The BORIS experience: evaluating omnichannel returns and repurchase intention

Consumer product returns are a recurring challenge for retailers and manufacturers (Wang et al., 2017; Daugherty et al., 2019; Jones et al., 2023). Incentivized by free shipping and free returns offered by retailers, a growing number of customers are bracketing, an activity described as buying multiple colors or sizes to keep the one that fits best and return the others (Balaram et al., 2022); 63% of customers reported bracketing in a 2022 survey (Hartmans, n.d.). As a result, products with no defects are often returned simply because of the mismatch between product characteristics and customer preferences (Nageswaran et al., 2020; Melacini et al., 2018; Shang et al., 2019; Ferguson et al., 2006). As omnichannel retailing evolves to integrate returns, information systems and inventories, companies’ returns processes have grown increasingly complex (Bernon et al., 2016; Ishfaq et al., 2016).

The increasing number of returns in omnichannel settings pushes retailers to offer a flexible range of return options (Yan et al., 2022). Among all returns strategies, the buy-online and return-in-store (BORIS) option is a popular cross-channel returns strategy (Yang et al., 2023). Research by Ertekin (2018) highlights the in-store return process as a crucial point for customer feedback and product improvement. Meanwhile, Huang and Jin (2020) note that BORIS allows retailers to have higher salvage values for returned products by facilitating immediate shelving. In 2023, nearly 90% of retailers updated their returns policies, including introducing charges for certain channel returns and promoting BORIS, as Optoro CEO Amena Ali reported (Yahoo Finance, 2023). BORIS, which combines the benefits of online and offline shopping, offers customers the convenience of in-store returns with immediate refunds while allowing retailers to verify returns’ authenticity in-store (Yan et al., 2022). Furthermore, retailers implementing the BORIS strategy aim to increase in-store traffic, which may potentially create additional opportunities for cross-selling activities and thus increase profits (Yang and Ji, 2022).

The evolution of digital supply chains is increasingly enabling companies to adopt a more customer-centric approach (Stanton and Melnyk, 2017). This shift has elevated customer service quality as a critical competitive edge for omnichannel retailers, significantly influencing customer expectations regarding order fulfillment and returns (Xie et al., 2023; Peinkofer et al., 2022; Jin et al., 2023; Murfield et al., 2017). To truly make supply chains more focused on the customer, companies must prioritize delivering an exceptional customer experience and respond swiftly to customer needs (Piccinini et al., 2015; Richey et al., 2022). Customer impatience is increasing, placing higher demands on logistics and supply chain professionals to deliver timely services and comprehensive information, enhancing transparency and visibility throughout the omnichannel shopping experience (Daugherty et al., 2019).

In the age of social media and online reviews, retail service failures can significantly harm a retailer’s reputation due to the powerful effect of negative word of mouth (Casidy et al., 2021). Although returns often indicate service failure and customer dissatisfaction (Mollenkopf et al., 2007; Griffis et al., 2012), returns also represent an opportunity for service recovery and improving customer satisfaction if managed well (Jones et al., 2023). Worldwide Business Research insights research shows a strong link between return experiences and customers’ future purchasing decisions, with 89% avoiding retailers after negative experiences, while 97% would return after positive ones (Retail Dive, 2022). Effective service recovery can thus transform failures into satisfaction, with personal interactions playing a pivotal role in building customer relationships (Jones et al., 2023; de Matos et al., 2007).

The BORIS returns process constitutes the primary direct interaction between online customers and retailers in omnichannel retailing (Chang and Li, 2022; Huang and Jin, 2020). The quality of this interaction is crucial for maintaining customer relationships (Jones et al., 2023; Russo et al., 2022). Enhancing perceptions of fairness during the returns process is essential for customer satisfaction and retention (Russo et al., 2022; Jones et al., 2023), highlighting the strategic importance of returns management in retail differentiation (Xie et al., 2023; Daugherty et al., 2019; Richey et al., 2005). Fair treatment in the returns process can mitigate initial dissatisfaction and contribute to sustainable customer relationships by offering a superior service experience during returns (Daugherty et al., 2019; Jones et al., 2023; Russo et al., 2021; Murali et al., 2016). Successfully managing BORIS returns can enhance customer satisfaction and loyalty, potentially increasing in-store purchases (Xie et al., 2023). However, research on the capabilities influencing the customer returns experience, particularly, in the BORIS context, remains limited (Xie et al., 2023; Daugherty et al., 2019). Russo et al. (2022) highlight service recovery resilience as a critical capability for ensuring procedural justice in service recovery and positively impacting customer satisfaction and loyalty in product replacement scenarios. Jones et al. (2023) explore how interpersonal and informational justice influence returns satisfaction, positive word-of-mouth and trust. Their study also investigates how the convenience of the returns process and the restrictiveness of the returns policy moderate the relationship between justice and returns outcomes.

Our research further examines the capabilities retailers need when handling customers’ online purchase returns to physical stores ( Appendix 2). We explore how the BORIS returns experience influences the retailer–customer relationships and customer shopping intentions. This study clarifies the current returns management process by addressing omnichannel returns through the following research questions: how does a retailer improve the customer’s in-store return experience by utilizing available store resources? And, how does the in-store return experience impact customer repurchase behavioral intentions in omnichannel retailing?

We examined how these capabilities influence customer perceptions of service quality through scenario-based experiments. A pilot study confirmed the validity of our constructs and manipulations. We aimed to guide retailers in enhancing the in-store return experience and using retail stores as part of a comprehensive returns strategy to increase sales. This study offers omnichannel retailers insights into improving customer service regarding returns, suggesting ways to enhance their services and integration for greater customer satisfaction.

Our research contributes to the academic literature and managerial practice by examining the capabilities needed to create a returns experience for retailers in the BORIS context. Additionally, we explore the effects of disclosing returns policy and loyalty program information on consumer repurchase behavioral intention, adding to emerging supply chain transparency literature and extending prior research on the benefits of information disclosure to customers. Our findings also augment previous logistics literature in the context of omnichannel networks. From a managerial perspective, the intended outcome is a practical understanding of the returns process so retailers may realign the returns policy and processes to advance omnichannel capabilities. Our findings highlight the need for retailers to disclose details regarding returns policies and loyalty programs and proactively communicate this information to consumers in the returns process. An informed returns strategy helps alleviate customer uncertainty in the returns process and ensures a value-driven customer experience. By so doing, an integrated returns management system can be developed in omnichannel retailing to minimize the costs of returns management and maximize the value of the customer relationship through future purchases.

The complexity of managing product returns in omnichannel retailing requires retailers to take a forward-thinking stance to develop capabilities to address service recovery (Russo et al., 2022; Jones et al., 2023). Growing customer impatience demands timely service recovery and transparent communication (Daugherty et al., 2019). Prior research has shown that a service recovery resiliency capability improves customers’ perceived fairness of the service recovery process in the context of product replacement (Russo et al., 2022) and enhances customer satisfaction and customer loyalty. Russo et al. (2022) further explored how improving communication during service recovery can foster perceived fairness, which, in turn, positively affects customer satisfaction and loyalty. Next, we delve into literature concerning service recovery and retail justice capability.

