Return Time Leniency in Online Retail: A Signaling Theory Perspective on Buying Outcomes

Abstract

Merchandise return policies (MRPs) have long been an important area of interest for operations and supply chain management researchers, who have identified some key advantages and disadvantages of offering comprehensive and convenient return policies. However, there is a lack of rigorous scholarship on one key dimension of MRPs—return time leniency. Understood as the amount of time that buyers have within which to return purchased items, return time leniency is often considered one of the leading indicators of a retailer's MRP friendliness. In the current study, we extend prior work on MRPs by introducing a signaling theory perspective to return time leniency. This allows us to develop a more nuanced interpretation of the underlying economics associated with extending the window of time during which returns are accepted. We do so by positioning our study in a retail context where such issues are highly important to consumers—online retail.

Taking stock of consumer returns: A review and classification of the literature

“…Regarding data analysis methods, our review reveals that regression and analysis of variance techniques are most commonly used. In general, secondary data studies employ regression methods, such as ordinary least squares regression (Hjort & Lantz, 2016), logistic regression (Shang, Pekgün, et al, 2017), and multilevel modeling (Rao et al, 2018). Structural equation modeling (SEM) methods are commonly used in survey and interview research (Mollenkopf et al, 2007;Powers & Jack, 2013) but also are used to analyze experimental data (Jeng, 2017;Zhang et al, 2017).…”

“…Section 5.2 focuses on the literature that resides at the intersection of RP and CB domains, in which the vast majority of the literature is concentrated. Section 5.3 is dedicated to the research in the intersection of return policy with PE and RM (2008), Bonifield et al (2010), Pei, Paswan, and Yan (2014), Rao, Lee, Connelly, and Iyengar (2018), Zhang, Li, Yan, and Johnston (2017), and Oghazi, Karlsson, Hellström, and Hjort (2018) Prospect (including endowment effect) Kahneman and Tversky (1979) Wood (2001), Wang (2009), andHeiman, Just, McWilliams, andZilberman (2015) Distributive (equity) and procedural justice Adams and Freedman (1976), Lind and Tyler (1988) Suwelack, Hogreve, and Hoyer (2011), Bower and Maxham III (2012), and Pei et al (2014) Attribution Kelley (1967) Bower and Maxham III (2012) Construal level Liberman and Trope (1998) Janakiraman and Ordóñez (2012) Norm Kahneman and Miller (1986) Kim and Wansink (2012) Cognitive dissonance Festinger (1957) Powers andJack (2013) domains. The complete classification of the empirical literature appears in Table A3.…”

“…Ten studies explore the impact of time leniency with respect to various CB classifiers. With respect to the CR classifier, a consistent finding is that higher time leniency is associated with a higher willingness-to-pay for product and return option; however, the relationship shows a diminishing return to scale (Heiman et al, 2015;Rao et al, 2018;Suwelack et al, 2011;Wang, 2009). Research on signaling quality suggests that time leniency can signal retailer service quality but may not signal product quality (d'Astous & Guèvremont, 2008;Zhang et al, 2017).…”

“…Integration also involves (a) conducting empirical-analytical hybrid studies, such as Davis et al (1998) Our classification is noteworthy by identifying several new and significant findings from the empirical literature on CB that have not yet been integrated into analytical studies. For instance, though a large proportion of customers apparently do not make a return (Petersen & Kumar, 2009;Abbey et al, 2018b), many customers value the return policy itself and are therefore generally willing to pay more for return policy leniency (Heiman et al, 2015;Rao et al, 2018;Wang, 2009). Further, an item will tend to exhibit a lower return rate when offered at a lower price than at a higher price (i.e., a within-item price effect) (Shang et al, 2018).…”

Where Should I Focus My Return Reduction Efforts? Empirical Guidance for Retailers

“…Heiman, McWilliams, Zhao, and Zilberman (2002) incorporate consumers' experience duration into their analytical model and show a positive value of a longer return time window. Some preliminary empirical evidence of this positive value is provided in Heiman, Just, McWilliams, and Zilberman (2015) through a survey instrument and in Rao et al (2018) through a field experiment on eBay. Other analytical work in this area has studied the coordination between restocking fee and return time window in combating fraudulent returns (Ülkü, Dailey, & Yayla-Küllü, 2013) or how to incorporate the return time window as a strategic decision for a fashion retailer who "circulates" returns in a closed-loop supply chain (Difrancesco, Huchzermeier, & Schröder, 2017).…”