UNDERSTANDING DETERMINANTS OF COMPLAINT

INTENTIONS IN ONLINE SHOPPING

The Perspectives of Justice and Technology

Ing-Long Wu

, Chi-Ying Huang

and Chu-Ying Fu

1, 2

Department of Information Management, National Chung Cheng University, Chia-Yi, Taiwan

Department of Information Management, WuFeng University of Science and Technology, Chia-Yi, Taiwan

Keywords: Online shopping, Complaint intention, Justice theory, Expectation-confirmation model.

Abstract: Consumers’ complaint behaviors are critical in determining repurchase behaviors in online shopping. An

understanding of complaint behaviors can provide insight to the failed service experience with consumers

and in turn, effectively redress consumers’ problems. Therefore, it is important to comprehend the

antecedents of complaint intentions in online shopping. The major issue is two-fold: behavior and

technology. This study thus integrates justice theory and expectation-confirmation model to examine the

antecedents of complaint intentions in terms of these two issues. Moreover, customer satisfaction is an

important mediator in the relationship structure. Data are collected from online shoppers with dissatisfied

experience. Structural equation modeling is used to analyze this model. The results indicate that distributive

justice and interactional justice are important in influencing customer satisfaction and complaint intentions

while interactional justice is not. Technology-based antecedents, such as perceived usefulness, are all

important in determining customer satisfaction and complaint intentions. Implications for managers and

scholars are discussed.

1 INTRODUCTION

Approximately half of the huge revenue is brought

by online shopping in B2C e-commerce. Although

online shopping market is relatively huge, however,

the growth rate has been decreasing recently. A

recent survey by market intelligent center in Taiwan

in 2009 has indicated the same situation in online

shopping market. Moreover, according to consumer

research report, acquiring a new customer is about

five to eight times more expensive than retaining an

existing one (Reichheld and Schefter, 2000, Chea and

Luo, 2008). As a result, research on online shopping

was previously focused on understanding consumer’s

acceptance/purchase behaviors, but more attention

has been paid recently to consumer’s post-adoption

behaviors (Chea and Luo, 2008; Kim and Son, 2009).

Consumer’s post-adoption/ repurchase behaviors are

the key to a firm’s survival in a highly competitive

e-marketplace (Chea and Luo, 2008, Kim and Son,

2009).

Research on post-adoption behaviors in online

shopping has been focused on customer satisfaction

and continuance intention to purchase (Gefen et al.,

2003, Finn et al., 2009). However, complaint

behaviors have often occurred to most buyers due to

dissatisfaction of online services (Voorhees and

Brady, 2005, Thogersen et al., 2009). Complaint

behaviors have been proven to play an important role

in consumers’ decision making of their purchase

(Breazeale, 2009). Little research on complaint

behaviors has been discussed in the online shopping

context.

Many researchers pointed out that online

consumers are different from traditional offline

consumers (Shankar et al., 2003, Teo, 2006). Online

consumers are buyers and at the same time they are

users of information systems (Shankar et al., 2003).

Accordingly, two major concerns need to be

considered in this study, behavioral issue and

technological issue. For behavioral issue, many

studies has claimed the importance of justice

perception in linking to customer satisfaction and

complaint intentions/behaviors (Maxham and

Netemeyer, 2002, Martinez-Tur et al. 2006,

Thogersen et al., 2009). However, the literature has

been a lack of considering its influence on complaint

intentions or behaviors in the online shopping

context.

For technological issue, expectation confirmation

473

Wu I., Huang C. and Fu C..

UNDERSTANDING DETERMINANTS OF COMPLAINT INTENTIONS IN ONLINE SHOPPING - The Perspectives of Justice and Technology.

DOI: 10.5220/0003311904730479

In Proceedings of the 7th International Conference on Web Information Systems and Technologies (WEBIST-2011), pages 473-479

ISBN: 978-989-8425-51-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

model of IS continuance (ECM) indicated the

relationships between technology acceptance model

(TAM) based features such as perceived usefulness

(Davis, et al., 1989), and customer satisfaction and

continuance intention to use (Bhattacherjee, 2001).

Furthermore, ECM-based features have been widely

used and extended to indicate two other

post-adoption behaviors: complaint and

recommendation intentions (Chea and Luo, 2008,

Finn et al., 2009). Grounding on justice theory and

extended ECM-based features, this study proposed a

novel research framework to understand the

antecedents of complaint intentions in the online

shopping context.