A positive customer logistics service experience has been found to build long-term customer relationships, e.g. strengthened sales, higher satisfaction, increased frequency of shopping visits, improved profitability and positive word-of-mouth communication (Grewal et al., 2008; Verhoef et al., 2009; Bagdare and Jain, 2013). The escalating service expectations of omnichannel retailing place extensive pressure on the retailer (Daugherty et al., 2019). Striving to distinguish themselves from competitors, retailers offer superior customer experience and exceptional service quality in a compressed timeframe, all while lowering costs. Murfield et al. (2017) investigated how three dimensions of logistics service quality (i.e. condition, availability and timeliness) impact consumer satisfaction and loyalty in two omnichannel retail scenarios: buy-online-pickup-in-store (BOPIS) and buy-in-store-ship-direct. Jin et al. (2023) further examined consumer reactions to cross-channel substitution options retailers offer when BOPIS stockouts occur. Their research shows that the effectiveness of retailer signals during the BOPIS service process is influenced by retailers’ operational capability and the consumer’s preference for the product category.

The returns experience is as necessary as the product fulfillment service process for an omnichannel retailer since contact with a retailer’s service provider influences customer satisfaction and repurchase intentions (Daugherty et al., 2019). Prior research finds that hassle-free and convenient returns strongly signal the seller’s product quality and service performance (Ahsan and Rahman, 2016). In the age of impatience, customers demand quick service, complete transparency and the ability to track their transactions (Daugherty et al., 2019). Failure to meet these expectations can lead to a diminished perception of service quality. Furthermore, Bustamante and Rubio (2017) discovered that unforgettable shopping encounters, including those in omnichannel settings, play a crucial role in retaining existing customers and attracting new ones to brick-and-mortar locations. We define the retailer reverse logistics service procedural capability (RRLSPC) as the capability that encompasses creating a returns process that aligns with customer expectations of fairness, focusing not just on the outcomes but on the fairness of the entire procedure, including process control, decision control and procedural adjustment. This capability is crucial for influencing customer perceptions of return fairness based on how the process is managed, including control, accessibility, timing, speed and flexibility. This capability ensures that customers perceive the returns process as just, enhancing their satisfaction and trust in the retail brand and potentially leading to positive service recovery outcomes.

Customer expectations of service brought by omnichannel retailing continue to rise in this age of impatience (Daugherty et al., 2019). Literature suggests that retail employees are essential facilitators in returns management (Lin et al., 2011; Chen et al., 2019). As key stakeholders, employees execute the operational strategies and represent the company through daily interactions with customers at the front end of the business (Chen et al., 2019). Prompt returns resolutions through well-trained personnel can increase repeat business and generate new customers, translating returns into a value-adding activity through retention (Stock and Mulki, 2009; Griffis et al., 2012; Autry et al., 2001; Mollenkopf et al., 2011; Chen et al., 2019). Though physical store returns typically only require short employee–customer interactions, the employee’s critical role as a facilitator of returns management impacts customer satisfaction and retention (Ertekin et al., 2020). A retail service employee informs customers about returns policies, provides product tracking, processes returned products and collects information on customer needs (Davis-Sramek et al., 2010). Unlike an initial purchase, a return might contain additional product fit information, and an employee’s familiarity with the retailer’s different returns policies and products is critical to generating the desired customer satisfaction from a return experience (Ertekin, 2018).

BORIS often represents customers’ sole face-to-face engagement with the retailer (Jones et al., 2023), making the perceived fairness of these return interactions a crucial element of returns service. Given the increasing complexity of returns management in omnichannel retailing, enhancing employees’ customer service knowledge, skills and abilities is crucial for maximizing the value of returned products (Chen et al., 2019; Goldstein, 2003). We define retailer interactional capability (RIC) as the ability of a service provider to engage with customers in a manner that adheres to the principles of interactional justice. This capability entails ensuring respectful, polite and appropriate customer interaction (Ekinci et al., 2008; Lin et al., 2011). It reflects the organization’s competence in managing customer service interactions so that customers feel valued and fairly treated (Francioni et al., 2018), directly influencing their perception of the service experience (Murali et al., 2016), especially during critical service recovery moments such as product returns (Vesel and Zabkar, 2009).

Return policies significantly influence customer purchase decisions, serving as a quality indicator and reducing purchasing risks (Jeng, 2017; Ülkü and Gurler, 2018). Lenient return policies can decrease product search time and are often reviewed by consumers before purchase (Oghazi et al., 2018; Janakiraman et al., 2016; Wood, 2001). Ülkü and Gurler (2018), Ülkü et al. (n.d.) and Xu and Jackson (2019) found that consumer valuation of returns policy remains consistent until the return deadline, underscoring the returns policy’s role from pre-purchase to the return period’s end. This policy reassures customers about potential returns (Ülkü and Gurler, 2018). Moreover, consistent policy execution affects post-return customer evaluations, with observed sensitivity to policy inconsistencies across channels (Pei et al., 2014). As Dabholkar et al. (1996) revealed, customers value reliability; some items refer to promises, and others to doing the service right. Retailers enhancing returns process transparency can boost customer satisfaction and perceived service quality, akin to findings in supplier transparency (Eggert and Helm, 2003) and process transparency (Buell et al., 2017), thus improving customer value and satisfaction through more transparent process communication.

Morgan et al. (2018) define supply chain transparency as sharing product history and current activities with stakeholders, enhancing traceability and incorporating feedback for improvements. Within this framework, retailers contribute to transparency by offering clear information about return policy processes and tracking returned products throughout the supply chain. The concept of transparent returns policy capability (TRPC) is thus described as an intentional approach to disclosing the detailed returns information that customers expect. It encompasses strategic and administrative dimensions: the strategic involves making returns policies clear to stakeholders such as employees, managers and customers, while the administrative focuses on the traceability of refunds and returned items across the supply chain, thereby facilitating a comprehensive understanding of transparency in handling customer returns.

Loyalty programs are designed to strengthen customer commitment to a brand (Uncles et al., 2003), increase purchase frequency (Lin and Bowman, 2022), bolster cross-over effects to other products and services (So et al., 2015), generate favorable word-of-mouth (Sundie et al., 2009) and increase willingness to pay a price premium (Meyer-Waarden, 2015). Retailers adopt loyalty programs to reward and encourage repeat patronage through incentives while maintaining brand share (Uncles et al., 2003; Henderson et al., 2011; Brashear-Alejandro et al., 2016). A loyalty program positively affects customers’ buying habits, evaluations and relationships with the retailer (Bolton et al., 2000). However, loyalty rewards expose repeat customers to repeat services, including potential service failures (Hwang et al., 2019). While a positive experience can also lead to higher, harder-to-meet expectations, Wagner et al. (2009) find that loyalty is adversely affected when a loyalty program member status is reduced.

