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Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal

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Past, present and future of Industry 4.0 : a systematic literature review and research agenda proposal

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Carsharing: a systematic literature review and research agenda

Journal of Service Management

ISSN : 1757-5818

Article publication date: 23 July 2021

Issue publication date: 17 December 2021

Following the recent surge in research on carsharing, the paper synthesizes this growing literature to provide a comprehensive understanding of the current state of research and to identify directions for future work. Specifically, this study details implications for service theory and practice.

Design/methodology/approach

Systematic selection and analysis of 279 papers from the existing literature, published between 1996 and 2020.

The literature review identified four key themes: business models, drivers and barriers, customer behavior, and vehicle balancing.

Practical implications

For managers, the study illuminates the importance of collaboration among stakeholders within the automotive sector for purposes of widening their customer base and maximizing utilization and profits. For policy makers, their important role in supporting carsharing take-off is highlighted with emphasis on balancing support rendered to different mobility services to promote mutual success.

Originality/value

This is the first systematic multi-disciplinary literature review of carsharing. It integrates insights from transportation, environmental, and business studies, identifying gaps in the existing research and specifically suggesting implications for service research.

• Nonownership
• Vehicle balancing
• Business model
• Access-based consumption

Nansubuga, B. and Kowalkowski, C. (2021), "Carsharing: a systematic literature review and research agenda", Journal of Service Management , Vol. 32 No. 6, pp. 55-91. https://doi.org/10.1108/JOSM-10-2020-0344

Emerald Publishing Limited

Copyright © 2021, Brenda Nansubuga and Christian Kowalkowski

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Environmental concerns and the specter of “peak car” – the idea that per capita distance traveled by an automobile will now fall, threatening the traditional mass-market car business – have prompted automotive manufacturers, service providers, NGOs, and policy makers worldwide to devise and offer a wide range of carsharing services. Carsharing refers to the temporary right to exclusive use of a car without the responsibilities of ownership, with payments linked to usage and/or subscription fees. Unlike traditional car rental and leasing, carsharing relies on platform mediation to identify appropriate matches between provider resources and users and to facilitate exchange ( Eckhardt et al. , 2019 ). While car rentals require a contractual agreement each time one rents a car, carsharing typically requires a membership and subscription. Lovelock and Gummesson (2004) suggested that such nonownership services – that is, marketing exchanges, which do not involve a transfer of ownership – differ distinctly from those that do, conveying benefits through temporary access rather than ownership. By securing the temporary right to use a good, customers avoid “the burdens of ownership” ( Moeller and Wittkowski, 2010 ), such as responsibility for maintenance and repair. Although accumulated service fees over time may be higher than the purchase price, individual payments are lower, making nonownership more affordable by comparison ( Schaefers et al. , 2018 ). Examples of carsharing include the pioneering Zipcar, which commenced operations in Massachusetts in 2000, and ShareNow, a collaboration between German automotive manufacturers BMW and Daimler. Achieving economic viability has proved challenging, and many projects are in retreat, for example, most of ShareNow's international operations have been closed down since 2019 ( Miller, 2019 ). Nevertheless, industry and public research centers continue to invest heavily in new initiatives ( Bocken et al. , 2020 ), and many carsharing service providers see long-term market potential. In 2019, the global carsharing market exceeded US$2.5 billion, with predicted growth of over 24% by 2026 ( Global Growth Trends, 2021 ).

Mirroring these developments, research interest in carsharing has increased across diverse fields that include transportation, environmental and energy studies, and business and management. This research activity has yielded a wide array of analyses and is also an indicator that carsharing is seen to have wide-ranging implications for society. Research on carsharing also reflects the growing interest in various nonownership services ( Schaefers et al. , 2016a ; Fritze et al. , 2020 ). However, despite extensive attention from transportation and environmental researchers – as well as increasing popular interest and media coverage – few of these studies are grounded in service management research.

Against this backdrop, we conducted a systematic review to synthesize the growing literature on carsharing. The dramatic increase in publications in recent years highlights the need for such a review, with more than 60 scientific journal articles published in 2020 alone. The review had three aims: (1) to consolidate existing knowledge and to extract key insights across disciplines; (2) to identify implications for managers and policy makers; and (3) to outline an agenda for service research. The study addresses two main gaps in existing literature reviews. First, as most of these reviews focus on specific issues and fail to acknowledge the full array of relevant research themes, they are not directly comparable. Among these, Jorge and Correia's (2013) review focused specifically on vehicle relocation. Although Ferrero et al. 's (2018) review was (we believe) the first attempt to comprehend the existing carsharing literature, it was not systematic, and their search covered only papers on access-based carsharing published during the period 2001–2016. As more than 180 papers on carsharing have since been published, an updated review is needed to capture newer and more relevant insights on subjects such as peer-to-peer (P2P) carsharing. Furthermore, Ferrero et al. 's study focuses on transportation-related outlets and issues, so neglecting important service management and marketing-related issues. On that basis, the present study addresses the need for an exhaustive thematic review.

The rest of the paper is structured as follows: Section 2 describes the methodology for the systematic selection and analysis of relevant publications. Section 3 presents the study findings, which are grouped under four themes: business models, drivers and barriers, customer behavior, and vehicle balancing. Section 4 outlines a carsharing research agenda that addresses key questions and challenges for service providers and customers. In conclusion, Section 5 identifies some key implications for managers and policy makers.

2. Methodology

The growing volume of fragmented research across disciplines makes it challenging to keep up with existing knowledge of carsharing, and a systematic literature review is needed to integrate findings and perspectives while minimizing bias ( Snyder, 2019 ). A systematic review is an effective means of critically evaluating a body of literature in an objective, transparent, unbiased, and rigorous way ( Boell and Cecez-Kecmanovic, 2015 ; Lyngdoh et al. , 2021 ) and increases the validity of the review process ( Palmatier et al. , 2018 ). Following Tranfield et al. (2003) , our approach involved three stages: planning, conducting and reporting the review.

2.1 Planning and conducting the review

In the first stage ( planning ), we assessed the need for a systematic review of the carsharing literature. In the second stage ( conducting the review), we identified and selected relevant publications, using the specified keywords (“carsharing,” “shared mobility,” “mobility services” and “mobility-as-a-service”) to search the Scopus database. As a leading database, Scopus is widely used for systematic reviews (e.g. Raddats et al. , 2019 ; Witell et al. , 2016 ). To ensure that the search was comprehensive, we used the Boolean operators “AND” and “OR” ( Cronin et al. , 2008 ). As keywords are the cornerstone of a systematic literature review ( Timmins and McCabe, 2005 ), we selected terms that were contextually relevant and reflected the scope of the carsharing literature, excluding related but distinct terms like “ride sharing” or “ride hailing.” Although terms like “shared mobility” and “mobility-as-a-service” tend to be broader in scope than carsharing, some such studies nevertheless relate to carsharing and are likely to provide relevant insights (e.g. Cohen and Kietzmann, 2014 ). More generic terms like “sharing economy” or “access-based services” were not included because a majority of these studies only refer to carsharing as an example among many other services rather than as the focal concern (e.g. Eckhardt et al. , 2019 ; Fritze et al. , 2020 ). Studies that discuss carsharing in detail, like Bardhi and Eckhardt (2012) , appear in searches using the term “carsharing,” but we deliberately excluded studies dealing with other nonownership services that do not involve shared car use, such as car leasing (e.g. Wittkowski et al. , 2013 ).

The search covered peer-reviewed journal papers published in English up to the end of 2020. To ensure a comprehensive overview of the field, the search was not confined to particular subjects or journals ( Snyder et al. , 2016 ), yielding an initial total of 1,417 papers. To begin the exclusion process, we read the abstracts and eliminated 1,051 papers that we deemed to be beyond the scope of our review. Some of the excluded papers related to topics like carbon emissions, autonomous vehicles, electric vehicle ownership, ride hailing, and ride sharing. Some addressed topics like mobile networks or mobility aids for the visually impaired but did not focus on automotive mobility, and these were also eliminated at this stage, leaving 366 papers in total. In the second round of the review, we read the remaining papers in full to assess their quality and to extract relevant information. We excluded any publications whose content failed to meet the specified criteria, for example, shared mobility studies with a very limited discussion of carsharing, or papers that focused on other vehicle sharing systems like bike sharing. As a result, a further 87 papers were excluded, leaving a final sample of 279 papers (see Appendix for subject areas, journals by subject, and number of papers published in each journal). Figure 1 summarizes the search and selection process.

To begin the review stage, the lead author read and categorized each paper in relation to the research topic. The research team then discussed the results, paying particular attention to papers in which the dominant theme was less easily discerned. For example, although electric vehicle carsharing was originally identified as a distinct theme, it was subsequently assimilated to other themes because it was the focus of interest in only a limited number of papers. This iterative analysis yielded four major themes: (1) the wide variety of carsharing business models ( n  = 62); (2) customer and service provider drivers and barriers ( n  = 56); (3) usage characteristics of carsharing customers, including behaviors and motives ( n  = 82); and (4) vehicle balancing, encompassing issues related to station location and vehicle relocation ( n  = 79). To assess the reliability of this categorization, another researcher independently reviewed the final sample of 279 papers. We assessed inter-judge reliability in terms of the proportional reduction in loss, which at 0.96 was well above the recommended 0.90 threshold for advanced practice ( Rust and Cooil, 1994 ). In cases where the two researchers differed – usually where a paper covered more than one theme – the research team jointly reviewed and discussed the study before agreeing a mutually acceptable categorization. Any additional relevant insights beyond a study's core theme are noted in the results.

2.2 Descriptive analysis of the field

Research on carsharing has grown rapidly, and the number of papers published in the last four years was more than double the total for all previous years combined. On average, less than two papers were published annually between 1996 (when the first paper appeared) and 2011. The topic began to attract greater research interest in 2012, when 15 papers were published, almost equaling the number of papers published in all previous years combined. Since then, the topic has attracted increasing research attention, and more than 60 papers were published in 2020 alone. Research initially focused on the nature of existing and potential business models for carsharing, customer reasons for engagement and customer behavior; over the years, interest has extended to key challenges like vehicle balancing. Table 1 shows the evolution of coverage of the four themes.

Overall, research on carsharing has most often appeared in transportation journals (125/279), with fewer (43/279) in business and management publications (including service and marketing) or environmental and energy journals (58/279) (see Appendix ). However, the three most cited papers for each theme appear in business and management journals (four papers), transportation journals (six papers), and environmental and energy journals (two papers) (see Table 2 ). While citation analysis is biased toward older publications, it is a useful way of identifying the main work in a given field ( Raddats et al. , 2019 ; Zupic and Čater, 2015 ). It is interesting that while the majority of papers were published in transportation journals, the two most cited papers were consumer studies that appeared in business and management publications. It is also notable that despite the growing importance for service and marketing researchers of concepts like the sharing economy and collaborative consumption (e.g. Eckhardt et al. , 2019 ; Guyader, 2018 ), few such studies have specifically investigated carsharing. As discussed below, it therefore seems useful to develop a fuller account of user needs and the challenges faced by carsharing service providers from the perspective of service management.

This section discusses the four themes elaborated in the third review stage (reporting and dissemination) ( Tranfield et al. , 2003 ). To begin, the evolution of carsharing is briefly summarized.

3.1 Evolution of carsharing

The concept of carsharing can be traced back to 1948 in Zurich, Switzerland, when the earliest known carsharing cooperative was formed by a group of private individuals. This initiative was followed soon after by similar ventures in other European countries, including France and the Netherlands ( Shaheen et al. , 1998 ). Early carsharing schemes operated on a round-trip basis, in which the customer returned the car to its original pick-up location at the end of each rent period. The earliest known one-way carsharing scheme (known as Procotip) was established in France in 1971, but it failed as a result of technological issues and a lack of appropriate control systems ( Shaheen et al. , 2015 ). Despite these early efforts, successful cases of carsharing were not identified until the late 1980 and 1990s, when various carsharing cooperatives in Switzerland (e.g. Mobility) were established. In 2008, Daimler formed the first free-floating carsharing scheme (then called Car2go and now known as ShareNow) in Ulm, Germany ( Firnkorn and Müller, 2012 ).

As concern grew about traffic congestion caused by increased car ownership ( Steininger et al. , 1996 ), carsharing was seen to offer multiple benefits to users, governments and service providers ( Tuan Seik, 2000 ). Beyond its initial appeal to environmentalists and community activists, today's users are motivated by factors like personal convenience and cost savings ( Orski, 2001 ). Other reported reasons for adopting carsharing include time savings, traffic mitigation, and individual attitudes ( Fellows and Pitfield, 2000 ). Shaheen et al. (1999) predicted that carsharing providers might combine their operational expertise with other advanced technology suppliers to offer mobility services with social, economic and environmental benefits. In many respects, this is true of carsharing today, which has been transformed by advances such as P2P services and technologies, growing stakeholder cooperation and integration, new automotive ventures, and a renewed emphasis on electric vehicles (EVs).

3.2 Carsharing business models

A business model describes how a business creates and captures value, articulating the value proposition, resources, and associated costs and revenue mechanisms ( Teece, 2010 ). Among carsharing services providers, business models vary widely from access-based services such as business-to-consumer (B2C) and business-to-business (B2B) models and cooperatives to platform-based peer-to-peer (P2P) models.

Most carsharing business models fall into the category of access-based services ( Bardhi and Eckhardt, 2012 ), which allow customers to access a product for a specified period in return for an access payment while the service provider retains legal ownership ( Schaefers et al. , 2016b ). Access-based services are considered suitable for convenience-seeking customers who do not care about the value of ownership and favor monetary savings ( Hazée et al. , 2017 ). According to Hazée et al. (2017) , such services entail a high level of customer involvement, minimal supervision by the service provider and high levels of interpersonal anonymity, as customers have minimal contact with other customers or employees when accessing the products.

3.2.1 Business-to-consumer carsharing

As the most common type of carsharing, B2C models have so far received the most research attention. This form of carsharing entails ownership of a fleet of cars by a firm, which rents them on demand to private individuals for short periods of time ( Münzel et al. , 2018 ). In addition to these independent service providers, car manufacturers also engage in B2C carsharing in their search for new markets ( Bellos et al. , 2017 ; Perboli et al. , 2018 ). As the owner (or lessee), the service provider assumes responsibility for car maintenance and marketing transactions ( Wilhelms et al. , 2017a ). B2C carsharing is generally usage-based, that is, members pay a service provider for access to cars at a given rate based on minutes or hours, or at a daily rate for distance traveled ( Schmöller et al. , 2015 ). Different membership types are often available – for example, pay-per-use or monthly subscription – according to the customer's expected level of usage ( Bocken et al. , 2020 ). B2C carsharing may be one-way or round-trip (in which the car is returned to the pick-up location after use) ( Cohen and Kietzmann, 2014 ; Le Vine et al. , 2014 ). Additionally, membership may be station-based or free-floating; in the former case, the customer must leave the car at a designated station within a given area, while a free-floating arrangement means they can leave the car at any location within the designated area ( Shaheen et al. , 2015 ). Users of free-floating and station-based services typically use the cars for short trips while round-trip customers mostly take longer trips ( Alencar et al. , 2019 ). Use of free-floating or station-based carsharing also varies according to trip length and purpose ( Heilig et al. , 2018 ); while station-based carsharing is favored mainly when a car is the most effective solution, the free-floating option is often preferred for nonregular trips ( Becker et al. , 2018 ) and saves time as compared to alternatives like public transport ( Becker et al. , 2017a ). Examples of B2C carsharing include Zipcar (founded in 2000 and acquired by Avis Budget Group in 2013) and ShareNow, a joint venture involving the German manufacturers BMW and Daimler.

3.2.2 Business-to-business carsharing

B2B carsharing – also referred to as corporate carsharing or employer-based carsharing ( Clark et al. , 2015 ) – is an option for organizations that do not wish to own or lease a fleet. Using this form of carsharing, employers provide access through a service provider to shared cars for employees' work-related trips ( Fleury et al. , 2017 ). B2B carsharing customers may be private or public sector organizations ( Clark et al. , 2015 ). B2B carsharing is usually offered by B2C service providers (e.g. ShareNow and Volvo Car's M), and the two models are broadly similar, aside from the fact that the B2B customers are organizations, and subscriptions or payments are made by the organization rather than the individual user. Typically, an employer signs a contract with the provider that specifies fees and terms of payment for the service. Potential customer benefits include lower travel costs as compared to car rental or taxis, as well as flexible fleet size, customized services and price models, and consolidated invoicing. For larger customers especially, service providers must have the necessary resources to provide personal support (e.g. through key account managers) and to ensure that the value potential is realized. Although one study of carsharing behavior in Toronto reported that B2B customers use carsharing more frequently than B2C customers ( Costain et al. , 2012 ), B2B carsharing has so far received much less research attention.

3.2.3 Cooperative carsharing

A third type of access-based carsharing business model is also known as the nonprofit car club ( Bonsall et al. , 2002 ) or self-regulating community ( Hofmann et al. , 2017 ), based on collective car ownership and usage within a defined or institutionalized local group such as friends, neighbors or a nonprofit organization ( Nitschke, 2020 ). This model is typically characterized by a communal interest in sharing cars rather than any profit motive ( Cohen and Kietzmann, 2014 ; Münzel et al. , 2018 ). Members of cooperative carsharing schemes usually pay an annual membership deposit or monthly mileage fees, with contractual obligations governing car maintenance and other administrative responsibilities ( Bocken et al. , 2020 ). As nonprofit organizations, cooperative carsharing schemes can often secure funding from government agencies and foundations. As they are usually community-based, these schemes tend to be small in size, favor a round-trip approach and offer only limited choices of car. One example of cooperative carsharing is the member-owned Modo, which was founded in 1997 in Vancouver, Canada.

