ANALYZING COMPLAINT INTENTIONS
IN ONLINE SHOPPING
The Perspective of Justice, Technology and Trust
Ing-Long Wu
1
and Chu-Ying Fu
2
1
Department of Information Management, National Chung Cheng University, Chia-Yi, Taiwan
2
Department of Information Management, WuFeng University of Science and Technology, Chia-Yi, Taiwan
Keywords: Customer Satisfaction, Complaint Intention, Justice Theory, Expectation-confirmation Model, Trust.
Abstract: Customers’ complaint behaviors are the key to repurchase intention in online shopping. An understanding of
the behaviors can provide insight to failed service experience and in turn, effectively redress their problems.
Therefore, it is important to examine the underlying drivers of complaint intentions in online shopping.
Online shopping is operated in a web-based virtual store. Online shoppers are both the buyers of products
and users of web-sites. Moreover, trust belief on e-vendors determines the psychological state of individual
shopping behaviors. Three major concerns particularly arise in this context, individual behavior, technology
use, and trust. This study thus proposed a novel research model in an integration of justice, technology use,
and trust issues to examine customer satisfaction and complaint behaviors. Data were collected from an
online survey with online-shopping experience. The empirical results indicated that distributive justice and
interactional justice contribute significantly to customer satisfaction and complaint intentions as procedural
justice does not. Technology-based features and trust belief are both important in determining the two target
variables. Implications for managers and scholars are further discussed.
1 INTRODUCTION
Online shopping was expected to be grown in a 8-
10% annual rate in the near future. However, recent
report has indicated a rapid increase in service
failure for online consumers. This causes a difficulty
to maintain customer satisfaction and in turn, a
decrease of sale revenue in a long-term basis.
Consumer’s post-purchase behaviors are the key to a
firm’s survival in a highly competitive e-
marketplace (Kim and Son, 2009). Research on post-
purchase behaviors has focused on customer
satisfaction and repurchase intention (Gefen et al.,
2003). However, complaint behaviors have often
occurred to most buyers due to dissatisfaction of
online services (Voorhees and Brady, 2005).
Complaint behaviors certainly play an important role
in consumers’ decision making of their repurchase
(Breazeale, 2009). The possible reasons why
consumers complain to online shopping are not fully
discussed in terms of their dissatisfaction
Online consumers are both the shoppers of
products and users of web-based systems in the
shopping process (Shankar et al., 2003). Moreover,
trust beliefs on e-vendors are also important in
determining the psychological state of individual
shopping behaviors (Palvia, 2009). Accordingly,
three major concerns particularly arise in the
complaint behaviors, individual behavior,
technology use, and trust belief. In the individual
behavior, many studies have claimed the importance
of justice issue in linking to customer satisfaction
and complaint intentions (Martinez-Tur et al., 2006).
However, the literature has been a lack of
considering its influence on complaint intentions in
online shopping.
In the technology use, expectation confirmation
model (ECM) of IS continuance indicated the links
between technology-based features such as
perceived usefulness and customer satisfaction and
continuance intention to use (Bhattacherjee, 2001).
In the trust belief, several studies have argued trust
as important determinant for the consumer’s
willingness to transact with e-vendors and further,
have identified the direct link between trust and
customer satisfaction in e-commerce context (Wu
and Chen, 2005; Kim et al., 2009). Grounding on
justice perception, ECM-based features, and trust
belief, this study proposed a novel research
framework to understand the major drivers of
complaint intentions in online shopping. Empirical
data is further used to examine this framework.
511
Wu I. and Fu C..
ANALYZING COMPLAINT INTENTIONS IN ONLINE SHOPPING - The Perspective of Justice, Technology and Trust.
DOI: 10.5220/0003880705110517
In Proceedings of the 8th International Conference on Web Information Systems and Technologies (WEBIST-2012), pages 511-517
ISBN: 978-989-8565-08-2
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)
2 LITERATURE REVIEW AND
HYPOTHESES
DEVELOPMENT
Based on the above discussion, Figure 1 provides a
pictorial depiction of this research framework. The
followings discuss the theoretical bases and
development of relevant hypotheses.
Perceived
usefulness
Expectation
confirmation
Customer
satisfaction
Intention
to complain
Distributive
justice
Procedural
justice
Interactional
justice
H1
H2
H3
H5
H6
H4
H9(-)
Trust
H7
H8
Figure 1: Research model.
