scholarly journals A Cross-National Service Strategy to Manage Product Returns: E-Tailers’ Return Policies and the Legitimating Role of the Institutional Environment

2021 ◽  
pp. 109467052198944
Author(s):  
Jana Gäthke ◽  
Katja Gelbrich ◽  
Shan Chen
Keyword(s):  
Institutional Environment ◽  
Eastern China ◽  
Repurchase Intention ◽  
Product Returns ◽  
Service Strategy ◽  
Return Policies ◽  
Context Variable ◽  
Product Return ◽  
Eastern Cultures ◽  
Multimethod Approach

Global e-tailers face product returns from across the world, but research on service strategies for successful product return handling in culturally diverse markets is virtually nonexistent. This study examines the drivers of product return–related customer behavior across Western and Eastern cultures. Using a multimethod approach comprising two surveys and one experiment, results from the major Western (United States) and Eastern (China) retail markets show varying patterns for product return behavior and a uniform pattern for repurchase intention. Specifically, return policies that imply high effort restrictiveness decrease product returns in Western but not Eastern cultures, while the perceived customer-oriented institutional environment increases product returns in Eastern but not Western cultures. For repurchase intention, we find that effort restrictiveness in both cultures decreases repurchase intention, while the perceived customer-oriented institutional environment increases repurchase intention. We also find self-interest and legitimacy as the mechanisms responsible for the effect of perceived institutional environment, an important context variable in international marketing that has been neglected in the product return context. These findings enhance our understanding of product returns in different cultural environments and offer valuable insights for an adequate service strategy in product return management by global e-tailers.

The impact of online reviews on product returns

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofei Li ◽  
Baolong Ma ◽  
Hongrui Chu
Keyword(s):  
Structural Equation ◽  
Secondary Data ◽  
Online Reviews ◽  
Quantitative Model ◽  
Negative Consequences ◽  
Product Returns ◽  
Content Type ◽  
High Expectations ◽  
Product Return ◽  
The Impact

PurposeThe value of online reviews has been well documented by academics and practitioners. However, to maximise the benefits of consumer reviews, online sellers must avoid the negative consequences associated with customer feedback, such as reputation loss, or product returns after purchase. In developing a better understanding of the relationships between online reviews and their potential for negative impacts, this research aims to explore product returns. Through a quantitative model, this research demonstrates why online reviews can result in product return behaviours.Design/methodology/approachThe hypotheses were tested via two studies. In Study 1, the authors examine the direct effects of review valence and review volume on product returns by analysing secondary data on 4,995 stores on China's Taobao.com. Study 2 further extends and validates the findings of Study 1 with a survey sample of 795 participants across several online shopping platforms. This analysis examines the mechanics and conditions that influence the relationships between online reviews and product returns through partial least squares-structural equation modelling (PLS-SEM).FindingsThe results show that both review valence (i.e. average star ratings) and the number of reviews can increase the probability of product returns due to the high expectations that result from positive online reviews. Further, the effect of review valence on product returns is stronger for first-time purchasers at a store. In terms of mitigation, the analysis shows that bilateral communications between sellers and buyers can temper the unrealistic expectations set by positive reviews, leading to fewer product returns.Originality/valueThis research adds to the literature on online reviews by exploring the negative consequences of online reviews and the role they play in online purchasing decisions. The findings also provide direct evidence as to why online reviews can result in more product returns, adding clarity to extant research which contains conflicting conclusions as to how online reviews affect product return behaviours.

Are Product Returns a Necessary Evil? Antecedents and Consequences

2009 ◽  
Vol 73 (3) ◽  
pp. 35-51 ◽  
Author(s):  
J. Andrew Petersen ◽  
V. Kumar
Keyword(s):  
Exchange Process ◽  
Data Availability ◽  
Marketing Communications ◽  
Buying Behavior ◽  
Product Returns ◽  
Product Return ◽  
Return Behavior ◽  
Process Factors ◽  
Necessary Evil

The firm–customer exchange process consists of three key parts: (1) firm-initiated marketing communications, (2) customer buying behavior, and (3) customer product return behavior. To date, the literature in marketing has largely focused on how marketing communications affect customer buying behavior and, to some extent, how past buying behavior affects a firm's decisions to initiate future marketing communications. However, the literature on product returns is sparse, especially in relation to analyzing individual customer product return behavior. Although the magnitude of the value of product returns is known to be high ($100 billion per year), how it affects customer buying behavior is not known because of a lack of data availability and understanding of the role of product returns in the firm–customer exchange process. Given that product returns are considered a hassle for a firm's supply chain management and a drain on overall profitability, it is important to study product return behavior. Thus, the authors empirically demonstrate the role of product returns in the exchange process by determining the exchange process factors that help explain product return behavior and the consequences of product returns on future customer and firm behavior. In addition, the authors demonstrate that product returns are inevitable but by no means evil.