3.2.4 Peer-to-peer carsharing

P2P carsharing business models can be classified under the broader term collaborative consumption , which is defined as the use of online marketplaces and social networking technologies to facilitate peer-to-peer resource sharing ( Barnes and Mattsson, 2016 ; Hamari et al. , 2016 ). P2P carsharing involves a triadic relationship between a car owner, a platform provider and a car renter (the customer) ( Wilhelms et al. , 2017a ). For the car owner, this involves renting out one's personal car and collecting the agreed monetary compensation ( Barbour et al. , 2020 ); for the customer, it entails renting a car from a private individual through an online platform ( Meelen et al. , 2019 ). As the platform provider does not own any cars, its role in P2P carsharing is to act as an intermediary between the car owner and customer ( Meelen et al. , 2019 ; Münzel et al. , 2018 ). P2P carsharing customers often pay a fee per use, and the platform provider takes a percentage of the rental fee paid by the customer. Under the P2P arrangement, the car owner assumes responsibility for maintaining and cleaning the car ( Hartl et al. , 2018 ) and determines the rental duration ( Bocken et al. , 2020 ). As P2P platform providers do not incur fleet investment costs, this form of carsharing has higher scalability potential than the access-based model ( Hampshire and Gaites, 2011 ; Meelen et al. , 2019 ).

While sharing of goods between individuals has always existed, the idea of P2P carsharing as a business is quite a recent development. Despite its growing popularity, this model has not received much research attention to date, and most studies focus on customer motivations (e.g. Barbour et al. , 2020 ; Dill et al. , 2019 ; Wilhelms et al. , 2017b ). Examples of P2P carsharing platforms include Getaround, which operates in the US and in some European markets (including France and the UK) and Turo, which operates in the US, Canada and the UK.

3.2.5 Business model typology

Based on the work of Hahn et al. (2020) and Wilhelms et al. (2017b) , Table 3 outlines the main characteristics of each business model. There are several differences between carsharing business models beyond value proposition, resources and revenue mechanism. Pricing is one of the most important factors, as it plays a role in determining the customer type and level of usage ( Balac et al. , 2017 ). Various strategies have been devised to make carsharing profitable, including dynamic pricing schemes based on availability and time ( Giorgione et al. , 2020 ); for a company targeting a younger clientele, for example, the ideal pricing model combines usage-based and monthly fees ( Hahn et al. , 2020 ). In the early phase of P2P carsharing, there were several experiments in the absence of an established pricing model; for instance, some operators allowed car owners to specify their own rental prices while others allowed the platform provider to set the price ( Barbour et al. , 2020 ).

Both P2P and cooperative carsharing have a cost advantage over B2C and B2B. As cooperative schemes are not profit-driven, and P2P car owners usually only want to earn some additional income from their cars, rental prices are usually lower than those of B2C and B2B providers, who need to make a profit on their investment ( Hofmann et al. , 2017 ; Münzel et al. , 2018 ). A second difference relates to fleet size; while cooperative schemes often have only a few cars, B2C and B2B providers range from hundreds to more than a thousand cars (depending on the city size), or indeed several thousand in the case of P2P ( Münzel et al. , 2018 ). In terms of required investment, this means that both B2B and B2C carsharing providers need significantly more capital than cooperative or P2P schemes. Additionally, as P2P cars are owned by individuals, the level of service provided may vary from one individual car owner to another.

3.2.6 Digital technologies

Digital technologies have had a major impact on the development of carsharing business models, especially P2P. Lagadic et al. (2019) observed that while carsharing is not a new service type, it has been completely transformed by advances in digital technologies. Although access-based services derive from traditional marketplace rental models, they have evolved through digital technology into a more collaborative and self-service approach ( Bardhi and Eckhardt, 2012 ). Similarly, the growth of carsharing has been aided by advances such as mobile apps, global positioning system (GPS), software that monitors fuel and battery usage, open-ended booking and smart locks that allow instant access ( Alfian et al. , 2015 ; Münzel et al. , 2018 ). In B2B carsharing setups, online dashboards can offer customers a consolidated overview of all user activities.

While technology plays an integral role in all types of carsharing, P2P carsharing relies more than other business models on technological innovations ( Julsrud and Farstad, 2020 ). In particular, P2P carsharing must make booking and key exchange as convenient as possible ( Sprei and Ginnebaugh, 2018 ), and this has become easier with smart locks that allow renters to access a car without having to meet the owner ( Münzel et al. , 2018 ). The increased incorporation of technology into carsharing services enhances the value proposition for the customer by making the service more flexible and convenient ( Lagadic et al. , 2019 ). As noted above, from a service research perspective, P2P carsharing differs from other carsharing models by virtue of its triadic structure. While many of the factors discussed here are relevant to all business models, most studies of carsharing explicitly or implicitly refer to B2C models or to access-based models in general. For that reason, it is important to highlight the differences between B2C and P2P models, especially with regard to customer behavior drivers and barriers.

3.3 Drivers and barriers

Individuals and business customers increasingly choose to rent objects for specific periods of time as needed rather than owning them; Lovelock and Gummesson (2004) refer to this as the rental/access paradigm. This is explained by Moeller and Wittkowski (2010) in terms of what they refer to as the burdens of ownership ; that is, product ownership entails certain burdens that users may prefer to avoid, such as risks related to product updates and obsolescence, responsibility for product maintenance and repair, and incurring the full cost of the product. In the same way, carsharing customers are driven by a desire to avoid the burdens of car ownership. Carsharing has multiple benefits as an affordable mobility alternative for lower-income groups like students and seniors, and as a substitute for transport alternatives such as walking and biking ( Cohen et al. , 2008 ). Most studies of the decision to use carsharing relate to B2C business models, and some recent studies have specifically discussed the issue in the context of B2B or communal carsharing, but few studies to date have dealt specifically with drivers and barriers in relation to P2P carsharing.

3.3.1 Key factors driving the demand and use of carsharing

Multiple studies have found that propensity to adopt carsharing in general is commonly influenced by sociodemographic, geographic and socioeconomic factors (e.g. Cartenì et al. , 2016 ; Coll et al. , 2014 ; Kang et al. , 2016 ). Other factors include convenience ( Orski, 2001 ; Peterson and Simkins, 2019 ), mobility patterns, family decisions, cost, and quality of alternative transport modes, lifestyle, and customer segment ( Chun et al. , 2019 ; Hahn et al. , 2020 ).

Sociodemographic factors like age and gender are often mentioned as influencing factors for adoption of carsharing (e.g. Carteni et al. , 2016 ; Juschten et al. , 2019 ; Shaheen and Martin, 2010 ). Some studies report that the average carsharing customer is young, male, middle class, more highly educated and from a household of below-average size (e.g. Becker et al. , 2017a ; Clewlow, 2016 ; De Luca and Di Pace, 2015 ; Hjorteset and Böcker, 2020 ; Tyndall, 2017 ). In a web-based study of vehicle availability in ten different European cities, Boldrini et al. (2019) found a positive correlation between carsharing demand and sociodemographic and urban indicators such as high educational attainment. In a survey of women's carsharing usage, del Mar Alonso-Almeida (2019) attributed the predominance of male carsharing customers to the fact that the mobile technology platforms on which carsharing services are based are less appealing to women than to men. Similarly, the predominance of younger carsharing customers can be attributed to their greater familiarity with technological devices like smartphones ( Burlando et al. , 2019 ; Guglielmetti Mugion et al. , 2019 ).

Geographically, carsharing demand is higher in urban centers than in rural areas ( Prieto et al. , 2017 ); this can be attributed to factors that make car ownership difficult and more expensive in urban centers, such as limited parking space. Carsharing adoption is positively affected by availability of other transport modes and scarcity of parking space ( Csonka and Csiszar, 2016 : Juschten et al. , 2019 ; Münzel et al. , 2020 ). In addition, close proximity to a carsharing station or to available cars increases the probability that individuals will use carsharing ( Diana and Ceccato, 2019 ; Jian et al. , 2016 ; Juschten et al. , 2019 ; Kent et al. , 2017 ; Zhou and Kockelman, 2011 ). A survey in Beijing by Shaheen and Martin (2010) established that one kilometer is the maximum distance that users are willing to walk to a carsharing station.

Regarding socioeconomic factors, the claim that carsharing is valued for its ability to provide cost savings for customers ( Duncan, 2011 ; Orski, 2001 ) is contradicted by Hjorteset and Böcker (2020) . In their investigation of propensity to adopt carsharing in Norway, they showed that carsharing appeals mainly to individuals who are unconcerned about reducing their expenditure, indicating that the choice is not motivated by cost reduction. Similarly, in a study of carsharing in Switzerland, Juschten et al. (2019) reported that high income increases the probability of adopting carsharing. However, Hjorteset and Böcker (2020) did not distinguish between P2P and B2C customers, and results may vary by carsharing type. While carsharing in the developed economies of Europe and North America is used predominantly by highly educated individuals ( Boldrini et al. , 2019 ; Clewlow, 2016 ; Coll et al. , 2014 ; Hjorteset and Böcker, 2020 ; Münzel et al. , 2020 ), Chun et al. (2019) reported that the opposite is true for developing economies, where highly educated individuals are more likely to favor car ownership.

Studies of the drivers of carsharing indicate a clear relationship between carsharing and urban population density (e.g. de Lorimier and El-Geneidy, 2013 ; Cohen et al. , 2008 ; Coll et al. , 2014 ; Csonka and Csiszar, 2016 ). For example, Becker et al. (2017b) found that free-floating carsharing in Switzerland scales with population density and social activity in a given area. As it is also more prevalent in areas with low car ownership and public transport, they concluded that carsharing serves as an alternative to these transport modes. Similarly, in their analysis of factors that favor carsharing, Coll et al. (2014) identified urban sprawl and a fairly good public transport system as drivers of carsharing.

Carsharing is often linked to general environmental benefits such as reduction of greenhouse gas emissions ( Cohen et al. , 2008 ; Fleury et al. , 2017 ). According to a study investigating choice of transport mode under uncertainty ( Kim et al. , 2017a ), improving the symbolic value of carsharing as an environmentally friendly solution by use of EVs makes it a more attractive option and increases market share. Carsharing is perceived as more environmentally friendly than private car ownership ( Münzel et al. , 2020 ) but less environmentally friendly than public transport ( Kim et al. , 2017a ). However, in a study of EV carsharing in Germany, Schwabe (2020) found that customer adoption of carsharing depends on more than perceived environmental friendliness. A study based on in-depth interviews with carsharing users in Norway ( Julsrud and Farstad, 2020 ) reported similar findings in relation to cooperative carsharing, revealing that in addition to environmental benefits, customers chose cooperative carsharing for its practical benefits, such as the convenience of access to a car. In short, as with carsharing in general, environmental consciousness may be one key driver but is insufficient alone.

Factors affecting demand may also depend on the type of carsharing. For example, if B2B corporate customers are to adopt carsharing, providers must meet high expectations in terms of flexibility and service quality ( Loose et al. , 2006 ). Fleury et al. 's (2017) survey of intention to use B2B carsharing in France indicates that motivating factors – especially for first time users – include tutorial videos, training and ease of access to the service provider. Probability of adoption is further increased if the service provided is perceived as environmentally friendly, which resonates with the growing emphasis on embedding sustainability goals in corporate strategies.

While most existing studies focus on access-based services (especially B2C), the factors that affect demand for P2P carsharing may differ for access-based carsharing models. In a study of five European cities, Münzel et al. (2020) reported that, in contrast to B2C, demand for P2P carsharing was not affected by population density. For car owners and service providers, the greater scalability of P2P overcomes the geographic limitations of B2C carsharing as it is not dependent on population density ( Dill et al. , 2019 ).

3.3.2 Key barriers to carsharing demand and use

The success of carsharing depends on making it more attractive to potential customers by overcoming perceived barriers ( Kim et al. , 2017b ). While previous research focused on the burden of ownership (e.g. Moeller and Wittkowski, 2010 ), Hazée et al. (2017) and Valor (2020) discuss the burdens of access and sharing, respectively. Hazée et al. (2017) classified the burdens of access in terms of functional and psychological barriers. Functional barriers included service complexity, which is associated with perceived access, usage, and understanding, and reliability, which refers to uncertainty about product performance and related technology. Psychological barriers included perceived contamination, referring to the fact that the product is also accessed and used by other (unknown) individuals, and responsibility, referring to customer concerns about being held accountable for their own and others' usage of available services.

These barriers also affect carsharing. In an early study, Shaheen et al. (1998) identified the limited availability of cars as one factor that limited the use of carsharing in residential areas. This finding relates to the issues of complexity and reliability identified by Hazée et al. (2017) as difficulty in accessing carsharing services when required undermines reliability and so acts as a barrier to adoption and use. Using a two-phase online survey, Kim et al. (2017a) confirmed that likelihood of carsharing use diminishes as waiting time increases. In the case of access-based services, cars must therefore be strategically located to service a wider pool of customers ( Kim et al. , 2019 ). In their study of Communauto in Montreal, Canada, de Lorimier and El-Geneidy's (2013) multilevel regression analysis showed that an efficient carsharing system must constantly attract new customers to compensate for lower use among seasoned members. To ensure customer satisfaction, the ideal situation combines high car availability and high car usage.

Low public awareness was identified by Zhou et al. (2017) as another significant obstacle to carsharing adoption in their survey of projected carsharing in Australia, Indonesia, Malaysia and Thailand. This problem relates to the compatibility barrier, where unfamiliarity can be a limiting factor. For example, lack of prior experience of similar services (such as car rental) is likely to deter certain potential customer groups from using carsharing. Both Duan et al. (2020) , who used a stated preference survey to quantify the impacts of potential carsharing demands in Shanghai, and Zhang and Li (2020) , who tested a model based on the theory of planned behavior in a study involving university students in Qingdao, China, reported that individuals who are familiar with car rental programs are more inclined to use carsharing.

In addition, nonmonetary costs (such as the inconvenience of having to reserve a car each time it is needed) may act as a barrier to carsharing ( Duncan, 2011 ). In a survey to understand the factors that influence carsharing adoption, Burlando et al. (2019) found that issues like comfort and independence, which are often associated with private car ownership, may discourage carsharing adoption. Table 4 summarizes the main customer drivers and barriers identified in the literature.

3.3.3 Drivers and barriers for service providers and car owners

While much of the research on carsharing drivers and barriers has focused on the customer (especially in relation to B2C carsharing), only a handful of studies have discussed the service provider perspective. Service providers offer carsharing services for different reasons. Among profit-driven ventures, pure B2C and B2B service providers are motivated mainly by the desire to make a profit on their services, while car manufacturers offer carsharing services mainly to expand their market and to explore untapped niches ( Bellos et al. , 2017 ; Perboli et al. , 2018 ). On the other hand, in a study of 235 car owners in Portland, Oregon, who rented out their cars for P2P carsharing, Dill et al. (2019) found that they were motivated by the prospect of generating extra income, low levels of car use and willingness to help others. Even if not in financial need, car owners in P2P carsharing schemes may be motivated by a desire to maximize car utilization or to make use of an otherwise underutilized asset ( Ballús-Armet et al. , 2014 ; Barbour et al. , 2020 ; Dill et al. , 2019 ). The claim that public awareness drives engagement in carsharing is further supported by the evidence that car owners who are familiar with the concept are more likely to offer their cars for P2P carsharing than those who are not ( Münzel et al. , 2019 ).

In addition to the high levels of investment required to engage in B2C and B2B carsharing, operational hurdles such as vehicle balancing (especially in one-way schemes, as discussed in detail in section 3.5 ) present serious challenges for service providers. While many of the barriers that affect platform-based services have also been identified in studies of access-based services, P2P carsharing must overcome some specific additional hurdles. P2P platform providers often face the double challenge of attracting both car owners and customers ( Wilhelms et al. , 2017b ). Based on 20 in-depth interviews with early adopters in Spain, Valor (2020) identified burdens for car owners that included emotional costs associated with the difficulties of sharing, the possibility of damage and perceived physical insecurity when driving the returned car. For customers, burdens included anticipated friction with owners, uncertainty about contractual conditions, limited reliability and availability, and perceived insecurity when driving a P2P-rented car.

Access-related barriers for P2P car owners include the perceived risk of limited access to their car ( Hazée et al. , 2020 ) and possible damage to the car while rented out. In a study of P2P carsharing in California, Ballús et al. (2014) noted that where the platform provider's package did not include insurance, fewer owners were willing to offer their car, as they would incur higher costs in the event of it being damaged. Hazée et al. (2020) discuss the image barrier as a common feature of collaborative consumption. As users are required to evaluate each other, car owners who worry that P2P carsharing may affect their standing in subsequent service requests may decide to withdraw.

3.4 Customer behavior

This theme relates to the travel behavior of carsharing customers, including specific customer traits and how they use carsharing services.

