Antecedents of continuance intentions towards online shopping an emprical study in vietnam

UNIVERSITY OF ECONOMICS HO CHI MINH CITY
International School of Business
------------------------------

Tran Thi Quynh Trang

ANTECEDENTS OF CONTINUANCE
INTENTIONS TOWARDS ONLINE
SHOPPING: AN EMPIRICAL STUDY
IN VIETNAM

MASTER OF BUSINESS (Honours)

Ho Chi Minh City – Year 2015


UNIVERSITY OF ECONOMICS HO CHI MINH CITY
International School of Business
------------------------------

Tran Thi Quynh Trang

ANTECEDENTS OF
CONTINUANCE INTENTIONS
TOWARDS ONLINE SHOPPING: AN
EMPIRICAL STUDY IN VIETNAM
ID: 22130084

MASTER OF BUSINESS (Honours)
SUPERVISOR: DR. NGUYEN THI NGUYET QUE

Ho Chi Minh City – 2015


Acknowledge

This thesis could not be finish without the help and support of many people who are
gratefully acknowledged here.
At the very first, I would like to express my deepest gratitude to my supervisor, Dr.
Nguyen Thi Nguyet Que. With her guidance, I could have worked out this thesis. She had
offered me valuable suggestions and criticisms with her profound knowledge in rich research
experience.
I am grateful to express my sincere to Prof. Nguyen Dinh Tho and Dr. Nguyen Thi Mai
Trang. I have learned from them a lot not only about research design, but also data analysis
technique. I am also extremely to give thankful to UEH – International School of business
(ISB) supported me in all process.
I would like to extent my sincere thanks to all my classmate and friends. Their kindness
and supports have contributed very much in my working process. Most important, I would like
to express my most sincere thanks to my father, my mother and my brother for their continuous
encouragement and support.


Abstract
Online shopping has emerged as a new e-commerce model and received attention in both
academics and practice. This research extends Mehrabian and Russell’s Stimulus-OrganismResponse model to investigate website stimuli that affect emotional reaction and perceived risk,
in turn consumers’ response systems. The purpose of this paper is to test a more comprehensive
model consisting of website quality (stimulus), cognition and emotion (organism) and
continuance intention (response).To examine research model, information and data is accessed
by using questionnaire for respondents purchasing on online shopping websites. This research
employed convenience sampling. The questionnaire was distributed in Ho Chi Minh City. In
total, 227 usable questionnaires were obtained in Ho Chi Minh City through online survey.

Confirmatory factor analysis (CFA) is used to test measurement scale and the structural
equation modeling (SEM) is used as the main method for analyzing research model and
hypotheses. Results in this study show that all four website quality dimensions had significant
negative effects on perceived risk and positive effects on emotional reaction, except for
customer service. Perceived risk had a slightly negative effect on consumers’ emotional
reaction. Meanwhile, perceived risk had a significant influence on continuance intention. In
addition, emotional reaction also had a positive impact on continuance intention in the context
of online shopping. The results may be generalized to a limited extent. With these results,
research framework can be seen fitted with data market. Study results also suggest that in order
to increase customer retention should not only consider about their strategies of eliciting
positive emotions and reducing perceived risk, they should a also take consideration of
increasing website quality.


TABLE OF CONTENTS
ABSTRACT
CHAPTER 1: INTRODUCTION .............................................................................................. 1
1.1 Research background ........................................................................................................ 1
1.2 Problems statement .......................................................................................................... 3
1.3 Research objectives and questions ................................................................................... 5
1.4 Methodology and scope of research ................................................................................ 6
1.5 Research significance ........................................................................................................ 7
1.6 Thesis structure ................................................................................................................ 7
CHAPTER 2: LITERATURE REVIEW .................................................................................... 8
2.1 Introduction ..................................................................................................................... 8
2.2 Theoretical background .................................................................................................. 8
2.2.1 S – O –R paradigm............................................................................................. 8
2.2.2 Website quality .................................................................................................. 9
2.2.3 Emotional reaction and perceived risk ............................................................ 10
2.2.4 Continuance intention ...................................................................................... 11

2.3 Hypotheses and proposed model ................................................................................... 12
2.3.1 Stimuli and organism ....................................................................................... 12
2.3.2 Organism and responses ................................................................................. 15
2.4 Summary ........................................................................................................................ 17
CHAPTER 3: METHODOLOGY ............................................................................................. 19
3.1 Introduction ................................................................................................................... 19
3.2 Research design ............................................................................................................. 19
3.3 Development of questionnaire ....................................................................................... 21
3.3.1 Measurement scale ................................................................................................ 21
3.3.1.1 Perceived risk scale ...................................................................................... 21
3.3.1.2 Emotional reaction scale .............................................................................. 22


