The current issue and full text archive of this journal is available at

www.emeraldinsight.com/2040-7122.htm

Effects of
Effects of reputation and website quality
website quality on online
consumers’ emotion,
33
perceived risk and
purchase intention Received 30 November 2011
Revised 14 March 2012
29 June 2012
Based on the stimulus-organism-response Accepted 31 July 2012

model
Jiyoung Kim
Department of Merchandising, University of North Texas,
Denton, Texas, USA, and
Sharron J. Lennon
Department of Fashion and Apparel Studies, University of Delaware,
Newark, Delaware, USA

Abstract
Purpose – This research extends Mehrabian and Russell’s Stimulus-Organism-Response model to
include both external (i.e. reputation) and internal source of information (i.e. website quality) as stimuli
which affect consumers’ response systems. The purpose of this paper is to test a more comprehensive
model consisting of reputation and website quality (stimuli), cognition and emotion (organism) and
purchase intention (response).
Design/methodology/approach – In total, 219 usable questionnaires were obtained at a large
Midwestern university through online survey. Structural equation modeling (SEM) was employed for
data analyses.
Findings – Reputation had a significant positive effect on consumers’ emotion and significant
negative effect on perceived risk. All four website quality dimensions had significant negative effects
on perceived risk and significant positive effects on emotion, except for customer service. Perceived
risk had a significant negative effect on consumers’ emotion, and both perceived risk and emotion had
a significant impact on purchase intention.
Research limitations/implications – This research employed convenience sampling, which
resulted in a majority of female respondents. The results may be generalized to a limited extent.
Originality/value – This study allows for empirical examination of the different effects of various
components of retail websites on emotion, perceived risk and behavioral intentions. This research will
add value to the related literature by filling the void of previous research and also will provide
practical implications for online retailers on designing and maintaining positive consumer response.
Strength of the research lies in its ecological validity, since respondents were not simply all reacting to
the same single stimulus.
Keywords Consumer behaviour, Web sites, Stimulus-Organism-Response model, Reputation, Journal of Research in Interactive
Website quality, Emotion, Perceived risk, Purchase intention, Interactive marketing, Marketing
United States of America Vol. 7 No. 1, 2013
pp. 33-56
Paper type Research paper q Emerald Group Publishing Limited
2040-7122
DOI 10.1108/17505931311316734
JRIM Introduction
7,1 Since Mehrabian and Russell (1974) 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
(Chebat and Michon, 2003; Richard et al., 2009). As online retailing has emerged as the
most rapidly growing form of retailing (Mulpuru et al., 2011), researchers have begun
34 to focus on various aspects of this new medium using the S-O-R framework. For
example, Richard and Chandra (2005) studied the relationship among web site
navigational characteristics, user characteristics, internal states, consumer responses,
and outcomes in the context of online communication. 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.
While these studies provide a framework for determining factors that contribute
to online retailers’ success, they have failed to recognize that the consumer decision
making process is based on more than factors internal to web sites (e.g. navigation,
color, and graphics). Web site quality, while important, only represents a subset of
potential online retail evaluative criteria. Other characteristics of online retailers
may have important roles in affecting consumer response. A number of researchers
argue that evaluation based on factors internal to the web site (i.e. web site quality)
cannot fully explain consumer behavior since consumers are influenced by both
external and internal factors in their decision making process (Lwin and Williams,
2006; Richardson et al., 1994; Zeithaml, 1988). Prior research has found that external
sources of information about a company are identified as important factors in the
overall evaluation of the firm and its service (Zeithaml and Bitner, 1997).
Online retailer reputation, an external source of information, may act as a
significant antecedent of consumer responses and future behaviors, along with
web site quality ( Jin et al., 2008; Lwin and Williams, 2006). However, little research
has emphasized the joint effect of factors external and internal to the web site on
consumers’ emotional, cognitive and behavioral responses in online retailing context.
While reputation was found to be a strong predictor of attitude and behavior ( Jin et al.,
2008; Lee and Shavitt, 2006; Purohit and Srivastava, 2001), existing literature lacks
explanation on the critical role of online store reputation in determining consumer
organismic and behavioral responses. In particular, no study thus far has tested the
relationship between online store reputation and consumer emotion in a
comprehensive model of consumer response in online retailing environment. In
order to fill this important research niche, this study extends the S-O-R framework of
stimulus, organismic reaction, and behavioral responses by adding an external
source of information (i.e. reputation) as a critical factor in the online shopping
situation. Therefore, the purpose of this study is to test a more comprehensive model
consisting of reputation and web site quality (stimuli), perceived risk and emotion
(organism) and purchase intention (response). More specifically, the primary
objectives of this research are:
 (1) to assess the effect of web site quality and reputation on emotion and perceived Effects of
risk; website quality
(2) to examine the relationship between emotion and perceived risk; and
(3) to test the effect of emotion and perceived risk on purchase intention.

