How much do online consumers really value free product returns? Evidence from eBay

• Published date: 01 November 2017
• Author: Michael Galbreth,Guangzhi Shang,Mark Ferguson,Pelin Pekgün
• DOI: http://doi.org/10.1016/j.jom.2017.07.001
• Date: 01 November 2017

How much do online consumers really value free product returns?

Evidence from eBay

Guangzhi Shang

a

,

*

, Pelin Pekgün

b

, Mark Ferguson

b

, Michael Galbreth

b

a

Florida State University, United States

b

University of South Carolina, United States

article info

Article history:

Received 11June 2016

Received in revised form

30 June 2017

Accepted 7 July 2017

Available online 2 August 2017

Accepted by Dr. Daniel R Guide.

Keywords:

Consumer returns

Online retailing

Marketing-operations interface

eBay

abstract

Consumer return rates have been steadily rising in recent years, resulting in growing costs for retailers

who must manage the returns process and the disposition of returned products. This cost pressure is

driven in part by extremelygenerous return policies, such as giving consumers a full refund upon return.

Interestingly, this common retail practice of full refunds is inconsistent with the recommendations of

many analytical models of returns, which nearly always show that a partial refund is optimal. Such in-

consistencies between theory and practice might arise when the decision drivers include d in the

analytical models do not match the decision drivers in practice. It might also be the case that retailers are

overly optimistic about the value that consumers assign to a full refund, and thus assume that the value

of such a policy outweighs its costs. In this paper, we use data collected from eBay, where identical

products are sold with different return policies, to investigate these open questions in the literature. We

analyze both the return policy drivers from the retailer's perspectiveand the return policy valuefrom the

consumer's perspective. Our results suggest that the value of a full refund policy to consumers may not

be as large as one might expect, and it also exhibits a large heterogeneity across buyers with different

levels of online purchase experience. In addition, we provide empirical evidence for what has long been

suspected by online retailers ethat a non-refundable forwardshippin g charge quickly erodes any value

that consumers assign to return policies. The generality of our results is limited by the fact that eBay

differs from traditional retail contexts in many respects, including the fact that eBay buyers may not be

representative of the general buyer population. However, our study of how eBay consumers value free

returns provides new insights into an understudied area, and it can serve as a starting point for future

studies of the value of return policies in other retail contexts.

©2017 Elsevier B.V. All rights reserved.

1. Introduction

An important business challenge for many retailers is the pro-

cessing of consumer returns, i.e. products that consumers return

within the retailer's pre-specified, and often “no-questions-asked,”

return window. Consumer returns are different than retailer over-

stock returns (to the manufacturer) and end-of-use returns by

consumers (typically occurring after many months or years of use).

The cost of consumer returns is significant. In 2015, the value of all

consumer returns received byU.S. retailers was estimated at $260.5

billion, an increase of approximately 50% from 2007 (National

Retail Federation, 2008; Ng and Stevens, 2015). A comparable

magnitude of increase has also been observed for return rates in the

past decade (National Retail Federation, 2008, 2016), from below

10% to around 14%. After returns are accepted by retailers, addi-

tional costs such as inspection, return logistics, and refurbishment

or disposal will incur at various steps along the “reverse supply

chain,”accounting for 5e6% of a typical OEM's revenue and 4% of a

typical retailer's sales (Douthit et al., 2011). It is reported that the

returns-associated reverse logistics costs alone are $40 billion for

the retail sector (Enright, 2003). Moreover, if returnedproducts are

sold to external liquidators and outlet stores, their salvage value is

often only 10%e20% of the original retail price (Stock et al., 2006).

As a result, the management of consumer returns has become one

*Corresponding author.

E-mail address: gshang@business.fsu.edu (G. Shang).

Contents lists available at ScienceDirect

Journal of Operations Management

journal homepage: www.elsevier.com/locate/jom

http://dx.doi.org/10.1016/j.jom.2017.07.001

0272-6963/©2017Elsevier B.V. All rights reserved.

Journal of Operations Management 53-56 (2017) 45e62

of the top managerial challenges faced by U.S. retailers according to

an industry study by UPS (Brill, 2015).

A significant contributor to the increasing volume (and cost) of

consumer returns is the popular trend of adopting lenient return

policies. In the past, restocking fees were common among con-

sumer electronic retailers such as mail-order computer dealers

(Chu et al., 1998). Today, however, almost all major retailers offer a

full refund. For example, Best Buy lifted their 15% restocking fee on

consumer electronics in 2010(Reisinger, 2010), while Wal-Marthas

long been offering a 90-day full refund policy. Clearly, retailers

generally believe that a full refund is the best return policy despite

the significant costs that offering such a policy entails. Given their

unwillingness to reduce the refund amount, most retailers simply

focus on preventing and managing returns through efforts such as

(1) reducing consumer product uncertainty before purchase, e.g.

through buyer assistance programs and virtual fitting technology,

and (2) streamlining the operations of the reverse supply chain

after the return (see Pinçe et al. (2016) for a recent example).

Spurred by the magnitude of the costs involved in managing

returns, academic researchers have produced a number of papers

on the topic. The vast majority of this work has been analytical,

with several models investigating the optimal returnpolicies under

various product, market, and consumer characteristics. For

example, Shulman et al. (2009) study a monopoly retailer selling

multiple products, while Hsiao and Chen (2012) consider a broad

notion of quality risk, which encompasses product uncertainty ea

common reason for returns eas a component. In sharp contrast to

practice, these theoretical results almost always recommend a

partial refund, that is, a refund of only a fraction of the original

price.