This study is grounded in two prominent theories: signaling theory and justice theory. Integrating both theories offers a more robust understanding of the interactions in the returns process between retailers and customers than each theory could individually explain. Signaling theory is the primary theoretical lens through which we examine the impact of retailers’ return policies and loyalty program disclosures on consumer repurchase behavioral intention. Signaling theory addresses the reduction of information asymmetry between parties involved in a transaction (via costly signals) (Mavlanova et al., 2012). A costly signal is a signal that requires significant effort to produce, ensuring that only those who genuinely possess the intention to communicate can afford to send it. This concept is crucial in reducing information asymmetry between parties, as the high cost of the signal makes it credible and trustworthy. In a typical signaling process, the signaler (e.g. firm, employee) sends costly and observable signals to the receiver (e.g. shareholder, consumers), such that the signaler forms and sends signals to reduce the asymmetry (Connelly et al., 2011). The signal communicates a specific quality about the signaler that would be ambiguous to the receiver, and the receiver observes the signal and interprets it (Connelly et al., 2011; Cheng et al., 2020). A signal helps reduce information asymmetry when it is profitable for high-quality retailers to send but unprofitable for low-quality retailers (Mavlanova et al., 2012). Signals then separate different quality retailers (Mitra and Fay, 2010).

This study proposes that retailers send an observable and clear signal by revealing the return policy and loyalty program information during the return process. In doing so, customers will quickly grasp how retailers handle their returns. Scholars suggest frequent communication of signals will likely capture the audience’s attention (Spence, 2002; Gao et al., 2008). Retailers must consistently communicate multiple signals and show only slight contradictions to enhance transparency and credibility. The customer will grasp the signals and subsequently use them to infer retailers’ reverse logistic service capability. Signaling theory describes how the disclosure of transparency leverages service capability in the returns process from the customer’s perspective.

Justice theory emphasizes the perceptions of fairness within the relationship (Greenberg, 1990). Research discloses that customers are sensitive to fairness perceptions in various social and economic transactions (Maxwell, 2002; Tokar et al., 2020; Russo et al., 2022; Jones et al., 2023). People are more likely to perceive decisions as legitimate if they view the decision-maker as providing fair procedures, see the decision-maker as interpersonally fair and believe they have equitable access to all relevant information (Taylor, 1974; Colquitt et al., 2005). Research shows that customers’ future behaviors depend on whether they are treated fairly (Ha and Jang, 2009; Jung et al., 2017; Russo et al., 2022; Jones et al., 2023). In the service context, interactional justice reflects how customers are treated with dignity and respect. This may occur when a frontline employee appropriately explains service failure (McColl-Kennedy and Sparks, 2003; Ha and Jang, 2009). Duffy et al. (2013) further note that interpersonal treatment during decision-making impacts an individual’s reaction to decision outcomes. Justice has proved beneficial in studying product returns process and reverse supply chain operations (Mollenkopf et al., 2007; Griffis et al., 2012; Russo et al., 2022; Jones et al., 2023). The retailer’s capability to handle returns during interaction enhances customer perception of (the) fairness of the returns process because customers are likely to evaluate the process to be fair and align with their expectations (Russo et al., 2022). While the outcome of the return is still a key element in the service experience, customer-perceived justice relies on different aspects of the recovery process (Peinkofer et al., 2022). Justice theory examines the customers’ perception of the fairness of reverse logistics services during the returns process.

We first hypothesize that retailers’ disclosure of the returns policy will affect consumers’ perceptions of returns-related procedural justice. As consumers are concerned about the frequency and consequences of their returns, an unsatisfying returns experience will increase their uncertainty about the retailer and affect their future shopping behavior. Judge and Colquitt (2004) argue that information sharing can significantly affect congruence among parties. Consumer perceptions of procedural fairness may depend on revelations about how retailers manage the return. Typically, consumers do not have full access to the information retailers use to determine a return’s legitimacy. Keeping such information from customers underlies a belief that they may not know what is best for them or would not make the ‘right’ decision. However, customers evaluate information concerning the returns process to manage uncertainty (Taylor, 1974).

A TRPC may be used as a cue to increase customers’ confidence in a retailer. Clear communication about returns policies helps in setting proper expectations and reduces confusion. Moreover, the administrative dimension, which includes efficient handling and traceability of returns, further enhances customer confidence by ensuring that returns are processed smoothly and accurately. This comprehensive approach to transparency in return policies reduces customer uncertainty and fosters a stronger relationship between the customer and the retailer. Customers are more likely to trust the retailer when they feel informed and understand the rationale behind tailored returns conditions. Transparency in these decisions reduced uncertainty (Taylor, 1974). In contrast, an opaque returns policy may trigger ambiguous considerations since it might focus only on the retailer’s profit. Research suggests that providing information about how an organization reached its decisions can lead to stronger decision acceptance (Mavlanova et al., 2012). Therefore, a transparent returns policy will significantly affect consumers’ perception of reverse logistics service quality. We offer the following hypothesis:

Customized loyalty program information will serve as another signal in the returns process. The retail strategy has moved toward a relational perspective emphasizing the customer rather than focusing on the product (Leenheer and Bijmolt, 2008). Loyalty programs create a competitive advantage by keeping current customers rather than continually replacing them (Leenheer and Bijmolt, 2008). A retailer pursues this competitive advantage by developing a deep understanding of customer needs, tailoring their offering to these needs as closely as possible and giving continuous incentives for customers to concentrate their purchases on them (Day, 2000). Noble et al. (2014) conclude that customers are more likely to value a loyalty program if they anticipate receiving frequent positive reinforcements.

With innovations in the data collection processes, retailers can use loyalty programs to seamlessly implement customized customer messaging to meet their increasingly diverse desires (Kumar, 2010), especially during direct contact with the customer in the returns process. Retailers may use loyalty programs to improve customer understanding and create higher customer value through differentiated offers (Leenheer and Bijimolt, 2008). As the customized and differentiated message is focused on customers’ needs and wants, a loyalty program’s transparency revealed in the return process enhances customer understanding of the loyalty program’s value. Communication through loyalty programs cultivates customer knowledge at direct touchpoints. Driving transparency into the loyalty program and across the returns process enhances customers’ perceptions of fairness and consistency. This transparency demonstrates the retailer’s commitment to providing a personalized, positive experience. When customers recognize that their specific profiles/ratings in loyalty programs are considered, the perceived fairness of the returns process is increased. Therefore, we propose the following hypothesis:

Reverse logistics service has long been seen as an opportunity to build a competitive advantage (Daugherty et al., 2005). Superior service quality is considered one of the keys to developing and retaining long-term returns (Daugherty et al., 2005, 2019). Murfield et al. (2017) investigate the positive association of logistics service quality with consumer satisfaction and loyalty. Using the return process to enhance a customer service rating is critical in support of retailing in the omnichannel era (Daugherty et al., 2019). Customers want handling and processing accomplished as quickly and smoothly as possible (Daugherty et al., 2005). Poor service and impatient customers can easily translate to lost business (Daugherty et al., 2019).

Kumar et al. (1995) conclude that one party’s perception of its partner’s procedural fairness strongly affects its willingness to invest in the relationship. It can be inferred that the perceived fairness of the returns process employed in determining these outcomes will influence the customers’ desire for continuity of the relationship and affect the degree of the customer’s long-term orientation. When mutual perceptions of procedural justice are high, both parties consider the relationship to be fair play (Luo, 2005). As the relationship continues, the confidence levels increase, which helps alleviate the fear of exploitation in the retailer-customer relationship (Liu et al., 2012). Similarly, when customers perceive a high level of procedural justice, they know that a retailer’s credibility protects their benefits well. Consequently, they are more willing to invest in and continue the relationship. Kim and Kim (2016) reveal that customers who are satisfied with services have significant positive tendencies to repurchase products and services in the future. Therefore, we propose:

According to justice theory, interactional justice focuses on how fairness in interpersonal interactions is perceived (Colquitt, 2001). It assesses the extent to which customers experience treatment characterized by politeness, dignity and respect during service processes (Liu et al., 2012). Retailer returns interactional justice encompasses how customers view retailing employee empathy, courtesy and problem-solving efforts (del Río-Lanza et al., 2009). A lack of patience and communication skills among employees of omnichannel retailers can lead to customer dissatisfaction with service quality across both online and physical channels (Xie et al., 2023). Thus, retailer returns interactional justice is critical in fostering consumer loyalty and improving the quality of relationships between retailers and their customers.