Literature review
Reputation in online retailing context
35
Proliferation of the internet shopping and online customer interfaces requires that
consumers learn new behavioral patterns or at least, adapt their existing ones to the
new medium. In the online retailing context, consumers experience greater transaction
risk with regard to the actual quality of the product or service that is being exchanged
(Park et al., 2005). Retailer reputation can be an important risk-reducing mechanism in
such environments. Assuming that all else is equal, consumers are more likely to buy
from an online retailer with better online reputation (Kotha et al., 2001).
While firms in the traditional marketplace have built good reputation through media
exposure, customer word of mouth, and branding, the internet is transforming the way
firms earn a reputation. Consumers now rely on social networking sites (e.g. Facebook,
Twitter), comparison shopping services (e.g. BizRate.com, Shopping.com), or customer
reviews on blogs in order to obtain information that enables them to evaluate the
retailers’ reputation. Through these resources, consumers not only aggregate product
and price information but collect and publish reviews and ratings of online retailers.
Such reviews and ratings may be viewed as measures of online retailer reputation as
they reflect the collective opinions of consumers toward the retailer (Gregg, 2009; Joo,
2007; Kim and Ahn, 2006). In order to evaluate and differentiate unfamiliar online
retailers when making purchasing decisions, consumers are found to rely on the diverse
resources available online (Luo and Cook, 2007). This study understands the critical
effect of online retailer reputation and posits that online retailer reputation, as an
external source of information, has a significant influence on consumer organismic
responses and behavioral intention, based on the S-O-R framework.

External stimuli: effect of reputation on emotion and perceived risk

In an online retail setting, stimuli that influence consumers’ emotion and perceived risk
encompass factors both internal and external to the web site (Lwin and Williams, 2006;
Richardson et al., 1994; Zeithaml, 1988). Following Olson (1977) and Olson and Jacoby
(1972), a web site can be defined as a set of information cues which can be categorized
as intrinsic cues (attributes of the web site) and extrinsic cues (factors that do not
constitute the web site itself). Before consumers decide to purchase from a web site,
they weigh the consequences of their action, and a positive evaluation will lead to
greater likelihood of positive responses. The determinants of positive consumer
response in an online environment are different from those in an off-line environment.
The search for information is more convenient and generally costs less in an online
environment because there are no transportation costs. However, it is not necessarily
easier to assess certain information, such as product and vendor evaluations, in an
online environment since consumers cannot directly interact with the seller or the
product (Chu et al., 2005).
When the intrinsic cues are insufficient for making accurate assessments of the
web site, consumers often rely on a web site’s extrinsic cues ( Jin et al., 2009). This study
JRIM proposes that online retailer reputation, a form of external reference, is a significant
7,1 antecedent of consumer responses and future behaviors. Retailer reputation is defined
as a collective total of all previous transactions of the retailer and requires consistency
of a retailer’s actions over extended periods of time (Herbig and Milewicz, 1995). Since
reputation is established by the flow of information from one user to another, sharing
of these opinions creates a halo effect which could become a source of external
36 reference ( Jin et al., 2009). The halo effect eventually can evoke a favorable response in
consumers even if performance is unsatisfactory (Thorndike, 1920). Further, a retailer’s
reputation affects consumer buying decisions as consumers are more likely to purchase
from established and reputable retailers than from unknown retailers (Lee and Shavitt,
2006). Reputation of the store acts as a schema, which has been developed through past
experiences with the retailer. This schema forms the basis for consumers’ expectations
of future experiences with the store (Estelami et al., 2004).
Despite the significant role reputation, as an external frame of reference, might play
in determining consumer organismic and behavioral responses evoked by stimuli in
the online store, there is little research that examined the effect of reputation on
generating positive or negative emotion. While reputation was found to be a strong
predictor of perceived risk (Purohit and Srivastava, 2001), no study thus far has tested
the relationship between online store reputation and consumer emotion. Research
results have suggested that reputation might have a positive influence on consumers’
perceptions or attitudes. Lee and Shavitt (2006) speculated that the reputation of a
store will influence perceptions of the store’s online site. Jin et al. (2008) found a
significant positive relationship between firm reputation, e-satisfaction, and e-trust.
Since online retailers lack person to person interaction that can serve as an initial source
of consumer affect, the primary source of consumer evaluation of the online retailer
may be the reputation that provides assurance. When this assurance is present, it is
reasonable to expect that consumers may experience positive emotion, even though this
assurance effect takes place unconsciously. Therefore, we hypothesize that online
retailer reputation will have a positive effect on emotion evoked by the online retailer:
H1. The better the online retailer’s reputation, the more positive consumers’
emotion toward the online retailer.
Reputation has been frequently suggested as a factor that reduces consumers’ perceived
risk in sales organizations (Doney and Cannon, 1997; Ganesan, 1994). According to
Chiles and McMackin (1996), firms with a good reputation are perceived to be reluctant
to jeopardize their reputational assets by failing to fulfill promises and obligations.
Consumers perceive less risk in purchasing from retailers who possess a reputation for
providing good service and quality products than from unknown retailers (Purohit and
Srivastava, 2001). Also, company reputation has been found to decrease consumers’
concerns with self-disclosure (Andrade et al., 2002). Dowling and Staelin (1994)
suggested that consumers engage in risk relieving activities in order to reduce their level
of perceived risk and hence their feelings of discomfort. These risk relievers were
found to limit the set of alternatives to well-known brands with good reputations
(Dowling and Staelin, 1994; Van den Poel and Leunis, 1995). Lwin and Williams
(2006) analyzed existing research on perceived risk and compiled a list of various risk
relievers in their study of perceived risk in the online retailing context. Their result
indicates that of the top six risk relievers, “retailer reputation” ranked third following
“warranty/money-back guarantee” and “brand/manufacturer reputation”. Therefore, Effects of
online retailer reputation is expected to reduce the level of consumers’ perceived risk website quality
regarding the online retailer:
H2. The better the retailer reputation, the lower perceived risk toward shopping at
the online retailer.