3.4.1 Usage characteristics of carsharing customers

Depending on the type of customer, carsharing services are used in different ways. In the case of B2C services, private individuals typically use carsharing for leisure travel and shopping ( Sioui et al. , 2012 ), while organizations use B2B carsharing for employees' work-related trips ( Clark et al. , 2015 ). In terms of demand, it may prove worthwhile to attract both private individuals and organizations as two customer groups that complement each other because they use cars at different times. In other words, private individuals might use the service for evening and weekend errands and leisure activities ( Costain et al. , 2012 ; de Lorimier and El-Geneidy, 2013 ), while organizations are likely to use the service during weekday working hours, so maximizing car utilization ( Loose et al. , 2006 ).

Charoniti (2020) investigated stated preferences for carsharing under uncertain travel times and noted that use of carsharing is strongly dependent on activity type and context. For example, Wu et al. 's (2020a) analysis of carsharing in London indicates that infrequent customers often use carsharing to transport bulky luggage while frequent customers are typically commuters. According to Jian et al. (2017) , while high-income customers who choose more luxurious brands use carsharing for leisure and business purposes, lower-income customers often use carsharing for special purposes like vans for moving or traveling with large groups of people.

Many individuals who register for carsharing schemes are not necessarily active customers and use the service only occasionally. In the case of Autolib, an early French EV carsharing initiative, frequency of use per customer continued to decline despite increasing membership; as many registered subscribers used the service infrequently, growth did not translate into increased revenues for the provider ( Lagadic et al. , 2019 ). This issue is less significant for P2P car owners, who incur very low marginal costs in comparison to B2C providers, who own or lease large fleets ( Meelen et al. , 2019 ).

For frequent users, carsharing is not their main mode of transport but is used in combination with other modes ( Ruhrort et al. , 2014 ). Carsharing customers are more likely than car owners to use other complementary mobility services such as bike sharing, walking and public transport ( Clewlow, 2016 : Mishra et al. , 2015 ; Münzel et al. , 2019 ). For example, in comparisons of free-floating carsharing and public transport in Spain and Denmark, respectively, Ampudia-Renuncio et al. (2020) and Carrone et al. (2020) reported that free-floating carsharing is often used as a substitute for public transport. Becker et al. 's (2017a) comparative study of station-based and free-floating carsharing schemes in Switzerland reports similar findings.

Multiple studies have discussed the impact of carsharing on car ownership, and whether the need for a personal car is reduced by replacing it with a shared one (e.g. Firnkorn and Müller, 2015 ; Jochem et al. , 2020 ; Le Vine and Polak, 2019 ). While findings vary, some studies report evidence of a reduction in car ownership as a result of carsharing. For example, in a survey of B2C carsharing members in Seoul, South Korea, Ko et al. (2019) estimated that each shared car replaced 3.3 private cars. Similarly, a study of B2C carsharing providers in Germany found that car owners who adopted carsharing tended to use their own car less frequently ( Loose et al. , 2006 ).

3.4.2 Trust

While trust is an important issue for all business models, its significance differs for B2C and P2P models. Several studies note the particular importance of trust for P2P carsharing; for example, in a study of German P2P providers, Wilhelms et al. (2017a) found that trust between car owners and customers was important, especially in relation to car usage behavior and maintenance. In their comparative study of B2C and P2P carsharing, Hartl et al. (2018) also found that because P2P carsharing involves transactions between strangers, the potentially higher economic risks and lack of regulation make trust more important. When offered a choice between B2C and P2P carsharing, customers tended to choose B2C because interacting with a company was perceived as less risky than with an individual. Similarly, Julsrud and Farstad (2020) reported that customers regarded B2C carsharing as (more) trustworthy and professional. According to Ma et al. (2020) , trust can affect customer loyalty both directly and indirectly, prompting service providers to address this challenge by eliminating factors that might lead to loss of customer trust, such as high transaction costs. Table 5 summarizes the main reported usage characteristics of carsharing customers.

3.4.3 Customer misbehavior

As a form of service cocreation, the carsharing system involves customer interdependency in ensuring that the car is in good condition for the next customer ( Bardhi and Eckhardt, 2012 ). Schaefers et al. (2016b) investigated misbehavior among users of sharing services such as carsharing and identified a parallel with broken windows theory, in that one customer's misbehavior affects the next customer's behavior – in other words, misbehavior can be contagious. However, as the opposite is true in groups exhibiting high communal identification, it was considered more useful for service providers to strengthen their brand identity rather than investing in surveillance. Bardhi and Eckhardt (2012) conducted 40 interviews with Zipcar customers and found no sense of perceived ownership when using a car; in some cases, users preferred not to be associated with carsharing services. According to Schaefers et al. (2016b) , the contagious effects of customer misbehavior suggest that deanonymizing service providers and customers may help to reduce misbehavior. In an experiment on motivating car inspection, Namazu et al. (2018) found that in the absence of a reminder, most customers did not inspect the car before use. Other undesirable behaviors such as late car return are more likely among P2P users than in B2C carsharing because customers may believe it is easier to discuss misbehavior with an individual than with a company ( Namazu et al. , 2018 ).

3.5 Vehicle balancing

This aspect of one-way carsharing remains challenging for many providers of access-based services. In one-way carsharing, a short-term imbalance of cars often occurs at certain locations because of the uneven distribution of cars, that is, cars may be unavailable where they are needed while oversupplied at redundant locations. This is a likely consequence of flexible return times and locations, and the uneven flow of cars between stations means there may be too few cars at popular pick-up points ( Nair and Miller-Hooks, 2011 ). The issue of vehicle imbalance is especially challenging for B2C providers as this business model most commonly offers one-way carsharing. The lack of adequate data makes it more difficult to resolve this problem ( Ren et al. , 2020 ), and some studies have developed demand forecasting models to predict carsharing demand in pursuit of optimal business performance and customer satisfaction (e.g. Moein and Awasthi, 2020 ; Müller et al. , 2017 ). The next section discusses the challenge of selecting appropriate locations for carsharing stations and refers to studies that address the issue of vehicle relocation.

3.5.1 Station location

Appropriate station selection includes identification of lucrative locations ( Cheng et al. , 2019 ) and ease of relocating cars between stations to meet demand ( Boyaci et al. , 2015 ). In many cases, limited access means that some potential customers may be excluded, while those who can access the cars may not use them. Clark and Curl (2016) , who studied UK bicycle and carsharing schemes, noted the importance of the station location in efforts to target potential and willing customers.

Hua et al. (2019) proposed an innovative framework for the deployment of one-way EV carsharing. They emphasized that identification of suitable stations is even more important for EV carsharing fleets because of issues such as battery charging and fluctuating travel time and demand. Seeking to identify optimal charging locations for one-way EV carsharing, Deza et al. (2020) showed how this can maximize balanced flow in carsharing networks. Given the uncertainty of travel demand, simulation models have been used to aid identification of optimal charging station locations for EV fleets (e.g. Brandstätter et al. , 2020 ; Kuwahara et al. , 2020 ; Lu et al. , 2020 ) and to assist fleet allocation (e.g. Deveci et al. , 2018 ). In their investigation of the factors that affect turnover at carsharing stations, Hu et al. (2018) found that in addition to spatial and temporal elements, station-level turnover and the relationship between transit and carsharing affect the use of shared cars. In identifying appropriate locations for carsharing stations, carsharing providers are encouraged to optimize efficiency, especially in densely populated areas, where cars occupy a significant amount of expensive parking space ( Hu et al. , 2018 ). Sai et al. (2020) designed a model for EV carsharing and concluded that city population, proportion of available travel modes, station construction costs and budget should be considered when selecting station locations.

3.5.2 Vehicle relocation

Spatio-temporal imbalances in demand for one-way carsharing mean that cars must be constantly redistributed ( Balac et al. , 2019 ), relocating them periodically between stations to ensure availability where they are most needed ( Kim and Lee, 2017 ). Any imbalance in distribution makes carsharing less reliable ( Balac et al. , 2019 ) and creates complexity barriers (cf. Hazeé et al. , 2017 ) for service providers, who must relocate cars to wherever they are required. Vehicle balancing efforts lead to various trade-offs; for example, Nourinejad and Roorda (2014) evaluated one-way carsharing performance by measuring effectiveness and proposed an optimization model for operational problems involving vehicle relocation. In the proposed model, increased fleet size means reduced time for relocation but also leads to increased costs. As another alternative, the same study proposed that fleet size could be reduced by extending reservation time to about 30 min. However, this might also result in reduced demand, as user privacy could be compromised in some instances by having to specify a return time window to facilitate vehicle relocation forecasting ( Repoux et al. , 2019 ).

Over time, vehicle relocation models have been developed for operator-based, user-based and combined vehicle relocation. While operator-based models require staff members to move cars, user-based models rely on customers to do so ( Brendel et al. , 2018 ). Testing the impact of vehicle relocation on competition among carsharing operators in Switzerland, Balac et al. (2019) found that to substantially affect demand, more employees are needed to perform the required relocations. Additionally, as cars are unavailable to customers while being relocated, no revenue is earned, making relocation less profitable ( Balac et al. , 2019 ). These issues were further highlighted by Kypriadis et al. (2020) , who noted that operator-based relocation requires multiple employees to ensure timely relocation. To minimize redundancy, it is also important to coordinate user-based and operator-based relocation ( Brendel et al. , 2020a ). This view is echoed by Huang et al. (2020) , who note that user-based and operator-based relocation can be combined on the basis of pricing and the number of shifts, respectively, to correct any vehicle imbalance in one-way carsharing systems. In creating a decision support system for vehicle relocation, Kek et al. (2009) found that customers could be offered incentives in the form of reduced travel costs to encourage compliance with user-based relocation. Alternatively, this can be achieved by allowing customers to choose their own incentive, such as vehicle delivery, alternate pick-up location, alternate drop-off location or paid user relocation ( Wu et al. , 2020b ). Table 6 summarizes key findings related to vehicle relocation models.

Many existing vehicle balancing models are incremental improvements of earlier models. For example, Correia et al. (2014) discussed further options for customer pick-up and car drop-off, and Jorge et al. (2014) combined a mathematical model and a simulation model for optimal vehicle relocation and real-time relocation policy. Unlike simulation models that focus on one-way carsharing, Nourinejad and Roorda (2015) described a bi-modal scheme combining round-trip and one-way modes as one solution to the vehicle relocation problem in one-way carsharing services. Although convenient for customers, one-way services are costly for service providers because they require constant vehicle relocation. In contrast, round-trip systems are convenient for service providers because there is no vehicle relocation requirement, but this is less convenient for customers, who must return the car to its original pick-up point at the end of each trip ( Jorge et al. , 2015 ). With the increasing popularity of EV carsharing fleets, more recent models take account of new requirements such as unplugging and recharging (e.g. Boyaci et al. , 2015 ; Weikl and Bogenberger, 2015 ). For example, based on two optimization models, Caggiani et al. (2020) proposed a shared (vehicle-to-grid) charging system that can help to minimize losses during charging times, so maximizing profits.

4. Discussion and research agenda

Despite the growing number of studies on carsharing, service scholars have shown surprisingly little interest to date, and there are ample opportunities for further research. As our review indicates, transport and engineering studies predominate, advancing solutions to challenges like vehicle balancing. Based on our systematic review of the literature, we identified four themes as the basis for a service research agenda encompassing theory development and managerial practice. The proposed agenda addresses critical factors for the further growth of carsharing, including customer requirements, balancing supply and demand, and the effects of carsharing on car shedding. While ongoing experimentation continues to explore why certain provisions work better in some locations and markets than in others, a wide range of tools and methods is already available to address pressing issues such as the societal relevance of carsharing ventures and how best to ensure their success.

The research agenda addresses four key areas and associated questions, including key issues for managers and policymakers (see Table 7 ). While several of these topics are specific to carsharing services, we believe they also have broader ramifications for other mobility service issues, including multi-modal mobility and the rental/access paradigm in general ( Lovelock and Gummesson, 2004 ). As our review illustrates, there is considerable heterogeneity in terms of business models and service systems across the various forms of mobility service. Even in the case of similar value propositions such as B2C and P2P carsharing, there are fundamental differences between their respective access-based and platform-based models and systems. Our findings also provide relevant insights and research directions for other forms of nonownership, shared use of other goods, or other types of short-term access to goods (e.g. bike sharing).

Carsharing is part of the wider shift from products to services commonly referred to as servitization . Although servitization research is among the most active domains in the field of service research and attracts interest from multiple disciplines, it has focused almost exclusively on B2B markets ( Raddats et al. , 2019 ). In light of the rapid growth of servitization initiatives in consumer markets (e.g. subscription-as-a-service, sharing economy offerings), there is a need for a systematic overview and a roadmap for future research directions. In this context, it also seems timely to look at carsharing from a wider service management perspective as a market driven by technological and digital transformation.

4.1 Critical factors for the growth of carsharing

Despite the evidence that some carsharing service providers are struggling, as seen in the closures across different markets, little of the existing research has sought to develop a better understanding of the key challenges. The present study identifies four main directions for future research. First, it will be important to identify the conditions necessary for the growth of carsharing. Carsharing is known to scale with population density and social activity – for example, in metropolitan cities ( Clewlow, 2016 ) – and is also more prevalent in areas with lower car ownership and efficient public transport ( Becker et al. , 2017b ). However, a comparison of carsharing services and levels of substitution for other transport modes shows that carsharing usage levels remain low ( Rotaris et al. , 2019 ). To address this issue, researchers should explore the factors impeding the progress of carsharing and how these can be managed.

Second, while studies point to high population density as a requirement for successful operation of carsharing, the required critical mass of users remains an unresearched issue. Studies of access-based carsharing have shown that most businesses need to achieve a critical mass to break even ( Acheampong and Siiba, 2020 ; Lagadic et al. , 2019 ; Terama et al. , 2018 ). However, this is effectively a chicken-and-egg problem; while a critical mass of service users is required for scalability, users must be able to see scalability before being convinced to abandon their private cars in favor of carsharing. Furthermore, many members of carsharing schemes are not necessarily active customers, as many of those who register for these services use them only occasionally. As mentioned earlier, cost issues make this a more significant problem for B2C providers than for P2P owners and platform providers. Further research on the critical mass of users needed for different business models may enable service providers to solve the problem of maximum vehicle utilization.

Third, carsharing research should explore the different business models in greater depth to understand how they can be harnessed to promote carsharing, either as standalone models or in combination. As most research to date has focused on B2C carsharing, further research should place greater emphasis on P2P and B2B models. For example, as trust is an especially important issue for P2P carsharing, it would be useful to analyze how peer service providers can enhance profits by gaining their customers' trust ( Benoit et al. , 2017 ). This in turn should contribute to the identification of underlying success factors that can be generalized to various forms of access-based carsharing. In practice, many commercial providers offer parallel B2C and B2B services and may have to do so to achieve the requisite utilization rate. Researchers should seek to determine how synergies between business models can best be managed and developed.

Finally, as technology plays an important role in the growth of carsharing, future research should explore how recent and imminent technological advances in the automotive sector can be harnessed to drive innovation and enhance the customer experience of carsharing. For example, some providers have incorporated EVs into carsharing fleets as a means of promoting sustainability and optimizing operational costs ( Jacquillat and Zoepf, 2018 ). However, the challenges associated with EV fleets, including customers' inexperience and the need for a distinct service system, remain to be addressed. Advances in driving automation technology also afford opportunities to utilize customer data to provide better services for customers. Further research on these issues can help carsharing service providers to develop the competences they need to manage the industry's digital transformation.

4.2 Key customer requirements

Carsharing adoption has been attributed to the potential for exploiting the benefits of car ownership without bearing the costs ( Shaheen et al. , 1998 ). At present, the known prerequisites include collaboration and transparency between stakeholders, demand synchronization, solutions for a diverse group of customers that are accessible and easy to use and adopt, the potential to combine mobility of people and goods, and public information about the availability of mobility services ( Eckhardt et al. , 2019 ). However, service researchers have made only limited efforts to identify the key customer requirements for an optimal carsharing experience. On that basis, we can identify three key areas for future research.

First, while extensive research has focused on the customer, including customer behavior, drivers and barriers (especially in relation to access-based business models), few studies have addressed the motives of carsharing service providers. Different types of providers (e.g. pure service firms or automotive manufacturers) may have different reasons for venturing into carsharing, reflecting two broad and fundamentally distinct business models: car sales and leasing (which is managed primarily through third-party dealers) and carsharing. Future studies on service providers can also contribute to the development of balanced solutions based on better alignment of the service provider and customer requirements. Additionally, the perspective of car owners who rent their cars for P2P carsharing has been neglected by existing research ( Hazée et al. , 2020 ).

A second direction for further research relates to payment schemes. To promote maximum car utilization and customer satisfaction, some studies (e.g. Le Vine et al. , 2014 ; Molnar and de Almeida Correia, 2019 ) have proposed the introduction of reservation systems to eliminate the current first-come-first-served approach. However, while such systems might seem to address the challenge of maximum utilization, they may have an opposite effect on long-term customer satisfaction, as customers are more willing to pay for a guaranteed reservation than for virtual queuing ( Wu et al. , 2020a ). The different types of carsharing providers have experimented with various pricing and payment schemes, such as pay-per-use or P2P's dynamic pricing based on time and usage. Lovelock and Gummesson (2004) proposed that pricing access-based services should relate to units of time but also noted that pricing schemes may need to be modified according to circumstance. Dowling et al. 's (2020) analysis of how users choose between different payment plans showed that carsharing customers typically opt for pay-per-use favor flexibility but tend to underestimate their usage. In general, further research is warranted on perceived utility and willingness to pay, as this may yield solutions to issues of scalability and stakeholder goal alignment.