3.3.1.3 Continuance intention scale ......................................................................... 23
3.3.1.4 Website quality scale ................................................................................... 23
3.3.2 Draft questionnaire ................................................................................................ 25
3.4 Data collection method ................................................................................................. 26
3.4.1 Pilot study .............................................................................................................. 26
3.4.2 Sampling method and sample size ......................................................................... 27
3.5 Data analysis techniques ................................................................................................ 28
3.5.1 Cronbach’s alpha ................................................................................................... 28
3.5.2 Exploratory factor analysis (EFA) ........................................................................ 29
3.5.3 Confirmatory factor analysis (CFA) ...................................................................... 29
3.5.4 Structural equation model (SEM) .......................................................................... 30
3.6 Summary ....................................................................................................................... 30
CHAPTER 4: ANALYSIS AND RESULTS ............................................................................ 31
4.1 Introduction ................................................................................................................... 31
4.2 Demographic statistic ................................................................................................... 31
4.3 Scale validation ............................................................................................................. 33
4.3.1 Preliminary results .................................................................................................. 33

4.3.2 Confirmatory factor analysis (CFA) ..................................................................... 35
4.4 Structural equation model ............................................................................................. 42
4.5 Discussion ...................................................................................................................... 43
4.6 Summary ........................................................................................................................ 45
CHAPTER 5: CONCLUSIONS AND LIMITATIONS ........................................................... 46
5.1 Conclusion .................................................................................................................... 46
5.2 Managerial implications ............................................................................................... 48
5.3 Limitations and future research ..................................................................................... 49
REFERENCES ........................................................................................................................... 51
APPENDICES ............................................................................................................................ 59


LIST OF FIGURE

Figure 2.1 Conceptual model ................................................................................................... 17
Figure 3.1 Research process ..................................................................................................... 20
Figure 4.1 CFA for the full model ............................................................................................ 36
Figure 4.2 Structural equation model ....................................................................................... 42


LIST OF TABLE

Table 3.1 Perceived risk scale .................................................................................................... 21
Table 3.2 Emotional reaction scale ............................................................................................. 23
Table 3.3 Continuance intention scale ........................................................................................ 23
Table 3.4 Website quality scale .................................................................................................. 24
Table 3.5 Cronbach’s Alpha Reliability Coefficient .................................................................. 28
Table 4.1 Demographic statistic .................................................................................................. 32
Table 4.2 Cronbach’s Alpha results ........................................................................................... 33
Table 4.3 Construct reliability, Factor loading and AVE in CFA ............................................... 38

Table 4.4 Convergent validity result ........................................................................................... 39
Table 4.5 The relationship between constructs in research model ............................................. 41
Table 4.6 Result of hypotheses testing ....................................................................................... 43


LIST OF ABBREVIATION

AVE Average Variance Extracted
CFA Confirmatory Factor Analysis
CR Construct Reliability
EFA Exploratory Factor Analysis
E-commerce Electronic commerce
ML Maximum Likelihood
SEM Structural Equation Model
S-O-R Stimuli – Organism - Response
SPSS Statistical software package


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CHAPTER 1: INTRODUCTION

1.1 Research background
The rapid growth of the Internet technology has resulted in the creation of a further form
of retail transaction – electronic commerce or online shopping. Electronic commerce,
commonly known as e-commerce, includes the purchase and selling of products or services
over electronic systems such as the Internet. A form of e-commerce is online shopping which is
the process whereby consumers directly buy goods or services from a seller in real time,
without an intermediary service, over the Internet. In recent years, the internet power, scope
and interactivity provide businesses with the potential to enhance their customers shopping

experience and in so doing increase their competitive positions (Doherty & Ellis-Chadwick,
2010). Retailers have more opportunities to reach their customers globally and directly via ecommerce and online shopping. More specifically, it is easier for e-retailers to expand target
markets, extend product lines, improve cost efficiency and enhance the customer relationships.
By and large, consumers have responded enthusiastically to these innovations (Doherty &
Ellis-Chadwick, 2010). Thereby, the Internet opens up new avenues for e-retailers in a manner
that traditional store cannot achieve.
As a result, there were variety of predictions, many of them highly optimistic, about the
scale, scope and impact of e-retailers. According to VECITA (2013), a company of market
research in US - eMarketer announced that online retail sales totaled $262.3 million, raised
16.3% from $222.5 million in 2012. Consequently, US continued to lead the world in ecommerce retail market with $156.1 million online shoppers and average online purchase per
shopper was $2.466 in 2013 (VECITA, 2013).
Similarly, consumers across Southeast Asia are going online in droves, particularly with
the rapid up-take of connected devices, and they are increasingly searching out online channels
to research and purchase the products and services they need and want. The growth of
connected device ownership across Southeast Asia is laying the foundation for a booming


