sales and returns models, and using decision support systems streamline operations, leading to improved efficiency, reduced returns, and informed decision making throughout the returns process. Moreover, examining data analysis methods and return forecasting effectiveness enables retailers to optimise the returns management capacities efficiently, providing a strategic advantage in mitigating returns-related challenges and maximising profitability. Understanding the factors influencing return channel loyalty in omni-channel retailing aids retailers in enhancing customer loyalty and effectively managing perceived risks, resulting in increased customer retention and brand advocacy. Studying returns management practices with a focus on return policies, product categories, preventive actions, and sustainability leads to reduced returns, improved operational efficiency, and alignment with environmentally conscious consumer preferences. Lastly, enhancing customer satisfaction and loyalty through service recovery resilience builds trust with customers, especially in product replacement scenarios, by emphasising fairness and transparency in retail interactions, ensuring long-term customer relationships and loyalty.

7.2. Future Research in Returns Management in E-Commerce

In order to present an overview of the possible research perspectives for future studies on the topic of returns management in e-commerce, we also analysed the future research described in the 54 articles from this review and combined them into generalised perspectives. By doing this, we ensured that the fourth and final step “energise” from [6] are taken into account and integrated. To do so, we started by grouping the articles with regard to the assigned RM-tasks. As previously explained, the categorisation of articles in terms of RM-tasks is not always clear or without overlaps, which is why a perspective may well fit into several categories. However, categorising the articles based on RM-tasks simplifies the review process, allows for more straightforward comparisons within each task area, and aids researchers and practitioners in efficiently finding literature pertinent to their specific interests in returns management. To reduce complexity, we have decided to only list each possible perspective once. The procedure makes it possible to present researchers or practitioners an organised and comparable overview of generalised future research perspectives that can be compared with one another. It is plausible that, by analysing the individual tasks research gaps within the respective task could be identified and targeted. To only consider the latest research streams, we mainly restricted the literature to the publication years 2019 and onwards. This approach, which focuses on predictive analytics, environmental considerations, and cost estimation, provides new momentum to returns management strategies and provides e-commerce with data-driven methods to optimise customer engagement and drive sustainable returns behaviour in a context-aware, commercially viable framework. For a better overview, each category is written in front of each task, e.g., preventive RM and (1) returns prevention.

Future research approaches for preventive RM and (1) returns prevention:

• Identify unique factors that demonstrate the link between the marketing efficiency of products and their returns.
• Conducting studies that examine the use of marketing instruments over time and across various market conditions and, based on this, develop strategies for retailers in relation to returns.
• Studies that segment customers according to their return behaviour and reasons for returns.
• Investigate why customers choose certain channels to return items and analyse whether channel choice can be encouraged by offering incentives and low-cost options.
• Examine how the type of a product and customer demographics (e.g., gender, age, education) affect product returns and customer loyalty in an omni-channel context.

Future research approaches for preventive RM and (2) returns avoidance:

• Create new scientific methodologies to measure the environmental effects of returns to support more sustainable business practices and the avoidance of product returns.
• Analyse the costs of returns (including handling costs, credit amounts, restocking fees, costs for different returns channels) for an e-commerce retailer to contextualise and differentiate returns avoidance strategies.
• Develop methodologies to assess the environmental footprints of online and brick-and-mortar shopping, aiming to inform and sensitise consumers about the ecological aspects.
• Focusing on techniques such as machine learning or deep learning to analyse more complex data structures and patterns to achieve accurate forecasting results using real-world data from companies from various sectors.
• Create helpful guidelines for researchers and practitioners to successfully apply predictive methods and develop individual models for various companies.

Future research approaches for preventive RM and (3) returns promotion:

• Conduct in-depth qualitative research to determine the motivations and values of customers who are encouraged and incentivised to return products.
• Advanced analyses of the economic and environmental potential of product returns.
• Studies analysing the impact of incentives for product returns on the return rate and customer satisfaction.
• Research the acceptance and implementation of sustainability practices in the area of product returns, especially in sectors like fashion or electronics.
• Conducting environmental and financial analyses comparing different return channels, laying the groundwork for an intelligent returns management system that guides customers towards the most sustainable option.