Internal stimuli: effect of web site quality on emotion and perceived risk
37
The other important stimuli in the online experience are those related to the web site
itself, which can be generally termed as web site quality. For online retailers, web sites
serve as repositories of information and offer transaction capabilities, providing a
mechanism to serve their consumers. Online retailers present different shopping
environments from the offline retailers as consumers interact with a technical interface
in a virtual space, rather than interacting with service personnel in a physical space.
Thus, 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, 2004b). In addition, online service quality has emerged as a critical
component in fulfilling expectations and enhancing customers’ evaluations of the
online retailer (Yoo and Donthu, 2001). Therefore, it is important to understand the
dimensions of web site quality in order to enhance the consumer experience and
facilitate the online interaction between a consumer and the online retailer.
In order to measure web site quality, different scales have been developed from various
viewpoints and suggest different dimensions for assessment. This study adopts etailQ
(Wolfinbarger and Gilly, 2003), a measurement scale that was developed to assess the
quality of web site experiences. Through offline focus group interviews, a sorting task, and
an online survey, four dimensions of web site quality were proposed: “web site design”,
“customer service”, “fulfillment/reliability”, and “security/privacy”. web site design
embraces diverse elements of the consumers’ interaction with the web site including
navigation, in-depth information and order processing (Wolfinbarger and Gilly, 2003);
customer service is described as responsive, helpful, and willing service that responds to
customer inquiries in a timely manner (Wolfinbarger and Gilly, 2003); fulfillment/reliability
measures the retailer’s ability to present accurate product information on the web site and
to deliver the right product within the time frame promised (Wolfinbarger and Gilly, 2003);
security/privacy represents the web sites’ security of credit card payment and privacy of
shared information (Wolfinbarger and Gilly, 2003).
Evaluations of web site quality are critical means of understanding whether the
retailer is providing the type and quality of information and interaction desired by
consumers (Kim and Stoel, 2004b). In examining the relationship between perceived
web site quality and emotion, a number of studies yielded significant results focusing on
different aspects of web site quality. Mummalaneni (2005) found that web site design
factors, including organization of layout, display and signage, have positive effects
on consumers’ pleasure and arousal. Eroglu et al. (2001) also proposed a
positive relationship between online cues such as layout and design on consumer
affective states. Yoo et al. (1998), in their study of in-store characteristics and in-store
emotional experiences, found that sales persons’ knowledge and responsiveness affect
positive and negative emotions of the customer. 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
JRIM shoppers’ expectations of sales persons’ service were met. Meanwhile, negative
7,1 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
38 of customer service may lead to more negative emotion. Therefore, we propose that
favorable evaluation toward the web site quality will lead to positive emotion:
H3. The better the consumers’ perceptions of the online retailer’s web site quality,
the more positive consumers’ emotion toward the online retailer.
H3a. The better the consumers’ perceptions of the online retailer’s web site design,
the more positive consumers’ emotion toward the online retailer.
H3b. The better the consumers’ perceptions of the online retailer’s customer
service, the more positive consumers’ emotion toward the online retailer.
H3c. The better the consumers’ perceptions of the online retailer’s fulfillment/reliability,
the more positive consumers’ emotion toward the online retailer.
H3d. The better the consumers’ perceptions of the online retailer’s security/privacy, the
more positive consumers’ emotion toward the online retailer.
Since web site quality is a critical factor when evaluating online retailers, favorable
evaluation of web site quality not only leads to positive emotion, but reduces perceived
risk. Previous literature examining the relationship between web site quality and
perceived risk supports the argument. Grewal et al. (2007) proposed that beliefs about the
perceived quality of the service provider will affect perceived risk. They also suggested
that information pertaining to the performance of service providers can be captured by
measuring consumers’ perception of service quality (e.g. responsive, reliable, empathetic,
and provided assurance to consumers). Further, their study found that perceived quality
of customer service was likely to affect the level of risk perceptions associated with future
service encounters. Delivery (e.g. on time delivery, product delivered undamaged,
product delivered matches web site description) has been challenging to online retailers
and has increased consumers’ perceived risk toward online shopping (Choi and Lee,
2003). In addition, initial research on e-commerce indicated that risk related to loss of
consumers’ privacy and security of personal information was an important barrier to
consumers’ internet adoption and use (Hui et al., 2007), stressing the importance of
web site quality related to security and privacy issues. For online retailers to prosper,
consumers must be confident in the seller’s ability and willingness to safeguard their
monetary information during transmission and storage (Pavlou et al., 2007). Further,
Gummerus et al. (2004) found that prompt response to customers’ requests is likely to
increase perceived convenience and diminish uncertainty thus leading to decreased
perceived risk. In line with these discussions, the following hypotheses are proposed:
H4. The better the consumers’ perception of the online retailer’s web site quality,
the lower perceived risk toward shopping at the online retailer.
H4a. The better the consumers’ perception of the online retailer’s web site design,
the lower perceived risk toward shopping at the online retailer.
 H4b. The better the consumers’ perception of the online retailer’s customer service, Effects of
the lower perceived risk toward shopping at the online retailer. website quality
H4c. The better the consumers’ perception of the online retailer’s
fulfillment/reliability, the lower perceived risk toward shopping at the
online retailer.
H4d. The better the consumers’ perception of the online retailer’s security/privacy, 39
the lower perceived risk toward shopping at the online retailer.