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online retail sector, with the number of consumers in the region making online purchases
increasing significantly in the past two years (Nielsen, 2014).
In 2014, online retail sales of China grew 63.9% over the previous year, with the
expected sales of $217.39 billion. It is expected that this growing rate will still be kept until
2018. Online shopping sales in China accounted for more than 50% of the total revenue of the
Asia – Pacific. This figure will reach 70% in 2018. Furthermore, eMarketer also forecast the
B2C e-commerce sale will account for $391.2 billion in 2016, up 13.6% over 2015 and will
reach $441.3 billion in 2017 (VECITA, 2014). Consequently, according to Nielsen (2014),
online shopping is set to continue its upward trajectory in the years ahead as consumers’
familiarity with, and trust in, online retail sites grows.
Looking back to Vietnam, along with the rapid development of online retailer in the

world, e-commerce or online shopping is not entirely an alien concept to Vietnam consumers.
The survey conducted by VECITA showed that the estimation of e-commerce sales per online
buyer accounted for approximately $145 in 2014 and total sales reached $2.97 billion, which
accounted only 2.12% of total retail sales. Besides, according to VECITA (2014), 41% of
surveyed enterprises having revenue of goods, services via e-commerce channels in 2014
increased, comparing with 2013 and 9% of respondents had decreased revenue. Meanwhile,
50% of respondents said this revenue experienced almost no change. Thus, many online
providers are struggling to find strategies to develop in this difficult period.
According to Reichheld and Schefter (2000), chief executives at the cutting edge of ecommerce – from Dell Computer’s Michael Dell to eBay’s Meg Whitman – care deeply about
customer retention and consider it vital to the success of their online operations. Besides that,
Reichheld and Schefter (2000) analyzed the relationship between the cost of serving loyal
customers and the volume of their purchases. The result indicated that increasing customer
retention rates by 5% increases profits by 25% to 95%. Those numbers startled many
executives, and the article set off a crush to craft retention strategies, many of which continue
to pay large dividends (Reichheld and Schefter, 2000). Such continuance is critical since the


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cost of attracting new consumers is considerably more expensive than retaining existing ones
(Al-Maghrabi et al., 2011). In particular, it is a financial efficiency for e-retailers, in
comparison with the bricks-and-mortar retailing environment. With a good understanding of
the web shopper’s continuance intention, e-retailers will be able to develop effective and
efficient e-commerce strategies to have more loyal customers. Nonetheless, web-shopping
continuance intention does not necessarily follow traditional consumer repurchase intention in
the traditional stores. Hence, online shopping continuance intention has recently emerged as a
prominent issue in research. Exploring and analyzing which factors influence customer
retention have significant meaning to online providers.

1.2 Problems statement

In Vietnam, the numbers of notification e-commerce websites which were confirmed till
the end of December 2014 reaching 5,082 websites (VECITA, 2014). Total transaction value in
2014 of 85 surveyed e-marketplaces reached 2,500 billion VND. The two leading emarketplaces were lazada.vn (21%) and sendo.vn (10%). Websites were mostly located in the
two big cities named Hanoi and Ho Chi Minh City. 46% of websites were set in Hanoi, 44% of
websites in Ho Chi Minh City, while 10% were in other cities/provinces (VECITA, 2014).
Along with development of e-commerce market, perceived risk and competition
between rivals also increase very strongly. More specially, Vietnamese culture whereby the
trading is conducted face to face and most people have inefficient knowledge on internet
transactions, so consumers perceived more risk with regard to online shopping.
It is evident that according to the survey of VECITA in 2014, the biggest challenge for
online shopping to Vietnamese consumers is the quality of products or services worse than
being advertised (81%). The others obstacles of online shopping are unprofessional logistic
services (51%), price not lower than buying in traditional shops or not clear (46%), personal
privacy disclosure (42%), and unprofessional website design (29%) (VECITA, 2014).