Service employees with the necessary skills are crucial for effectively meeting customer needs during service interactions (Jung et al., 2017). Positive service experiences enhance customer loyalty and commitment to the retailer over time. Buell et al. (2017) found that customer appreciation for service increases when they notice employees exerting considerable effort. Furthermore, the respect and courtesy shown by service employees increase customer preference for the retailer, serving as reciprocal gratitude for receiving fair treatment (Ertekin, 2018). Therefore, we propose that:

Next, we hypothesize that a transparent return policy in the returns process impacts consumer repurchase behavioral intentions. According to Spence (2002), signaling reduces transaction uncertainty by distinguishing high-quality retailers who can afford to communicate transparently from their lower-quality counterparts. A transparent return policy, characterized by the dissemination of clear, observable information (signals) by retailers (signalers) about their return policies, acts as a strong signal that a retailer is cooperative and supportive, thereby boosting consumer confidence in the retailer’s reliability (Duan et al., 2021). This relationship is supported by the theory that effective signaling can reduce perceived risks associated with repurchases (Bower and Maxham III, 2012; Petersen and Kumar, 2009), encouraging sustained customer engagement. This hypothesis suggests that a transparent return policy, as a clear and costly signal of retailer quality, not only diminishes information asymmetry and perceived transaction risks but also fosters a stronger, more committed relationship between consumers and retailers, ultimately promoting repurchase intentions.

A TLPC requires retailers to implement information technology across multiple channels to connect and analyze transaction information and give customers a more personalized experience during reverse logistics. As signal receivers in the omnichannel retailing context, customers perceive costlier signals to be associated with more credibility (Liu, 2007). To disclose customized customer loyalty program information, omnichannel retailers must expend additional efforts to build connections and maintain transparency, resulting in higher associated signaling costs (Hartmann and Moeller, 2014). Such costly signals will be considered higher quality (Connelly et al., 2011). As a result, the disclosure of a transparent loyalty program signals a retailer’s willingness to enhance a superior returns experience, which has a more significant impact on consumers regarding their attitudes and purchase intentions. We, therefore, hypothesize that:

We argue that customer perceptions of a TLPC can moderate the impact of returns policy capability on customers’ perceived retailer service procedural justice. Communicating with the customer is significant in creating the customer experience, especially during direct contact (Xie et al., 2023; Jones et al., 2023). Credible communication helps retailers reduce the information asymmetry between retailers and customers (Connelly et al., 2011). The TLPC is conceptualized as the revelation of comprehensive customer loyalty information—including rewards, status and customized returns offers—that serves as a multifaceted signal to consumers. This breadth of information, when communicated effectively, can significantly enhance a customer’s cognitive and emotional engagement with the retailer, fostering a more profound sense of trust and commitment (Connelly et al., 2011; Janney and Folta, 2006). Such trust directly impacts customers’ perception of the retailer’s procedural justice in managing returns. On the other hand, a TRPC focusing on the disclosure of detailed returns information, aiming to clarify the return process for all stakeholders, signals the retailer’s integrity and reliability in returns operations. This strategic and administrative disclosure serves as a direct signal of the retailer’s integrity and reliability, crucial for managing customer expectations and perceptions regarding procedural justice in returns (Griffis et al., 2012). In line with signaling theory, transparency reduces information asymmetry, allowing customers to more accurately assess the retailer’s quality and fairness in handling returns (Spence, 1974; Connelly et al., 2011). The interaction between the transparent loyalty program and return policy capabilities thus plays a critical role in shaping customers’ perceptions, with a strong alignment between the two signaling consistent quality and enhancing perceptions of procedural justice. This alignment, or signal fit, between the loyalty program and return policy transparency can bolster customer trust and satisfaction, further emphasizing the importance of coherent signaling in retail returns management (Connelly et al., 2011; Kirmani and Rao, 2000).

A misfit, such as high transparency in loyalty programs coupled with low transparency in return policies, might introduce confusion and reduce perceived procedural justice due to inconsistent signals regarding the retailer’s commitment to fairness and customer value. This scenario underscores the critical importance of maintaining signal coherence across different aspects of retail returns operations to effectively leverage transparency in enhancing customer perceptions of procedural justice. Therefore, our hypothesis suggests that the effect of transparent return policies on perceived procedural justice in retail reverse logistics is stronger when the transparency of the loyalty program is perceived as high, facilitated by a coherent signal strategy that aligns the transparency levels of both components to optimize customer trust and satisfaction.

Further review of H3 and H4 leads to the potential moderating influence of retailer returns interactional capability when considering how a reverse logistics procedural capability affects repurchase behavioral intention. When service employees are perceived to be adept at meeting customers’ needs, it demonstrates genuine concern for the customer’s welfare. The retailer’s entire service quality may be increased in the customer’s eyes (Daugherty et al., 2005). In service recovery studies, actions deployed to impact another party’s perceived justice of the resolution are suggested as tools to correct an adverse event’s damage (Wang et al., 2014; Cheng et al., 2020) and improve customer satisfaction (Jones et al., 2023).

Capable employees are more likely to identify customer needs, clarify confusion regarding policies and procedures and increase customer satisfaction (Poujol et al., 2013). The overall experience is influenced by how individuals perceive and process information the service provider communicates (Peinkofer et al., 2022; Russo et al., 2022). For example, when the customer initiates the product replacement, the service provider’s prompt and effective communicative effort can improve customer perceived fairness (Russo et al., 2022). In the context of the returns process, when a retailer’s service employees have a lower level of RRLSPC, reverse logistics interactional capability becomes even more critical concerning repurchase behavioral intention. Individuals seek explanations and information better to understand cause and effect in the service recovery; the information provided through interactions alters the outcome.