A final area of concern is that much of the existing research has focused on developed countries in Europe, North America and Asia (including China) while neglecting developing economies with lower purchasing power parity. These less developed countries (in Africa, Asia and Latin America) account for the greater part of the world's population and stand for essentially all world population growth. Many of these markets are undergoing rapid urbanization but often lack efficient public transport and suffer from traffic congestion and poor air quality. As Lovelock and Gummesson (2004) noted, people in developing countries often find ways of improving their prospects through innovative sharing of goods and services. While such practices may not be directly replicable, mobility solutions in these markets may provide valuable insights for improving overall quality of life. Future research should seek to identify the key requirements for progressing carsharing in different customer groups and how these can be implemented in markets with service systems or institutional arrangements that differ fundamentally from high-income economies.

4.3 Addressing the balance between supply and demand

This problem occurs mainly in schemes that operate one-way carsharing although it is also relevant to the challenges of vehicle balancing management in general. To date, research in this area has been largely confined to the fields of computer science, engineering and mathematics, focusing mainly on simulation models to assist vehicle balancing. We contend that service researchers can address the imbalance between vehicle supply and demand in three areas.

First, it is clear from the various trade-offs associated with different approaches to vehicle balancing that there is no quick fix for this problem in the context of one-way carsharing. Specifically, service providers must choose between increased costs, reducing the time spent relocating vehicles and reduced customer demand. Service researchers should examine ways of striking a better balance between fleet size/mix and fixed and variable costs by incorporating factors such as customer preferences and behavior.

Second, while a number of studies have advanced user-based vehicle relocation models or a combination of user-based and operator-based models as cost-saving alternatives for vehicle balancing, there is some skepticism about customers' willingness to participate in vehicle relocation, regardless of incentives offered ( Brendel et al. , 2020b ). The question of whether user-based relocation models offer a viable solution remains unanswered in the absence of empirical studies to test these claims. Further research should therefore include empirical studies of user-based vehicle relocation as an alternative or a complement to operator-based relocation, as well as the range of customer motivations and their effects on engagement, usage and loyalty.

Finally, despite growing research interest and ongoing development of new relocation models, some issues remain unresolved. For example, as some relocation models are context-specific and refer to parameters for the city in question, generalizability is limited (e.g. Boyaci et al. , 2015 ; Di Febbraro et al. , 2018 ). Nevertheless, any lessons learned from these context-specific models should be disseminated to enhance general understanding and to inform further research on vehicle balancing strategies. Additionally, the interdependence of supply and demand is a crucial issue for the design of carsharing schemes but is often neglected in vehicle relocation models ( Jian et al. , 2019 ). Finally, questions remain to be answered regarding the optimal inventory level at each carsharing station ( Laporte et al. , 2018 ).

4.4 The effect of carsharing on car shedding

One matter of longstanding debate is whether carsharing might reduce traffic congestion by encouraging car shedding. Dowling and Simpson (2013) predicted that people's relationship with their cars would change, and that young people in particular would increasingly favor carsharing. This resonates with industry forecasts of a cultural shift in which the car ceases to be an object of ownership and instead becomes part of a service or a network of vehicles for collective use. However, in a study examining the effects of free-floating carsharing on the purchase of new vehicles in cities, Schmidt (2020) found that the claim that one shared car eliminates roughly three car purchases annually does not apply to high status cars such as SUVs or larger vehicles like vans. Similarly, Li and Kamargianni's (2020) research on how carsharing affects private car ownership and public transport demand established that people who take long trips are less likely to give up their private car in favor of carsharing. As one may reasonably assume that P2P sharing is also a less attractive option in such cases, the debate around carsharing as a means of reducing car traffic remains unresolved.

Further research on this topic should address three issues. First, the conditions under which carsharing might encourage car shedding remain unclear, and further research is needed. Ikezoe et al. 's (2020) survey of car owners in Japan found that car owners tend to have an emotional attachment to their cars and would only abandon them if carsharing tapped into factors beyond economic rationality (such as convenience). To position access-based and platform-based carsharing services as an attractive alternative to car ownership, researchers should explore the nature and impact of these underlying factors.

A second research direction relates to the fact that customer willingness to forego car purchases after joining a carsharing scheme may be contingent on carsharing type ( Mishra et al. , 2015 ). For example, round-trip customers are more likely to abandon private ownership if they reduce that dependency by adopting other modes of mobility such as walking or biking. In contrast, one-way customers employ carsharing to replace modes such as taxi and ride hailing ( Lempert et al. , 2019 ). Further research should therefore strive for a deeper understanding of how the various carsharing models impact car shedding. Such insights would help to develop an understanding of service outcomes that can guide resource allocation in carsharing schemes.

Finally, more research is needed to specify the ideal conditions for the co-existence of carsharing and other transport modes such as public transport and biking, both as separate services and for multimodal mobility. Contrary to popular belief, carsharing alone may not suffice to encourage car shedding but is more likely to succeed if combined with other transport modes. In fact, access-based carsharing is often most successful when combined with other complementary transport options ( Csonka and Csiszár, 2016 ; Shaheen et al. , 1998 ). Future research should also explore how digital technology can be used to facilitate seamless multi-modal mobility services that align customer preferences and policy goals.

5. Conclusions and implications for practice

As evidenced by a growing number of publications over the last five years, carsharing has attracted significant attention from scholars, industry stakeholders and government organizations, reflecting ongoing challenges and the need for innovative mobility solutions. The aims of this literature review were to synthesize the extensive existing literature and to identify key areas for future research. Using a systematic approach, the review analyzed 279 studies published between 1996 and 2020, addressing issues that include existing business models, customer behavior, and user drivers and barriers, as well as the ongoing challenge of vehicle balancing. On that basis, we advanced a research agenda to address critical issues and to propose how existing challenges and opportunities can best be managed.

5.1 Managerial implications

The review identifies three main implications for carsharing service providers. First, managers need to understand the importance of building relationships with a wider range of stakeholders, especially when dealing with EV fleets. They must also understand the importance of customer and market factors; for example, while providers may be involved in the design of greenfield service systems from an early stage, initiatives in densely populated areas are different because parking space and charging infrastructure are scarce resources. Similarly, the requirements for P2P carsharing may differ significantly from B2C and B2B contexts. In every case, managers must understand customer needs, shape expectations and articulate potential benefits in terms of the appropriate service business model and ecosystem.

Second, managers must pursue innovations that will help to make carsharing attractive to the wider population; for example, Jian et al. (2020) suggested providing shared parking spaces for carsharing customers rather than simply providing carsharing services as is the current norm. This would enable carsharing service providers to make a profit by renting parking spaces from parking suppliers and then renting the same spaces to carsharing customers at a higher price. This would in turn deliver a higher utilization rate and a further increase in profits. Managers should also look to exploit ongoing technological advances to achieve synergies between business models, for instance, by leveraging customer data to reveal user patterns and so facilitate maximum utilization.

Finally, by targeting a broad base of customers through collaboration with other mobility providers (such as public transport), managers can identify key competences that can be utilized or synchronized to improve services. To that end, it will also be important to assess the potential market size in order to anticipate the impact of other mobility modes and to identify usage patterns that will optimize utilization rates and customer engagement.

5.2 Policy implications

For those making and implementing public policy, the present review has two main implications. First, given the potential of carsharing to resolve many pressing societal and environmental challenges such as traffic congestion and air pollution, such initiatives warrant the support of public and private stakeholders alike. However, policy interventions cannot simply support carsharing at the expense of public transport, nor can they enforce policies that simply make carsharing attractive to non-car owners; instead any such intervention must balance and direct provisions to maximize societal benefit. While service providers are often motivated by profit, regulatory bodies are driven by their duty to protect the interests of both users and providers ( Lindloff et al. , 2014 ) and must therefore base supports on assessments of local need and the common good. Rather than undermining support for public transport and funding, policy can make carsharing a more attractive alternative to car ownership through a combination of rewards and penalties, for example, lower taxes on revenues from P2P carsharing in combination with more restricted or more expensive public parking. Policies should not simply aim to make carsharing more attractive, as this may prove counterproductive by attracting non-car owners who would otherwise use public transport.

Secondly, to improve traffic conditions and pollution, more support should be provided for EV carsharing fleets, engaging actively with challenges such as the provision of optimized charging stations. In their study of the optimal use of EVs in carsharing, Abouee‐Mehrizi et al. (2021) identified three key factors: charging speed, charging station availability and battery range (in that order). Even if most carsharing users are not driving long distances, providers need to ensure that their fleets are charged – ideally, where they are parked. Automotive manufacturer Tesla owns and operates the largest global-charging network in the world (25,000+ Superchargers as of June 2021), but too much reliance on commercial actors may create brand-specific lock-in effects that inhibit growth and provision of the necessary infrastructure for local ventures. In short, public decision makers can play a more active role in facilitating the shift to electrification and shared mobility.

Literature search and selection process

Publications by topic and year

Most cited papers by theme

Main usage characteristics of carsharing customers

User-based and operator-based relocation models

Research agenda and managerial implications

Reviewed journals and research streams

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Acknowledgements

This research received financial support from the CarE-Service project (which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 776851), and the SE:Kond 2 LIFE project (2019-04463), which was funded by Sweden's innovation agency Vinnova.

Corresponding author

About the authors.

Brenda Nansubuga is a Ph.D. student in industrial marketing at Linköping University, Sweden, and her research interests include service-based business model innovation, with focus on the automotive sector.

Christian Kowalkowski is a Professor of Industrial Marketing at Linköping University and is affiliated with the Centre for Relationship Marketing and Service Management at Hanken School of Economics in Helsinki. Dr. Kowalkowski's research interests include service growth strategies, solutions marketing, service innovation and subscription business models. His work has been published in journals such as Journal of Service Research, Journal of Service Management, Journal of Service Theory and Practice, Service Industries Journal, Industrial Marketing Management and Journal of Business Research . He is the servitization editor for the Journal of Service Management , associate editor of the Journal of Services Marketing and advisory board member of Industrial Marketing Management .

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Purpose-This study applies bibliometric analysis to explore the evolution of the research paradigm of agility related to management and organisations. Design/methodology/approach-Authors prepared a quantitative study of the review of selected articles using co-citation analysis and bibliographic coupling. Based on the bibliometric analyses, the evolution of the agility field (past, present, and future of agility research) was prepared. Findings-Emergent themes focus on the importance of agility in interpreting organisational responses in the context of issues as diverse as information systems and business intelligence systems, market orientation, strategic alignment and social computing. Future research needs to focus on digitisation in conjunction with informatisation, an important topic for creating a new organisational culture and knowledge management through increased collaboration between humans and machines. Originality/value-As the authors are aware, this study is one of the first to choose to show the overall development and importance of agility through quantitative bibliometric methods used to assess the value and contribution of scientific productivity and its impact on development.

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Forecasting e-commerce consumer returns: a systematic literature review

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• Published: 21 May 2024

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• David Karl   ORCID: orcid.org/0000-0002-0326-5982 1  

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The substantial growth of e-commerce during the last years has led to a surge in consumer returns. Recently, research interest in consumer returns has grown steadily. The availability of vast customer data and advancements in machine learning opened up new avenues for returns forecasting. However, existing reviews predominantly took a broader perspective, focussing on reverse logistics and closed-loop supply chain management aspects. This paper addresses this gap by reviewing the state of research on returns forecasting in the realms of e-commerce. Methodologically, a systematic literature review was conducted, analyzing 25 relevant publications regarding methodology, required or employed data, significant predictors, and forecasting techniques, classifying them into several publication streams according to the papers’ main scope. Besides extending a taxonomy for machine learning in e-commerce, this review outlines avenues for future research. This comprehensive literature review contributes to several disciplines, from information systems to operations management and marketing research, and is the first to explore returns forecasting issues specifically from the e-commerce perspective.

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1 Introduction

E-commerce has witnessed substantial growth rates in recent years and continues growing by double-digit margins (National Retail Federation/Appriss Retail 2023 ). However, lenient consumer return policies have resulted in $212 Billion worth of merchandise being returned to online retailers in the U.S. in 2022, accounting for 16.5% of online sales (National Retail Federation/Appriss Retail 2023 ). While high rates of consumer returns mainly concern specific sectors and product categories, online fashion retailing is particularly affected (Diggins et al. 2016 ). Recent studies report average shipment-related return rates for fashion retailers in the 40–50% range (Difrancesco et al. 2018 ; Karl and Asdecker 2021 ). In addition to missed sales and reduced profits (Zhao et al. 2020 ), consumer returns pose operational challenges (Stock and Mulki 2009 ), including unavoidable processing costs (Asdecker 2015 ) and uncertainties regarding logistics capacities, inventory management, procurement decisions, and marketing activities. Hence, effectively managing consumer returns is an essential part of the e-commerce business model (Urbanke et al. 2015 ).

Similar to the research conducted by Abdulla et al. ( 2019 ), this work focuses on consumer returns in online retailing (e-commerce), excluding the larger body of closed-loop supply chain (CLSC) management, which encompasses product returns related to end-of-life and end-of-use scenarios involving raw material recycling or remanufacturing. In contrast to CLSC returns, retail consumer returns are typically sent or given back unused or undamaged shortly after purchase, without any quality-related defects. These returns should be reimbursed to the consumer and are intended to be resold “as new” (de Brito et al. 2005 ; Melacini et al. 2018 ; Shang et al. 2020 ).

Regarding forecasting aspects, demand forecasting is a crucial activity for successful retail management (Ge et al. 2019 ). In contrast to demand and sales, returns constitute the “supply” side of the return process (Frei et al. 2022 ). Consequently, forecasting becomes a complex task and a significant challenge in managing returns due to the inherently uncertain nature of customer decisions regarding product retention (Frei et al. 2022 ). Moreover, return forecasts are interconnected with sales forecasts and promotional activities (Govindan and Bouzon 2018 ; Tibben-Lembke and Rogers 2002 ). Hence, forecasting objectives may vary, encompassing return quantities, timing (Hachimi et al. 2018 ), and even individual return probabilities. Minimizing return forecast errors is critical to reduce and minimize reactive planning (Hess and Mayhew 1997 ). Accurate forecasts rely on (1) comprehensive data collection, e.g., regarding consumer behavior, and (2) information and communications technology (ICT) for data processing, such as big data analytics. Despite extensive research in supply chain management (SCM), Barbosa et al. ( 2018 ) noted a lack of relevant publications exploring the "returns management" process of SCM in conjunction with big data analytics. Specifically, “the topic of forecasting consumer returns has received little attention in the academic literature” (Shang et al. 2020 ). Nonetheless, precise return forecasts positively impact reverse logistics activities’ economic, environmental, and social performance, primarily concerning quantity, quality, and timing predictions (Agrawal and Singh 2020 ). Hence, forecasting returns holds significant relevance across various supply chain stages.

1.1 Previous meta-research

Hess and Mayhew ( 1997 ) emphasized the need for extensive data analysis concerning reverse flows, which forms the basis for returns forecasting. Subsequently, research on consumer returns and reverse logistics has proliferated. Thus, before collecting data and reviewing the topic of consumer returns forecasting, we first examined existing reviews and meta-studies relevant to the subject matter. To accomplish this, we referred to Web of Science, Business Source Ultimate via EBSCOhost, JSTOR and the AIS Electronic Library as primary sources of knowledge (search term: "literature review" AND "return*" AND "forecast*”). As a secondary source, we appended the results of Google Scholar, Footnote 1 for which a different search term was used (intitle:"literature review" ("product return" OR "consumer return" OR "retail return" OR "e-commerce return") forecast) due to unavailable truncations and to reduce the vast amount of literature with financial focus the search term “return” would lead to. Table 1 presents the most pertinent literature reviews related to the scope of this paper.

Agrawal et al. ( 2015 ) identified research gaps within the realm of reverse logistics, finding “forecasting product returns” as a crucial future research path. However, among 21 papers focusing on “forecasting models for product returns”, the emphasis was predominantly on CLSC, reuse, remanufacturing, and recycling, which do not align with the aim of this review. Agrawal et al. also noted a lack of comprehensive analysis of underlying factors in returns forecasting, such as demographics or consumer behavior.

Similarly, Hachimi et al. ( 2018 ) addressed forecasting challenges within the broader context of reverse logistics. They classified their literature using various forecasting approaches: time series and machine learning, operations research methods, and simulation programs. The research gaps they identified included a limited number of influencing factors taken into account, the absence of established performance indicators, and methodological issues related to dynamic lot-sizing with returns. Although this review focused on reverse logistics, the call for research into predictors of future returns is equally applicable to consumer returns in e-commerce.

The review of Abdulla et al. ( 2019 ) centers on consumer returns within the retail context, particularly in relation to return policies. While they discuss consumer behavior and planning and execution of returns, they do not present any sources explicitly focused on forecasting issues.

Micol Policarpo et al. ( 2021 ) reviewed the literature on the use of machine learning (ML) in e-commerce, encompassing common goals of e-commerce studies (e.g., purchase prediction, repurchase prediction, and product return prediction) and the ML techniques suitable for supporting these goals. Their primary contribution is a novel taxonomy of machine learning in e-commerce, covering most of the identified goals. However, within the taxonomy developed, the aspect of return predictions is disregarded.