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

Confirmation

Satisfaction

Intention

to complain

Distributive

justice

Procedural

justice

Interactional

justice

Justice Theory

Expectation Confirmation Model

H1(-)

H2(-)

H3(-)

H10(-)



Figure 1: Research model.

2.1 Justice Theory

Colquitt et al. (2001) comprehensively reviewed 183

justice-related studies from the literature and

integrated into three major justice constructs:

distributive, procedural and interactional justice.

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 (Thibaut and Walker, 1975,

Alexander and Ruderman, 1987).

Bies and Moag (1986) separated out the

interpersonal aspect of procedural justice that is

termed interactional justice. Interactional justice

refers to the perceived fairness of interpersonal

treatment that individuals receive in the decision

making process. Recently, one study on online

context defined interactional justice as the degree to

which online users perceive online companies as

honest and trustworthy in complying with their

promises related to information privacy (Son and

Kim, 2008). 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. Tax et al. (1998) argued that complaint is

the behavior after consumers have injustice

treatments in distribution, procedure and interaction

with retailers. Some studies reported that the three

justice components are all considered to be the

important drivers of complaint intentions in a failed

service experience (Maxham and Netemeyer, 2002).

Other studies unequivocally suggested that higher

levels of distributive, procedural, and interactional

justice will decrease the likelihood of complaint

intentions (Blodgett et al., 1993, Clemmer, 1993).

Hence, it is hypothesized that:

H1. Distributive justice has a negative effect on

complaint intention in online shopping.

H2. Procedural justice has a negative effect on

complaint intention in online shopping.

H3. Interactional justice has a negative effect on

complaint intention in online shopping.

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

WEBIST 2011 - 7th International Conference on Web Information Systems and Technologies

474

also concluded the importance of the three

components in impacting customer satisfaction in

service industries (Tax et al., 1998, Voorhees and

Brady, 2005). Hence, it is hypothesized that:

H4. Distributive justice has a positive effect on

consumer satisfaction in online shopping.

H5. Procedural justice has a positive effect on

consumer satisfaction in online shopping.

H6. Interactional justice has a positive effect on

consumer satisfaction in online shopping.

2.2 Expectation Confirmation Model

Bhattacherjee (2001) proposed expectation

confirmation model of IS continuance (ECM) by

integrating expectation confirmation theory (ECT)

(Oliver, 1980, 1981) and TAM-based studies, such as

perceived usefulness (Davis et al., 1989), to explore

user satisfaction continuance intention to use. ECT

was originally proposed by Oliver (1980) for

consumer behavior research to examine consumer

satisfaction and post-purchase behaviors.

Bhattacherjee (2001) argued that IS user’s

continuance decision, in general, is similar to

consumer’s repurchase decision in ECT. Furthermore,

a post-expectation of IS use is considered to be

included in ECM while ECT only examines the

effect of pre-expectation in the purchase decision.

Based on TAM-based studies, perceived usefulness is

considered as an appropriate post-expectation in the

IS continued use. Accordingly, this model is

indicated as a similarity to the lower part of Figure 1.

Moreover, ECM-based features have been widely

used were 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 for the extension of

ECM.

Since online shopping context, in essence, is a

website-based technology, post-adoption behaviors,

are also the major concern of consumer’s repurchase

decision (Chea and Luo, 2008). The online complaint

behaviors may be explained by an extension of

ECM-based features in a technological 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-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; Kang et al., 2009). Hence, it is hypothesized

that:

H7. 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

performance (Oliver, 1980). Positive confirmation

arises when the perceived performance of customers

exceeds their pre-expectation. Positive confirmation

indicates a positive effect on customer satisfaction.

Previous studies on ECM in online environment also

indicated empirical evidence in terms of the effect of

disconfirmation on customer satisfaction (Chea and

Luo, 2008, Finn et al., 2009). Hence, it is

hypothesized that:

H8. 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 in consumer behavior

research, 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). Hence, it is hypothesized

that:

H9. Perceived usefulness has a positive effect on

customer satisfaction in online shopping.