Griffis et al. (2012) identified that updates on refund status ease customers’ uncertainty during the product returns process. Indeed, a perceived justice capability with a lower degree of procedural fairness can be balanced out by a high degree of interactional fairness because communication improvement can help clarify the procedures used within a process (Hulland et al., 2012; Russo et al., 2022). Similarly, employees responsible for addressing customers’ return needs and politely and promptly communicating accurate information during the interaction with customers can enhance the impact of customers’ assessment of procedural justice on repurchase intentions. Explanations about the process can increase a customer’s understanding and control over the process (Russo et al., 2022). Thus, retailers with returns interactional capability can enhance customers’ evaluation of the returns process, resulting in positive customer outcomes. Following this reasoning, we postulate the following hypothesis:

According to justice theory, when individuals perceive fairness in a process, they are more likely to accept the outcome because it is deemed fair (Martinez-Tur et al., 2006). In the context of returns management literature, perceived procedural justice is considered an antecedent to customer attitude and behavior (Russo et al., 2022). Procedural justice encompasses critical elements of the service recovery and returns process that customers value, such as processing time, speed and flexibility. As customer demand for transparency increases, the administrative dimension of TRPC provides tracking and tracing of returns, which eases the obstacles to effective decision-making. This is especially true for returns with no printed receipts and fraudulent returns. Thus, we contend that a transparent returns policy will enhance customer perceptions of procedural justice, further influencing customer behavior intentions. Formally stated:

We adopted a scenario-based vignette experiment to investigate our proposed relationships, as shown in Figure 1. Scenario-based experiments have been used in supply chain research to isolate effects that are difficult to observe in noisy field experiments (Ta et al., 2018; Duan and Aloysius, 2019; Duan et al., 2021). The experimental validation used in this study consists of three phases: two pilot tests and one main study. We conducted two pilot tests to develop and refine the experimental stimuli to capture appropriate variable information (Knemeyer and Naylor, 2011) and isolate the causal relationships between independent and dependent variables. The experiment features eight conditions corresponding to eight scenarios (see  Appendix 1) describing a physical store’s returns situation. The experiment design for our study was a 2 (low TRPC vs. high TRPC) $×$ 2 (low TLPC vs. high TLPC) $×$ 2 (low RIC vs. high RIC) between subjects.

The vignettes were designed to describe a hypothetical returns scenario at the physical store. Participants were instructed that they were about to return one misfit clothing item bought online to a retailer’s physical store (BORIS). Apparel was selected for the vignette as it is one of the most returned product categories in the omnichannel context. To eliminate any potential brand effects, we did not specify the brand name and used a general term: “your favorite brand.” After drafting several iterations of the vignette, five well-known supply chain management and marketing scholars reviewed the experimental conditions. They provided invaluable feedback on the instructions and clarity of the writing.

We adopted a multi-segment experimental approach like that of Bendoly et al. (2010). All participants were provided detailed instructions (see  Appendix 1) to ensure the experimental procedure’s consistency. Segment one involved a pre-qualified survey modeled after Barisione and Iyengar (2016) to investigate ethnicity issues. Demographic and background questions, including ethnicity, were carefully blended to minimize priming effects (Ta et al., 2018). In segment two, each subject was randomly assigned to a scenario with specific treatment exposure. Segment three used follow-up questions to measure research constructs, manipulation effectiveness, participant attentiveness, social desirability bias and other participant characteristics used as control variables. We randomized all scenarios and answered options to minimize ordering effects (Abbey and Meloy, 2017). Our between-subject design and subtle manipulation prevented the participants from guessing the purpose of the research, reducing the unwanted demand effects (Lonati et al., 2019). Following Schoenherr et al.’s (2015) recommendation, we included attention-check questions and retained a quality filter based on participants’ prior histories. Lastly, we prevented the same individual from entering the same study multiple times by accepting only one participant per Internet Protocol (IP) address (Zhu et al., 2018).

As recommended by Rungtusanatham et al. (2011), we tested the extent to which participants perceived our scenarios as realistic on a seven-point Likert scale. We included several manipulation check questions in the study to evaluate the efficacy of the TRPC, TLPC and RIC. A single-item direct attention check question assesses and eliminates careless responses and increases data quality (Abbey and Meloy, 2017; Meade and Craig, 2012 Schoenherr et al., 2015). To mitigate the impact of social desirability bias, participants were guaranteed anonymity in their responses, emphasizing that honest feedback was of utmost importance (Fisher, 1993). Further, we include six items from the Crowne and Marlowe (1960) social desirability scale in the post-experience survey. This approach tests the direct path from the social desirability measure to the dependent variable (Hartmann and Moeller, 2014).

Retailer reverse logistics service procedural capability was measured with items adapted from Maxham and Netemeyer (2002) and Fang et al. (2011) to handle returns management in-store. Retailer interactional justice capability was examined in this research, reflecting the degree to which retailer employees put forth effort on returns handling and treated customers with respect, courtesy, fairness and honesty throughout the returns processes. We adapted the measurement items from Vesel and Zabkar (2009) to suit the retail returns service setting. The construct of customer repurchase intentions was assessed through a six-item using a 7-point Likert scale, adjusted from Duarte et al. (2018) to suit this study’s context better. The scale for measuring repurchase intentions across omnichannel retail touchpoints, aiming to facilitate a cohesive shopping experience, is further confirmed by Xie et al. (2023). Additionally, where appropriate, we accounted for various personal attributes such as age, gender and income, which have been demonstrated to influence the decision-making processes of individuals, as identified by Overstreet et al. (2022). Other variables include education, ethnicity, previous experience and shopping frequency in the past year. “Shopping frequency” captured a person’s shopping times in the past year and was measured by one item adapted from Tokar et al. (2020).

Utilizing the concept and framework of supply chain visibility and traceability developed by Morgan et al. (2018), the constructs for measuring TRPC and TLPC were identified. This development was further refined by integrating findings from return policy and related literature for enhanced transparency in returns and loyalty program capabilities, respectively. The development of these measurement scales underwent three critical stages: item generation, purification and validation. Initially, a comprehensive item list was generated through a thorough literature review. This list and definitions of the transparent return policy and loyalty program capabilities were then evaluated for content validity by ten subject experts with backgrounds in logistics and supply chain management. The feedback from these experts, which broadly affirmed the item list’s adequacy while incorporating specific insights from our field study, was instrumental in refining the measurement scales. These refined scales were subsequently tested in a pretest, ensuring they captured the construct of interest. The scales’ reliability and validity were assessed and verified through two pilot studies, establishing the final scale items for use in our research.

In the initial pilot study, we recruited 603 business college students from two prominent public universities in the southern USA, offering course bonus credits as an incentive for participation. Student samples have been shown to be appropriate for supply chain research contexts when studying the consumer (Kees et al., 2017; Peinkofer et al., 2016). Of the responses received, 235 were disqualified due to rapid completion, suggesting speeding. Our measurement scale employed reverse-coded items, a method validated by Schoenherr et al. (2015), to ensure respondent attentiveness. Responses were excluded if they showed a uniform pattern of identical values (e.g. all 1s) across the survey’s Likert-point scale, particularly, in reverse-coded items. This uniformity indicates a significant lack of engagement with the material, implying that the questions were not read thoroughly. Further, 25 respondents who indicated they had taken the survey before were also eliminated, resulting in 343 responses eligible for detailed analysis. Even though the loss rate is relatively high, it is within the range of attention check results reported in other experimental operations studies (Abbey and Meloy, 2017). Table 1 presents details of sample characteristics.

Our dependent variable was adapted from the literature to fit the study context, with the unit of analysis at the subject level.  Appendix 3 provides an overview of the measures used. Previous literature indicates that customer behavior and similarity perceptions may vary by individual characteristics (Bregman et al., 2015; Ta et al., 2018).

