Search results

This research aims to examine the factors that influence customers' product return intentions and proposes that YouTube product reviews can mitigate customers' desire to return a product.

The proposed theoretical research model and hypothesized relationship were investigated using a quantitative process. This study used 302 data from Indonesian young adult respondents to examine the structural model, which was analyzed using the SmartPLS 3.2 software package.

The results show that YouTube product reviews, product fit uncertainty and customer satisfaction are the key determinants of customers' product return intention. Furthermore, the results show that the credibility of YouTube product reviews has a major impact on customers' familiarity with a product, satisfaction and the likelihood of returning goods to sellers.

In the e-commerce industry, increasing the use of YouTube product reviews will help businesses eliminate unnecessary product returns. Sellers are also encouraged to collaborate with YouTube producers to review specific products, which can benefit companies by raising brand awareness and gaining customer feedback. Furthermore, YouTube online product reviews can help consumers avoid having an unpleasant shopping experience that causes emotional reactions and lowers satisfaction.

Most research has not considered antecedents in observing the product return phenomenon; this study observes a prerequisite of consumer product returns (i.e. information asymmetry and product familiarity) and investigates the relationships between YouTube product reviews, customer satisfaction and product return intention.

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

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

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

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

Motivated by the real-world practice that the boom of the online selling induces a higher product return as well, selecting which online channel mode indicates who takes ownership over the product and thus bears the loss of the product return. This paper aims to seek the optimal online channel modes for the two members in a platform supply chain in the presence of product returns.

This study aims to develop a platform supply chain that consists of one platform company and one supplier. Along with an offline distribution channel, the supplier can choose two alternative online selling modes (i.e. the reselling and agency modes) to sell its product through the online marketplace. This paper applies Stackelberg game to derive the equilibrium with different business scenarios and selects the optimal online channel modes for two parties, respectively. Moreover, this paper extends to a different supply chain with a reverse channel leadership and a different product return policy for testing the robustness.

Several interesting and important results are derived in this paper. Firstly, it is found that the relative pricing are largely relied on the costs of two channels. Secondly, the platform supply chain may benefit from a pure channel rather than the dual-channel when this channel enjoys a relatively low cost and/or a sufficiently high consumer preference. Then, the platform and the supplier act contradictorily when selecting their optimal online channel modes. To be specific, the platform motivates to choose the online reselling mode when both the commission rate and the slotting fee are relatively low, whereas the supplier is likely to select the online agency mode under this circumstance. Finally, a win-win situation in regards to the optimal online channel mode for two parties is achievable with numerical experiments.

Based on the analytical studies, the results derived in the authors’ work can provide managerial insights to assist the supplier and the platform company in determining the operational decision and selecting the optimal online channel mode to deal with consumer returns. In addition, appropriate commission rate along with slotting fee will make both parties achieve a win-win situation in determining their optimal online channel mode.

To the authors’ best knowledge, this paper makes the first move to determine the optimal online channel mode in the content of consumer returns and study how it is affected by different product return policies.

Research regarding the quality of e-tailers’ service during product returns is sparse and the little that has been performed treats returns as recovery from failure. However, that view is outdated. E-tailers' product return practices have substantially evolved and customers' return behavior has considerably increased, in turn, influencing expectations of customers. Thus, a need arises to revise the understanding of how customers evaluate the quality of e-tailers' service during product returns. This study conceptualizes customer-perceived e-return service quality, identifies e-return's current dimensions, and offers directions for measurement.

This study is conducted in two stages. The first stage follows an abductive approach, with a continuous back-and-forth movement between existing theory and two qualitative data sets to identify the dimensions of e-return service quality. Scale development process is started in the second stage to offer directions for measurement based on the empirically grounded dimensions.

The conceptualization of e-return service quality identifies six dimensions: (1) owning of responsibility, (2) return convenience, (3) return remedies, (4) service team support, (5) site's return friendliness, and (6) returns diligence. The factor analysis supports the six-factor solution that can be employed for developing a valid scale in future.

The study suggests that e-tailers who are looking to differentiate themselves through superior e-return service quality should focus on customizing their service through excellent performance on these dimensions.

The paper updates and refines the understanding of service quality in the context of product returns service provided by e-tailers and thus offers a novel contribution.

This study aims to investigate the conditions for the financial feasibility of an incentive-based model for self-drop or crowdsourced drop of the product to be returned at designated drop boxes (thereby ensuring a contactless process).

Constraint-based non-linear mathematical modeling was done for cost differential with and without crowdsourcing. This was analyzed against returns on investment for the installed infrastructure. Scenarios were looked into from the linear, iso-elastic and logarithmic demand functions to identify the optimal incentive policy. The results were further evaluated using “willingness to return” for customer willingness for product returns via drop boxes.

Crowdsourcing is viable when product returns are no more than 15%–20% of the overall products, with a logistics cost differential of 15%–25%. These were only viable when the product return incentive was within the range of 15%–20% of the product cost, as well as the penalty was in the range of 25 to 40% for wrong returns.

The findings are expected to aid the organizations in successfully designing product return policies while adhering to the post-COVID-19 norms, including contactless transactions and social distancing.

The study provides a look into the viability sensitivity of effective gains/profitability against the required level of service for returns, wrong returns, penalties and incentives for crowdsourcing in a developing country like India.

The purpose of this paper is to investigate the dynamic acquisition pricing strategy for collecting used products (also known as cores or returns) in a finite planning horizon. In particular, this paper studies a cost-minimization model in which a firm offers acquisition price that impacts the quantity of the returns, and remanufactures the used product to satisfy the customer demand.

