Return Policy Leniency Impacting Customers' Purchase Intention

Return Policy Leniency Impacting Customers’ Purchase Intention – A Viable Strategy for E-

Tailers?

David Karl

University of Bamberg

Kilian Vornberger

University of Bamberg

Björn Asdecker

University of Bamberg

Cite as:

Karl David, Vornberger Kilian, Asdecker Björn (2022), Return Policy Leniency Impacting Customers’

Purchase Intention – A Viable Strategy for E-Tailers?. , Proceedings of the European Marketing Academy

50th, (111790)

Return Policy Leniency Impacting Customers’ Purchase Intention – A Viable

Strategy for E-Tailers?

Abstract

Return policy can reduce e-commerce consumer returns by subjecting high-returning

customers to a stricter return policy. Besides return behavior, purchase intention is affected. In

an online survey of 197 participants, return policy leniency strongly influences purchase

intention. Other variables, such as perceived trust, show a weaker impact on purchase

intention than return policy directly. Managerially, this paper improves companies’

understanding of how different return policies affect customer behavior. Academically, the

research on return policy and purchase intention is complemented by examining three

different return policy manifestations under control of trust, fairness, opportunism, and return

difficulty.

Keywords: Consumer returns; return policy leniency; purchase intention

1 Introduction

The importance of consumer returns is increasing due to the steady growth of the online B2C

market. The measures associated with the COVID-19 pandemic further accelerated the

expansion of e-commerce to new firms, business areas, and customers (OECD, 2020).

Growing e-commerce also increases consumer returns (Xia & Zhang, 2010). Consumer

returns cause high costs (Asdecker, 2015) and impact emissions (Khusainova, 2019).

Nevertheless, to increase customer satisfaction, retailers usually offer return policies (RP)

characterized as lenient. Leniency means how conveniently a customer can return an item

(Abdulla, Abbey, & Ketzenberg, 2019). We use two of the dimensions developed by

Janakiraman, Syrdal, and Freling (2016): (1) Time leniency (how long can items be returned),

and (2) monetary leniency (fees for shipping or returning), and add one more dimension,

payment leniency.

RP influences customer behavior. Past research suggests that a generous return policy leads to

more returns and orders du to more impulsive purchases (Lantz & Hjort, 2013). About 86 %

of customers report that return policies influence their purchase decisions (Olick, 2019).

Accordingly, return policy can reduce returns but equally increase sales (Bahn & Boyd,

2014). Abbey, Ketzenberg, and Metters (2018) show that a small proportion of customers are

responsible for a large share of the total return volume. They advocate categorizing customers

according to their return behavior and tailoring return policies accordingly. Nevertheless, how

do individualized return policies influence purchase behavior? We aim to improve this

understanding, taking into consideration confounding variables, with this research question:

How does return policy leniency influence an e-commerce customer’s purchase intention?

2 Literature Background

For B2C returns management in general, we refer to a review by Abdulla et al. (2019), who

pointed out return policy as an essential research subject. The following studies have

investigated return policy in the context of purchase intention (PI). According to Bonifield,

Cole, and Schultz (2010), customers exposed to more lenient return policies rate the retailer’s

quality higher and show increased PI. Hsieh (2013) find that lenient return policies and

information credibility negatively impact perceived opportunism and positively impact trust,

while perceived opportunism negatively affects trust, which influences stickiness intention

positively. Pei, Paswan, and Yan (2014) state that return policy positively influences PI and

perceived fairness, moderated by a higher reputation or lower competition among e-tailers.

Perceived fairness positively affects perceived trust, which in turn has a positive effect on PI.