Prior research revealed that complaint intentions

arise when they encounter a dissatisfied circumstance

in online shopping (Velazquez et al., 2006,

Thogersen et al., 2009). Many studies also supported

a direct relationship between customer satisfaction

and complaint intentions in online environment

(Voorhees and Brady, 2005, Chea and Luo, 2008). In

other words, when consumers feel more dissatisfied,

the complaint intentions increase. Hence, it is

hypothesized that:

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

UNDERSTANDING DETERMINANTS OF COMPLAINT INTENTIONS IN ONLINE SHOPPING - The Perspectives of

Justice and Technology

475

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 in online shopping,

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 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. Complaint intention was measured with

items based on Singh (1988), Liu and McClure

(2001), Chea and Luo (2008). It comprises 5 items.

3.2 Sample Design

Empirical data was collected via consumers who had

a failed or dissatisfied service experience when they

shopped online for products or services. This online

survey was placed on online communities for their

users as the potential respondents. 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

1072 questionnaires were received and 699 are valid

with a failed service experience (65%). Of the

respondents, 453 are female (65%) and 246 are male

(35%), 607 are between 20 and 39 years old (87%),

and 600 are at least college degree (86%). The

number of respondents whose online shopping

experience was more than three years is 472 (68%).

3.3 Measurement Model

This study used a structural equation modeling (SEM)

technique with AMOS 7.0 software to test the

proposed model. There are 699 valid questionnaires

and it is enough to execute SEM with a sample size

of 10 times of the total measuring items (Hair et al.,

2006). The testing results report a goodness of model

fit with the indices of χ2/df (884.39/391=2.26), TLI

(0.90), CFI (0.91), and RMSE (0.08). Next, item

loadings range from 0.72 to 0.90, composite

reliabilities range from 0.81 to 0.91, and AVEs range

from 0.54 to 0.82. This indicates reliability and

convergent validity in a highly acceptable level. The

correlation matrix for discriminant validity indicates

that each construct’s square root of AVE is above its

correlations with other constructs. This indicates

divergent validity in a highly acceptable level.

4 HYPOTHESES TESTING

The structural model was used to examine path

significance of the hypothesized relationships and

variance explained for the endogenous variables

(

). The testing results of the structural mode are

shown in Figure 2.

Perceived

usefulness

C onfirm ation

Sa tisfactio n

Intention

to com p lain

Distributive

justic e

Procedural

justic e

Interactional

justic e

E xpectatio n C onfirm ation M odel

0.26*

0.03

0.17*

-0.24*

0.04

-0.22*

0.36*

019*

0.47*

-0.28*

= 0.41

= 0.69

= 0.29

Justice Theory

Figure 2: Results of the structural model. Value on path:

Standardized coefficients,

: Coefficient of

determination, *: p<0.01.

For justice components, distributive justice and

interactional justice are two important predictors of

complaint intentions (β=-0.24 and -0.22) while

procedural justice is not significant in its influence

(β=0.04). Therefore, Hypotheses 1 and 3 are

supported, but Hypothesis 2 is not supported. Next,

distributive justice and interactional justice are two

important antecedents in determining customer

satisfaction (β=0.26 and 0.17) and procedural justice

indicates no significance in its influence (β=0.03).

Therefore, Hypotheses 4 and 6 are supported and in

contrast, Hypothesis 5 is not supported. For

ECM-based components, confirmation of

expectations plays an important role in determining

perceived usefulness and customer satisfaction

(β=0.47 and 0.36). Therefore, Hypotheses 7 and 8 are

supported. Moreover, confirmation of expectation

explains 29% variance of perceived usefulness

(

=0.29). Perceived usefulness is a significant

influencer of customer satisfaction (β=0.19).

Therefore, Hypotheses 9 is supported. Moreover, the

three justice components and two ECM-based

WEBIST 2011 - 7th International Conference on Web Information Systems and Technologies

476

components jointly explain 69% variance of

customer satisfaction (

=0.69). Finally, customer

satisfaction is an important predictor of complaint

intention (β=0.28). Therefore, Hypotheses 10 is

supported. Moreover, the three justice components

and customer satisfaction jointly explain 41%

variance of complaint intention (

=0.41).