We evaluate the perceived treatment of assigned scenarios on a seven-point Likert scale. Testing shows that means were significantly different from low-level and high-level for return policy capability, indicating a successful treatment manipulation. The t-test shows that high-level cases were statistically significantly different from the low-level scenario for RIC. These two measurements helped us assess whether the experimental manipulation was held, following Bachrach and Bendoly (2011). The average realism check measure score was 5.6, with a standard deviation of 1.05. The results demonstrated that our participants could imagine the hypothetical returns experience happening to them. However, the obtained result revealed that the manipulation of the TLPC was not working as expected. After reviewing the data and discussing possible improvements with three scholars, we altered the detailed treatment instrument. Then, we conducted a second pilot test by collecting another 120 responses from Amazon Mechanical Turk (Mturk). The responses indicated that the alterations had the intended effect. We present the final version of the vignette in  Appendix 1.

The procedure used in the main study replicated the procedures used in two pilot tests, with a few minor modifications of the wording based on respondents’ feedback. Subjects for the experiment were recruited through Mturks and were paid $1.8 after completing an online survey built using Qualtrics. We created a human intelligence task in Mturk to collect data from willing participants with online purchasing experience (Sheehan and Pittman, 2016). To mitigate the effects of cultural variance, we recruited US-based participants (Ta et al., 2018; Overstreet et al., 2022). Of 620 responses, 94 were disqualified for reasons like repeated attempts or failed attention checks, yielding 512 participants for our primary study analysis.

We conducted a confirmatory factor analysis of dependent variables, independent measures and moderator variables and found evidence of discriminant validity. Our five-factor model was established, including repurchase behavioral intention, RRLSPC, TRPC, TLPC and RIC. The fit statistics are CFI = 0.943, RMSEA = 0.075 and SRMR = 0.046, supporting our model (Hair et al., 2017). We assessed the reliability of the dependent and independent variables by means of Cronbach’s alpha and composite reliability statistics. The values of Cronbach’s alpha exceeded conventional thresholds of 0.5 (Burton et al., 1994). Convergent validity was established through the average variance extracted (AVE). The AVE for each factor exceeded the recommended threshold of 0.5, and all three alpha values exceeded 0.8 (Fornell and Larcker, 1981). We assess convergent validity by checking standardized factor loadings for each item. The item factor loading is above the 0.5 threshold and significant at p < 0.001, suggesting convergent validity. The AVEs for latent variables are above 0.5, offering adequate construct reliability, as shown in Table 2. Harman’s one-factor test with unrotated factor solutions was utilized to address potential common method bias. The findings indicated that a single factor accounted for 43.046% of the total variance in the sample, which falls below the recommended threshold of 50% (Podsakoff et al., 2012). Consequently, this result suggests that common method bias does not pose a concern in this study.

To test $H1$⁠, $H2$ and $H7$⁠, we ran a regression with TRPC as the independent variable and the TRPC as a moderator and perceived RRLSPC as the dependent variable, using PROCESS model 1 (Hayes, 2013) (mean-centered for all constructs, a bootstrap sample of n = 5,000). The results, shown in Table 3, indicated a significant direct effect of TRPC on customer-perceived RRLSPC, supporting $H1$⁠. Surprisingly, no significant direct effect was observed from the TLPC on perceived retailer reverse logistics service procedural justice, $H2$ is not supported. $H7$ hypothesized that TRPC and TLPC interact to produce RRLSPC perception. The data support this expectation, indicating that the perceived relationship between TRPC and RRLSPC is more robust when the TLPC is high instead of low (Figure 2). This finding suggests that customers increase their RRLSPC perception with a higher TLPC if they are given the same level of transparency in the returns policy. This result suggests that transparency is crucial for retailers to improve their perceived capability in the retail returns experience. It emphasizes the need for consistent signaling across various aspects of retail returns operations to effectively use transparency in boosting customer perceptions of procedural justice. Retailers can significantly enhance customers’ perceptions of retail service procedural justice through transparency. This enhancement is achieved by reducing information asymmetry via signaling, differentiating retailers and increasing the signaling effectiveness.

To further explore how enhanced retailer reserves logistics service procedural capability impacts customer repurchase behavioral intention, we ran the PROCESS 14 and 22 model to test the hypotheses $H3$⁠, $H4$⁠, $H5$⁠, $H6$ and $H8$ (Hayes, 2013), which matches the structure of our conceptual model. We run the regression with TRPC as an independent variable, the TLPC as the first moderator, RRLSPC as the mediator, RIC as the second moderator and customer repurchase behavioral intention as the dependent variable. The control variables were also included. The result shown in Table 3 support $H3$⁠, that customers’ perception of reverse logistics procedural capability is positively associated with customers’ repurchase behavioral intention. This finding aligns with other studies’ findings; Lin et al. (2011) found that procedural justice can result in increased intentions to return and a reduced propensity for spreading negative word-of-mouth, highlighting the impact of fairness perceptions on consumer retention behaviors. $H4$ anticipates a positive link between customers’ perceptions of RIC and their repurchase intentions, a relationship confirmed by the findings in Table 3. This is consistent with Peinkofer et al. (2022) finding that the overall consumer experience is determined by how individuals perceive and interpret the information conveyed by the service provider, underlining the critical role of interactional capabilities in driving repurchase behavior.

Hypothesis H5 suggests a positive correlation between the TRPC and customers’ intentions to repurchase, a relationship that is corroborated by the evidence presented in Table 3. To test hypothesis H9, we conducted a moderated mediation analysis with RRLSPC as a mediator; the result is shown in Table 3. The PROCESS 4 test examines the mediation between transparent return policy and repurchase behavioral intention through retail reverse logistics procedural justice. A bootstrapping approach was used because it employs repeated sampling and provides narrower confidence intervals for estimates and, therefore, greater asymptotic accuracy (Duan and Aloysius, 2019). The transparent returns policy enhances customer perceptions of procedural justice, further influencing customer behavior intentions. Hence, $H9$ was supported. We use the PROCESS macro (model 22, n = 5,000) to test the predicted interaction effect between the RIC and RRLSPC (⁠$H8$⁠). However, we could not find evidence that RIC attenuates the effect of reverse logistics procedural service on repurchase behavioral intentions, as shown in Figure 3. Therefore $H8$ is not supported. We speculate that the procedural justice enhanced by transparency offered during the returns process may diminish customers' emphasis on their perception of interactional justice, suggesting a pivotal threshold in how justice is perceived during such experiences. This concept aligns with research findings that examine the different influences of justice types on customer outcomes. Notably, distributive justice is considered to have a more substantial impact than procedural or interactional justice in service recovery contexts due to its more tangible nature (Martinez-Tur et al., 2006; Maxham and Netemeyer, 2003). Martinez-Tur et al. (2006) indicate that distributive justice is particularly pivotal in scenarios with minimal socio-emotional engagement. Furthermore, Jones et al. (2023) demonstrate that the impacts of interpersonal justice on returns satisfaction are more pronounced than those of informational justice, further elaborating on the nuanced effects of justice perceptions on customer responses. The mediation paths are summarized in Table 4.