Organismic and behavioral responses: emotion, perceived risk and purchase intention
According to the S-O-R framework, the organism is represented by affective and
cognitive intermediary states and processes that mediate the relationship between the
stimulus and response. Several research studies applying the S-O-R framework include
both emotional and cognitive states in their research models (Eroglu et al., 2001;
Holbrook and Hirschman, 1982; Richard, 2005; Wang et al., 2009).
Affective responses reflect emotions and feelings evoked by environmental stimuli
(Batra and Ray, 1986; MacKenzie and Lutz, 1989). Most work in environmental
psychology focuses on the pleasure, arousal, and dominance (PAD) dimensions of
affective responses as expected reactions to environmental stimuli (Mehrabian and
Russell, 1974). However, the PAD dimensions have been criticized as being too narrow
in scope and not encompassing the range of possible variations in emotional reactions
(Machleit and Eroglu, 1998). Emotion typologies which include a more comprehensive
set of emotional responses (Izard, 1977) have been recommended for use (Eroglu et al.,
2001). As this study is interested in assessing the effect of consumer emotional states,
we focus on consumption emotion which can be broadly defined 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).
The present study adopts Jang and Namkung’s (2009) measure of consumption
emotion, which was developed for a retail service setting based on Izard’s (1977)
typologies.
According to Mehrabian and Russell (1974), consumer emotions lead to
various consumer response behaviors such as purchase intention (Ha and Lennon,
2010; Wu et al., 2008) and approach behaviors (Eroglu et al., 2003; Menon and Kahn, 2002;
Wu et al., 2008). A number of research studies have found that consumer emotions play a
major role in purchasing behavior, evaluation, and decision making processes
(Ladhari et al., 2008). The study of Donovan and Rossiter (1982) showed that pleasure is a
major predictor of retail outcomes (time spent browsing the store’s environment, the
tendency to spend more money than originally planned, and the likelihood of returning
to the store). Similarly, Baker et al. (1992) found that consumer emotional state was
positively related to willingness to buy. Therefore, it is reasonable to hypothesize that
consumers’ emotional state will affect purchase intention at a particular online retailer:
H5. The more positive consumers’ emotion shopping at the online retailer, the
greater consumers’ purchase intention toward the online retailer.
JRIM Cognitive responses describe consumers’ internal mental processes and states, and
7,1 involve memory, knowledge structures, imagery, beliefs and thoughts (Holbrook and
Hirschman, 1982). 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. Perceived risk toward
an online retailer can be one of the cognitive responses consumers experience while
40 shopping online. According to Forsythe and Shi (2003), perceived risk in the online
retailing context can be defined as the subjectively determined expectation of financial,
performance, psychological and time/convenience risk by an internet shopper in
planning a particular online purchase.
Consumers are apprehensive when they are uncertain if their purchase will allow
them to achieve their buying goals (Cox and Rich, 1964). Perceived risk is a function of
the uncertainty about the potential outcomes of a behavior and the possible
unpleasantness of these outcomes (Forsythe and Shi, 2003) and it represents consumer
uncertainty about loss or gain in a particular transaction (Murray, 1991). Van den Poel
and Leunis (1995) suggested that consumers’ perceptions of risk play a major role in
determining patronage decisions; non store shopping is perceived to be more risky
than in-store shopping. This is because shoppers lack the opportunity to physically
examine or test the products and they fear not getting what they want (Mitchell, 1999).
Vijayasarathy and Jones (2000) also found that consumers’ perceived risk is a critical
factor influencing their online shopping behavior. In their study of online apparel
products, Park et al. (2005) found a negative relationship between perceived risk
and purchase intention in an online context. Thus, it is reasonable to hypothesize that
perceived risk will negatively influence consumers’ purchase intention:
H6. The lower perceived risk of shopping at the online retailer, the greater
consumers’ purchase intention toward the online retailer.
According to the appraisal theory, emotions arise as a result of cognition (Arnold, 1960;
Frijda, 1989; Ortony et al., 1988; Roseman, 1984; Scherer, 1993). Lazarus (1991) further
asserts that cognitive appraisal is both necessary and sufficient for the formation of
emotions. Appraisal theorists claim that emotional responses to events or stimuli are not
dependent on the events or stimuli itself, but on the meaning an individual gives to the
events in the context of the individual’s needs and coping potential (Frijda, 1993;
Lazarus, 1974). This can explain why the same event may evoke different emotions for
different individuals or why the same person may feel different emotions at different
times when experiencing the same event. When an individual is faced with different
events, specific emotions arise depending on the meaning a person assigns to
these events (Frijda, 1993). Arnold (1960) suggested that emotions arise after people
appraise events as risky or beneficial. Also, Roseman et al. (1990) indicated that hope and
fear result from events appraised as uncertain and joy is caused by events appraised as
motive-consistent and certain.
In studying consumers, a few researchers investigated some aspects of the
appraisal-emotion relationship and found that consumers’ cognitive appraisals result
in consumers’ emotional responses (Folkes, 1984; Nyer, 1997; Ruth et al., 2002). Chebat
and 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.
Their study revealed that the best fitting model supports the cognitive theory of Effects of
emotion, indicating cognition has significant influence over consumers’ mood. The website quality
framework proposed by Roseman et al. (1996) explained that a particular combination
of cognitive appraisals such as unexpectedness, probability and control potential,
determine which emotions (e.g. surprise, hope, joy, relief, liking, pride, fear, and
sadness) will be experienced in a given situation. As perceived risk is defined as a
function of the uncertainty about the potential outcomes of a behavior in this study, we 41
are able to predict that perceived risk will influence consumer emotion in an online
shopping situation. According to this logic, we propose that increased perceived risk
will lead to a more negative emotion (Figure 1):
H7. The greater the perceived risk of shopping at the online retailer, the more
negative consumers’ emotion toward the online retailer.