The most exhaustive literature review to date regarding product returns, conducted by Ambilkar et al. ( 2021 ), analyzed 518 papers and adopted a holistic reverse logistics approach encompassing all supply chain stages. The authors categorized the papers into six categories, including “forecasting product returns”, for which they found and concisely described 13 papers. Due to the broader research scope, none of the analyzed papers focused on consumer returns within the retail context.

The review by Duong et al. ( 2022 ) employed a hybrid approach combining machine learning and bibliometric analysis. Regarding forecasts of product returns, they identified three relevant papers (Clottey and Benton 2014 ; Cui et al. 2020 ; Shang et al. 2020 ) within the “operations management” category. They explicitly call for further research on predicting customer returns behavior in the pre-purchase stage, highlighting the importance of a better understanding of online product reviews and customers’ online interactions.

1.2 Research gaps and research questions

Why is a systematic literature review necessary for investigating consumer returns and forecasting? On the one hand, there are empirical and conceptual papers that touch upon this topic, including brief literature reviews that align with the subject’s focus (e.g., Hofmann et al. 2020 ). However, narrative reviews lack transparency and replicability (Tranfield et al. 2003 ) and often induce selection bias (Srivastava and Srivastava 2006 ) as they tend to approach a field from a specific perspective. In contrast, systematic reviews strive to present a holistic, differentiated, and more detailed picture, incorporating the complete available literature (Uman 2011 ). On the other hand, existing systematic reviews provide structured yet relatively superficial overviews of literature on end-of-use and end-of-life forecasting (Shang et al. 2020 ), but they do not specifically address consumer returns. Furthermore, we contend that a review dedicated to general reverse logistics forecasting would not adequately capture the distinctive context and requirements inherent in the consumer-retailer relationship within the realm of e-commerce (Abdulla et al. 2019 ).

Consequently, based on existing reviews and papers, we have identified research gaps worth examining more in detail: (1) Returns forecasting techniques and relevant predictors for the respective underlying purposes, especially in the context of e-commerce (RQ1 and RQ2); (2) the integration of return forecasts into an existing but incomplete taxonomy of machine learning in e-commerce (Micol Policarpo et al. 2021 ; RQ3); and (3) future research directions pertaining to e-commerce returns forecasting (RQ4). Therefore, this review aims to shed more light on consumer returns forecasting in the retail context. The following research questions outline the primary objectives:

RQ1: What key research problems (e.g., forecasting purposes, technological approaches) have been addressed in the literature on forecasting consumer returns over time?

RQ2: What are the …

Publication outlets and research disciplines,

Research types and methodologies,

Product categories and industries,

Data sources and characteristics,

Relevant forecasting predictors,

Techniques and algorithms

… used to address these key problems?

RQ3: How can returns forecasting be integrated into a taxonomy of machine learning in e-commerce?

RQ4: What are promising or emerging future research directions regarding forecasting consumer returns?

The paper is organized as follows: Sect.  2 describes selected fundamental concepts and the delimitation of the research field on consumer returns forecasting. Section  3 contains the methodology for the review, drawing on the PRISMA guideline (Page et al. 2021 ) while integrating the approaches of Denyer and Tranfield ( 2009 ) and Webster and Watson ( 2002 ). Section  4 presents the review’s main results, answering RQs 1 (Sect.  4.1 ), RQ2 (Sects.  4.2 – 4.5 ), and RQ 3 (Sect.  4.6 ). A research framework developed in Sect.  5 structures the discussion regarding future research directions (RQ4). Section  6 subsumes the overall contribution of this review.

2 Consumer returns and forecasting

2.1 consumer returns and return reasons.

Reverse product flows, commonly referred to as product returns, can be classified into three categories: manufacturing returns, distribution returns, and consumer returns (Shaharudin et al. 2015 ; Tibben-Lembke and Rogers 2002 ). Among these, consumer returns are further differentiated between returns in brick-and-mortar retail or mail-order/e-commerce returns (Tibben-Lembke and Rogers 2002 ) and are also known as commercial returns (de Brito et al. 2005 ) or retail (product) returns (Bernon et al. 2016 ). With sky-rocketing e-commerce sales, online consumer returns have emerged as the dominant segment, making them a highly relevant field of research (Abdulla et al. 2019 ; Frei et al. 2020 ). Additionally, the digitization of retail provides numerous opportunities for data collection, as digital customer accounts facilitate more efficient analytical monitoring of customer behavior (Akter and Wamba 2016 ). Simultaneously, as competitive pressures intensify in e-commerce due to increased price transparency and substitution possibilites, retailers aiming to stimulate impulse purchases face hightened return rates (Cook and Yurchisin 2017 ; Karl et al. 2022 ).

The spatial decoupling of supply and demand introduces a higher level of uncertainty for e-commerce customers regarding various product attributes compared to bricks-and-mortar retailing (Hong and Pavlou 2014 ). As consumers are unable to physically assess the products they order, this translates into returns being essential part of the e-commerce business model. Besides fit uncertainty, other reasons for returns exist. Stöcker et al. ( 2021 ) classify the drivers triggering consumer returns into consumer behavior related reasons (e.g., impulsive purchases, showrooming), fulfillment/service related reasons (e.g., wrong/delayed delivery) and information gap related reasons (product fit, insufficient visualization). By mitigating customers’ return reasons, retailers try to reduce the return likelihood (“return avoidance”) (Rogers et al. 2002 ). Another, but less promising way of reducing returns, is preventing customers who intend to return from actually doing so (e.g., by incurring additional effort or by rejecting returns) (Rogers et al. 2002 ).

Adapted from Abdulla et al. ( 2019 ) and Vakulenko et al. ( 2019 ), a simplified parallel process of a return transaction from the consumer’s and retailer’s perspective is visualized in Fig.  1 . Retailers can use forecasting in all transaction phases (Hess and Mayhew 1997 ). Targeting customer interventions pre-purchase (real-time forecasting) could be implemented by using dynamically generated (Dalecke and Karlsen 2020 ) digital nudging elements (Kaiser 2018 ; Thaler and Sunstein 2009 ; Zahn et al. 2022 ) in case of a predicted high return propensity. In the post-purchase phase, forecasting could stimulate different interventions (e.g., customer support) or can be helpful for logistics and inventory planning activities (Hess and Mayhew 1997 ). In the phase after the return decision, data analysis, including segmentation on different levels, e.g., for customers, products, or brands (Shang et al. 2020 ), can support managerial decision-making regarding assortment or (individualized) return policies for future orders (Abdulla et al. 2019 ). In other words, forecasting (or modeling) of returns in later phases of the process can substantiate interventions in earlier phases of the process (e.g., a temporary return policy change, or the suspension of product promotions due to particular forecasts). However, such data-driven interventions itself also represent an influencing factor to be taken into account in future forecasts; thus, different forecasting purposes can be linked, at least when it comes to the data required. All these interdependencies hint at the circularity of the returns process, with an adequate management of returns representing an opportunity for generating customer satisfaction and retention (Ahsan and Rahman 2016 ; Röllecke et al. 2018 ).

Purchase and return process concerning forecasting issues (adapted from Abdulla et al. 2019 ; Vakulenko et al. 2019 )

Although primarily focussing on the online retailers’ process, it is worth noting that the issue at hand is equally applicable to brick-and-mortar retail (Santoro et al. 2019 ), which can benefit from the application of advanced data analysis techniques for forecasting purposes (Hess and Mayhew 1997 ).

2.2 Forecasting purposes and corresponding techniques

Accurate forecasting holds significant importance in the realm of e-commerce. Precise demand forecasts (“predictions”) play a pivotal role in inventory planning, pricing, and promotions and ultimately impact the commercial success of retailers (Ren et al. 2020 ). Forecasting consumer returns affects similar business aspects and resorts to comparable existing technical procedures. The data science and statistics literature offers diverse methods and algorithms for forecasting consumer returns. The choice of approach depends on the specific objective, with the outcome variable being scaled accordingly. For instance, when forecasting whether a single product will be returned, the dependent variable is either binary or expressed as a propensity value ranging form 0 to 1. On the other hand, forecasting the quantitay or timing of returns entails continuous outcome variables. As a result, various techniques, from time-series forecasting to machine learning approaches can be applied, which will be briefly outlined in the subsequent sections.

2.2.1 Return classifications and propensities

A naïve method for determining the propensity or return decision forecast is using lagged (historical) return information (return rates), either for a given product, a given customer, or any other reference, to calculate a historical return probability (Hess and Mayhew 1997 ). Return rate forecasts are a reference-specific variant of forecasting return propensities.

Simple causal models based on statistical regression methods utilize one or more independent exogenous variables. The logistic regression (logit model) is employed when the dependent variable is binary or contains more nominal outcomes (multinomial logistic regression). For each observation, the binary logistic regression assesses the probability that the dependent variable takes the value “1” (Hastie et al. 2017 ). Consequently, this approach finds application for return decisions and return propensities. Comparatively, linear discriminant analysis (Fisher 1936 ) bears a resemblance to logistic regression by generating a linear combination of independent variables to best classify available data. This classification process involves determining a score for each observation, subsequently compared to a critical discriminant score threshold, and distinguishing between return and keep.

More sophisticated machine learning (ML) techniques such as neural networks, decision tree-based methods, ensemble learning, and boosting methods are highly suitable for this forecasting purpose. For a general exposition of ML techniques in the domain of e-commerce, we refer to Micol Policarpo et al. ( 2021 ). Additionally, for a comparative study of several state-of-the-art ML classification techniques, see Fernández-Delgado et al. ( 2014 ). Artificial Neural Networks (NN) consist of interconnected nodes (“neurons”) organized in layers, exchanging signals to ascertain a function that accurately assigns input data to corresponding outputs. Typically, supervised learning techniques such as backpropagation compare the network outputs with known actual values (Hastie et al. 2017 ). Notably, neural networks are the most popular machine learning algorithm in last years’ e-commerce research (Micol Policarpo et al. 2021 ), and deep learning extensions like Long Short-Term Memory (Bandara et al. 2019 ) are gaining attention. Decision Trees (DT) manifest as hierarchical structures of branches representing conjunctions of specific characteristics and leaf nodes denoting class labels. This approach endeavors to construct an optimal decision tree for classifying available observations. Many decision tree algorithms have been introduced to serve this purpose (e.g., Breiman et al. 1984 ; Pandya and Pandya 2015 ). Ensemble learning methods adopt a voting mechanism involving multiple algorithms to enhance predictive performance (Polikar 2006 ). Analogously, boosting and bagging techniques are incorporated in algorithms like AdaBoost or the tree-based Random Forest (RF) to augment the input data, aiming at more generalizable forecasting models less prone to overfitting issues (Hastie et al. 2017 ). Support Vector Machines (SVM) stand as another example of a supervised ML algorithm, having demonstrated efficacy in tackling classification problems within e-commerce (Micol Policarpo et al. 2021 ).

2.2.2 Return timing and volume forecasts

For product returns, timing is crucial in forecasting end-of-life, end-of-use, or remanufacturing returns that can occur years after the initial purchase (Petropoulos et al. 2022 ). In contrast, for consumer returns, the possible time window in which products are regularly returned in new condition with the aim of a refund is much shorter (usually less than 100 days and mostly less than 30 days), and priorities are more on forecasting return volumes. Forecasting return volumes can be multi-faceted, ranging from forecasting the total return volume a retailer has to process within its logistics department through forecasting product-specific return numbers up to forecasting costly return shares, e.g., return fraud volume. Because returns depend on fluctuating sales, time-series forecasting of return volumes performs only well with constant sales volumes or under risk-pooling (Petropoulos et al. 2022 ). Thus, for a naïve return volume forecast, sales forecasts for a given timeframe are multiplied by the lagged return rate (historical data of products/consumers or any other reference). Possible algorithms for estimating historical return rates include time series forecasting to causal predictions comprising ML approaches (Hachimi et al. 2018 ).

Time-series techniques, e.g., single exponential smoothing (SES) or Holt-Winters-approaches (HW), are based on the assumption that the future development of an outcome variable (e.g., return volume) is dependent on its past numbers, while time acts as the only predictor. Most of these models can be generalized as autoregressive moving averages (ARIMA) models, for which numerous extensions are available. These models can approximate more complex temporal relationships. Similarly, time-series regression models use univariate linear regression with time as a single exogenous variable.

The mentioned multivariate regression models are essential statistical tools and can predict metric variables such as return volume or time. The logic is to fit a linear function of a given set of input variables (“features”) to the outcome variable with the criteria of minimizing the residual sum of squares (Hastie et al. 2017 ). Many variants of regression models are derived from this logic (e.g., generalized linear models), and various extensions are built upon this base (e.g., LASSO for variable selection, Tibshirani 1996 ).

Emerging from more complex statistical methods and using the possibilities of continuously increasing computing power, IT-based machine learning (ML) approaches were developed. Some of these approaches have already been presented in Sect. 2.2.1, being suitable for predicting metric variables in addition to classification tasks, e.g., neural networks, decision tree algorithms, and especially ensemble techniques like random forests.

3 Methodology

Methodologically, the research process of this review follows the PRISMA guideline (Page et al. 2021 ) where applicable and is structured in five steps (Denyer and Tranfield 2009 ; Webster and Watson 2002 ): (1) question formulation; (2) locating studies; (3) study selection and evaluation; (4) (concept-centric) analysis and synthesis; and (5) reporting and using the results for defining an agenda for future research.

The first step refers to the research questions already formulated in the introduction. The second step involves selecting the databases and defining the search terms. In that respect, five scientific databases were selected, aiming at journal as well as conference publications: AIS Electronic Library (AISeL), Business Source Ultimate (BS) via EbscoHost, JSTOR (JS), Science Direct (SD), and Web of Science (WoS). To ensure inclusivity and to account for potential variations in spelling or phrasing, the final search strings incorporate truncations where applicable. The search query utilized in this review comprises two key components. Firstly, it pertains to consumer returns, encompassing products returned by consumers, primarily in the context of e-commerce, to the retailer. While it is recommended to use reasonably general search terms, the term “return” alone would yield results for various stages of reverse logistics and a vast amount of financial literature. Therefore, we conducted a more specific search using the phrase “consumer return*” and the related terms “e-commerce return*”, “product return*”, “return* product”, “customer return*”, and “retail return*”. Secondly, this paper specifically focuses on forecasting (“forecast*”), which can be alternately referred to as “predict*” or “prognos*”. The combination of these terms was searched for in the Title, Abstract and Keywords fields.

The search includes results up to the middle of 2022 and resulted in 725 initial search hits (see Fig.  2 ). As this review aims to identify papers dealing with consumer returns and forecasting, the inclusion criteria for eligibility were:

The title or keywords referred to consumer returns or forecasting (in a broader sense, including data preparation). A connection to the respective subject area and applicability to the retail domain should at least be plausible.

Manuscript in English: No important study would be written and published in a language different than English.

The paper has undergone a single- or double-blind peer-review process, either as a journal publication or as a publication in peer-reviewed conference proceedings.

Research process flow diagram

In the third step, duplicates were removed, resulting in a set of 650 unique records. Subsequently, the papers underwent screening based on title, keywords, and language to determine whether they warranted further examination. This preliminary screening phase reduced the number of papers to 85. These papers’ abstracts and full texts were thoroughly reviewed to assess their relevance. This step encompasses all papers pertaining to returns forecasting for retailers or direct-selling manufacturers while excluding those focused on closed-loop supply chain management or remanufacturing, recycling, and end-of-life returns. Ultimately, a final sample of 20 publications was identified, serving as a foundation for identifying additional relevant papers (vom Brocke et al. 2009 ; Webster and Watson 2002 ) through a forward search using Google Scholar and snowballing via backward search. This process yielded an additional five papers, resulting in a total of 25 papers included for review (Table  2 ).

The fourth step comprises the analysis and synthesis of the relevant papers. Data, including bibliographic statistics, were collected in accordance with the research questions. A two-way concept-centric analysis, as described by Webster and Watson ( 2002 ), was conducted, encompassing confirmatory aspects based on the fundamentals outlined in Sect.  2 of this paper, as well as exploratory elements aimed at enriching existing categories and concepts. The objective was to comprehensively describe the relevant concepts, approaches, and dimensions discussed in the literature.

Moving on to the fifth and final step (Denyer and Tranfield 2009 ), the results are presented. Initially, the main scope of the papers included in the analysis is presented. Next, bibliographic data pertaining to the included papers are provided to offer a concise overview of the research area and its recent developments, followed by a content analysis and synthesis of the relevant literature to delve into the current state of research and highlight key findings. Finally, Sect.  5 outlines a research agenda for the domain (vom Brocke et al. 2009 ).

4 Results of the systematic review

After outlining the main scope of the relevant publications (4.1), a short bibliographic characterization (4.2) is given. Next, this section presents the results of the systematic review, focussing on the methodology and datasets used (4.3), predictors used for returns forecasting (4.4), and forecasting techniques employed (4.5). The integration of consumer returns forecasting into an existing taxonomy for e-commerce and machine learning (Micol Policarpo et al. 2021 ) summarizes and concludes the presentation of the results.

4.1 Overview and main scope of the relevant publications

Table 3 provides an overview of the forecasting purpose of the papers, the data source for the forecasting, the algorithms employed, and the predictors used in the forecasting models. The contributions of the respective papers regarding forecasting issues are summarized in the Appendix.

For identifying research streams, the publications are analyzed regarding the intention and main scope, as described in the abstract, the respective research questions, and the remainder of the papers. Most papers were assigned to an unequivocal research scope, while some contributed to two key topics (Fig.  3 ).