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Besides, according to Nielsen (2014), credit card security remains a key concern for
consumers across the region with five of the six Southeast Asia markets ranking above the
global average with respect to their concern around providing credit card information online.
Filipinos are the most cautious when it comes to paying online by credit card (67% do not trust
giving their credit card information online), followed by Thais (62%), Indonesians (60%),
Vietnamese (55%), Malaysians (52%) and Singaporeans (41%), compared to 49 percent of
consumers globally. In order to reduce perceived risk consumers online retailers must look for
opportunities to provide reassurances around online payment security. With many problems,
websites in Vietnam are facing many difficulties in attracting customers as well as keeping
existing customers (Nielsen, 2014).
Customer retention is a necessary topic for helping online retailers increase their
competitive advantages and maintain their market. Since Mehrabian and Russell suggested that

environmental stimuli (S) lead to an emotional reaction (O) that evokes behavioral responses
(R), the model has been applied in various retail settings to explain the consumer decision
making process (Chang & Chen, 2008). Because consumers interact with websites to shop for
products and services and because they have abundant choices in selecting online stores,
website characteristics are crucial components of the online shopping environment (Jiang et al.,
2010). Eroglu et al. (2001) developed a model proposing that online atmospherics such as
colors, graphics, layout and design can provide information about the retailer as well as
influence consumers’ emotional and behavioral reactions. Mummalaneni (2005) also applied
the S-O-R model to the online retailing setting and found that the model is useful in
understanding the relationships among web site characteristics, emotional responses, and
purchasing behaviors of the consumer. More importantly, the quality of an e-commerce website
plays an important role in influencing consumers’ purchasing decisions (DeLone and McLean,
2004) because consumers are more likely to shop at well-designed websites (Liang and Lai,
2002) (as cited in Hsu & Tsou, 2011). However, scholars are uncertain what element of website
quality acts as stimulus to influence online consumers’ emotional, cognitive and behavioral


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responses in context of online shopping. Further, the S-O-R paradigm has been used widely in
research in the industrialized world, less commonly applied to developing countries including
Vietnam. Addressing these research gaps, drawing from S-O-S framework, this study
investigates the website stimuli that influence the online consumer’s emotional reaction and
perceived risk which in turn derives continuance intention towards online shopping in Vietnam.
Notably, this study also analyzes the relationship between emotional reaction and perceived
risk.

1.3 Research objectives and questions
The purpose of this study is to test a more comprehensive model including web site
quality (Stimulus), emotional reaction and perceived risk (Organism), and continuance

intention (Response) in the online shopping environment. To be more specific, this research
examines the effect of website quality on emotional reaction and perceived risk, respectively.
Notably, the relationship between emotional reaction and perceived risk is analyzed. Finally,
this paper determines the impact of emotional reaction and perceived risk on continuance
intention towards online shopping in Vietnam.
To fulfill the purpose of the study, the research questions have been formulated by
basing on the background and problem statement as following:
Q1. Does website quality influence the emotional reaction towards online
shopping in Vietnam?
Q2. Does website quality influence the perceived risk towards online shopping in
Vietnam?
Q3. Does perceived risk influence the emotional reaction towards online
shopping in Vietnam?
Q4. Does perceived risk influence the continuance intention towards online
shopping in Vietnam?


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Q5. Does emotional reaction influence the continuance intention towards online
shopping in Vietnam?

1.4 Methodology and scope of research
Collecting data process of this study is designed into two stages. First is a pilot study,
second is main survey to collect data for examining research model. Pilot study is to in-depth
interview with sample 7 respondents to examine the questionnaire. Main study is quantitative
research with sample size 227 respondents.
Author accesses information and collect data by using questionnaire. Respondents have
ever purchased on online shopping websites. Sample will be selected by using non-probability
sampling method – convenience sample. The survey will be distributed in Ho Chi Minh City –

the principal business center in Vietnam.
Purpose of this research is to examine conceptual model. In this main study, exploratory
factor analysis (EFA) and confirmatory factor analysis (CFA) will be used to assess
measurement scales. The structural equation model (SEM) will be used as the main method for
analyzing the research model and hypotheses in AMOS software package.

1.5 Research significance
This research is important to the literature by investigating the combination of the
effects of website quality, emotional reaction and perceived risk on continuance intention in
Vietnam e-commerce context.
Additionally, this paper makes a contribution to e-vendors, understanding consumer
continuance intention in depth can lead to changes in firm profitability.