According to Zhang, Li, Yan, and Johnston (2017), consumers perceive a return under a

lenient policy as easier than under a more strict return policy, and thus, perceived return

difficulty and perceived service quality positively influence PI. Oghazi, Karlsson, Hellström,

and Hjort (2018) show that perceived return policy leniency positively influences PI with trust

as a mediator, while a direct influence of leniency on PI cannot be confirmed. Wang,

Anderson, Joo, and Huscroft (2020) conclude that leniency positively affects perceived

fairness, perceived return service quality, and repurchase intention; perceived fairness and

perceived service quality also positively impact consumers' repurchase intention.

The selected papers cover most of the return policy dimensions identified by Janakiraman et

al. (2016) and suggest a direct influence of return policy on PI while uncovering other indirect

relationships. However, leniency dimensions are primarily examined in separate studies or as

different variables. We consider the return policy integrally and integrate the payment

dimension, which has not been mentioned in this research strand so far. E.g., paying by credit

card elicits less pain than paying in cash because payment is decoupled from the timing of

consumption (Prelec & Loewenstein, 1998). Garnefeld, Feider, and Boehm (2017) show that

payment after receiving the goods increases returns compared to payment before delivery.

Based on these considerations, we investigate the RP’s influence on PI, perceived trust,

fairness, return difficulty, and their interrelationships.

3 Hypotheses

In e-commerce, information asymmetries exist because physical distance causes uncertainties,

and customers cannot evaluate items before purchase. Retailers can reduce asymmetries

through a signal (Spence, 2002). According to Signaling Theory (Spence, 1973), signaling

describes a signal sent by an agent observable by a principal to reduce pre-contractual

information asymmetries (Kirmani & Rao, 2000). Since return policy leniency acts as an

information mechanism in the relationship between online retailers and customers (Wang et

al., 2020), this could reduce information asymmetries: For example, lenient return policies

signal customers being able to act flexibly because they can avoid costs of a wrong purchase

decision (Wood, 2001). Thus, leniency could positively influence PI. Pei et al. (2014), and

Wang et al. (2020) support this assumption. Therefore, we hypothesize:

H1: Customers’ purchase intention is positively associated with return policy leniency.

According to Equity Theory, perceived fairness results from the ratio between profit and

investment in an exchange (Adams, 1965). Concerning Procedural Justice Theory as part of

Equity Theory, people are interested in fair distribution and fair processes (Lind & Tyler,

1988). People prefer their own advantage or positive inequality (Bower & Maxham, 2012).

We assume that customers value fair treatment and prefer a customer-friendly return policy.

Customers feeling mistreated are less likely to shop at a retailer in the future and vice versa

(Bower & Maxham, 2012). Pei et al. (2014) and Wang et al. (2020) indicate that return policy

leniency positively influences perceived fairness, promoting PI. Accordingly, we hypothesize:

H2a: Customers’ perceived fairness is positively associated with return policy leniency.

H2b: Customers’ purchase intention is positively associated with perceived fairness.

Trust is crucial to reduce uncertainties in e-commerce (Hsieh, 2013). Trust is the willingness

of a party to expose itself to the actions of a second party, anticipating that the second party

will fulfill the expectations of the first party without control (Mayer, Davis, & Schoorman,

1995). According to Agency Theory, in a relationship between two or more economic entities

in which a principal instructs an agent to perform a service, information asymmetries exist

between buyers and sellers (Jensen & Meckling, 1976). By reducing incomplete information

through a deliberate signal, higher trustworthiness could be achieved (Spence, 2002). Return

policy leniency could represent this kind of signal. Oghazi et al. (2018) and Hsieh (2013)

show a relationship between perceived trust and return policy. Therefore, we hypothesize:

H3a: Customers’ perceived trust is positively associated with return policy leniency.

A lack of trust can harm attitudes toward e-commerce (McKnight, Choudhury, & Kacmar,

2002). Conversely, Kim and Peterson (2017) show that trust promotes PI. Pei et al. (2014)

and Oghazi et al. (2018) confirm this relationship for return policies. Accordingly, it seems

essential to foster trust for increasing future purchases. We hypothesize:

H3b: Customers’ purchase intention is positively associated with perceived trust.