Method
Procedure
An online survey was used to collect data. Students, who were recruited from classes in
a large Midwestern University, participated in the online survey in exchange for course
credit. Only those who had previously visited (browsed) or purchased at an online
retailer were included in the survey in order to ensure respondents’ familiarity toward
the subject matter.
Consumers across the age spectrum shop online, but generation Y aged 18-31 have
been identified as the internet’s “hottest” market and a prime source of future growth in
online sales (Temkin, 2009). Gen Y adults, who comprise 19 percent of the US online
population, are the most likely to own multiple connected devices: more than

Reputation

H1

Website Quality H2

Website H3a Emotion

Design H5
H4a

H3b
H7 Purchase
Fulfillment Intention
/Reliability H4b

H3c Perceived H6
Risk
Customer
H4c
Service
Figure 1.
H3d
An extended S-O-R model
H4d with reputation, web site
Security quality, emotion,
/Privacy perceived risk and
purchase intention
JRIM two-thirds of these adults own more than one connected device, including roughly
7,1 one-third who own three or more (Forrester Research Report, 2011). They are heavy
users of the internet and have more access to this medium than most other population
segments. The most significant difference between Gen Y and the older generations is
that Gen Y is the first generation to grow up with digital technologies embedded in
every aspect of their social lives (Temkin, 2009). Since they started using these
42 technologies at such formative ages, their social connections with other people have
been formed around digital devices (Temkin, 2009). Gen Y is also leading the digital
segment from the number of devices they own to the amount of time they spend on the
internet (Forrester Research Report, 2010).