5 FINDINGS AND DISCUSSIONS

In the justice components, distributive and

interactional justices remain to be important

predictors of complaint intentions, as in many prior

studies with physical stores. These results are quite

interesting to online vendors. First, while the

Internet-based mechanisms is highly penetrable for

their users to share beliefs, thoughts and behaviors,

such as virtual communities, blogs, and facebook,

online consumers are often in an easy way to

compare the products or services offered by online

stores, such as the quality of products and prices,

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 tend to complain it to

online vendors. Next, interactional justice for the

online stores indicates the importance of the design

of system interface. The design of system interface

should be presented both in an honest and

trustworthy manner and in a user-friendly mode for 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 has no significant

impact on complaint intentions and customer

satisfaction, which is not consistent with prior studies

(Clemmer, 1993; Blodgett et al., 1997). The reasons

behind this may be explained as below. The history

of e-commerce has been for a long time in a mature

form 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 embedded into system

architecture and are operated without any

interference from human being. It is convenient for

experienced and inexperienced shoppers to follow

these rules in a straight forward manner. Online

shoppers do not regularly feel inconsistent in the

purchase procedures applied to them and are treated

in a relatively fair form. While most shoppers are

well known with these rules defined in the online

system, there is less possibility to give rise to

unfairness in the purchase process. In contrast, there

is more possibility to introduce unfairness in a

human-handling purchase process while these rules

intend to be amended by service representatives from

time to time. Next, procedural justice also is not

correlated with customer satisfaction. As above

discussion, it can be considered as a regular norm in

the online shopping and consumers feel procedural

justice as not important to their online shopping. As

result, their correlation would not be built

significantly in the online shopping.

In the ECM-based components, confirmation of

expectations, perceived usefulness, and customer

satisfaction reveal positive relationships between

them. Overall, this study indicates the importance of

a technological perspective with ECM-based features

in the online shopping. Specifically, confirmation of

expectations has positive impact on perceived

usefulness and further, both of them significantly

influence customer satisfaction. This may explain the

importance of confirmation of expectations in

initially driving the activities of online shopping.

Before consumers can be ready for doing their online

shopping, they need to first confirm what they

originally expect from the online shopping as a

convenient and efficient way to get their products or

services. In that, the better way to find and search

purchase items in online stores can be termed

perceived usefulness. Finally, complaint intentions

are well demonstrated with its explained ability. This

may indicate a fact that an integration of both just

theory and ECM-based features is in a position to

effectively explain the complaint intentions of online

shopping while this idea is rooted in a consideration

of both behavioral and technological aspects.

6 CONCLUSIONS

AND SUGGESTIONS

The results indicated that distribution and

interactional justices are more influencing in

determining complaint intentions and customer

satisfaction than procedural justice. The findings

with justice perception are unique and important in

the particular online shopping context. This is a

major contribution of this research. Next,

ECM-based components were also applied

successfully to examine complaint intentions in this

context. The results showed the significant

relationships among these technology-based

components. Overall, the proposed model provides

UNDERSTANDING DETERMINANTS OF COMPLAINT INTENTIONS IN ONLINE SHOPPING - The Perspectives of

Justice and Technology

477

insights into explaining and predicting complaint

behaviors in the online shopping.

Several important practical implications arise

from our findings. The survey result reported a high

proportion of online shoppers (over 65%) that has the

experience of a service or produce failure in online

shopping. Consumers not only want to express

negative feelings and to seek for redress, but also

want to give advices for improving the process of

products and services offered in online stores.

Therefore, online stores should take account of

consumer’s complaints, and pay attention on the

communication channel between online stores and

consumers. Specifically, distributive and interactional

justices and ECM-based components have indicated

well as the underlying drivers in determining

complaint intentions. The communication channel

should be considered from both marketing and

technological aspects and can be effectively

improved accordingly.

For marketing aspect, the products and treatments

of online stores play an important role in consumer’s

perceived justice. Online stores should maintain and

improve the quality of products and treat consumers

fairly with the implementation of customer

relationship management. For technological aspect,

online stores need to improve front-end and back-end

mechanisms simultaneously. In the front-end part,

online stores should implement new and useful

information and communication technologies, design

user-friendly system interface, build effective

searching engines, and develop easy understanding

form of layout. In the back-end part, online stores

can provide useful customized information to fulfill

consumer requirements and let consumers manage

their orders, payments, and deliveries in a more

efficient way.

Finally, a limitation of 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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