1.6 Thesis structure
The thesis consists of five chapters, excluding summary and abstract. The first chapter
will generally introduce the background of online shopping context in the world and Vietnam


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in particular. The problem statement also explains what importance the study has. In addition, it
gives questions to clarify the issue and shows the main purpose of study. Finally, the structure
thesis is summarized.
The next chapter will include the related literature review. These literatures will support
to find the central concept of the research. Based on the discussion, the hypotheses are derived.
Chapter three will introduce the research methodology. The measurement scale and
variables will be explained in detail. Besides, research design, sampling and data collection
method will be generally described.
The fourth chapter is about data analysis and findings. Based on the result of data
analysis, the finding will be brought into the context of the research. Finally, these findings will

be summarized and evaluated.
The last chapter will be conclusive of the conclusion and research limitation.
Furthermore, future directions will be mentioned.


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CHAPTER 2: LITERATURE REVIEW

2.1 Introduction
Chapter 2 mainly introduces theoretical background and research model of the study.
First, literatures of S-O-R framework in online shopping context are discussed. Next, this
chapter reviews the theories of website quality, emotional reaction, perceived risk and
continuance intention towards online shopping. Accordingly, the existing researches employed
various approaches to measure website quality and emotional reaction. Hence, concepts and
instruments of website quality and emotional reaction are analyzed systematically. Finally,
research model, its constructs and relationship hypothesized among the constructs are
discussed.

2.2 Theoretical background
2.2.1 S – O – R paradigm
Mehrabian and Russell (1974; as cited in Kim & Lennon, 2013) proposed the stimulus
(S)-organism (O)-response (R) paradigm that describes the effects of environmental stimuli on
emotions and behavioral responses. According to the paradigm, various environmental stimuli
(e.g. color, music, light, and scent) induce emotions (e.g. pleasure, arousal, and dominance)
which in turn influence approach-avoidance behaviors (Mehrabian and Russell, 1974; as cited
in Ha & Im, 2012). By applying the empirically demonstrated that traditional or online store
environmental stimuli affect emotional reactions that in turn influence response behaviors such
as purchase and revisit (Eroglu et al., 2003). Researchers found that store stimuli (e.g. color,
light) elicit not only emotional but also cognitive responses within organisms and that cognitive

responses also affect approach/avoidance behaviors (Babin et al., 2003; as cited in Ha & Im,
2012).In an online store setting, both emotional and cognitive responses are also found to play
an important role in the relationship between online store atmosphere and behavioral responses
(Eroglu et al., 2003; Ha & Im, 2012). In the S-O-R paradigm, approach or avoidance behaviors


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are generally operationalized as the response behavior or behavioral intention and measured
with behavioral outcome variables such as desire to explore the store (Ha and Lennon, 2010b;
Wu et al., 2008) and time/money spent (Smith and Sherman, 1993) (as cited in Ha & Im,
2012).
2.2.2 Website quality
In the e-commerce context, website quality is considered as an important internal factor
for consumers to evaluate criteria of online retailers (Hsu & Tsou, 2011; Kim & Lennon,
2013). Researchers have showed that online store atmospherics include all the cues used to
design the web site and its layout, with examples including the background colour and pattern,
hyperlinks, icons, overall colour scheme, typeface and web borders (Chang and Wang, 2008;
Eroglu et al., 2001).Internet users are classified into internet shoppers (those who have made
purchases on the internet) and internet browsers (those who have browsed online for products
or services but have not made purchases) (Forsythe and Shi, 2003). Aladwani & Palvia (as
cited in Chang & Chen 2008) set out to develop a sound instrument to measure web site quality
from the user’s perspective and identified four underlying dimensions: technical adequacy,
content quality, specific content and appearance. However, this study primarily focused on
evaluating web site quality from the shoppers’ perspective and developed multi-item scales to
measure the entire online buying experience that includes the pre-purchase and post-purchase
experiences of the customer. Hence, this study adopted the four dimensions of web site quality
proposed by Wolfinbarger & Gilly (2003). Website quality includes: (a) Website design is
consumers’ interaction including navigation, in-depth information and order processing; (b)
Customer service, that is, response, helpful and willing service that answers the consumers’

questions in a timely manner; (c) Fulfillment/ Reliability, that is, capability of providing
accurate product information and delivering the right product within the time frame promised
and (d) Security/privacy, that is security of card payment and privacy of consumer’s
information.