Adherence to fairness positively impacts trust (Bies & Tripp, 1995; Pei et al., 2014).

Accordingly, a signal of fairness can reduce information asymmetries, leading to increased

trust (Waterman & Meier, 1998). We hypothesize in the context of trust:

H3c: Customers’ perceived trust is positively associated with perceived fairness.

In internet-based exchange relationships, online retailers may behave opportunistically

(Liang, Laosethakul, Lloyd, & Xue, 2005). Opportunistic behavior describes the lack of

honesty as well as pronounced self-interest in transactions (Williamson, 1975). In a buyer-

seller relationship, the seller puts his own goals above the buyer's benefit (Hsieh, 2013).

Information asymmetries between buyer and seller facilitate opportunistic behavior (Mishra,

Heide, & Cort, 1998; Waterman & Meier, 1998). Hsieh (2013) shows that a lenient return

policy contributes to mitigating perceived opportunism. Accordingly, this study conjectures

that return policy leniency can counter perceived opportunism:

H3d: Customers’ perceived opportunism is negatively associated with return policy leniency.

Li, Browne, and Wetherbe (2006) argue that credible behavior is perceived as reliable, but

unmet expectations damage trust. Opportunistic behavior can be understood as an unmet

expectation. Moreover, the retailer is assumed to behave opportunistically (Eisenhardt, 1989;

Mishra et al., 1998). The signal sent to the customer to reduce information asymmetries may

also be harmful (Connelly, Certo, Ireland, & Reutzel, 2011). Thus, this study assumes that

opportunistic behavior harms trust. Li et al. (2006) and Hsieh (2013) describe a negative

relationship between opportunism and trust. Consequently, we hypothesize:

H3e: Customers’ perceived trust is negatively associated with perceived opportunism.

Perceived return difficulty is the customer’s perceived inconvenience in returning an item to

receive a refund (Zhang et al., 2017). Both return depth and return time impact the perceived

return difficulty. For example, if customers perceive a potential return as difficult, they

perceive an increased risk of unpredictable costs. Since customers tend to avoid wrong

decisions preventively (Mitchell, 1999), we hypothesize:

H4a: Customers’ perceived return difficulty is negatively associated with return policy

leniency.

H4b: Customers’ purchase intention is negatively associated with perceived return difficulty.

4 Methodology

4.1 Survey Description

For data collection, this research used an online survey conducted in February 2021. The

questionnaire consists of three parts. Before the actual questionnaire, a virtual cover letter

informs the participants about the survey's background and assures them anonymity. Next, we

queried essential characteristics of the respondents. In the central part, each participant goes

through two scenarios. The participants are asked to imagine purchasing an item from a

fictitious online fashion retailer and to answer several items on PI, perceived fairness,

perceived trust, perceived return difficulty, and perceived opportunism. Scenario 1 is balanced

characterized by neither particularly strict nor lenient return policy elements. Scenario 2

involves one of three randomly assigned manipulations, i.e., either a strict, balanced, or

lenient scenario (Garnefeld et al., 2017; Lantz & Hjort, 2013; Raghubir & Srivastava, 2008;

Wood, 2001) (Table 1).

Table 1. Randomly Assigned Return Policies.

Scenario

Strict

Balanced

Lenient

Shipping costs

Yes

Return costs

Yes

Payment period

Immediate

14 days

30 days

Return period

14 days

30 days

100 days

1,214 participants started the survey, of which 302 subjects completed the questionnaire

(24.9%). After removing 105 samples due to missing return experience or inconsistent

responses, the final sample consists of 197 participants, almost all from Germany. The

average age is 29.4; 58.9% had at least a college degree, 71.6% were female. The average

completion time was 6.5 minutes. Regarding the gender imbalance in our sample, no

significant differences for the mean and variance of the PI were observed.

4.2 Manipulation Check

A one-factor ANOVA checks the manipulation by the scenarios. In addition, post hoc tests

provide information about which groups differ from each other, using the mean values of PI.