Instrument
Scales to measure each of the variables in the model were adopted or developed based
on previous literature. First, respondents were asked to name the online retailer they
had purchased products from most often in the past year. This approach has limitation
as it may cause the respondents to answer the questions with their most favored
retailer in mind. As a result, the study may not be able to capture such cases where
negative evaluation toward the web site results in positive purchase intention. Despite
the limitation, this method is widely used in retailing literature (Kim and Niehm, 2009;
Macintosh and Lockshin, 1997) as it allows researchers to ensure that respondents
possess sufficient experience to answer questions about the online retailer. This is
important for the current study because when the respondents have ample experience
at an online retailer, they are more likely to have clear and firm perceptions and
intention toward the web site. In addition, non-frequent shoppers who have little
experience with the web site may not have developed meaningful perceptions of the
web site (Kim and Stoel, 2004a), thus leading them to answer the questions without
careful consideration. In what follows, all variables were measured using five-point
Likert scales (1 ¼ strongly disagree, 5 ¼ strongly agree), except for the extent of online
shopping behavior and demographic variables.
In order to assess reputation, three items developed by Doney and Cannon (1997)
was modified to fit the research context. Reliability of 0.78 was reported in their study.
To measure consumer evaluation of web site quality, etailQ developed by
Wolfinbarger and Gilly (2003) was used. The measure consisted of 14 items that
assess four dimensions of web site characteristics: web site design, customer service,
fulfillment/reliability, and security/privacy. Reliability of 0.83 for web site design, 0.79
for customer service, 0.88 for fulfillment/reliability and 0.84 for security/privacy were
reported in their study. To measure emotion, nine items were adopted from Jang and
Namkung (2009). While many researchers suggest that the measure of emotion is a
ubiquitous bipolar continuum of pleasantness-unpleasantness (Russell, 1983), several
limitations in its application to consumer related emotion studies have been recognized.
Researchers (Abelson et al., 1982; Babin et al., 1998; Westbrook, 1987) suggest that the
unipolar view is more appropriate in understanding consumption emotion as it enables
respondents to indicate that they feel happiness and unhappiness at the same time.
Jang and Namkung (2009) generated emotion items based on the unipolar view in their
study of restaurant service based on in-depth interviews. They reported a reliability of
0.91 for positive emotion and 0.94 for negative emotion. In our survey, respondents
were asked to indicate the extent to which they felt a certain way (e.g. joy, distress)
after visiting the online retailer they had specified. For analysis purpose, scores from Effects of
the negative emotion items were reversed. Four perceived risk items used in this study website quality
were developed by Forsythe and Shi (2003), who examined and selected measurement
items based on the 10th User Surveys of Graphic, Visualization and Usability (GVU)
Center from Georgia Institute of Technology (GVU, 1998). They developed four items
to capture four dimensions of risk perceived specifically in the online retailing context;
financial, performance, psychological and perceived time/convenience loss. Four 43
purchase intention items were adapted from the study of Kim and Lennon (2008).
Reliability was reported as 0.90. Respondents also completed demographic items and
four items regarding online shopping behavior.

Analysis
Sample
Usable questionnaires were obtained from 219 participants. A majority of the
respondents was female (93.2 percent). Respondents tended to be young (mean
age ¼ 20.98, 95.3 percent of the sample was younger than 25) and well-educated
(89.0 percent were attending college, or had college degree and/or an advanced degree).
Slightly more than 81 percent of the respondents referred to an apparel online retailer
(e.g. Abercrombie & Fitch, Gap, Nordstorm, Neiman Marcus) for the items related to
evaluating the web site quality. The rest included electronics goods (6.4 percent),
household goods (3.5 percent), and other. Approximately 95 percent of respondents
spent 0-2 h browsing or purchasing on the particular web site per visit, and
56.1 percent spent $1-$100 purchasing products on the particular web site during the
past year. Table I presents the demographic characteristics and online shopping
behavior of the respondents.

Preliminary analysis and evaluation of measures

Before hypotheses testing, we performed Confirmatory Factor Analysis (CFA) to
confirm the validity of each construct using LISREL 8.80. For model respecification,
items were considered for deletion if they:
.
displayed a significantly lower item reliability than that of the other items
posited to measure the same construct, as indicated in the squared multiple
correlations;
.
showed insignificant path coefficients for the expected construct;
.
showed large residuals with other indicators;
.
shared large variance with other indicators, due to error and thus unexplainable
variance; or
.
shared common variance with indicators posited on some other constructs.

The respecification decision was made based on both statistical and content
considerations as suggested by Anderson and Gerbing (1988). After reviewing the
initial CFA result six items (four items from web site quality, one item from perceived
risk and one item from emotion) were found problematic and were deleted from further
analysis.
The final CFA result yielded acceptable fit: x 2 ¼ 589.75, df ¼ 311 ( p-value ¼ 0.00),
GFI ¼ 0.86, AGFI ¼ 0.82, NFI ¼ 0.91, CFI ¼ 0.93, RMR ¼ 0.049, RMSEA ¼ 0.057.
JRIM Frequency %
7,1
Gender
Male 15 6.8
Female 204 93.2
Age
18-20 98 44.8
44 21-25 109 49.8
26-33 13 4.7
Education
High school graduate 24 11.0
Some college, no degree 173 79.0
Associate degree, academic 11 5.0
Bachelor’s degree 10 4.6
Online retailer
Apparel 164 81.2
Electronic goods 13 6.4
Groceries 1 0.5
Household goods 7 3.5
Sports equipment 4 2.0
Books and CDs 6 3.0
Other 13 6.4
Hours spent for each visit
Less than 1 h 160 73.1
1-2 h 49 22.4
3-4 h 6 2.7
5-6 h 2 0.9
7-8 h 1 0.5
Dollar amount spent the past year
$1-$50 73 33.3
$51-$100 50 22.8
$101-$200 39 17.8
$201-$300 27 12.3
$301-$400 8 3.7
Table I. $401-$500 7 3.2
Demographic $501-$1,000 6 2.7
characteristics and $1,001 þ 5 2.3
online shopping behavior
of the sample Note: n ¼ 219