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2.2.3 Emotional reaction and perceived risk
In addition, based on the various researchers’ view, emotional reaction and perceived
risk are psychological states that are affective (Wang et al., 2010; Hsu & Tsou, 2011) or
cognitive responses (Cho & Lee, 2006; Chang & Chen, 2008) that may be affected by an
individual’s interaction with a situation. The theory of perceived risk has been applied to
explain consumer’s behavior in decision making since the 1960s. Forsythe & Shi (2003)
mentioned that perceived risk is a function of the uncertainly potential outcomes of a behavior
and the possible unpleasantness of these outcomes. Additionally, perceived risk is powerful at
explaining consumers’ behavior because consumers are more often motivated to avoid mistakes
than to maximize utility in purchasing (Mitchell, 1999). The definition of perceived risk has
changed since online transactions have become popular. In the past, perceived risks were
primarily regarded as fraud and product quality (Wu and Wang, 2005).Today, perceived risk
refers to certain types of financial, product performance, social, psychological, physical and
time risks when consumers make transactions online (Boksberger et al., 2007; Chang, 2008;
Corbitt et al., 2003; Lim, 2003; Mitchell, 2001; Smith and Sivakumar, 2004, as cited in Chang
& Chen, 2008). According to Kim et al. (2008), perceived risk is considered as a consumer’s
belief about the potential uncertain negative outcomes from the online transaction. As
perceived risk is an individual’s biased assessment of a risk situation, its assessment is highly
dependent on the individual’s psychological and situational characteristics (Cho and Lee,
2006). In the context of online retailing, the cognitive state concerns issues regarding how
online shoppers interpret information provided online and form thoughts and beliefs toward the
service/product being provided.

It is important to note that the emotional factor is a form of affect, and is a response to
online service delivery. “Consumption emotions are the affective responses to one’s
perceptions of the series of attributes that compose a product or service performance” (Dubé &
Menon, 2000; as cited in Yu & Din, 2001). Mehrabian & Russell scales offer a bipolar
framework for emotional response as expected reactions to environmental cues including


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pleasure, arousal and dominance (Hsu & Tsou, 2011). Nevertheless, the scale has been
criticized as being limited in its applications to consumption-related emotion studies, previous
studies argued that the unipolar view is more suitable for evaluating consumption experiences.
In recent marketing studies, emotions are represented by only two dimensions: pleasure –
arousal. Previous research indicates that there is a positive and significant relationship between
emotions and behavioral intentions (Westbrook, 1987; Han & Back, 2007; Ladhari, 2007;
Bigne et al., 2008, as cited in Bigdeli et al., 2014). Additionally, Eroglu et al. (2001) have
recommended using emotion typologies which consist of a more comprehensive set of
emotional responses. In line with the arguments of previous research, this paper defines
consumer emotional reaction as:
the set of emotional responses elicited specifically during product usage or consumption
experiences, as described either by the distinctive categories of emotional experience and
expression (e.g. joy, anger, and fear) or by the structural dimensions underlying emotional
categories, such as pleasantness/unpleasantness, relaxation/action, or calmness/excitement
(Westbrook and Oliver, 1991, p.85; as cited in Kim & Lennon, 2013).

Thus, this study conceptualizes emotion and perceived risk as organism that evoked by
environmental stimuli.
2.2.4 Continuance intention
Online shopping continuance intention in recent years has increasingly become a
concerned problem in the technology and marketing areas. Researchers define online customer

retention in a variety of ways, such as “online repurchase intention”, “continue to shop online”,
“customer intention to return”, “web site stickiness”, and “continued information systems/IT
intention”(Wen et al., 2011). Zeithaml et al. (1996) defined behavioral intentions as: “signal
whether customers will remain with or defect from the company”. They grouped behavioral
intentions into favorable and unfavorable groups: Favorable behavioral intentions (positive
word of mouth, recommending, remaining loyal, spend more, and paying a price premium),
and unfavorable behavioral intentions (negative word of mouth, switching to another company,


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complaining to external agencies, less business with company). This study focuses on
continuance intention and defines it as consumers desire to shop again on the Internet regarding
users who used to shop online in the past.
Accordingly, this paper posits that web site quality (stimuli) has positive effect on
consumers’ emotional reaction and negative effect on perceived risk (organism), which in turn
may impact consumers’ continuance intention towards the online retailer (response).