Classification of main scopes (n = 25; not mutually exclusive)

At first, we identified a stream of literature regarding the comparison of different forecasting models and algorithms (Asdecker and Karl 2018 ; Cui et al. 2020 ; Drechsler and Lasch 2015 ; Heilig et al. 2016 ; Hess and Mayhew 1997 ; Hofmann et al. 2020 ; Imran and Amin 2020 ). These papers use existing approaches, adapt them for individual forecasting purposes, apply models to one or more datasets, and compare and evaluate the resulting forecasting performance. One paper claims that the difference in forecasting accuracy of easily interpretable algorithms is relatively small compared to more sophisticated ML algorithms (Asdecker and Karl 2018 ). This statement is partially confirmed (Cui et al. 2020 ), as the ML algorithms show advantages over simpler models in the training data set but have lower prediction quality due to overfitting issues in the test data. Nevertheless, fine-tuned ML approaches (e.g., deep learning with TabNet) outperform simpler models and gain accuracy when correcting class imbalances during the data preparation phase (Imran and Amin 2020 ). When confronted with large class imbalances (e.g., low return rates), boosting algorithms like Gradient Boosting work well without oversampling (Hofmann et al. 2020 ). Fundamentally, ensemble models incorporating different techniques show the maximum possible accuracy (Asdecker and Karl 2018 ; Heilig et al. 2016 ). Forecasting of return timing is more erroneous than return decisions, and split-hazard-models outperform simple OLS approaches (Hess and Mayhew 1997 ). Time series prediction only works reliably when return rates do not fluctuate heavily (Drechsler and Lasch 2015 ).

The second stream we identified focuses on feature generation or selection and dataset preparation (Ahmed et al. 2016 ; Ding et al. 2016 ; Hofmann et al. 2020 ; Rezaei et al. 2021 ; Samorani et al. 2016 ; Urbanke et al. 2015 , 2017 ). Besides this central topic, some papers also compare different forecasting algorithms (Ahmed et al. 2016 ; Hofmann et al. 2020 ; Rezaei et al. 2021 ; Urbanke et al. 2015 , 2017 ). For example, random oversampling of data with large class imbalances can improve the performance of different forecasting algorithms, while models based only on sales/return history perform worse than models with more features (Hofmann et al. 2020 ). Two similar approaches are based on product, basket, and clickstream data, using different algorithms for feature extraction (Urbanke et al. 2015 , 2017 ). The first developed a Mahalanobis Feature Extraction algorithm, proving superior to other algorithms like principal component analysis or non-negative matrix factorization (Urbanke et al. 2015 ). The second develops a NeuralNet algorithm to extract interpretable features from a high-dimensional dataset, showing superior performance and giving reasonable interpretability of the most important factors (Urbanke et al. 2017 ). For the automated integration of different data sources into single flat tables and the generation of discriminating features, a rolling-path algorithm is developed, improving performance when data is imbalanced (Ahmed et al. 2016 ). Similarly, the software “Dataconda” can automatically generate and integrate relational attributes from different sources into a flat table, which is often the required prerequisite for forecasting algorithms (Samorani et al. 2016 ). A different selection approach clusters the features into groups and applies selection algorithms to the groups, aiming to select a smaller set of attributes (Rezaei et al. 2021 ). As quite an offshoot, one paper predicts a seller’s overall daily return volume dependent on his current “reputation” measured by tweets (Ding et al. 2016 ), which needs sentiment analysis to be integrated into the forecast.

A quite heterogenous research stream belongs to the development of algorithms, heuristics, and models that go beyond a straightforward adaption of existing approaches (Fu et al. 2016 ; Joshi et al. 2018 ; Li et al. 2018 ; Potdar and Rogers 2012 ; Rajasekaran and Priyadarshini 2021 ; Shang et al. 2020 ; Sweidan et al. 2020 ; Zhu et al. 2018 ). Potdar and Rogers ( 2012 ) developed a methodology for forecasting product returns based on reason codes and consumer behavior data. Fu et al. ( 2016 ) developed a conditional probability-based statistical model for predicting return propensities while revealing return reasons and outperforming some baseline benchmark models. Li et al. ( 2018 ) describe their “HyperGo” approach as a ‘framework’ and develop an algorithm for forecasting return intention after basket composition. Zhu et al. ( 2018 ) describe a “LoGraph” random walk algorithm for predicting returned customer/product combinations within their framework. Although Joshi et al. ( 2018 ) label their approach as a “framework”, they describe a specific two-stage algorithm for forecasting return decisions based on network science and ML. Rajasekaran and Priyadarshini ( 2021 ) developed a hybrid metaheuristic-based regression approach to predict return propensities.

Seven papers deal with concepts, meta-models, or substantial frameworks for returns forecasting (Fu et al. 2016 ; Fuchs and Lutz 2021 ; Heilig et al. 2016 ; Hofmann et al. 2020 ; Li et al. 2018 ; Shang et al. 2020 ; Zhu et al. 2018 ). A generic framework for a scalable cloud-based platform, which enables a vertical and horizontal adjustment of resources, could enable the practical real-time use of computationally intensive ML algorithms for forecasting returns in an e-commerce platform (Heilig et al. 2016 ). Two papers (Fuchs and Lutz 2021 ; Hofmann et al. 2020 ) are based on design science research (DSR, Hevner et al. 2004 ) for developing artifacts like meta models and frameworks. The first also refers to CRISP-DM, the “Cross Industry Standard Process for Data Mining” (Wirth and Hipp 2000 ), and develops a shopping-basket-based general forecasting approach suitable across different industries without domain knowledge and attributes needed (Hofmann et al. 2020 ). In a similar approach, based on the basket composition and user interactions, a generic model for real-time return prediction and intervention is developed (Fuchs and Lutz 2021 ) and prepared for integration into an ERP system. Fu et al. ( 2016 ) present a generalized return propensity latent model framework by decomposing returns into different inconsistencies (unmet product expectations, shipping issues, and both factors combined) and enriching the derived propensities with product features and customer profiles. Li et al. ( 2018 ) developed a “HyperGo” framework for forecasting the return intention in real-time after basket composition, including a hypergraph representation of historical purchase and return information. Similarly, Zhu et al. ( 2018 ) developed a “HyGraph” representation of historical customer behavior and customer/product similarity, combined with a “LoGraph” random-walk-based algorithm for predicting customer/product combinations that will be returned. Shang et al. ( 2020 ) discuss two opposing forecasting concepts, demonstrating that their predict-aggregate framework is superior to common and more naïve aggregate-predict approaches.

The last stream covers the detection and forecasting of return fraud and abuse (Drechsler and Lasch 2015 ; John et al. 2020 ; Ketzenberg et al. 2020 ; Li et al. 2019 ). On the employees’ side, one paper tries to automatically predict fraudulent return behavior of agents (employees), e.g., regarding unjustified refunds, by a penalized logit model, enabling a lift in detection (John et al. 2020 ). On the customers’ side, misused returns as a cost-incurring problem are the forecasting purpose of different time series prediction models (Drechsler and Lasch 2015 ). Instead of focussing on fraudulent transactions, a trust-aware random walk model identifies consumer anomalies, enabling retailers to apply targeted measures to specific customer groups (selfish, honest, fraud, and irrelevant customers) (Li et al. 2019 ). Similarly, returning customers can be categorized into abusive, legitimate, and nonreturners (Ketzenberg et al. 2020 ). Based on the characterization of abusive return behavior, a neural network classifier recaptures almost 50% of lost profits due to return abuse (Ketzenberg et al. 2020 ).

One paper (Sweidan et al. 2020 ) could not be assigned to the other scopes. It applies a single algorithm (RF) to a given dataset, and it contributes to the idea that only forecasted return decisions with high confidence should be used for targeted interventions due to their overproportional reliability.

4.2 Bibliographic literature analysis

Forecasting consumer returns has gained more research attention since 2016 (Fig.  4 ). The majority of the sample are conference publications, a couple of years ahead of the rise in journal publications. Compared to the publications on returns forecasting in the broader context of reverse logistics, which emerged in 2006 (Agrawal et al. 2015 ), the research on consumer returns moved into the spotlight about ten years later. This development is linked to a massive increase in e-commerce sales pre- and in-pandemic (Alfonso et al. 2021 ).

Publication trend by publication outlet

Out of 9 journal publications in the final sample, only two are published in the same journal (Journal of Operations Management). Out of 16 conference papers, 6 are published at conferences of the Association for Information Systems. In total, 16 of the 25 papers found are published in Information Systems (IS) and related outlets. Others can be assigned to the Management Science / Operations Research discipline (3), Strategy & Management in a broader sense (4), Marketing (1), and Research Methods (1) (Fig.  5 ).

Distribution of publication disciplines

Regarding the researchers’ geographical perspective, one paper was jointly published by authors from the US and China, 10 of 25 papers were authored from North America, followed by authors from Germany (7), India (3), China (1), and one paper each from Bangladesh, Singapore, and Sweden.

The most cited paper (200 external citations Footnote 2 ) from Hess and Mayhew ( 1997 ) could be thought of as the root of this research field (Table  4 ). However, only 10 out of 24 papers reference this work. Although Urbanke et al. ( 2015 ) received only 15 citations in total, within the sample, it is the second most cited paper (8 citations) and could eventually be classified as a research strand and origin of returns forecasting in the IS domain. Concerning the remaining papers, no unique strands of literature are recognizable based on citation analysis.

4.3 Methodology and data characterization

Regarding methodology, most of the papers start with a short narrative literature review regarding their respective focus. Not a single paper was based on interviews, surveys, questionnaires, or field experiments. 3 out of 25 papers formulated and tested conventional hypotheses. All of the publications use quantitative data for analysis and forecasting in a “case study” style, including numerical experiments based on real or simulated data.

Table 5 lists further details about the data used in the publications. 4 out of 25 papers rely on simulated data, and 23 out of 25 integrate actual data gained from a retailer. Two papers use both data types. 5 papers use more than one dataset (Ahmed et al. 2016 ; Cui et al. 2020 ; Rezaei et al. 2021 ; Samorani et al. 2016 ; Shang et al. 2020 ). The most frequently studied industry is fashion/apparel (10 papers), followed by five consumer electronics datasets. Two publications are based on data from a Taobao cosmetics retailer, and two datasets originate from general and wide assortment retailers. Two datasets incorporate building material and hardware store articles, and the detailed products are not named for three publications. Based on the previous studies, it is evident that consumer returns forecasting is most relevant for e-commerce, as 19 of the 25 publications refer to e-tailers. Nevertheless, 7 publications refer to brick-and-mortar retailing. Direct selling/marketing is represented in 2 data sets.

4.4 Predictors for consumer returns

There is an individual stream of research into factors that influence or help avoid consumer returns (e.g., Asdecker et al. 2017 ; De et al. 2013 ; Walsh and Möhring 2017 ), which is not part of this review. Nevertheless, the forecasting literature gives insights into return drivers, as the input variables (features, predictors, exogenous variables) for forecasting models represent some of these factors. Table 6 presents the most used predictors and tries to map these to the return driver categorization from Sect.  2.2 (Stöcker et al. 2021 ).

Although only a part of the publications interprets the predictors, some insights can be extracted. For total return volume , sales volume is the most critical predictor (Cui et al. 2020 ; Shang et al. 2020 ). Historical return volume trends can include behavioral aspects (e.g., impulse purchases) in a given timeframe (Cui et al. 2020 ; Shang et al. 2020 ). The product type significantly impacts the volume of returns (Cui et al. 2020 ), confirmed by widely varying return rates between different industries/sectors. Adding transaction-, customer-, or product-level predictors led to a surprisingly small forecasting accuracy gain (4% reduction of RMSE, Shang et al. 2020 ). The latter input variables may be more critical in forecasting return decisions and propensities.

Regarding product attributes , product or order price is one of the most common predictors, while some papers also include price discounts. In most models, price is hypothesized to increase returns (e.g., Asdecker and Karl 2018 ; Hess and Mayhew 1997 ). Promotional (discounted) orders also seem to result in more returns (Imran and Amin 2020 ), which could be explained by the stimulation of impulse purchases. Footnote 3 Brand perception influences return decisions (positive brands, lower returns) (Samorani et al. 2016 ). The order and return history of products are also relevant for predicting future orders and returns (Hofmann et al. 2020 ). Fit importance as a product attribute does not significantly change return propensities (Hess and Mayhew 1997 ).

Concerning customer attributes , gender seems essential, as female customers return significantly more items than men (Asdecker and Karl 2018 ; Fu et al. 2016 ). Younger customers show a slightly lower propensity to return (Asdecker and Karl 2018 ), but age played a more prominent role in predicting return fraud among employees than in customers (John et al. 2020 observed more fraud among younger employees). Customers with low credit scores returned more (Fu et al. 2016 ). The return history of a customer is possibly the most important predictor of future return behavior (Samorani et al. 2016 ). Some papers argue that consumer attributes, including purchase and return history (e.g., number and value of orders), are more relevant predictors than product or transaction profiles, reflecting more or less stable consumer preferences (Li et al. 2019 ).

Basket interactions are significant (Urbanke et al. 2017 ) in returns prediction. E.g., the larger the basket, the higher the return propensity will be (Asdecker and Karl 2018 ). Selection orders (same product in different sizes or colors) increase the return propensity (Li et al. 2018 ). Logistics attributes like delivery times only show minor effects (Asdecker and Karl 2018 ). Regarding the payment method, prepaid products are sent back less frequently than those with post-delivery payment options (Imran and Amin 2020 ), confirming other research results (Asdecker et al. 2017 ).

One literature stream focuses on the automated generation of features , as different and large-scale data sources need to be integrated and prepared for forecasting algorithms. Thus, possible interrelationships are complex to find manually, and ML approaches might outperform human analysts (Rezaei et al. 2021 ). While some approaches generate a large number of features that are hard to make sense of (Ahmed et al. 2016 ), the approach of Urbanke et al. ( 2017 ) aims to maintain the interpretability of automatically generated input variables. Some unexpected but meaningful interrelations might be found by automatic feature generation, e.g., the price of the last returned orders (Samorani et al. 2016 ). Nevertheless, automatic feature generation might be computation-intensive; thus, a parallel integration of feature selection could be advantageous for large data sets (Rezaei et al. 2021 ).

A remarkable research path based on artificial intelligence is integrating qualitative information like product reviews as predictors, going beyond numerical feedback (Rajasekaran and Priyadarshini 2021 ) or tweets. These data can be processed and made accessible for forecasting with ML-based sentiment analysis techniques (Ding et al. 2016 ).

4.5 Forecasting techniques and algorithms

To describe the techniques and algorithms employed, we sorted the papers by forecasting purpose as described in Sect.  2 , then assigned them to different algorithms, either from time series forecasting, statistical techniques, or ML algorithms. Table 7 lists all papers for which an assignment was possible, and the respective techniques used. If a comparison was possible, the best-performing algorithm is marked in this table.

The approaches listed in Table  7 are overlap-free, but some papers use more than one version of an approach, i.e., more than one algorithm from a category. E.g., TabNet is a DeepLearning version of neural networks (NN), and different variants of GradientBoosting are compared in one paper (CatBoost/LightGBM, not differentiated in the table below) (Imran and Amin 2020 ).

The algorithm used most frequently (Fig.  6 ) is the Random Forest algorithm (RF, 10 papers), followed by Support Vector Machines (SVM, 8 papers), Neural Networks (NN, 6 papers), logistic regression (Logit, 6 papers), GradientBoosting (5 papers), Ordinary Least Squares regression (OLS, 4 papers), Adaptive Boosting (AdaBoost), Linear Discriminant Analysis (LDA), and CART (Classification and Regression Trees, 3 papers each).

Most frequently used algorithms (used in at least three papers)

The papers focusing on return volume use time series forecasts like (AutoRegressive) Moving Averages (MA), Single Exponential Smoothing (SES), and Holt-Winters Smoothing (HWS) more frequently than ML algorithms. Nevertheless, when considering a predict-aggregate approach as proposed by Shang et al. ( 2020 ), these ML techniques could be helpful in forecasting return decisions first and cumulating the propensity results for the volume prediction in the second step.

In forecasting binary return decisions, Random Forests (RF) (Ahmed et al. 2016 ; Heilig et al. 2016 ; Ketzenberg et al. 2020 ), Neural Networks (NN) (Imran and Amin 2020 ; Ketzenberg et al. 2020 ), as well as Adaptive Boosting (AdaBoost) (Urbanke et al. 2015 , 2017 ) showed high prediction performance. The performance of different algorithms varies depending on the data set, the implementation, and the parameterization used. For this reason, it is hardly possible to make a generally valid statement regarding performance levels. Combining several algorithms in ensembles (Asdecker and Karl 2018 ; Heilig et al. 2016 ) seems advantageous, at least for retrospective analytical purposes, when the required computing resources are less relevant.

When evaluating different forecasting algorithms for return decisions, imbalanced classes (especially evident for low return shares in non-fashion datasets) seem to be handled differently depending on the algorithms. Class imbalances might distort comparison results in some publications. Random oversampling as a measure of data preparation can solve this problem (Hofmann et al. 2020 ).

High-performance algorithms are needed for real-time predictions, e.g., graph and random-walk-based (Li et al. 2018 ; Zhu et al. 2018 ). According to Li et al. ( 2018 ), the proposed algorithm “HyperGo” performs best for most performance metrics.