The Levene test indicates that equality of variance between the groups can be assumed

(p>.05). Significant differences in the mean values exist between all groups (F=193.345;

p<.001). The Bonferroni posthoc test and the Scheffé procedure confirm the manipulation

functionality. Thus, the subjects show a significantly different PI depending on the scenario.

4.3 Operationalization of Constructs

We tested the hypothesized relationships using structural equation modeling (SEM) to

integrate multiple exogenous and endogenous latent and manifest variables (Ullman &

Bentler, 2013). The focal constructs of our study, namely purchase intention (PI), perceived

fairness (FA), perceived opportunism (OPP), perceived trust (TR), and return difficulty (DI),

were operationalized with multi-item scales. We adopted them from existing studies showing

statistical validity and reliability of these constructs (Table 2). All items were measured on a

5-point Likert type scale, ranging from 1 (“strongly disagree”) to 5 (“strongly agree”).

Table 2. Measurement Scales and Summary Statistics.

Construct

Source

Items

used

Relia-

bility

AVE

Sq. Multiple

Correlation

Kukar-Kinney, Xia, and Monroe (2007); Wang et

al. (2020)

.98

.87

.945

Pei et al. (2014); Kukar-Kinney et al. (2007)

.93

.77

.622

OPP

Hsieh (2013)

.88

.71

.319

Hsieh (2013)

.95

.83

.674

Jarvenpaa, Tractinsky, and Vitale (2000)

.96

.90

.542

4.4 Reliability and Validity Check

To check the unidimensionality of the item structure, we conducted an exploratory factor

analysis for each construct (principal axis analysis and Promax). As a measure of sample

adequacy, the Kaiser-Meyer-Olkin criteria of each construct all show values >.6 (Kaiser &

Rice, 1974). Bartlett's test can be rejected for all constructs (p < .001), indicating data fit for

analysis (Dziuban & Shirkey, 1974). Two items showed a communality <.5 and were not

further considered. The results of the individual explorative factor analyses confirm the one-

dimensionality of the constructs. Cronbach's alpha indicates high reliability on the construct

level (Table 2).

We conducted a confirmatory factor analysis for parameter estimation to ensure reliability and

validity based on the second-generation quality criteria. Since no construct correlation is >.9,

no parameter is excluded. Indicator reliability for all items is >.4, so we assume acceptable

reliability (Bagozzi & Baumgartner, 1994). Reliability at the construct level is determined by

factor reliability. Factor reliability exceeds .6 for all constructs, confirming construct

reliability. Since all constructs have an AVE>.5, we assume convergence validity (Fornell &

Larcker, 1981). We assume construct validity for the reflective measurement models, as the

requirements for discriminant validity are met according to the Fornell/Larcker criterion.

5 Results and Discussion

The SEM was estimated by the maximum-likelihood method (Table 3, Figure 1). The indices

of the measurement model show an acceptable fit. All coefficients except for two are

significant. 95% of the PI variance is explained by the model (Table 2). The standardized

coefficients of DI to PI and TR to PI are significantly <.2, while all other standardized

coefficients exceed this threshold for meaningfulness (Chin, 1998).

Figure 1. Research Model with Factor Loadings.

The largest significant positive coefficient in the model indicates that a more lenient return

policy increases PI (1.323; p<.001). The data accordingly support H1. We suggest return

policy leniency to signal quality and thus to reduce purchase decision conflict (Wood, 2001),

which confirms the results of Pei et al. (2014) and Wang et al. (2020). The results also show

that the more lenient the return policy, the fairer the customer feels treated (1.287; p<.001). In

addition, higher perceived fairness positively affects PI (.341; p<.001). Thus, H2a and H2b

are supported and confirm the findings of Pei et al. (2014) and Wang et al. (2020). H3a is

confirmed by the data (.377; p=.003), supporting the research findings of Hsieh (2013) and