2.3 Hypotheses and proposed model
2.3.1 Stimuli and organism
Store environmental cues affect consumer emotions both in traditional (Babin et al.,
2003; Baker et al., 1992; Crowley, 1993; Donovan et al., 1994) and online (Eroglu et al., 2003;
Fiore et al., 2005; Ha and Lennon, 2010a, b; Menon and Kahn, 2002) settings (as cited in Ha &
Im, 2012). Before setting up major online marketing programs, online service providers should
focus on enhancing website quality in order to increase consumers’ positive emotions and on
implementing superior-quality services. A well-designed web site can enhance the probability
of a favorable impression as the viewer responds to visual cues, and a viewer with a favorable
impression of a web site is more likely to become a customer (Albert, 2004). For e-vendors,
websites gather company and product information that give consumers a brief understanding
them and offer transaction capabilities, providing a mechanism to serve their customer. As a

consequence, it is necessary to have a good understanding of website quality to improve the
consumer experience.
Numerous empirical studies analyzed the effect of website quality on emotional
reaction. Web site cues such as font color, background color, animated images, and
interactivity features positively influence the emotions or moods felt by online shoppers
(Eroglu et al., 2003; Wu et al., 2008). Kim & Lennon (2013) found that website quality
including website design, fulfillment and security are predictors of individual’s emotional
reaction, except customer service. Consumers’ evaluation of customer service may not have


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yielded a significant emotional response because of the negative perception tied to contacting
the customer service (Kim & Lennon, 2013). However, Hsu & Tsou (2011) argued that service
quality has a significant impact on positive and negative emotion. Online service quality is a
critical means of understanding whether the retailer is providing the type and quality of
information and interaction desired by consumers (Kim and Stoel, 2004; as cited in Kim &
Lennon, 2013).Similarly, it is found that the knowledge and responsiveness of sale person are
correlated with customers’ emotional reaction (Yoo et al., 1998). When store personnel
delivered excellent service, customers felt pleased, excited, content, and attractive. Also,
positive emotions like pleasure, pride, attractiveness, and contentment were observed when
shoppers’ expectations of sales persons’ service were met. Meanwhile, negative emotions such
as anger, anxiety, displeasure, and nullification were induced when customers received
incompetent or unkind service. Further, Griffith and Krampf (1998) insisted that a lack of
prompt response, especially to e-mail inquiries, is the most common negatively perceived
phenomenon in online retailing. On the other hand, as online retailers lack physical interaction
with the store personnel, a negative impression of customer service may lead to more negative
emotion.
Additionally, when shopping on the Internet, the consumers cannot communicate the
vendor directly so the web interface becomes the first impression. Fung & Lee (1999) stated

that more attractive and user-friendly web interface design can make the consumers feel
enjoyment and easier to trust in the vendor. It is showed that people who experience more
pleasure and arousal by the web site design may evaluate the web site and product information
more favorably (Ha & Im, 2012). An impressive web interface design should be easy to
navigate, access in-depth information and quickly to order. The more difficult it is to do this,
the less chance of consumers making a purchase or considering future purchases via the web
site. Hence, the website has instruction pages and internal search engine that help visitors easier
to search or locate the required information. Koufaris (2002) suggested that the use of a search
engine impacts shopping enjoyment. Further, privacy refers to the degree to which the online


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shopping web site is safe and protects the customers’ information (Chiu et al., 2009; as cited in
Lee et al., 2011). If customers are not sure of protection of privacy, they will be unwilling to
repurchase online, but if privacy is assured, they will be more willing to repurchase online (Lee
et al., 2011). A research investigated by Ha and Stoel (2009) shows that consumers are likely
experience greater enjoyment and have more fun when shopping at an e-store that sets up high
quality information and impressive attributes. Goode and Harris (2007) define perceived online
reliability as the extent to which the site consistently responds and functions as expected
(without broken links, broken pages or dead end links). Ndubisi (2011; as cited in Lee et al.,
2011) showed that service reliability leads to customer orientation and satisfaction, and
indirectly to loyalty which is mediated by satisfaction. It has been argued that to attract new
customers and to retain existing customers, the perceived reliability of web sites is of pivotal
importance (Goode & Harris, 2007). Goode & Harris (2007) found that where existing
customers find evidence of unreliable service or online performance (for example, broken
links, failed java script, scripting errors and missing graphics), such shoppers will often leave
the site, disappointed with the online provision. On the basis of previous research, the
following hypotheses are, therefore, derived:
H1. The website quality has a positive influence on consumers’ emotional reaction.