2.1 Justice Theory
The literature has defined three major justice
constructs: distributive, procedural and interactional
(Colquitt et al., 2001). Distributive justice refers to
the perceived fairness where individuals assess the
fairness of an exchange by comparing their inputs to
outcomes to form an equity score. Procedural justice
refers to the perceived fairness of policies,
procedures and criteria used by decision makers in
reaching the outcome of a dispute or negotiation
(Alexander and Ruderman, 1987). Interactional
justice refers to the perceived fairness of
interpersonal treatment that individuals receive in
the decision making process. Accordingly,
interactional justice is in a position to reflect the
perceived fairness of a communication between
system interface and consumers.
Justice perception can be used not only in
exploring service recovery process such as post-
complaint behaviors, but also in understanding entire
failed service experience in consumer purchase
context (Turel et al., 2008; Sangareddy et al., 2009).
Online shopping process can be considered as an
exchange of time, effort, and money for receiving
products or services. The consideration of justice
should be the major concern of online shoppers in
the post-purchase process. Therefore, this paper used
justice theory to investigate consumer’s complaint
intentions in the online shopping context. The
following discusses the development of relevant
hypotheses.
Martinez-Tur et al. (2006) argued that justice
components, distributive, procdural, and
interactional justice, are all improtant predictors of
customer satisfaction in the study of hotel and
restaurant industries while distributive justice is
more influential than procedural and interactional
justice. Maxham and Netemeyer (2002) indicated
the support of an influence of three justice
components on customer satisfaction in the study of
service industries. They further found that
procedural and interactional justices are more
influential in forming satisfaction than distributive
justice. Other studies also concluded the importance
of the three components in impacting customer
satisfaction in service industries (Tax et al., 1998;
Voorhees and Brady, 2005). We propose following
the hypotheses.
H1. Distributive justice has a positive effect on
consumer satisfaction in online shopping.
H2. Procedural justice has a positive effect on
consumer satisfaction in online shopping.
H3. Interactional justice has a positive effect on
consumer satisfaction in online shopping.
2.2 Expectation Confirmation Model
Bhattacherjee (2001) proposed expectation
confirmation model (ECM) of IS continuance by
integrating expectation confirmation theory (ECT)
(Oliver, 1980) and TAM-based studies, such as
perceived usefulness (Davis et al., 1989), to explore
user satisfaction and continuance intention to use.
ECT was originally proposed by Oliver (1980) for
consumer behavior research to examine consumer
satisfaction and post-purchase behaviors.
Furthermore, a post-expectation of IS use is added to
ECM when ECT only examines the effect of pre-
expectation in the purchase decision. ECM has been
widely extended to include two other post-adoption
behaviors, complaint and recommendation
intentions, in e-commerce (Yen and Lu, 2008; Finn
et al., 2009). In this study, we only considered
complaint intentions in its extension. The complaint
behaviors in this context may be predicted partially
by ECM in a technology use perspective.
The following discusses the development of
relevant hypotheses. According to TAM, perceived
ease of use is positively related to perceived
usefulness (Davis et al., 1989). Previous studies
indicated that perceived ease of use and
confirmation are similar because they are cognitive
constructs stemming from a consumer’s post-
WEBIST2012-8thInternationalConferenceonWebInformationSystemsandTechnologies
512
consumption expectation after the initial use of
online shopping (Bhattacherjee, 2001). Moreover,
many studies also argued that confirmation has a
positive impact on perceived usefulness in online
environment (Chea and Luo, 2008). We propose the
hypothesis.
H4. Confirmation has a positive effect on
perceived usefulness in online shopping.
According to ECT, confirmation is determined by
the combination of pre-expectation and perceived
outcome (Oliver, 1980). Positive confirmation arises
when the perceived outcome of customers exceeds
their pre-expectation. Positive confirmation indicates
a positive effect on customer satisfaction. Previous
studies on ECM in online environment also showed
empirical evidence in terms of the effect of
disconfirmation on customer satisfaction (Chea and
Luo, 2008; Finn et al., 2009). We propose the
hypothesis.
H5. Confirmation has a positive effect on
consumer satisfaction in online shopping.
Consumers are more likely to form favorable
feelings of satisfaction when the online shopping
website is perceived to be useful (Bhattacherjee,
2001). Perceived usefulness as drawn from TAM is
considered to be post-expectation in IS use (Thong
et al., 2006). According to ECT, post-consumption
expectation is a predictor to consumer satisfaction
(Bhattacherjee, 2001; Chea and Luo, 2008).
Furthermore, prior studies also showed that
perceived usefulness is an important antecedent of
consumer satisfaction (Chea and Luo, 2008; Chiu et
al., 2009). We propose the hypothesis.