Oghazi et al. (2018). Thus, lenient return policies appear to build trust. However, H3b and the

results of Pei et al. (2014) and Oghazi et al. (2018) that trust positively affects PI cannot be

Return Policy

Leniency

Perceived

Opportunism

Perceived Trust

Perceived

Fairness

Perceived Return

Difficulty

Purchase

Intention

PF1

e3 PF3

PF4

PF2

e1 RPL

PO2

PO1

PO3

PT4

e10

PT2

PT1

e11

PT3

e12

e17PI5

e15PI3

e14PI2

e16PI4

e13PI1

e18PI6

e20

RD2

e19

RD3

e21

RD1

e22

e23

e26

e25

e24

.79

-.74

.11

.73

.31

.07

.32

-.56

.27

-.37

.89

.79

.62

.96

.85

.77

.92

.93

.73

.60

.32

.83

.82

.87

.69

.67

.76

.67

.93

.95

.92

.86

.90

.84

.74

.95

.96

.97

.95

.94

.81

.91

.92

.93

.88

.66

.54

.93

.97

.87

.95

H1 (✓)

H2a (✓)

H2b (✓)

H3a (✓)

H3b (x)

H3c (✓)

H3d (✓)

H3e (✓)

H4a (✓)

H4b (x)

confirmed due to a slightly positive but insignificant effect (.111; p>.05). H3c is supported

(.194; p=.003) in agreement with the results of Pei et al. (2014): Customers seem to repay fair

treatment with trust in the online retailer. Furthermore, the data confirm (-.643; p<.001) that a

more lenient return policy makes the customer perceive less opportunism from the online

retailer. Moreover, we found that perceived opportunism significantly reduces perceived trust

(-.376; p<.001). Consequently, H3d and H3e are supported, consistent with Li et al. (2006)

and Hsieh (2013). Perceived return difficulty decreases significantly as the return policy

becomes more lenient (-1.179; p<.001). Thus, H4a is supported, confirming the results of

Zhang et al. (2017). We cannot confirm the postulated negative effect of the perceived return

difficulty on PI. Contrary to the conjecture, the coefficient is positive but significant (.125;

p=.021). The data do not support H4b, which contradicts the research of Zhang et al. (2017).

Table 3. Path Coefficients and Results of Hypothesis Tests.

Hypothesis

Path

Coefficient

Sign.

Conclusion



1.323

.152

8.682

<.001

Support

H2a



1.287

.115

11.175

<.001

Support

H2b



.341

.066

5.132

<.001

Support

H3a



.377

.126

2.985

.003

Support

H3b



.111

.081

1.375

.169

Reject

H3c



.194

.065

2.967

.003

Support

H3d

OPP



-.643

.088

-7.34

<.001

Support

H3e



OPP

-.376

.07

-5.406

<.001

Support

H4a



-1.179

.102

-11.603

<.001

Support

H4b



.125

.054

2.301

.021

Reject

Fit indices: χ

=368,447, df=180, χ

/df=2.047,

GFI=.836, CFI=.965, RMSEA=.074

6 Conclusion, Contribution, and Future Research

In summary, return policy leniency strongly influences PI and, at the same time, affects other

variables, which influence PI partly and with smaller effect sizes. Return policy thus

represents an instrument for influencing customer behavior not only regarding return behavior

but rather pre-purchase. A lenient return policy can increase trust and the fairness perceived

by the customer. In turn, it reduces the perceived opportunism and the perceived difficulty of

a consumer return and can thus contribute to higher customer satisfaction. On the downside,

as suggested by Abbey et al. (2018), individual and strict return policies can discourage

unwanted customers already from purchasing.

This paper extends previous research on consumer return policy leniency by a more holistic

approach integrating time, costs, and payment modalities, rather than focussing on individual

parts of return policies. Moreover, this study formulates three different return policies and

thus breaks the previous dichotomous view. Using SEM, we incorporate several influencing

variables, which have already partially been investigated in this context.