H1a. The website design has a negative influence on consumers’ emotional reaction.
H1b. The privacy has a negative influence on consumers’ emotional reaction.
H1c. The fulfillment has a negative influence on consumers’ emotional reaction.
H1d. The customer service has a negative influence on consumers’ emotional reaction.
Previous literature has proposed that website quality not only leads to positive
consumers’ emotional reaction but also reduce perceived risk. Lim (2003; as cited in Chang &
Chen, 2008) argued that the consumers’ perceived risk is technology-related and includes
issues such as download delays, limitations in the interface, search problems, inadequate
measurement of web application success, security weakness and a lack of internet standards.
Consumers appreciate simplicity or clear design in e-commerce web sites as they reduce the


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perceived risks of wasted time, deception and frustration, and customers may get annoyed
when they see the interface design and format of interface elements varying among the
different pages of a web site (Wang and Emurian, 2005). Chang & Chen (2008) showed that
web site quality presents tangible cues that can be deliberately used to build consumer trust in a
retailer’s ability, integrity and benevolence, and in turn, decreased customer-perceived
uncertainty and risk towards the online retailer. Meanwhile, Grewal et al. (2007; as cited in
Kim & Lennon, 2013) found that perceived quality of customer service was likely impact the
level of risk perceptions related with future service encounters. Furthermore, for online
retailers, risk is correlated with the loss of consumers’ privacy and security of personal
information that makes a vital barrier to customers’ internet adoption and use (Hui et al., 2007).
In addition, Gummerus et al. (2004) indicated that instant response to customers’ inquiries
probably raises perceived convenience then leading to reduce perceived risk. Following to the
findings of Kim & Lennon (2013), a well-design, easy to navigate website is likely to reduce
risk towards online shopping. In line with these discussions, it is hypothesized that:
H2. The website quality has a negative influence on perceived risk.
H2a. The website design has a negative influence on perceived risk.

H2b. The privacy has a negative influence on perceived risk.
H2c. The fulfillment has a negative influence on perceived risk.
H2d. The customer service has a negative influence on perceived risk.
2.3.2 Organism and responses
According to the appraisal theory, emotions arise as a result of cognition (Arnold, 1960;
Frijda, 1989; Ortony et al., 1988; Roseman, 1984; Scherer, 1993, as cited in Kim & Lennon,
2013). Lazarus (1991; as cited in Kim & Lennon, 2013) claimed that cognitive is both
necessary and adequate for the emotional formation. When an individual is faced with different
events, specific emotions arise depending on the meaning a person assigns to these events
(Frijda, 1993; as cited in Kim & Lennon, 2013). Arnold (1960; as cited in Kim & Lennon,
2013) suggested that emotions arise after people appraise events as risky or beneficial. A few


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researchers investigated some aspects of the appraisal-emotion relationship and found that
consumers’ cognitive appraisals result in consumers’ emotional responses (Ruth et al., 2002).
Chebat & Michon (2003), in their experiment of testing the effect of store ambient scent on
shoppers’ spending, compared the fit of two different models; ambient scent-emotions
cognition- spending model and ambient scent-cognition-emotion-spending model. Positive
mood induced by a moving image decreases perceived risk (Park et al., 2005). As perceived
risk is defined as a function of the uncertainty about the potential outcomes of a behavior in this
study, it is suggested that:
H3. Perceived risk has a negative influence on consumers’ emotional reaction.
According to Chung et al. (2003), the outcome of the research is that perceived
consumer risk shows a negative relationship with the repurchase intention. Further, Chiu et al.
(2014) indicated that a higher level of perceived risk reduces the effect of utilitarian value an
increase the effect of hedonic value on repeat purchase intention. Simultaneous, they found that
perceived risk affects repeat purchase intention negatively. Moreover, risk factors including
natural disaster risk, physical risk, political risk, and performance risk as major risk types to

affect travel satisfaction and the traveler’s repurchase intention (An et al., 2010). It is evident
that according to the survey of VECITA in 2014, the biggest challenge for online shopping to
Vietnamese consumers is the quality of products or services worse than being advertised
(81%). The others obstacles of online shopping are unprofessional logistic services and price
not lower than buying in traditional shops or not clear (VECITA, 2014). Besides, according to
Nielsen (2014), credit card security remains a key concern for consumers across the region with
five of the six Southeast Asia markets ranking above the global average with respect to their
concern around providing credit card information online. Based on the literature review, this
study includes the hypothesis that:
H4. Perceived risk has a negative influence on shopping online continuance intention.
Additionally, the emotional responses experienced after a purchase plays an impactful
role on repeat purchase intention. Emotional reaction contributes to guiding an individual’s