Shopping plans, buying motivations, and return policies: impacts on product returns and purchase likelihoods

2015 ◽  
Vol 27 (4) ◽  
pp. 645-659 ◽  
Author(s):  
Joon Yong Seo ◽  
Sukki Yoon ◽  
Milena Vangelova
Keyword(s):  
Product Returns ◽  
Return Policies

scholarly journals The Influence of Substituting Prices, Product Returns, and Service Quality on Repurchase Intention

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jian Xue ◽  
Zeeshan Rasool ◽  
Mohsin Ali Khan ◽  
Ahmad Imran Khan ◽  
Farooq Khan ◽  
...  
Keyword(s):  
Customer Satisfaction ◽  
Service Quality ◽  
Textile Industry ◽  
Switching Costs ◽  
Sample Survey ◽  
Survey Method ◽  
Repurchase Intention ◽  
Product Returns ◽  
Repurchase Intentions ◽  
Business To Business

The textile industry is production-intensive and incorporates diverse transactions made by multiple suppliers, corporate buyers, and supply chain members. In the business-to-business context, the cost for attracting a new customer is notably much higher than that needed to retain a present one. Customer loyalty in terms of customer repurchase intention has, therefore, been considered as a key determinant for textile companies to improve their efficiency and competitive advantage. This study aims to investigate the business-to-business repurchase intentions of Pakistan textile and clothing industry customers. The study framework specifically consolidates the mutual dynamics of appealing (service quality), facilitating (product returns), and averting (switching costs) factors altogether and the effect of these variables on customer satisfaction and thus on customer retention (repurchase intent) in the textile’s transactional scenario. A sample survey method is used for this study. The data collected through self-administered questionnaires (n = 325) from All Pakistan Textile Mills Association enlisted the employees of the companies. The structural equational modeling technique was applied to examine the study hypotheses. The findings contended that service quality and switching costs are essential determinants that shape the repurchase intentions. Therefore, product returns do not contribute toward customer satisfaction and also do not shape the intentions of business-to-business customers to repurchase from the same supplier after having even a good product return experience in past.

Opportunistische Retouren

2013 ◽  
Vol 54 (4) ◽  
pp. 20-24 ◽  
Author(s):  
Björn Asdecker ◽  
Alexander Weigel
Keyword(s):  
Mail Order ◽  
Super Bowl ◽  
Product Returns ◽  
Return Rates ◽  
Product Return ◽  
Managerial Implications ◽  
Consumer Perspectives

Seit geraumer Zeit berichten Fachmedien, dass nach Großereignissen wie dem Super Bowl oder dem Oktoberfest die Retourenquoten für Fernseher bzw. Trachtenmode ansteigen. Offensichtlich bestellen manche Verbraucher Waren für konkrete Anlässe, nutzen diese vollumfänglich und machen anschließend von ihrem Widerrufsrecht Gebrauch. Die vorliegende Arbeit untersucht die Problematik opportunistischer bzw. unethischer Rücksendungen erstmalig für den deutschen Versandhandel. Dabei werden sowohl Händler- als auch Verbraucherperspektive berücksichtigt und Handlungsempfehlungen aufgezeigt. There have been numerous reports that product return rates for TVs or traditional Bavarian dresses increase considerably after Super Bowl Sunday and the Octoberfest, respectively. It is obvious that some consumers order products for specific occasions, use these at no cost and then exercise their right to return the product. This paperisthe first to examine the problem of unethical/opportunistic product returns in the German mail order industry. Both, merchant and consumer perspectives are considered. Finally, managerial implications are discussed. Keywords: widerrufsrecht, verbrauchersicht, verbraucherbefragung, rücksendung, händlersicht

Managing return flow of end-of-life products for product recovery operations

2017 ◽  
Vol 7 (1) ◽  
pp. 169-177
Author(s):  
Seval Ene ◽  
Nursel Öztürk
Keyword(s):  
End Of Life ◽  
Reverse Logistics ◽  
Product Recovery ◽  
Forecasting Model ◽  
Return Flow ◽  
Data Sets ◽  
Product Returns ◽  
Data Set ◽  
Product Return ◽  
Proposed Model

Increased consciousness on environment and sustainability, leads companies to apply environmentally friendly strategies such as product recovery and product return management. These strategies are generally applied in reverse logistics concept. Implementing reverse logistics successfully becomes complicated for companies due to uncertain parameters of the system like quantity, quality and timing of returns. A forecasting methodology is required to overcome these uncertainties and manage product returns. Accurate forecasting of product return flows provides insights to managers of reverse logistics. This paper proposes a forecasting model based on grey modelling for managing end-of-life products’ return flow. Grey models are capable for handling data sets characterized by uncertainty and small sized. The proposed model is applied to data set of a specific end-of-life product. Attained results show that the proposed forecasting model can be successfully used as a forecasting tool for product returns and a supportive guidance can be provided for future planning. Keywords: End-of-life products, grey modelling, product return flow, product recovery; 

scholarly journals MEDICINE INVETORY CONTROL BY CONSIDERING EXPIRY PERIODS AND PRODUCT RETURNS USING THE ALWAYS BETTER CONTROL (ABC) ANALYSIS AND THE HANDLEY WITHIN MODEL OF ECONOMIC ORDER QUALITY (EOQ) AT PHARMACIES IN INDONESIA