From a managerial point of view, this study supports e-tailers in understanding the

interdependencies between return policy and PI as well as other factors important to this

relationship. For reducing consumer returns, individual return policies cannot be implemented

without taking PI and other variables into consideration. Following the approach of

individually adjusting the return policy of customers with excessive returns (Abbey et al.,

2018), retailers must balance these trade-offs to determine the suitable level of leniency and

the critical thresholds. This study reveals that a stricter return policy can significantly reduce

future purchases, allowing to manipulate the structure of the customer base in a smoother way

than closing down customer accounts (Safdar & Stevens, 2018). Vice versa, individually

adjusted, more lenient conditions might increase future revenues of low-returning customers.

Nevertheless, the results hint at some future research required. A longitudinal study could

validate the results in a non-pandemic context. In addition, our sample is restricted to the

European market. Furthermore, we examine only two of the five return policy dimensions

identified by Janakiraman et al. (2016). Overall, an integrated analysis of return policy effects

on actual purchases and returns would supplement the findings of our study.

References

Abbey, J., Ketzenberg, M., & Metters, R. (2018). A More Profitable Approach to Product

Returns. MITSloan Management Review, 60(1), 71–74.

Abdulla, H., Abbey, J., & Ketzenberg, M. (2019). How Consumers Value Retailer's Return

Policy Leniency Levers: An Empirical Investigation. SSRN Electronic Journal. Advance

online publication. https://doi.org/10.2139/ssrn.3664228

Adams, J. S. (1965). Inequity In Social Exchange. In L. Berkowitz (Ed.), Advances in

Experimental Social Psychology (Vol. 2, pp. 267–299). Academic Press.

https://doi.org/10.1016/S0065-2601(08)60108-2

Asdecker, B. (2015). Returning mail-order goods: Analyzing the relationship between the rate

of returns and the associated costs. Logistics Research, 8(1), 1–12.

https://doi.org/10.1007/s12159-015-0124-5

Bagozzi, R. P., & Baumgartner, H. (1994). The Evaluation of Structural Equation Models and

Hypothesis Testing. In R. P. Bagozzi (Ed.), Principles of Marketing Research (pp. 386–

422). Cambridge: Blackwell.

Bahn, K. D., & Boyd, E. (2014). Information and its impact on consumers׳ reactions to

restrictive return policies. Journal of Retailing and Consumer Services, 21(4), 415–423.

https://doi.org/10.1016/j.jretconser.2014.03.002

Bies, R. J., & Tripp, T. M. (1995). The Use and Abuse of Power: Justice as Social Control. In

R. Cropanzano & M. K. Kacmar (Eds.), Organizational Politics, Justice, and Support:

Managing Social Climate at Work (pp. 131–145). London: Greenwood Publishing Group.

Bonifield, C., Cole, C., & Schultz, R. L. (2010). Product returns on the Internet: A case of

mixed signals? Journal of Business Research, 63(9), 1058–1065.

https://doi.org/10.1016/j.jbusres.2008.12.009

Bower, A. B., & Maxham, J. G. (2012). Return Shipping Policies of Online Retailers:

Normative Assumptions and the Long-Term Consequences of Fee and Free Returns.

Journal of Marketing, 76(5), 110–124.

Chin, W. W. (1998). Commentary: Issues and Opinion on Structural Equation Modeling. MIS

Quarterly, 22(1), 7–16.

Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling Theory: A

Review and Assessment. Journal of Management, 37(1), 39–67.

https://doi.org/10.1177/0149206310388419

Dziuban, C. D., & Shirkey, E. C. (1974). When Is a Correlation Matrix Appropriate for Factor

Analysis? Some Decision Rules. Psychological Bulletin, 81(6), 358–361.

https://doi.org/10.1037/h0036316

Eisenhardt, K. M. (1989). Agency Theory: An Assessment and Review. The Academy of

Management Review, 14(1), 57–74. https://doi.org/10.2307/258191

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with

Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1),

39–50. https://doi.org/10.1177/002224378101800104

Garnefeld, I., Feider, L., & Boehm, E. (2017). Managing the Necessary Evil: Can Payment

Methods Reduce Product Returns. In R. Chandy, J. Inman, & C. Moorman (Eds.),

Proceedings of the 28th Winter AMA Conference (Vol. 28, F-30-F-31). Orlando.

Hsieh, P.‑L. (2013). Perceived opportunism (PO) in e‐return service encounters. Managing

Service Quality: An International Journal, 23(2), 96–110.

https://doi.org/10.1108/09604521311303390

Janakiraman, N., Syrdal, H. A., & Freling, R. (2016). The Effect of Return Policy Leniency

on Consumer Purchase and Return Decisions: A Meta-analytic Review. Journal of

Retailing, 92(2), 226–235. https://doi.org/10.1016/j.jretai.2015.11.002

Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store.

Information Technology and Management, 1(1/2), 45–71.

https://doi.org/10.1023/a:1019104520776

Jensen, M. C., & Meckling, W. H. (1976). Theory of the Firm: Managerial Behavior, Agency

Costs and Ownership Structure. Journal of Financial Economics, 3(4), 305–360.

https://doi.org/10.1016/0304-405X(76)90026-X

Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological

Measurement, 34(1), 111–117. https://doi.org/10.1177/001316447403400115

Khusainova, G. (2019). There Is No Such Thing As A Free Return. Retrieved from

https://www.forbes.com/sites/gulnazkhusainova/2019/03/28/there-is-no-such-thing-as-a-

free-return/. (Last accessed: July 25, 2021).

Kim, Y., & Peterson, R. A. (2017). A Meta-analysis of Online Trust Relationships in E-

commerce. Journal of Interactive Marketing, 38(1), 44–54.

https://doi.org/10.1016/j.intmar.2017.01.001

Kirmani, A., & Rao, A. R. (2000). No Pain, No Gain: A Critical Review of the Literature on

Signaling Unobservable Product Quality. Journal of Marketing, 64(2), 66–79.

https://doi.org/10.1509/jmkg.64.2.66.18000

Kukar-Kinney, M., Xia, L., & Monroe, K. B. (2007). Consumers’ perceptions of the fairness

of price-matching refund policies. Journal of Retailing, 83(3), 325–337.

https://doi.org/10.1016/j.jretai.2007.03.005

Lantz, B., & Hjort, K. (2013). Real e-customer behavioural responses to free delivery and free

returns. Electronic Commerce Research, 13(2), 183–198. https://doi.org/10.1007/s10660-

013-9125-0

Li, D., Browne, G. J., & Wetherbe, J. C. (2006). Why Do Internet Users Stick with a Specific

Web Site? A Relationship Perspective. International Journal of Electronic Commerce,

10(4), 105–141. https://doi.org/10.2753/jec1086-4415100404

Liang, H., Laosethakul, K., Lloyd, S. J., & Xue, Y. (2005). Information Systems and Health

Care-I: Trust, Uncertainty, and Online Prescription Filling. Communications of the

Association for Information Systems, 15(1), 41–60.

Lind, E. A., & Tyler, T. R. (1988). The Social Psychology of Procedural Justice. New York:

Springer Science & Business Media.

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model Of

Organizational Trust. Academy of Management Review, 20(3), 709–734.

https://doi.org/10.5465/amr.1995.9508080335

McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and Validating Trust

Measures for e-Commerce: An Integrative Typology. Information Systems Research,

13(3), 334–359. https://doi.org/10.1287/isre.13.3.334.81

Mishra, D. P., Heide, J. B., & Cort, S. G. (1998). Information Asymmetry and Levels of

Agency Relationships. Journal of Marketing Research, 35(3), 277–295.

https://doi.org/10.1177/002224379803500301

Mitchell, V.‑W. (1999). Consumer perceived risk: conceptualisations and models. European

Journal of Marketing, 33(1/2), 163–195. https://doi.org/10.1108/03090569910249229

OECD (2020). E-Commerce in the time of COVID-19. (Last accessed: March 25, 2021).