H6. Perceived usefulness has a positive effect on
customer satisfaction in online shopping.
2.3 Trust
E-commerce is a less verifiable and controllable
environment in which online services or transactions
are offered without physical face-to-face contact and
simultaneous exchange of services and money.
Thus, without reducing social complexity and risk
resulting from the undesirable opportunistic
behaviors of e-vendors, only short-term transactions
would be possible between buyers and e-vendors
(Pavlou and Gefen, 2004). This would create a
potential impact of their transactions on buyers’
dissatisfaction due to a lack of trust on e-vendors to
nurture long-term customer relationships (Pavlou,
2003; Palvia, 2009). Trust plays an important role in
determining online shopping. The following
discusses the development of hypotheses.
Trust is one of the determinants of perceived
usefulness, especially in an online environment,
because part of the guarantee that consumers will
sense the expected usefulness from the web site is
based on the sellers behind the web site (Gefen et
al., 2003). Moreover, trust is recognized to have
positive effect on perceived usefulness since trust
allows consumers to become vulnerable to e-vendors
to ensure that they gain the expected useful
information and service (Pavlou, 2003). We propose
the hypothesis.
H7. Trust has a positive effect on perceived
usefulness in online shopping.
One study discussed the potential linkage between
trust and customer satisfaction in terms of an
exploration in pre-purchase and post-purchase
behaviors in online shopping (Kim et al., 2009).
Another was in an attempt to understand consumers’
loyalty intention in online shopping while proposing
a direct effect of trust on customer satisfaction (Chiu
et al., 2009). We propose the hypothesis.
H8. Trust has a positive effect on customer
satisfaction in online shopping.
Finally, prior research revealed that complaint
intentions arise when they encounter a dissatisfied
circumstance in online shopping (Thogersen et al.,
2009). Many studies also supported a direct
relationship between customer satisfaction and
complaint intentions in online environment
(Voorhees and Brady, 2005). In other words, when
consumers feel more dissatisfied, the complaint
intentions increase. We propose the hypothesis.
H10. Customer satisfaction has a negative effect
on complaint intention in online shopping.
3 RESEARCH DESIGN
3.1 Instrumentation
A survey method was conducted to collect empirical
data. The instrument contains a two-part
questionnaire, a nominal scale for basic information
and a seven-point Likert scale for research
constructs. Basic information collects the
information about consumer characteristics,
including gender, education level, job, online
shopping experience, and failed service experience.
The measuring items for the three justice
components were adapted from the measurement
developed by Blodgett et al. (1997), Martinez-Tur et
al. (2006), and Turel et al. (2008). They contain 4
items, 4 items and 4 items respectively. The
ANALYZINGCOMPLAINTINTENTIONSINONLINESHOPPING-ThePerspectiveofJustice,TechnologyandTrust
513
measuring items for confirmation, perceived
usefulness and satisfaction were adapted from the
measurement developed by Bhattacherjee (2001),
Olsen (2002), and Finn et al. (2009). They contain 3
items, 3 items and 5 items respectively. The
measuring items for trust were adapted from Gefen
et al. (2003) and Kim et al. (2009). Four items are
included. Complaint intention was measured with
items based on Singh (1988) and Chea and Luo
(2008). It comprises 5 items.
3.2 Sample Design
For the survey, qualified respondents are the
shoppers with previous experience in online
shopping. Furthermore, they were asked to reflect on
a recent experience of online shopping for products
or services (within the past three months) that served
as the basis for completion of the survey. This study
employed an online survey during the period of
February-April in 2011. Public notice of the survey
questionnaire was published in a number of bulletin
board systems and forums. There is also a reward
system offered for the respondents. Initially, pretest
was conducted for the scale. The scale was carefully
examined by selected practitioners and academicians
in this area, including translation, wording, structure,
and content. After the questionnaire was finalized,
the online survey was carried out in terms of the
above procedure. A total of 1057 respondents were
received with shopping experience and 40 were
incomplete in their responses. This results in a valid
sample size of 1017 for this study. Of the
respondents, 64.9% is female, 72.9% is 21-40 years
old, 49.5% is less than 3 years of online shopping
experience, and 65.7% is failed service experience
3.3 Measurement Model
This study employed structural equation modeling
(SEM) technique with AMOS 7.0 software to test
the proposed model. The most common SEM
estimation procedure is maximum likelihood
estimation (MLE). Theoretically, the sample size for
executing MLE requires it at least 10 times of the
total number of measuring items. There are 1017
valid questionnaires and it is enough for executing
MLE to analyze data.