This paper uses multi-period stochastic dynamic programming theory to model a remanufacturing system that faces the random demand for remanufactured products. The number of the returns at each period is uncertain and increases linearly with the acquisition price offered.

The study shows that when the uncertainty of demand for remanufactured products increases, the remanufacturer should hold a higher core stock level to minimize the expected total cost and thus a higher acquisition price is needed to attract returns. However, given demand uncertainty, the optimal price decreases in the initial core stock level in each period. It also indicates that the optimal acquisition price increases in the variance of the returns, but decreases in the mean of the returns.

The findings suggest that a remanufacturer could reduce the expected total cost by adjusting the acquisition price according to the number of returns periodically.

Introducing the impact of supply uncertainty on the acquisition price of used products, this paper uses a multi-period dynamic model, instead of single period model in previous studies, to examine the remanufacturer’s dynamic acquisition pricing policy.

Forecasting product returns plays an important role in the operations of reverse logistics (RL). However, their contribution to sustainability performance is yet to be explored. The purpose of this paper is to explore the product returns in Indian electronics industry and examine the relationship of forecasting product returns with triple bottom line performance of RL.

In this study, based on past literature review, four hypotheses, relating to forecasting of product returns and its association with performance, were developed. A questionnaire was sent to 700 respondents from the Indian electronics industry. Overall, 208 received responses were found suitable for the research. The necessary statistical analysis was carried out to ensure the reliability and validity of the questionnaire. In order to test different hypotheses, partial least square path modelling (PLSPM) technique of structural equation modeling was utilized.

Measurement model had shown sufficient data fit for the modeling. PLSPM results reveal that the accuracy in forecasting product returns is positively associated with operational performance of RL. It also plays an important role in the sustainability efforts of an organization.

Managers can utilize results of study for exploring and emphasizing issues of product returns for improving RL performance. One of the limitations is that data are collected only from Indian electronics industry. Another limitation is that only product returns are considered for the operational and TBL performance of RL. In future, study may be carried out considering different factors in other sectors and countries.

The intent of forecasting product returns is considered to be operational efficiency. It can make significant contributions to the sustainability efforts of an organization. Review of the past literature indicates that research in the field of RL is in developing stage, and issues related to forecasting product returns are under-represented. The paper adds value to the few available articles on product returns.

The purpose of this paper is to investigate how online apparel retailers make trade-offs in achieving efficiency in handling consumer returns, attempting to reduce the number of consumer returns they are faced with and increasing sales through returns management.

The authors use literature to develop propositions and employed a case study research design to understand how online apparel retailers make trade-offs in returns management practices in order to verify the propositions. Case study subjects have been anonimized.

The authors have developed and verified five propositions with the aim to understand how retailers make trade-offs in reducing the number of online consumer returns, increasing the effectiveness of handling online consumer returns and increasing sales through returns management.

The research is limited by the use of interview data from cases, a focus on apparel retail only and by the use of companies located in the Netherlands only.

The propositions help managers make trade-offs in reducing the number of returns versus increasing sales through returns management versus increasing the effectiveness of handling consumer returns.

Consumer returns lead to a significant flow of items from consumers back to online retailers, in particular in fashion. Reduction of this flow decreases social and environmental impacts through reduced transport and handling requirements.

Several authors identify that more empirical research is welcome in the area of returns management. The research aims to contribute to this gap by focussing at how online apparel retailers make trade-offs in achieving efficiency in handling returns, increasing sales through returns management and attempting to reduce the number of consumer returns they receive.

Product returns management (PRM) is a core supply chain management process. Though the importance of value creation and appropriation is acknowledged, extant studies on value in product returns tend to be limited to the residual asset value (cost recovery) of the returned products. Further, value discussion in PRM is limited to the value implications for a single party in the returns transaction rather than all the product returns chain entities. The purpose of this paper is to explore value creation and appropriation in a triad of supplier-retailer-3PL in the product returns chain.

The study uses an inductive qualitative approach. Semi-structured interviews with executives in a triad of organisations formed the primary data source for the study.

The paper identifies six value drivers and develops a value creation and appropriation framework. It shows that facilitation, value orientation, process alignment and relational factors are key drivers of value creation and appropriation in PRM.

The findings reinforce the view that value creation and appropriation are the outcomes of multi-party interactions in a product return chain. The framework presented contributes to the literature by showing the linkages amongst key drivers.

The findings show the important role of external process facilitation and how a 3PL contributes to value creation in a triadic relationship.

Research based on a triadic product returns chain is a unique dimension of this study.

The purpose of this paper is to empirically explore supply chain integration (SCI) enabling practices, their benefits and barriers in a retail product returns process context.

The study adopts a case study research strategy. It draws on a single case, comprised of an original equipment manufacturer (OEM) and its two retailers. It utilizes an in-depth semi structured interviewing approach, combined with walk-through observations.

The study finds that management of retail product returns can significantly benefit both an OEM and its customers when appropriate SCI enabling practices are deployed. While these practices are similar to those in forward supply chain processes, barriers are driven by the characteristics of product returns processes.

The limitations of this study stem primarily from its methodological design. A single case research strategy provides a limited opportunity for external generalization of the research findings.

This study illustrates the value of SCI initiatives in product returns processes and informs managers ' decision making in the planning and execution of similar SCI implementations in product returns processes.

This research claims to be one of the first works that systematically and empirically explores SCI in reverse supply chain processes, as opposed to forward supply chain processes.