4.6 E-Commerce and machine learning taxonomy extension

In their literature review regarding the use of ML techniques in e-commerce, Micol Policarpo et al. ( 2021 ) propose a taxonomy to visualize specific ML algorithms in the context of e-commerce platforms. This novel kind of taxonomy is based on direct acyclic graphs, i.e., all input variables need to be fulfilled to reach the target. The first level of the taxonomy represents different target goals for the use of ML in e-commerce. While returns forecasting (“product return prediction”) is identified as an essential goal among others (purchase prediction, repurchase prediction, customer relationship management, discovering relationships between data, fraud detection, and recommendation systems), it was excluded from the taxonomy they developed, possibly because the review comprised only two relevant papers on this topic (Micol Policarpo et al. 2021 ). The review at hand proposes an extension of Micol Policarpo’s taxonomy, renaming the goal to “consumer returns forecasting”. This extension reflects and synthesizes the consumer returns forecasting studies reviewed.

The middle level of the taxonomy represents properties and features that support this superordinate goal. On this level, our extension does not include return fraud detection, which we propose to be integrated into the existing category of “fraud detection”, separated into transaction analysis and consumer analysis (Micol Policarpo et al. 2021 ). Circles represent the necessary data to execute the analysis, referring to categories introduced in (Micol Policarpo et al. 2021 ), with an additional “return history” category. The bottom level presents the algorithms described frequently, while some streamlining is required regarding the tools and approaches that seem the most common or most appropriate.

The schematic above (Fig.  7 ) is to be read as follows: In the context of E-Commerce  +  Artificial Intelligence (Layer 1), Consumer Return Forecasting (Layer 2) is an essential goal among six other goals. Layer 3 presents different purposes of analysis, which are the base for return forecasting. Realtime Basket Analysis is based on clickstream data and basket composition (browsing activities) to target interventions. Basket analysis benefits from customer and product information (dotted line). Graph-based approaches (Li et al. 2018 ; Zhu et al. 2018 ) are promising for real-time analysis due to their lower computing requirements, although cloud-based implementation of more complex algorithms or ensemble models might be feasible (Fuchs and Lutz 2021 ; Heilig et al. 2016 ; Hofmann et al. 2020 ). Customer Analysis and Product Analysis (e.g., Potdar and Rogers 2012 ) require adequate Data Preparation in the sense of input variable generation, extraction, and selection (Urbanke et al. 2015 , 2017 ). For these purposes, data regarding return history (e.g., Hofmann et al. 2020 ; Ketzenberg et al. 2020 ), purchase history (e.g., Cui et al. 2020 ; Fu et al. 2016 ), customer personal information (e.g., Heilig et al. 2016 ; Ketzenberg et al. 2020 ), clickstream data, and browsing activities are required as input (shown by cross-hatched circles). For each purpose, one or more possible algorithms are shown.

Proposed consumer returns forecasting extension to the E-commerce and Machine Learning techniques taxonomy of Micol Policarpo et al. ( 2021 , p. 13)

Compared to predicting purchase intention, return predictions seem to require more levels of data. Nevertheless, even simple rule-based interventions can promise benefits, e.g., selection orders that inevitably lead to a return shipment can be easily recognized (Hofmann et al. 2020 ; Sweidan et al. 2020 ). Different ML techniques are helpful for data preparation and input variable (feature) extraction and generation when considering more complex interrelations. NeuralNet is one example of an automatic selection of relevant features (Urbanke et al. 2017 ). These approaches are not only able to enhance forecasting accuracy (Rezaei et al. 2021 ) but can also render the many possible variables interpretable about their content.

5 Discussion

The analysis of the papers above revealed that research in this discipline seems heterogeneous and partly fragmented, and clear-cut research strands are still hard to identify. Thus, the existing literature calls for further publications to render this research field more comprehensive. Below, research opportunities are derived and embedded in a conceptual research framework derived from the results of the existing literature, also integrating the extension of the E-Commerce and Machine Learning taxonomy (Fig.  7 ). A conceptual framework improves the understanding of a complex topic by naming and explaining key concepts and their relationships important to a specific field (Jabareen 2009 ; Miles et al. 2020 ). Thus, this framework aims to organize problems and solutions discussed in the consumer returns forecasting literature and to embed and classify potential future research topics in the existing knowledge base (Ravitch and Riggan 2017 ). The subsections following the framework outline some potential research avenues (P1–P6) that have been touched on in the past but still leave considerable opportunities for further insights. These proposals should not be seen as comprehensive due to numerous other research opportunities in this field but rather as prioritization based on the current literature.

The framework derived (Fig.  8 ) underlines the interdisciplinary nature of this research field, integrating different perspectives (information systems research, marketing and operations perspective, and strategy and management perspective). From a managerial point of view, the literature included in this review is biased towards the information systems perspective. Thus, in contrast to the framework developed by Cirqueira et al. ( 2020 ) for purchase prediction, we do not take a process perspective but instead emphasize the interdependencies and interactions between research topics and highlight the managerial need to take a strategical perspective similar to the framework developed by Winklhofer et al. ( 1996 ). Consequently, a meta-layer on forecasting frameworks and practices includes the mainly technical development frameworks in this review but also accentuates the need for further research regarding actual organizational forecasting practices (e.g., P2, P5, P6). Around this meta-layer, some related research strands are linked in order to embed the topic of returns forecasting in the research landscape. E.g., in general, forecasting purchases and returns could be linked (P6), also effecting inventory decisions.

Conceptual Consumer Return Forecasting Framework

The center of the framework consists of three dimensions, namely purposes and tasks, predictors, and techniques. Depending on the strategical purpose, tasks are derived that determine (1) the data (predictors) needed and (2) the usable techniques to execute the forecasting. Different forecasting techniques require an individual set of predictors, whereas the availability of specific data allows and determines the use of more or less sophisticated algorithms.

In the literature, some forecasting purposes were more pronounced (return decisions or propensities), while others have gained less attention (return timing, P1). Regarding the data necessary for accurate forecasting, the return predictors discussed often were hardly comparable, as they originated from different data sources, different industries, were related to different dimensions, or were aggregated in another way. Systematically linking forecasting predictors and research on return drivers and reasons could contribute significant insights (P4) that, from a marketing perspective, may support the development of effective preventive instruments. Furthermore, the literature mainly refers to the fashion or consumer electronics industry, leaving room to validate the findings in the context of other industries (P3).

When (automatically) selecting or creating predictors, the boundaries between predictors and prediction techniques are blurred as machine learning algorithms prepare the input data before executing a forecasting model. Regarding forecasting techniques, time series forecasting was seldom used in recent publications. Machine learning algorithms were the most popular subject of investigation, with random forests, support vector machines, and neural networks as the most popular implementations. Classical statistical models like logit models for return decisions or OLS regression gained less research attention. Literature on end-of-life return forecasting could complement the research on techniques and their accuracy. Most publications used technical indicators for assessing the accuracy of forecasting models, which is the information systems perspective. From a managerial position, evaluating (monetary) performance outcomes (e.g., Ketzenberg et al. 2020 ) of forecasting systems should be more relevant.

5.1 Research proposal P1: return timing for consumer returns

Toktay et al. ( 2004 ) encouraged the integrated forecasting of the return rate and the return time lag. In line with this, Shang et al. ( 2020 ) criticize the missing focus on the timing of return forecasts. The reviewed literature confirms that forecasting return propensities and decisions are more prominent than timing and volume forecasts. While the knowledge of when a return is expected is vital in managing end-of-life returns that occur over the years, for retail consumer returns, return periods are mostly 14–30 days. Thus, the variability of return timing seems limited compared to end-of-life returns in this context, which makes this forecasting purpose less critical. Nevertheless, some retailers offer up to 100 days of free returns (e.g., Zalando). Consequently, more studies about the importance of return timing forecasts in the e-commerce context from a business and planning perspective and their interdependence with return processing or warehousing issues could shed light on this topic and complement the current literature (Toktay et al. 2004 ; Shang et al. 2020 ).

5.2 Research proposal P2: realtime forecasting systems

Another research gap became apparent regarding the real-time use of forecasting systems and the associated activities and interventions, building on the initial research and the frameworks already published (e.g., Heilig et al. 2016 ; Urbanke et al. 2015 ). The generic framework developed by Fuchs and Lutz ( 2021 ) could serve as a launching pad for this stream of research.

The paper from Ketzenberg et al. ( 2020 ) could act as a stimulus and inspiration for a similar approach, not only focusing on return abuse as already examined but on return forecasting in general, the possible associated interventions for various consumer groups, and the resulting consequences for the retailer’s profit. Even the methodology of customer classification could be helpful for many retailers in targeting interventions.

Before real-time return forecasting is implemented, associated preventive return management instruments need to be designed and evaluated. Many of these measures are discussed (e.g., Urbanke et al. 2015 ; Walsh et al.  2014 ), but an overview of which preventive measures (for some examples, see Walsh and Möhring 2017 ) are effective in general (1) and how forecasting accuracy interdepends with their usefulness (2) is still missing, to substantially link the topics of forecasting and interventions. No answers could be found to the call by Urbanke et al. ( 2015 ) for field experiments to investigate such a link.

Thanks to cloud and parallelization technologies and the associated scalability of computing power (Bekkerman et al. 2011 ), algorithm runtimes are becoming less relevant. However, especially for real-time use, it should be evaluated which algorithms and underlying datasets exhibit an appropriate relationship between the targeted forecasting accuracy, the expected benefit, and the required computing power.

Recommendations concerning the algorithms and techniques can be derived (Urbanke et al. 2015 ), and a generic implementation framework was developed (Fuchs and Lutz 2021 ). However, from a business perspective, no contributions could be found regarding the actual implementation of real-time forecasting systems, the interventions involved, and their impact on consumer behavior or profit (also see proposal P5). In addition, the implementations of such systems need to be analyzed concerning the cost-effectiveness of the required investments.

5.3 Research proposal P3: cross-industry and multiple dataset studies

Many publications rely on a single data set from a specific industry or retailer. Only a few compare several retailers (e.g., Cui et al. 2020 ). Studies including and comparing different countries are missing, which is especially interesting since legal regulations for returns vary. For example, in contrast to the U.S., citizens within the EU are granted a 14-day right of withdrawal for distance selling purchases. Footnote 4 Although in most developed countries, liberal and broadly comparable returns policies are standard in practice due to competitive pressure, the generalizability of the results is frequently limited. One remedy for this problem is to use multiple data sets from different retailers (e.g., electronics vs. jewelry, Shang et al. 2020 ). Admittedly, it is challenging to simultaneously collaborate with several retailers and to combine different data sets, due to reasons of preserving corporate privacy and synchronizing various data sources. Nevertheless, research needs to draw conclusions from single data points, as well as logically replicate or falsify those results by integrating more data points to find patterns of similarities and differences, either within or cross-study (Hamermesh 2007 ). Therefore, we suggest that future studies acquire industry-related datasets from several retailers at once or replicate existing studies, which aligns with the aim and scope of Management Review Quarterly (Block and Kuckertz 2018 ). Cross-industry or cross-country manuscripts, which go beyond the mere assertion of an industry-agnostic approach (Hofmann et al. 2020 ) and jointly investigate data from several sectors, would promise an additional gain in knowledge and could be less challenging from a privacy perspective.

5.4 Research proposal P4: extended study of relevant predictors in forecasting applications

Although not the main focus of this review, predictors of consumer returns are especially interesting for marketing and e-commerce research, for example, regarding preventive measures for avoiding returns. In the past, many consumer return papers highlighted single aspects or a limited selection of return drivers or preventive measures employed but rarely attempted to model return behavior as comprehensively as possible. However, the latter is the very objective of returns forecasting, which is why the findings on influencing factors in articles with a forecasting focus tend to be more holistic, although not sufficiently complete (Hachimi et al. 2018 ). Some return reasons named in the literature (e.g., Stöcker et al. 2021 ) have not yet been included in forecasting approaches, and vice versa, only a part of the influencing factors investigated could be mapped to a return reason categorization. The reason categories assigned (Sect.  4.4 , Table  6 ) still contain some uncertainty. For example, a customer’s product return history may reflect the general returning behavior of a customer to some extent, while it can not be ruled out that repeated logistical problems caused the returns. Product attributes may reflect information gaps that consumers can only assess after physically inspecting the product, whereas product price–frequently cited and influential product attribute—is only related to information gaps when considering the price-performance ratio (Stöcker et al. 2021 ). Technical information about the web browser or device used by the customer is difficult to categorize, as it may reflect behavioral (impulse-driven mobile shopping) as well as informational (small display with few visible information) aspects. The payment method chosen by a customer, for example, could not be linked to one of the reason categories.

This reasoning should serve as a basis for linking forecasting predictors and return reasons more closely in the future. For example, the respective relative weighting of return drivers is more likely to be obtained considering as many factors involved as possible, minimizing the unexplained variation. From the reviewed literature, we extracted 18 different return predictor categories. For instance, seven papers (Cui et al. 2020 ; Fu et al. 2016 ; Ketzenberg et al. 2020 ; Li et al. 2018 , 2019 ; Urbanke et al. 2015 , 2017 ) integrated more than five predictor categories. But even though some papers integrate more than 5,000 features for automated feature selection (Ketzenberg et al. 2020 ), there are still combinations of input variable categories that have not been investigated and, more importantly, interpreted yet. Therefore, we call for more comprehensive research on return predictors and their interpretation, including associated preventive return measures, in the context of return forecasting.

5.5 Research proposal P5: descriptive case studies and business implementations surveys

This review identified a lack of publications regarding the actual benefit and the diffusion of consumer returns forecasting systems in different scopes and industries, building on the papers presenting return forecasting frameworks. In 2013, less than half of German retailers analyzed the likelihood of returns (Pur et al. 2013 ). Most of those who did were using naïve approaches that might be outperformed by the models presented in this review. Still, we do not know the status quo regarding the degree of adoption and implementation of forecasting systems for consumer returns in e-commerce firms (e.g., see Mentzer and Kahn 1995 for sales forecasting systems), country-specific and internationally.

Furthermore, the impact of return forecasting practices on company performance should be examined not only based on modeling, but on retrospective data (e.g., see Zotteri and Kalchschmidt 2007 for a similar study on demand forecasting practices in manufacturing). A possible hypothesis to examine might be that accuracy measures like RMSE or precision/recall and subsequently even the choice of the most accurate machine learning algorithm (e.g., see Asdecker and Karl 2018 ) are less relevant from a business perspective: (1) No algorithm clearly outperforms all other algorithms, and (2) the correlation between technical indicators and business value is unstable (Leitch and Tanner 1991 ). Methodologically, implementations of consumer returns forecasting in e-commerce should thus be surveyed and analyzed with multivariate statistical methods to examine critical factors and circumstances of return forecasting systems – similar to publications on reverse logistics performance (Agrawal and Singh 2020 ).

5.6 Research proposal P6: holistic forward and backward forecasting framework for e-tailers

Some publications present frameworks for forecasting returns (Fuchs and Lutz 2021 ). Nevertheless, in the past, forecasting in retail and especially e-commerce commonly focused more on demand (Micol Policarpo et al. 2021 ) than returns. Current approaches for demand forecasting try to predict individual purchase intentions based on click-stream data, online session attributes, and customer history (e.g., Esmeli et al. 2021 ). Our systematic approach could not identify any paper that connects and integrates both directions in e-commerce forecasting, neither conceptual (frameworks) nor with a quantitative or case-study-like approach. Nevertheless, first implementations of return predictions in inventory management are presented (e.g., Goedhart et al. 2023 ). Subsequently, similar to Goltsos et al. ( 2019 ), we call for research addressing both demand and return uncertainties by providing a holistic forecasting framework in the context of e-commerce.

6 Conclusion

To date, no systematic literature review has undertaken an in-depth exploration of the topic of forecasting consumer returns in the e-commerce context. Previous reviews have primarily focused on product returns forecasting within the broader context of reverse logistics or closed-loop supply chain management (Agrawal et al. 2015 ; Ambilkar et al. 2021 ; Hachimi et al. 2018 ). Regrettably, the interdisciplinary nature of this subject has often been overlooked, also neglecting the inclusion of results from information systems research.

The review first aims to provide an overview of the existing literature (Kraus et al. 2022 ) on forecasting consumer returns. The findings confirm that this once novel topic has significantly evolved in recent years. Consequently, this review is timely in examining current gaps and establishing a robust foundation for future research, which forms a second goal of systematic reviews (Kraus et al. 2022 ). The current body of work encompasses various aspects from different domains, including marketing, operations management/research, and information systems research, highlighting the interdisciplinary nature of e-commerce analytics and research. As a result, future studies can find suitable publication outlets in domain-specific as well as methodologically oriented journals and conferences.

Scientifically, the algorithms and predictors investigated in previous research serve as a foundational reference for subsequent publications and informed decisions regarding research design, ensuring that specific predictors and techniques are not overlooked. Researchers can utilize this review and the research framework developed as a structuring guide, e.g., regarding relevant publications on already examined algorithms or predictors.

Managerially, the extended taxonomy for machine learning in e-commerce (Micol Policarpo et al. 2021 ) can serve as a guideline for implementing forecasting systems for consumer returns. This review classifies possible prediction purposes, allowing businesses to apply them based on their respective challenges. Exploring the most frequently used predictors reveals the data that must be collected for the respective purposes. This review also offers valuable insights into data (pre-)processing and highlights popular algorithms. Furthermore, frameworks are outlined that support the design and implementation phase of such forecasting systems, supporting analytical purposes or enabling direct interventions during the online shopping process flow. As an exemplary and promising application, return policies could be personalized (Abbey et al. 2018 ) by identifying opportunistic or fraudulent basket compositions or high-returning customers, thereby reducing unwanted returns (Lantz and Hjort 2013 ).