The testing results reported a goodness of
model fit with the indices of χ2/df
(1036.23/495=2.09), TLI (0.92), CFI (0.93), and
RMSE (0.08). Next, item loadings range from 0.73
to 0.92, composite reliabilities range from 0.85 to
0.91, and AVEs range from 0.56 to 0.79. This
indicates reliability and convergent validity in a
highly acceptable level. Each construct’s square root
of AVE is above its correlations with other
constructs. These results indicate discriminant
validity in a highly acceptable level.
4 HYPOTHESES TESTING
The structural model was used to examine
hypothesized path and variance explained for the
endogenous variables (
2
R
), as indicated in Figure 2.
Perceived
usefulness
Expectation
confirmation
Customer
satisfaction
Intention
to complain
Distributive
justice
Procedural
justice
Interactional
justice
0.28*
0.06
0.22*
0.28*
0.18*
0.36*
-0.28*
Trust
0.27*
0.20*
R
2
= 0.30
R
2
= 0.62
R
2
= 0.42
Figure 2 Results of the structural model Value on path:
Standardized coefficients,
2
R
: Coefficient of
determination, *: p<0.01.
In the justice components, distributive justice
and interactional justice are two important
antecedents in determining customer satisfaction
(β=0.28 and 0.22) and procedural justice indicates
no significance in its influence (β=0.06). Therefore,
Hypotheses 1 and 3 are supported, but Hypothesis 2
is not supported. In the ECM-based components,
confirmation of expectations plays a critical role in
determining perceived usefulness and customer
satisfaction (β=0.36 and 0.28). Therefore,
Hypotheses 4 and 5 are supported. Perceived
usefulness is an important predictor of customer
satisfaction (β=0.18). Therefore, Hypotheses 6 is
supported. In the trust construct, it shows a
significant influence on perceived usefulness and
customer satisfaction (β=0.27 and 0.20). Thus,
Hypotheses 7 and 8 are supported. Moreover,
confirmation of expectation and trust jointly
explains 30% variance of perceived usefulness
(
2
R
=0.30). Next, justice components, ECM-based
features, and trust jointly explain 62% variance of
customer satisfaction (
2
R
=0.62). Finally, customer
WEBIST2012-8thInternationalConferenceonWebInformationSystemsandTechnologies
514
satisfaction is viewed as a mediator in achieving
complaint intentions through its antecedents and
indicates a significant impact on complaint
intentions (β=-0.28). Therefore, Hypotheses 9 is
supported. Moreover, customer satisfaction explains
42% variance of complaint intentions (
2
R
=0.42).
5 FINDINGS AND DISCUSSIONS
In the justice components, distributive justice and
interactional justice remain to be important
predictors of customer satisfaction and in turn,
complaint intentions, as in many prior studies with
physical stores (Smith et al., 1999; Martinez-Tur et
al., 2006). These results are quite interesting to
online vendors. First, while the Internet-based
mechanisms are highly penetrable for their users to
share beliefs, thoughts, and behaviors, such as
virtual communities, blogs, and face book, online
consumers are often in an easy way to compare the
products or services offered by online stores, such as
quality and price, with other buyers for the fairness
of their exchange or purchase. If the outcome of
their exchange is unfair, that is, the failure of
distributive justice, consumers will definitely feel
dissatisfaction and tend to complain it to online
vendors. Next, interactional justice for the online
stores indicates the importance of designing a better
shape of system interface. System interface should
be presented both in a trustworthy manner and in a
user-friendly mode to enable a good communication
to online consumers. More specifically, it can be
trusted by online consumers in terms of the features
of privacy, security, and accuracy in the interaction
with online stores.
Besides, procedural justice indicates non-
significant impact on customer satisfaction, which is
consistent with some previous studies (Martinez-Tur
et al., 2006; Chiu et al, 2009). The reasons behind
this may be explained as below. The history of e-
commerce has been defined in a mature form for a
long time since the advent of the Internet and
communication technologies in a decade ago. The
procedures or policies for dealing with online
shopping, such as trading rules, payment, return,
delivery, and so on, have been defined clearly for
most online firms. They are well embedded into
system architecture and are operated without any
interference from human being. It is easy and
convenient for experienced and inexperienced
shoppers to follow these rules in a straightforward
manner. Online shoppers do not regularly feel
inconsistent in their purchase process and are treated
in a relatively fair form. While most shoppers are
well known with the trading rules defined in online
systems, there is less possibility to give rise to
dissatisfaction and unfairness in the purchase
process. In contrast, it would be more likely to
introduce dissatisfaction and unfairness in a human-
oriented purchase process while these rules intend to
be amended by employees from time to time.