This study’s findings make essential contributions to the logistics, supply chain management and omnichannel retailing literature by uncovering the influences of the in-store return experience on customer repurchase behavioral intention based on signaling theory and justice theory. Even though customers have been recognized as critical, active participants in supply chain execution (Ta et al., 2015), their reactions, perceptions and supply chain performance assessments were not traditionally considered (Esper and Peinkofer, 2017). Our study meticulously identifies and empirically evaluates four crucial capabilities essential for enhancing the customer interface within omnichannel retailing: TRPC, TLPC, RRLSPC and RIC. It responds to the scholarly demand for a detailed understanding of the capabilities necessary for effective omnichannel returns management (Daugherty et al., 2019). This exploration and assessment sheds light on areas where businesses can improve to ensure a more seamless and satisfying customer experience in the BORIS process. Consequently, our research enriches the academic conversation (Rasool et al., 2023) regarding omnichannel retailing strategies and offers practitioners practical guidance for refining their returns operations, aligning both theory and practice toward the common goal of optimizing omnichannel returns management and dealing with end-of-life product issues (Wilson and Goffnett, 2022). By understanding customer perceptions of retailer omnichannel capabilities during the returns experience, this study allows the retailers and their supply chains to better plan for customer needs and preferences and make corresponding improvements to respond to customer demand.

Our findings contribute to the growing supply chain research domain of transparency by examining how disclosure of a returns policy and loyalty program during the returns process impacts customer perceptions of RRLSPC and repurchase behavioral intention. The finding that perceived transparency of the returns process affects customer repurchase behavioral intention is consistent with signaling theory (Brady et al., 2012) and confirms long-held expectations that customers who can observe how and why rules are applied in the returns process take this as a reliable signal of service quality via justice evaluation (Bonifield et al., 2010). This approach contributes to signaling theory by demonstrating the role of transparency as a signal in contexts beyond initial purchase decisions, extending into the post–purchase experience. Our research makes a novel contribution to signaling theory by delving into how transparency in returns policies and loyalty program capabilities during BORIS returns shape consumer perceptions and behavior. By investigating how consumers decode information about returns services in physical stores, our study underscores the significance of clear information disclosure in influencing consumer actions. This consumer-centric approach (Ishfaq et al., 2022; Esper and Peinkofer, 2017) aids in generating insights for refining supply chain management strategies and addresses the challenges of escalating customer returns in omnichannel contexts (Yan et al., 2022; Yang et al., 2023) and the imperative to reduce return costs (Daugherty et al., 2019).

When considering paths to build responsiveness into logistics strategy, our study explores the dynamics between transparency in loyalty programs and returns policy capabilities, identifying their pivotal role in molding consumer perceptions. The congruence, or signal fit, between these elements signals consistent quality to consumers, enhancing their perceptions of the retailer’s reverse service procedural justice, which, in turn, can strengthen customer repurchase intention. This finding aligns with and expands upon existing literature (Connelly et al., 2011; Kirmani and Rao, 2000), emphasizing the critical importance of cohesive signaling in managing retail returns. Conversely, signal misfit, characterized by discrepancies such as high TLPC and low TRPC, may spread confusion and diminish perceptions of fairness, spotlighting the adverse effects of inconsistent signaling on customer perceptions of retailer integrity.

This study contributes to signaling theory in three specific ways. First, it clarifies questions about the role of returns service information in a reverse logistics context, including detailed returns policies and personalized loyalty programs, as signals of returns service experience. Second, it examines long-standing questions concerning how consumers navigate and interpret both congruity within these signals and the discrepancies between them during the returns process in physical stores (Autry et al., 2001; Petersen and Kumar, 2009; Wood, 2001). Thirdly, it demonstrates the impact of these consumer interpretations on their behavioral responses, particularly their repurchase intentions. By providing a nuanced understanding of how transparent returns policies and loyalty programs serve as signals in the retail environment and influence consumer behavior, our research offers valuable insights for omnichannel retailers striving to enhance customer repurchase intention through effective communication and policy transparency. A broader implication of our findings is that more extensive applications of signaling theory may be helpful in reverse logistics. Our research highlights how transparency affects customer perceptions of RRLSPC and their intentions to repurchase. We found that different characteristics vary in their ability to influence perceptions of service quality, indicating a spectrum of signal strengths from strong to weak.

Our research contributes significantly to the justice literature by delving into the BORIS returns process within omnichannel retailing, specifically regarding service recovery and the speed of returns. Echoing previous studies that have highlighted the role of perceived justice in service recovery leading to customer satisfaction and positive behavior (Mollenkopf et al., 2007; Griffis et al., 2012; Russo et al., 2022; Jones et al., 2023), our study further explores the nuanced impacts of interactional and procedural justice in this context (Blodgett et al., 1997; Martínez-Tur et al., 2006), including how interactional justice interacts with procedural forms (Goodwin and Ross, 1992; Russo et al., 2022). Our findings extend the dialogue by emphasizing the crucial roles of interpersonal and procedural justice in the BORIS returns process, showcasing their profound effects on customer satisfaction and engagement. This enhanced understanding aids retailers in refining their returns management strategies to enhance customer experiences and outcomes in the omnichannel landscape. Our findings indicate that as retailers treat their customers fairly regarding returns processes and interactions, consumers reciprocate by engaging in behaviors to strengthen the relationship. This reinforces the view that retailers need to make themselves attractive to consumers by offering value to consumers not only in terms of products but also in terms of in-store competency and RRLSPC. This study confirms that one such source of value is how fairly customers feel they are treated. The result from the quasi-experimental design strongly supports the notion that consumers may rely heavily on returns procedures when assessing the overall fairness of returns events rather than the actual outcomes. Results indicate that RRLSPC fairness has a more substantial impact on satisfaction than interactional fairness, suggesting that how the returns are handled is significant for retailer-customer relationship management. A core contribution of this study is to expand our understanding of returns management by theorizing and confirming that what customers learn about the returns process matters: consistent with signaling theory, these perceptions are strong indicators of retailer service quality and consistent with justice theory, they have significant effects on repurchase behavioral intentions.

Our research contributes to the understanding of omnichannel returns management. While it is challenging to fully understand the relationship between the setup of omnichannel supply chain structures and the facilitation of customer returns, this study outlines the supporting role of brick-and-mortar store locations in reverse logistics processes. Direct contact communication with customers has been regarded as an effective method to create the customer experience (Daugherty et al., 2019). Our findings indicate that a TRPC increases customers’ fairness perceptions of the procedural justice of retailers’ reverse logistics service. The information technology deployment across omnichannel platforms shared between retailers and customers is essential to gaining complete visibility of customer interactions (Daugherty et al., 2019).

The complexity of product returns is increasing with the development of an omnichannel structure. For omnichannel retailers aiming to curtail returns costs while leveraging the customer return experience to foster repurchase intentions, incorporating transparency at crucial human interaction points becomes essential (Jones et al., 2023). Particularly for retailers grappling with behaviors like bracketing or wardrobing, implementing customized return policies (Overstreet et al., 2022) and loyalty programs emerges as a strategic response. Such transparency clarifies returns procedures and tailors the returns experience to discourage abuse of return policies, signaling a commitment to fair and customer-focused service practices. The justice perceived by customers returning items in-store is significantly influenced by the transparency of return policies, which includes clear communication about return policies and processes, tracking returned items and the provision of customized solutions for those exploiting return policies. Also, loyalty programs offering return-related discounts and utilizing member-specific information further enhance this perception. In an era where consumer expectations from omnichannel retailers exceed those from traditional outlets, the signals conveyed through customer service interactions become pivotal. These signals shape consumer expectations regarding both the process and outcomes of returns, emphasizing retailers’ need to manage the service information they disseminate consciously. Furthermore, in-store product availability checks and additional recommendations from the employee can serve as capabilities in customers’ channel choices in omnichannel retailing. This study contributes to understanding consumer return behavior in an omnichannel setting by examining the association between customer in-store returns experiences and subsequent shopping behavior. Retailers considering expanding their omnichannel capability (Ishfaq et al., 2016) and rethinking their physical store roles in the complex omnichannel environment can use this study’s findings to evaluate their strategy and identify the level of realignment effort needed. Our exploration of customer responses to retailer offerings for in-store returns management will guide retailers in developing requisite operational capabilities.