1

This is also true in studies that examine the effect on a re-

tailer's return policy decision from competition (Shulman et al.,

2011), manufacturer-retailer coordination (Su, 2009), the joint de-

cisions of return policy and inventory (Ketzenberg and Zuidwijk,

2009), open-box sales (Akçay et al., 2013), and end-of-season

markdown pricing (Altug and Aydinliyim, 2016).

At a high level, there are two possible explanations for the

discrepancy between the recommendations from the theoretical

literature (which advises offering partial refunds) and actual retail

practice (offering full refunds). First, it might be that the decision

drivers included in the analytical models, whose goal is to provide

high-level insights, do not closely match the decision drivers used

in practice. That is, the stylized representations of reality in these

models might be inconsistent with how retail managers actually

make decisions regarding return policies. Indeed, although

analytical models of the decision can be quite complex, anecdotal

evidence from industry surveys and news articles suggests that the

reason for choosing full over partial refund in practice can be quite

simple. For example, a majority of consumers review return policies

before purchase (Hsiao and Chen, 2012) and dislike any return-

related fees (O'Neill and Chu, 2003), influencing retailers to claim

that offering a lenient return policy is simply necessary to keep

them competitive (Chao, 2015). A second possible explanation is

that retailers may be overly optimistic about the value that con-

sumers assign to a full refund, and thus assume (perhaps incor-

rectly) that the value of such a policy outweighs its costs. While the

specific return-related costs (e.g. reverse logistics costs) are rela-

tively easy to quantify for a retailer, the value that consumers

attribute to a return policy is more difficult to measure. Given the

limited empirical evidence on the return policy value, it is difficult

for retailers to make informed decisions regarding the relative costs

and benefits of offering full refunds.

This study contributes to the consumer returns literature by

offering empirical insights on both the benefits from offering a full

refund and the possible drivers behind this important decision for

the retailer in the eBay context.First, using eBay auction data where

multiple retailers sell the same products through the same channel

but with different return policies, we present an assessment of

whether the three return policy drivers assumed in the analytical

literature (specifically: competitive environment, product salvage

value, and retailer reputation) influence an eBay retailer's return

policy choices. Secondly, we leverage the eBay auction data to es-

timate the value of a full refund policy to consumers using its

impact on the final auction prices. In the extant literature, Anderson

et al. (2009) was the first to attempt to empirically quantify the

value of a return policy in the context of a mail-order apparel

retailer, which only offers a full refund for all of the products sold.

The approach used in their paper is to fit a structural choice model

to catalog apparel sales (revealed preference data) and approxi-

mate the decrease in the consumer's utility when no refund was

offered by using counterfactual analysis. Because their data is only

from a single retailer that does not change its return policy during

the time of the study, a counterfactual analysis is the only option

available for their study. Heiman et al. (2015) provide another es-

timate for apparel products through a survey (stated-preference

data) where consumers are asked about the discount they are

willing to accept if the return policy is hypothetically removed. As

described in detail below, both our data and our empirical approach

are significantly different from these previous studies since we

study revealed-preference data in a setting where different re-

tailers sell the exact same products, but some offer full refunds

while others offer no refund. The presence of different retailers

offering different return policies allows us to provide a more direct

comparison. Moreover, our focus on the eBay context enables us to

examine the extent to which the non-refundable shipping charge

might be perceived as a “latent restocking fee”by consumers. In

addition, we examine how more experienced eBay shoppers might

better estimate their cost of product uncertainty and hence assign a

larger value to a full refund policy. This consumer heterogeneity

aspect adds an important nuance to our findings that has not been

previously explored.

We provide a brief overview of our research goals and approach

here. Our focus is on small to medium size online retailers (SMEs),

as represented by our dataset of eBay sellers. For SMEs that face the

decision of what type of return policy to offer, we provide an es-

timate of the additional dollar value that consumers place on a

product sold with versus without a full refund policy. Since the

retailers should be able to more easily estimate their internal costs

of offering full refunds, our estimate of the benefit side of the

equation should enable them to make more informed decisions. For

retailers who have already made the decision to offer free returns,

we provide an estimate of the premium they can charge for their

products when competing against similar retailers that do notoffer

free returns.

To help answer our research questions, we collected data on

consumer electronics products sold through eBay auctions. We

chose eBay as our data source due to the fact that it includes

identical products sold with different return policies (i.e. full refund

vs. no refund) through different sellers which are mostly small to

medium in size. The reason for our focus on consumer electronics is

two-fold. First, the National Retail Federation (2016)estimates that

return rates for “hard goods,”including consumer electronics, are

50% more than the grand average across product categories, while

return rates from online sales are twice as large as in brick-and-

mortar stores (Ng and Stevens, 2015). Second, an examination of

the return policies of major online electronics retailers and

1

Full refund does appear as an optimal solution under certain conditions, such as

when product cost and salvage value are both zero, and degree of productdiffer-

entiation are medium in Shulman et al. (2009, see Case 2 in Table 4).

G. Shang et al. / Journal of Operations Management 53-56 (2017) 45e6246