Oghazi, P., Karlsson, S., Hellström, D., & Hjort, K. (2018). Online purchase return policy

leniency and purchase decision: Mediating role of consumer trust. Journal of Retailing and

Consumer Services, 41(1), 190–200. https://doi.org/10.1016/j.jretconser.2017.12.007

Olick, D. (2019). The surge in online-shopping returns has boosted the warehouse sector.

Retrieved from https://www.cnbc.com/2019/01/04/surge-in-e-commerce-gift-returns-has-

boosted-the-warehouse-sector.html. (Last accessed: June 15, 2021).

Pei, Z., Paswan, A., & Yan, R. (2014). E-tailer׳s return policy, consumer׳s perception of

return policy fairness and purchase intention. Journal of Retailing and Consumer Services,

21(3), 249–257. https://doi.org/10.1016/j.jretconser.2014.01.004

Prelec, D., & Loewenstein, G. (1998). The Red and the Black: Mental Accounting of Savings

and Debt. Marketing Science, 17(1), 4–28.

Raghubir, P., & Srivastava, J. (2008). Monopoly Money: The Effect of Payment Coupling

and Form on Spending Behavior. Journal of Experimental Psychology: Applied, 14(3),

213–225. https://doi.org/10.1037/1076-898X.14.3.213

Safdar, K., & Stevens, L. (2018). Banned From Amazon: The Shoppers Who Make Too Many

Returns. Retrieved from https://www.wsj.com/articles/banned-from-amazon-the-shoppers-

who-make-too-many-returns-1526981401. (Last accessed: August 20, 2021).

Spence, M. (1973). Job Market Signaling. The Quarterly Journal of Economics, 87(3), 355–

374.

Spence, M. (2002). Signaling in Retrospect and the Informational Structure of Markets. The

American Economic Review, 92(3), 434–459. https://doi.org/10.1257/00028280260136200

Ullman, J. B., & Bentler, P. M. (2013). Structural Equation Modeling. In I. B. Weiner (Ed.),

Handbook of Psychology (pp. 661–690). Hoboken: Wiley.

https://doi.org/10.1002/0471264385.wei0224

Wang, Y., Anderson, J., Joo, S.‑J., & Huscroft, J. R. (2020). The leniency of return policy and

consumers' repurchase intention in online retailing. Industrial Management & Data

Systems, 120(1), 21–39. https://doi.org/10.1108/IMDS-01-2019-0016

Waterman, R. W., & Meier, K. J. (1998). Principal-Agent Models: An Expansion? Journal of

Public Administration Research and Theory, 8(2), 173–202.

https://doi.org/10.1093/oxfordjournals.jpart.a024377

Williamson, O. E. (1975). Markets and Hierarchies: Analysis and Antitrust Implications: A

Study in the Economics of Internal Organization. New York.

Wood, S. L. (2001). Remote Purchase Environments: The Influence of Return Policy

Leniency on Two-Stage Decision Processes. Journal of Marketing Research, 38(2), 157–

169. https://doi.org/10.1509/jmkr.38.2.157.18847

Xia, Y., & Zhang, G. P. (2010). The Impact of the Online Channel on Retailers'

Performances: An Empirical Evaluation. Decision Sciences, 41(3), 517–546.

https://doi.org/10.1111/j.1540-5915.2010.00279.x

Zhang, J., Li, H., Yan, R., & Johnston, C. (2017). Examining the Signaling Effect of E-tailers’

Return Policies. Journal of Computer Information Systems, 57(3), 191–200.

https://doi.org/10.1080/08874417.2016.1183989