The x 2 value was higher than expected. However, reliance on the x 2 test as the sole
measure of a model fit is not recommended because the test is sensitive to sample size.
Small deviations from a true model can reject the hypothesized model in large samples,
and large deviations of the hypothesized model from a true model may not be detected
(Bagozzi and Edwards, 1998). Therefore, researchers have sought alternative indices to
assess model fit such as normed x 2 (x 2/df) (Wheaton et al., 1977). Our ratio of x 2 to the
degrees of freedom was 1.9, indicating good fit, as suggested by Tabachnick and Fidell
(2007) (below 3.0), Carmines and McIver (1981) (below 3.0) and Wheaton et al. (1977)
(below 5.0). Therefore, we decided to accept the CFA result as all the indices indicated
fairly good model fit. Table II shows the CFA result of indicators used for the
structural model after the respecification of the measurement model.
 Effects of
Factor loading Cronbach’s a
website quality
Web site design 0.87
The web site is well designed in order not to waste my time 0.77 *
The web site provides in-depth information 0.74 *
It is quick and easy to complete a transaction on this web site 0.73 *
Customer service 0.89 45
The company is willing and ready to respond to customers’ needs 0.76 *
Inquiries are answered promptly 0.83 *
Fulfillment/reliability 0.83
The product delivered was represented accurately by the web site 0.78 *
What I received after purchase was what I expected 0.71 *
The product is delivered on time as promised by the company 0.81 *
Security/privacy 0.79
I feel my privacy is protected on this web site 0.82 *
I feel safe in my transactions with this web site 0.85 *
Reputation 0.90
This web site is a large company that everyone recognizes 0.91 *
This web site is well-known 0.85 *
This web site has a good reputation 0.90 *
Perceived risk 0.94
I do not trust that my credit card number will be secure at this web site 0.91 *
It is difficult to judge quality of a product/service on this web site 0.89 *
I do not trust that my personal information will be kept private 0.79 *
Emotion 0.91
Positive
Joy 0.73 *
Excitement 0.82 *
Peacefulness 0.82 *
Negative
Anger 0.71 *
Distress 0.79 *
Disgust 0.88 *
Fear 0.89 *
Purchase intention 0.95
I will buy an item I viewed at this web site in near future 0.94 *
I will buy an item from this web site if I find something that I like 0.91 *
I will probably buy the item I saw at this web site for myself in near
future 0.88 *
I will visit this web site when I want to buy certain items in near future 0.94 * Table II.
Measurement items after
Notes: Significant at: *p , 0.05; x 2 ¼ 589.75, df ¼ 311 ( p-value ¼ 0.00), GFI ¼ 0.86, AGFI ¼ 0.82, confirmatory factor
NFI ¼ 0.91, CFI ¼ 0.93, RMR ¼ 0.049, RMSEA ¼ 0.057 analysis

To assess unidimensionality of the latent constructs in the model, a separate exploratory

factor analysis (EFA) for each construct was conducted (Kumar and Dillon, 1987). Results
yielded a single underlying factor for each construct. Reliabilities (all above 0.7)
(Nunnally, 1978), corrected item-to-total-correlations within each construct (all above 0.5)
(Doll and Torkzadeh, 1988) and factor loadings (all above 0.4) were all high, suggesting
unidimensionality of each construct (Steenkamp and Trijp, 1991). Convergent validity was
assessed using CFA. Significant t-values of each item’s estimated path coefficient on its
posited latent construct and high squared multiple correlations for the individual items
JRIM indicated convergent validity (Lusch and Brown, 1996; Steenkamp and Trijp, 1991). All
7,1 estimated path coefficients had t-values that were significant at the p , 0.01 level. In
addition, composite reliability (all above 0.60), and average variance extracted (AVE) (all
above 0.50) exceeded the minimum criteria suggested by Bagozzi and Yi (1988). All items
exceeded the criterion for individual item reliability (i.e. squared multiple correlations)
(above 0.50). In order to test discriminant validity, shared variance of each pair of
46 constructs was compared against the AVE of each construct (Hair et al., 2006). The AVE
for each construct was larger than the shared variances. This result is presented in
Table III.

Hypothesis testing and result

Structural equation modeling (SEM) using the maximum likelihood method (LISREL
8.80) was employed to test the hypotheses. SEM estimates multiple and interrelated
dependence relationships (Hair et al., 2006), thus is an ideal technique to test the
hypotheses given the complex relationships among the constructs. The overall fit of
the model was acceptable (x 2 ¼ 721.6, df ¼ 290 ( p-value ¼ 0.00), GFI ¼ 0.88,
AGFI ¼ 0.89, NFI ¼ 0.92, CFI ¼ 0.94, RMSEA ¼ 0.056, RMR ¼ 0.049). Normed x 2
(x 2/df) was 2.49, indicating good fit.
The results support all postulated paths, except for one path from customer service
to emotion. Reputation had a significant positive effect on emotion supporting H1. This
indicates that if consumers perceive a retailer’s reputation to be high, they will tend to
experience more positive emotion. Reputation also yielded significant negative effect
on perceived risk supporting H2 and the previous literature.
Web site design had a significant positive impact on emotion and significant
negative effect on perceived risk, supporting H3a and H4a. This supports the previous
stream of literature that indicated the positive influence of a well-designed, easy to
navigate web site on pleasurable online experience and reduced perceived risk.
Customer service had a significant negative effect on perceived risk supporting H4b,
yet did not have significant impact on emotion, rejecting H3b. These findings suggest
that although prompt response and helpful service from the web site reduced perceived
risk, the same service did not make the consumer feel excited or joyful. Consumers
usually seek customer service when they find the information presented on the web site