2020 ◽  
Vol 15 (Number 2) ◽  
pp. 20-32
Author(s):  
Irwan Sukendar ◽  
Andre Sugiyono ◽  
Munfiqotusshifa Munfiqotusshifa
Keyword(s):  
Previous Method ◽  
Economic Order ◽  
Optimal Order ◽  
Product Returns ◽  
Total Cost ◽  
Product Return ◽  
Group A ◽  
Abc Analysis ◽  
Group B ◽  
Expired Medicines

This study aims to control medicine inventories by considering the expiration period and the product return using The Always Better Control (ABC) analysis and The handley within model of Economic Order Quantity (EOQ). The results of this study indicate that there are 21% of medicines or 22 types of medicines belonging to group A with the use of 74.64%. for group B there were 25% drugs or 28 types of drugs with a budget use of 15.31% of all medicines. Meanwhile, there are 55% of medicines or 60 types of medicines belonging to group C with the use of a budget of 10.05% of the total medicines and the calculation using the EOQ method by considering the expiration period and product returns in this research has a more optimal order size compared to the previous method so that it can minimize expired medicines and estimate which medicines will expire at the end of the cycle so that the total cost of supplies at the pharmacy is more optimal.

scholarly journals AN OVERVIEW ON RETAIL REVERSE LOGISTICS

2014 ◽  
Author(s):  
Ioana Olariu
Keyword(s):  
Marketing Strategy ◽  
Theoretical Approach ◽  
Reverse Logistics ◽  
Environmental Concern ◽  
Retail Industry ◽  
Return Flow ◽  
Competitive Pressure ◽  
Product Returns ◽  
Return Policies ◽  
Effective Manner

This article is a theoretical approach on retail reverse logistics. Environmental concern and the current marketing strategy have spurred retailers to implement strategies to facilitate product returns from end customers. Reverse logistics, indicating the process of this return flow, encompasses such activities as the movement of returned products, facilities to accommodate returned items, and overall remedy process for returned items. The retail industry, under great competitive pressure, has used return policies as a competitive weapon. Grocery retailers were the first to begin to focus serious attention on the problem of returns and to develop reverse logistics innovations. Grocery retailers first developed innovations such as reclamation centers. Reclamation centers, in turn, led to the establishment of centralized return centers. Centralizing returns has led to significant benefits for most firms that have implemented them. Over the last several years, retailers have consolidated. Now, more than ever, reverse logistics is seen as being important. This reverse distribution activity can be crucial to the survival of companies, because the permanent goodwill of the company is at stake. Businesses succeed because they respond to both external and internal changes and adjust in an effective manner to remain competitive.

scholarly journals A Local Algorithm for Product Return Prediction in E-Commerce

2018 ◽  
Author(s):  
Yada Zhu ◽  
Jianbo Li ◽  
Jingrui He ◽  
Brian L. Quanz ◽  
Ajay A. Deshpande
Keyword(s):  
Large Scale ◽  
Local Algorithm ◽  
Data Sets ◽  
Product Returns ◽  
Product Return ◽  
Large Scale Data ◽  
Rich Information ◽  
The Rich ◽  
Scale Data ◽  
Large Scale Data Sets

With the rapid growth of e-tail, the cost to handle returned online orders also increases significantly and has become a major challenge in the e-commerce industry. Accurate prediction of product returns allows e-tailers to prevent problematic transactions in advance. However, the limited existing work for modeling customer online shopping behaviors and predicting their return actions fail to integrate the rich information in the product purchase and return history (e.g., return history, purchase-no-return behavior, and customer/product similarity). Furthermore, the large-scale data sets involved in this problem, typically consisting of millions of customers and tens of thousands of products, also render existing methods inefficient and ineffective at predicting the product returns. To address these problems, in this paper, we propose to use a weighted hybrid graph to represent the rich information in the product purchase and return history, in order to predict product returns. The proposed graph consists of both customer nodes and product nodes, undirected edges reflecting customer return history and customer/product similarity based on their attributes, as well as directed edges discriminating purchase-no-return and no-purchase actions. Based on this representation, we study a random-walk-based local algorithm for predicting product return propensity for each customer, whose computational complexity depends only on the size of the output cluster rather than the entire graph. Such a property makes the proposed local algorithm particularly suitable for processing the large-scale data sets to predict product returns. To test the performance of the proposed techniques, we evaluate the graph model and algorithm on multiple e-commerce data sets, showing improved performance over state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document