Future research approaches for curative RM and (4) effective returns processing:

• Conducting more studies on returns processing focussing on various sectors (furniture, fashion, office equipment etc.) in order to identify similarities and differences.
• Further development and refinement of prototypes and systems, e.g., those that classify incoming return packages according to the number of products.
• Developing advanced analytical methods using data from corporate ERP-systems and different data sources currently used for returns processing.
• Simulation studies to analyse the status of returns before, during, and after their occurrence in order to optimise the processing of returns in terms of resources.
• Examine how return experiences and any issues in the returns process contribute to bad feedback from customers, especially in the age of social media.

8. Conclusions

This systematic literature review illustrates the evolution and diversification of returns management in e-commerce. We observed the geographical development of research activities in this domain, particularly focusing on specific sectors and tasks within returns management. Our comprehensive analysis provides an in-depth understanding of the current landscape and highlights avenues for future research presented. The key findings of our systematic literature review shed light on the dynamic evolution of returns management in e-commerce. Our research reveals a surging interest in this field since 2007, as evidenced by the increasing number of articles dedicated to various aspects of returns management tasks. Notably, returns prevention emerges as a pivotal focus, underscoring the importance of strategies to mitigate returns in e-commerce operations. Our analysis also highlights the predominance of empirical studies, followed by conceptual analyses and modelling methods, showcasing the diverse approaches adopted within this domain. Furthermore, the geographical distribution of research shows contributions from countries such as Germany, the US, the UK, and China, reflecting a global interest in returns management practices. Sector-wise, our review underscores the significance of sectors like fashion and electronics, while also exploring additional industries like home and living, media, and miscellaneous categories in the context of returns management in e-commerce. Looking ahead, future research directions emphasise the need for enhanced data analysis, sustainable practices, and the development of intelligent systems to drive informed decision making and promote eco-friendly return processes that align with both economic and environmental sustainability goals. These key findings provide a comprehensive understanding of the research landscape in returns management, offering valuable insights for future academic and practical endeavours in this dynamic field. The methodology employed in this study for examining returns management in e-commerce is designed to offer a thorough and structured analysis of the subject. The research initially identified a need to explore current trends in returns management, leading to the formulation of specific research questions aimed at providing a broad and inclusive perspective. These questions focus on various aspects, such as the depth of research in returns management, the different tasks within returns management, the global distribution of research efforts, the sectors of e-commerce being studied, and the identification of future research needs. Following a systematic approach inspired by Ketchen’s research steps, the study integrated key components such as summarising existing literature, synthesising findings to reveal patterns, conceptualising a thematic framework, and proposing future research avenues. The methodology involved conducting a comprehensive search across numerous academic databases, filtering out irrelevant articles, and including only peer-reviewed publications for thorough examination. The study progresses through distinct sections to outline the methodological process and ensure a rigorous analysis and interpretation of the research findings. In essence, this methodological approach aims to provide a clear and detailed understanding of the current landscape of returns management in e-commerce. By addressing research gaps and outlining future research directions, the study endeavours to offer valuable insights that contribute to the advancement of knowledge in this dynamic field. This study acknowledges possible limitations. Our examination was confined to articles published either in English or German, potentially overlooking significant studies from major e-commerce markets published in other languages. Despite this, incorporating a broader linguistic range would have posed practical challenges due to the necessity of translating titles, abstracts, and keywords to ensure comprehensive search results. Additionally, a differentiated application of keywords across published works posed a challenge to the identification process as our confidence in predetermined search and exclusion criteria might not have secured all relevant literature. Limited access to subscription-based databases may have further constrained the breadth of literature included in the review. Despite these limitations, we aim to have provided a well-informed contribution to the research on returns management in e-commerce. Moving forward, we aspire to support future research endeavors with the insights gleaned from our study.