Finally, a limitation of this review is the exclusion of forecasting algorithms for end-of-use returns, which could potentially be applicable to forecasting shorter-term retail consumer returns. However, the closed-loop supply chain and reverse logistics literature has been systematically excluded. Hence, future reviews could synthesize previous reviews on reverse logistics forecasting with the more detailed findings presented in this paper.

The use of Google Scholar for systematic scientific information search is controversely discussed (e.g., Halevi et al. 2017 ) due to the missing quality control and indexing guidelines, as well as limited advanced search options. But as an additional database for an initial search, the wide coverage of this search system can enrich the results.

External citations according to Google Scholar, which is preferable for citation tracking over controlled databases (Halevi et al. 2017 ).

Other literature also describes a counteracting effect of a reduced price due to lowered quality expectations or a higher perceived value of the “deal” itself (e.g., Sahoo et al. 2018 ).

It should be noted that the relevance of the forecasting topic depends on the maturity of the e-commerce sector. In most developing countries, B2C e-commerce is comparatively young and consumer returns are not yet a common phenomenon, which is why research on return forecasts is relatively insignificant for these countries.

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Appendix: Author-centric content summary (with focus on forecasting issues)

1.1 journal publications.

Hess and Mayhew ( 1997 ) describe a forecasting approach, taking the example of a direct marketer for apparel with a lenient consumer return policy (free returns anytime). The analysis can plausibly be applied to a general retailer, although return time windows are somewhat different. A regression approach and a hazard model are compared. The regression approach itself is split into an OLS estimation of return timing (with poor fit) and a logit model of return propensities, which is in turn used for the split function of the box-cox-hazard approach for estimating the probability of a return over time. The accuracy was measured by fit statistics regarding the absolute deviation from the actual cumulative return proportion, with the split-hazard model outperforming the regression model. Besides price, the importance of fit of the respective product is used as a predictor.

Potdar and Rogers ( 2012 ) propose a method using reason codes combined with consumer behavior data for forecasting returns volume in the consumer electronics industry, aiming at the retailer stage as well as the preceding supply chain stages. The subject of their study is an offline retailer, which allows generalization for e-tailers due to a similar return policy (14 days free returns with no questions asked). In a multi-step approach, the authors are using essential statistical methods (moving averages, correlations, and linear regression), but use sophisticated domain and product knowledge like product features or price in relation to past return numbers, aiming to rank different competing products regarding their quality, and to predict the volume of returns for a given product for each given period of time.

Fu et al. ( 2016 ) derive a framework for the forecasting of product- and consumer-specific return propensities, i.e., the return propensity for individual purchases. Their study is directed at online shopping and is evaluated using the data from an online cosmetic retailer selling via Taobao.com. The predictors are categorized into inconsistencies in the buying and in the shipping phase of a transaction. A latent factor model is introduced for return propensities capturing differences between expectations and performance. This model is extended by product (e.g., warranty) and customer information (e.g., gender, credit score). The model is based on conditional probabilities, and an iterative expectation–maximization approach derives its parameters. MAE and RMSE, precision/recall, and AUC metrics assess the forecast accuracy. As benchmark models, two matrix factorization models and two memory-based models (historical consumer or product return rates) are compared, while the proposed model outperforms the references. Furthermore, this model allows identifying various return reasons, e.g., return abuse and fraud.

Building on the work of Fu et al. ( 2016 ), Li et al. ( 2019 ) investigate underlying reasons for consumer returns, taking the example and data of an online cosmetic retailer via Taobao.com. They examine the customers’ return propensity for product types, aiming at detecting abnormal returns suspecting abuse. Different from purchase decisions, they find customer profile data to be more important predictors for return decisions than product information or transaction details. The authors detect “selfish” or “fraud” consumers based on this rationale. For estimating return propensities for a given consumer and product, they calculate the return behavior depending on the return decision of similar consumers (“trust network”) and the amount of trust in these other consumers. MAE and precision-recall-measures are used to assess the prediction of different random walk models. The employed trust-based random walk model outperforms the other models on most indicators, building the basis for anomaly detection of consumers to cluster them into groups (honest/selfish/fraud) and individually address the return issues of these groups.

Although the paper from Cui et al. ( 2020 ) aims at product return forecasts from the perspective of the manufacturer, their case can be generalized for classic e-tailers, as the manufacturer is responsible for the return handling in their scenario—a task often performed by the retailer. They used a comprehensive data set from an automotive accessories manufacturer aiming to forecast return volume for sales channels and different products. The observed return rates lower than 1% are uncommonly low, and therefore the results must be interpreted with caution. First, a hierarchical OLS regression step-by-step incorporates up to 40 predictors regarding sales, time, product type, sales channel, and product details, including return history. The full model shows a significantly increased performance measured by a more than 50% decrease of MSE, which was used as the primary performance measure. Interestingly, relatively small differences in model quality (R 2 ) led to overproportional changes in the MSE. Using a machine-learning approach for predictor selection (“LASSO”), another MSE reduction of about 10% was achieved. Data Mining approaches (random forest, gradient boosting) could not outperform the LASSO approach. Forecasting performance was strongly dependent on the variation of the data. The two best predictors for return volume were past sales volume and lagged return statistics. The authors were wondering about the importance of lagged return information, failing to acknowledge that this predictor includes the consumer reaction to detailed product information, which has not been a significant predictor.

Ketzenberg et al. ( 2020 ) segment customers and target detecting the small number of abusive returners, as these are unprofitable for the retailer and generate significant losses over a long time. In general, high-returning customers are usually more profitable. The data used for this study is from a department store retailer with various product groups in the assortment. Predictors are transactional data and customer attributes. For classification, different algorithms like logit, Support Vector Machines (SVM), Random Forests (RF), Neural Networks (NN) are used in combination with different shrinkage methods like LASSO, ridge regression, and elastic net. Random Forests and especially Neural Networks outperform the other algorithms, assessed by sensitivity, precision, and AUC. In conclusion, a low rate of false positives could assure retailers of using abuse detection systems.

Shang et al. (Shang et al. 2020 ) developed a predict-aggregate (P-A) model adaptable both for retailers and manufacturers for forecasting return volume in a continuous timeframe, in contrast to commonly used aggregate-predict (A-P) models. Instead of aggregating data first (i.e., sales volume and returns volume), they first aggregate product-specific return probabilities and then aggregate the purchases by addition of the individual probabilities. As predictors, they only use timestamps and lagged return information. They tune and assess their models on two datasets from an offline electronics and an online jewelry retailer. ARIMA and lagged return models known from end-of-life forecasting (de Brito et al. 2005 ) are used as benchmarks, using RMSE as an assessment criterion. The authors show that even a basic version of their approach outperforms the benchmark models in almost all observed cases by up to 19%, though using only lagged returns and timestamps as input. Different extensions, e.g., including more predictor variables, can easily be integrated and are shown to further improve the forecasting performance.

John et al. ( 2020 ) try to predict the rare event of return fraud from customer representatives that make use of exactly knowing the e-commerce company’s return policy framework and buying and returning items fraudulently. Therefore, predictors range from transaction details to customer service agent attributes. A penalized likelihood logit model was chosen by the authors and was evaluated by precision and recall, focussing on maximizing recall and minimizing false negatives. The most important predictors were communication type and reason for interaction.

The paper by Rezaei et al. ( 2021 ) introduces a new algorithm to automatically select attributes from high-dimensional databases for forecasting purposes. As a demonstration sample, they use simulated data as well as the publicly available ISMS Durable Goods dataset (Ni et al. 2012 ) for consumer electronics. The results are assessed by AUC, precision, recall, and f1-score. They compare different configurations. For the simulated data, LASSO as shrinkage method generally works best, outperforming RF and BaggedTrees. For real-world data, based on a forecast with a logit model, they show that the proposed selection algorithm performs similar or better compared to LASSO, SVM, and RF, while the complexity of the chosen variables is lower.

1.2 Conference publications

Urbanke et al. ( 2015 ) describe a decision support system to better direct return-reducing interventions at e-commerce purchases with highly likely returns. They compare different approaches for extracting input variables for return propensity forecasting. They use a large dataset from a fashion e-tailer, aiming to reduce the input variables regarding consumer profile, product profile, and basket information from over 5,000 binary variables to 10 numeric variables by different algorithms (e.g., principal component analysis, non-negative matrix factorization, etc.). The results are then used to predict return propensities with a wide variety of state-of-the-art algorithms (AdaBoost, CART, ERT, GB, LDA, LR, RF, SVM), thus also revealing both feature selection and prediction performance. The proposed Mahalanobis feature extraction algorithm used as input for AdaBoost outperforms all other combinations presented, while interestingly, a logit model with all original inputs delivers relatively precise forecasts.

Building on some parts of this study, the paper of Urbanke et al. ( 2017 ) presents a return decision forecasting approach and aims at two targets, (1) high predictive accuracy and (2) interpretability of the model. Based on real-world data of a fashion and sports e-tailer, they first hand-craft 18 input variables and then use NN to extract more features and compare this approach to other feature extraction algorithms based on different forecasting algorithms. For assessment, they measure correlations between out-of-sample-predictions and class labels and AUC. The best performing classifier was AdaBoost, while the contribution of NN-based feature extraction shows interpretability as well as superior predictive performance.

Ahmed et al. ( 2016 ) focus on the automatic aggregation and integration of different data sources to generate input variables (features). They use return forecasting just as an exemplary classification problem for their data preparation approach, using various ML algorithms, e.g., RF, NN, DT-based algorithms, to detect returned purchases of an electronics retailer. Based on AUC measure, the results of their GARP-approach are superior to not using aggregations while generating an extensive amount of features with no pruning approach. In general, SVM and RF work best in combination with the proposed GARP approach. The data is based on the publicly available ISMS durable goods data sets (Ni et al. 2012 ).

A similar group of authors published another paper (Samorani et al. 2016 ), again using the aforementioned ISMS dataset as an example for data preparation and automatic attribute generation. Besides forecasting performance, in this paper, they want to generate knowledge about important return predictors; e.g., a higher price is associated with more returns, but only as long price levels are below a 1,500$ threshold. AUC is used to assess different levels of data integration, confirming that overfitting might happen when too many attributes are used.

Heilig et al. ( 2016 ) describe a Forecasting Support System (FSS) to predict return decisions in a real environment. First, they compare different forecasting approaches for data from a fashion e-tailer, assessed by AUC and accuracy metrics. The ensemble selection approach outperforms all other classifiers, with RF being the closest competitor. Computational times grow exponentially when using more data. Based on these results, they secondly describe a cloud framework for implementing such ensemble models for live use in a real shop environment.

Ding et al. ( 2016 ) present an approach to predict the daily return rate of an e-commerce company based on sentiment analysis of tweets regarding this company in the categories of news, experience, products, and service. Therefore, they use sophisticated text mining technologies, while the forecasting approach of an econometric vector autoregression is more or less common. The emotion of posts regarding different variables (news, product, service) impacts the returns rate negatively, while the emotion of purchasing experience impacts it positively, showing that the prediction accuracy enhances through classifying social network posts.

Drechsler and Lasch ( 2015 ) aim at forecasting the volume of fraudulent returns in e-commerce over several periods of time. They present different approaches multiplying the sales volume and the relative return rate, the first referring to Potdar and Rogers ( 2012 ), estimating the rate of misused returns directly based on time-lag-specific return rates. In a second approach referring to Toktay et al. ( 2000 ), they estimate the overall returns rate and multiply it by the time-specific ratio of fraudulent returns. The return rates were forecasted by moving averages and exponential smoothing techniques. Assessment criteria for performance comparison based on simulated data were MAE, MAPE, and TIC, showing the first approach to be superior, but both methods are not sufficiently robust. Therefore, the authors include further time-specific information (like promotions or special events, which could foster fraudulent returns) in a model using a Holt-Winters approach, showing superior performance. All of the models are highly dependent on low fluctuation in return rates, showing a shortcoming of these more or less naive forecasting techniques.

Asdecker and Karl ( 2018 ) compare the performance of different algorithms for forecasting binary return decisions: logit, linear discriminant analysis, neuronal networks, and a decision-tree-based algorithm (C5.0). Their analysis is based on the data of a fashion e-tailer, including price, consumer information, and shipment information (number of articles in shipment, delivery time). For the assessment of different algorithms, they use the total absolut error (TAE) and relative error. An ensemble learning approach performs best and similar to the C5.0 algorithm. Though, differences in performance are relatively small, while only about 68% of return decisions are forecasted correctly.

Li et al. ( 2018 ) propose a hypergraph representation of historical purchase and return information combined with a random-walk-based local graph cut algorithm to forecast return decisions on order (basket) level as well as on product level. By this, they aim to detect the underlying return causes. They use data from two omnichannel fashion e-tailers from the US and Europe to assess the performance of their approach, using precision/recall/F 0.5 /AUC metrics while arguing that precision is the most important indicator for targeted interventions. Three similarity-based approaches (e.g., a k-Nearest Neighbor model) are used as reference. The proposed approach performs best regarding AUC, precision, and F 0.5 metrics.

Zhu et al. ( 2018 ) developed a weighted hybrid graph algorithm representing historical customer behavior and customer/product similarity, combined with a random-walk-based algorithm for predicting customer/product combinations that will be returned. They report an experiment based on data from a European fashion e-tailer suffering from return rates as high as 50%. For assessment, they use precision, recall, and F 0.5 metrics. Their approach is superior to two reference competitors (similarity-based and a bipartite graph algorithm). As predictors, they use product similarities and historical return information, while their approach can be enriched with detailed customer attributes.

Joshi et al. ( 2018 ) model the return decisions based on the data of an Indian e-commerce company, especially dealing with returns for apparel due to fit issues. In a two-step approach, they first model return probabilities using concepts from network science based on a customer’s historical purchase and return decisions, and secondly use a SVM implementation with return probabilities as a single input to classify for the return decision. Assessed by F 1 /precision/recall scores, their approach is superior to a reference random-walk baseline model.

Imran and Amin ( 2020 ) compare different forecasting algorithms (XGBoost, CatBoost, LightGBM, TabNet) for return classification based on the data of a general e-commerce retailer from Bangladesh. As input variables, only order attributes, including payment method and order medium, are used. For evaluation, they use metrics like true negative rate, false-positive rate, false-negative rate, true positive rate, AUC, F 2 -score, precision, and accuracy. In the end, they chose TPR, AUC, and F 2 -score, claiming that misclassifying high return probability objects were the first thing to avoid. According to these metrics, TabNet as a deep learning algorithm outperforms the other models. The most important predictors were payment method, order location, and promotional orders.

As returns are most prominent in fashion e-commerce, most of the forecasting papers take this industry as an example, as forecasting models are more precise when returns are more frequent. Hofmann et al. ( 2020 ) develop a more generalized order-based return decision forecasting approach, appropriate for different industries and suitable also for low return rates. For their analysis, they use a dataset from a german technical wholesaler with a return rate as low as 5%. Input variables were just basket composition and return information. For assessment, they used precision and recall metrics. RF did not perform superior to a statistical baseline approach, nor with oversampling as data preparation, to deal with the group imbalance. The DART algorithm makes use of the group imbalance correction by random oversampling. In general, gradient boosting performs best with imbalanced groups, also without oversampling, but forecasting quality is lower than with more specialized forecasting approaches as described for fashion. Furthermore, results were more accurate on basket level than on single-item level.

Fuchs and Lutz ( 2021 ) use Design Science Research (DSR) principles to design a meta-model for the real-time prediction of returns. The goal is to influence consumer decisions by triggering a feedback system based on the basket composition and its return probability. For forecasting, which is not the primary focus of their paper, they build upon a gradient boosting model taken from existing research (Hofmann et al. 2020 ) and describe possible implementations into an ERP system regarding asynchronous communication requirements and possible architecture.

The paper by Sweidan et al. ( 2020 ) evaluates the forecasting performance of a random forest model for a shipment-based return decision, using real-world data of a fashion e-tailer. For their model, they use customer (e.g., lagged return rate) and order information as inputs. They find that predictions with high confidence are very precise (i.e., low false-positive rate). Thus, interventions can be targeted at such orders already when the items are in the consumers’ basket without risk of a misdirected intervention. For assessment, accuracy, AUC, precision, recall and specificity are used. Regarding the predictors, they note that selection orders (a product in different sizes) are the best predictor for order-based returns.

Rajasekaran and Priyadarshini ( 2021 ) develop a metaheuristic for forecasting the product-based return probabilities. In the first step, they determine return probabilities based on product feedback, time, and product attributes regarding manufacturer return statistics. Secondly, they compare different algorithms (OLS, RF, Gradient Boosting) by MAE, MSE, and RMSE metrics. Interestingly, linear regression performs best in all metrics, but no explanation and a misinterpretation regarding the best algorithm are given.

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Karl, D. Forecasting e-commerce consumer returns: a systematic literature review. Manag Rev Q (2024). https://doi.org/10.1007/s11301-024-00436-x

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Received : 24 August 2023

Accepted : 12 April 2024

Published : 21 May 2024

DOI : https://doi.org/10.1007/s11301-024-00436-x

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