Concerning insight into the retailer’s interactional capabilities, the result demonstrates a high RIC increases customer repurchase behavioral intentions. Of our four direct paths influencing customer repurchase behavioral intention, an RIC has the second-largest standardized path coefficient and significantly affects customers’ behavioral intentions. This result emphasizes the need to reinforce the retailer’s capability to handle returns and the relationship with the customer. The findings motivate the idea that a training program may be a tactic to improve the in-store returns experience. An interesting question is whether retailers will train frontline employees to handle returns. Retailers usually have a training program to increase sales, but it is not tailored to handle returns. They may conduct store-wide training of all representatives handling returns or focus on some representatives specifically handling returns across the organization. Capable frontline employees with increased knowledge of customer needs can respond appropriately to retain customers through customer returns experience.

Retailers can use information from each customer’s product returns behavior to realize long-term relationship growth and maximize each customer’s profitability. Our findings suggest that developing returns strategies that match customers’ varying needs could be an effective way of turning customers making a return into customers showing loyal behavior. Improving retail service quality with a less costly investment is possible. Processing time length is one essential measurement of customer satisfaction (Ertekin et al., 2020) and allows retailers to improve service quality without high costs. Customers are less satisfied with the return process time, and retailers should consider dedicating resources to enhancing the efficiency of customer returns, such as providing a designated returns counter with clear signage from the entrance. The primary objective of the BORIS is to enhance customer returns service quality by ensuring easy access to return services. However, recent reports emphasize the significant operational challenges this creates for store employees, particularly in handling the surge in Amazon Prime day returns, known as “Amazombies” (O’Donovan, 2024). This influx places considerable strain on store resources and staff morale, thereby impacting overall store efficiency. Therefore, while having a dedicated BORIS desk may streamline the return process for customers, retailers should also consider the operational challenges and employee well-being when implementing such initiatives.

As with any research, our study contains certain limitations. It is possible that customers would behave differently during an actual returns experience than in our experiment. However, given that one cannot easily access or control an omnichannel environment and that gaining the ability to manipulate factors of interest is exceptionally difficult, our work represents a solid attempt to answer questions at the retailer-customer interface in the omnichannel scenario.

Our study only targets respondents from the United States. Exploring customers from other countries with an omnichannel retail environment at different phases could be worthwhile. A comparative study examining the different perceptions of the omnichannel retail environment from the retailer and customer perspective would also be interesting. Further studies could simultaneously collect data from customers and retailers to explore our proposed constructs' different perceived values and quality. Future studies could extend our analysis by examining the influential factors of customer returns intention in each channel an omnichannel retailer offers. Our study only examined BORIS, but future studies could cast a broader net to collect data on customers’ usage across different returns channels.

Although the previously mentioned limitations must be addressed in future research, they do not discredit our findings. Instead, the results presented here serve as a foundation for research on reverse logistics transparency in retailer-customer relationship management. Customers shopping at omnichannel retailers must decide which retailer to choose in the traditional shopping environment and which channel to choose. Our study did not examine the common capabilities in returns experience impacting customer channel selection. Future research is required to fully understand these capabilities and identify potential maturation paths followed by omnichannel retailers in pursuit of an optimal omnichannel returns management strategy.

Finally, using experimental design can limit the number of questions that can be asked. The complexity of the returns process and the apparent link between sustainability and reverse logistics openly call for a renaissance in returns and reverse logistics research. Given a better understanding of BORIS and the consumer, many new research questions can be examined. Future research should ask: What are the long-term impacts of transparent returns processes on customer repurchase behavior? What training and policies can improve interactional justice in the returns process? What are the operational benefits of optimizing procedural justice in reverse logistics? What types of compensation (refunds, exchanges, in-store credits) are perceived by customers as just? What are the customer and operational benefits of integrating technology in reverse logistics? How do cultural differences impact customer expectations and perceptions of fairness in returns? What are the best practices for managing returns in a global retail environment? How do sustainable returns practices influence customer perceptions of justice and fairness? What are the ethical implications of different reverse logistics strategies? What elements of loyalty programs are most effective in enhancing the returns experience? These research opportunities can provide valuable insights into managing reverse logistics and returns processes, enhancing customer satisfaction and building stronger customer relationships through improved justice and transparency. Scholars can contribute to a more comprehensive understanding of theory and practice by exploring these questions. We hope this research has the potential to drive innovative studies and ensure that both businesses and consumers benefit from a well-managed reverse logistics system.

The scenario of the different returns process. 3 Treatments: (transparent returns policy, transparent loyalty program, RIC). Below are background details about our case study. Please read the description carefully.

Today is Saturday morning. You wake up to organize your home; then you find that the clothes you purchased online (from your favorite brand) a few days ago did not meet your expectations (return reason code defined, exogenous variable?). It is hard to find a box to fit this item in, and your printer is down to print out the shipping label. As you know, there is a physical store near you and you decide to return the product at the brick-and-mortar store to skip the process of packaging and taping.

You walk into the store; the representative greets you warmly and checks if you need assistance. You tell them you came in for an online product return and then the representative leads you to the return counter. When you get there, the associate firstly appreciates you shopping with them and actively checks your information and returned products to help you. The associate looks up the available inventory and asks if you’d like to exchange the product or refund. (high level)/you walk into the store and can’t see any associates available to help. Following the signage at the store, you finally find out where the return counter is. You notice the associate at the counter is staring at the cell phone and giggling. After seeing you are right in line. The associate asks you how to help you. You tell them you come here for an online product return. The representative does not answer you and directly takes the returned product. The associate doesn’t mention any options of exchange or refund. (low level) (Retailer Interactional Capability).

When processing the returns, you can get the loyalty program info in simple words and see your loyalty member information on a computer in a few seconds. You can also see whether your product is qualified for return from your loyalty program purchase history. You can easily track the receipt and your loyalty status, rewards points and whether the discount voucher is available for the next purchase. (High level)/you can NOT see any loyalty program information around the counter and have to check with the associate again. Moreover, you are not provided with your loyalty program-related information, your purchase history, e-receipt and rewards points in the whole returns process (low level) (Transparent Loyalty Program Policy).

When you are waiting for processing, you see the returns policy posted on the wall at the front counter, which states what products can justify an acceptable return in simple words. You have been told how far you are away from the return deadline for this product. Finally, you are told the time frame for the refund and provide the tracking information (high level)/you do NOT see any information about the company’s returns policy around the counter or on the wall, in the whole returns process, you are not provided with the returns policy and how your products qualify for returns. When it is done, you are told the return has been processed and you are free to go. You are not given the time frame of the refund and tracking information. (Low Level) (Transparent Returns Policy).

Table A1