In the ECM-based features, confirmation of
expectations, perceived usefulness, and customer
satisfaction show positive relationships between
them. In general, this study indicates the importance
of considering technology use perspective in terms
of ECM-based features in the online shopping
context. Specifically, confirmation of expectations
has positive impact on perceived usefulness and
further, both of them significantly influence
customer satisfaction toward using online systems.
This may be explained by the importance of
confirmation of expectations in initially driving the
activities of online purchase. Before consumers can
be ready for doing online shopping, they need to
first confirm their original expectation from online
stores as a convenient and efficient way to get their
products or services. In that, the better way for
consumers to search and find purchase items in
online stores can be termed perceived usefulness.
For the trust beliefs, it has direct impact and
indirect impact through perceived usefulness on
customer satisfaction. Trust is the underlying basis
for the effect of a consumer’s belief on online
purchase. As discussed previously, perceived
usefulness was defined as a post-adoption or
repurchase-intention belief toward online purchase
behavior. Without a building of initial trust beliefs,
perceived usefulness, in essence, would not be
constructed in favor of online purchase. Next, by the
same token, customer satisfaction is in a similar
definition with a post-consumption behavior driving
by an initial trust belief of reducing trading
uncertainty. In sum, complaint intentions are well
demonstrated with its explained ability from these
proposed antecedents in this research.
6 CONCLUSIONS AND
SUGGESTIONS
Overall, the proposed model with justice, technology
use, and trust drivers provides useful insights into
explaining and predicting complaint behaviors in the
online shopping. Several important practical
implications arise from our findings. A high
proportion of online shoppers (over 65%) has the
ANALYZINGCOMPLAINTINTENTIONSINONLINESHOPPING-ThePerspectiveofJustice,TechnologyandTrust
515
experience of a service or produce failure in online
shopping. Therefore, online stores should carefully
take account of consumers’ complaints as their
major concern for maintaining long-term
relationship. The primary work, in general, focuses
on improving the communication channel between
online stores and consumers.
Specifically, justice was found to be the
important drivers in determining complaint
intentions. This implies a consideration from
marketing aspect to effectively improve the
communication channel. The marketing activities
which are related to the products and treatments
offered by online stores play a critical role in
determining consumer’s justice perception.
Customer relationship management (CRM) intends
to deeply understand customer requirements in an
individual basis and to eventually build long-term
relationship. CRM would be an important
mechanism to effectively communicate with
consumers for maintaining the quality of products
and treatments.
Next, ECM-based features are the necessary for
customers to impact complaint intentions. This
indicates an understanding of technological aspect
for improving the communication channel. Online
stores need to improve both front-end and back-end
mechanisms at the same time. In the front-end part,
online stores should develop high accessible and
speedy hardware, user-friendly system interface,
effective searching engines, and ease-operating
system navigation. In the back-end part, online
stores can analyze useful customized information to
fulfill consumer requirements and allow consumers
to manage their orders, payments, and deliveries in a
more efficient way.
Finally, while trust belief is also important in
predicting consumer complaints, this implies that a
psychological state aspect needs to be built for
enhancing consumer’s confidence before their
willingness to accept the communication channel.
The effort from online stores may focus on two
possible ways. The first is to send a
signal/advertising message in both physical and
virtual manners for promoting consumer’s
recognition of sellers. The second is often to conduct
a survey research for understanding the real
requirements of consumers in order to reduce the
gap between sellers and buyers.
Some theoretical implications are also noted
from the findings. First, to the best of our
knowledge, there are few studies about complaint
intentions in the online shopping context. It is
important to explore complaint behaviors in order to
understand and recover service failure in online
shopping while this shopping has increasingly
become very important in our life. Second, while
justice perception has been applied mostly in the
physical context, few studies have been found in the
online context. We think that it is important to
consider the role of justice perception in the
complaint behaviors of online shopping.
Finally, although this research has produced
some interesting results, a number of limitations
may be inherent in it. First, a limitation may be the
sampling method employed in this study while this
is an online questionnaire survey. However, we have
tried our best to place the questionnaire
simultaneously on several larger online communities
for covering a larger/wider variety of data sources
for being more representative in the study sample.
Second, this study showed that approximately 65%
and 35% of the respondents are female and male
respectively. The result may not reflect properly the
regular population distribution of gender and cause a
potential bias against the current findings. However,
in fact, women are more likely to do online shopping
than man and this would, in essence, reflect the
actual situation.
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