WD CS FR SP REP RISK EMO PURCH

WD 0.56 0.50 0.46 0.48 0.26 0.49 0.42 0.46

CS 0.71 0.63 0.52 0.56 0.45 0.62 0.35 0.48
FR 0.68 0.72 0.59 0.59 0.50 0.56 0.56 0.52
SR 0.69 0.75 0.77 0.70 0.46 0.56 0.45 0.50
REP 0.51 0.67 0.71 0.68 0.79 0.64 0.62 0.66
RISK 0.70 0.79 0.75 0.75 0.80 0.75 0.61 0.67
EMO 0.65 0.59 0.75 0.67 0.79 0.78 0.65 0.62
PURCH 0.68 0.69 0.72 0.71 0.81 0.82 0.79 0.84
Notes: Correlations are below diagonal, squared correlations are above the diagonal, and AVE
Table III. estimates are presented on the diagonal; WD – web site design; CS – customer service; FR –
AVE and shared fulfillment/reliability; SP – security/privacy; REP – reputation; RISK – perceived risk; EMO –
variance estimates emotion; PURCH – purchase intention
is insufficient, when an error has occurred, or when the product has not been delivered Effects of
as expected. After experiencing helpful and responsive service, consumers may assess website quality
the risk of shopping at the web site as not considerable, thus proving greater customer
service would lead to reduced perceived risk. We hypothesized a similar effect of
customer service on emotion. However, since the intensity of emotion is often difficult
to recall with accuracy, consumers may rely on the memory of behavior ensuing from
the event (e.g. I must have felt stressed; I had to contact customer service) (Lynch et al., 47
1988). Therefore, consumers’ evaluation of customer service may not have yielded a
significant emotional response because of the negative perception tied to contacting the
customer service (e.g. I would not have contacted customer service if my product was
delivered on time/if I could find the information I needed on the web site on the first
place). Therefore, the emotion elicited by the customer service experience may not be
identical with the emotion generated by the overall experience shopping at the online
retailer and may have led to the insignificant relationship between customer service
and consumer emotion toward the online retailer.
Fulfillment/reliability had a significant positive effect on emotion and a significant
negative effect on perceived risk supporting H3c and H4. The result indicates that
accurate and prompt offline service delivery enhanced consumer emotion and reduced
risk toward shopping at the web site. Security/privacy had a significant positive impact
on emotion and a significant negative effect on perceived risk supporting H3d and H4d.
Perceived risk had a significant negative influence on purchase intention and
emotion, supporting H6 and H7. This is in accordance with the previous literature that
when the perceived risk is low, consumers experience positive emotion and are more
willing to purchase at the web site. This may be crucial for the online business since
risk (financial, performance, psychological and perceived time/convenience loss) is
greater than in offline stores; in the online context consumers not only disclose their
credit card and personal information, they also need to rely on the online store to safely
and promptly deliver the ordered merchandise as promised. Emotion had a positive
effect on purchase intent, supporting our H5 and previous literature. Results of the
hypotheses tests are shown in Figure 2.

Discussion
Academic and managerial implications
Since online retailing is evolving and more and more consumers seek to purchase
products and services online, it is critical to determine what factors influence
consumers to shop online. In previous studies based on the S-O-R framework, consumer
evaluation of web site quality was found to significantly impact consumer response
toward retailer product/service offerings. However, web site quality is a factor that is
internal to the web site, which focuses on what is being offered by the retailer, and does
not reflect external sources of reference. Therefore, this study provides theoretical
implications by including reputation (i.e. external reference) along with web site
quality (i.e. web site internal factors) as antecedents to consumers’ emotional, cognitive
and behavioral responses, in order to develop a more comprehensive model of consumer
experience in online retailing context. By suggesting an extended S-O-R framework
to include reputation as an antecedent of perceived risk and emotion, this study allows
for empirical examination of the effects of various components of retail web sites on
emotions, perceived risk and behavioral intentions.
JRIM
7,1 Reputation
ξ1
0.69(γ11)
–0.70(γ21)

Website
48 Design
0.81(γ12)
Emotion
ξ2 η1 0.49(β31)
–0.59(γ22)

Purchase
Customer –0.05(γ13) –0.61(β21) Intention
Service
η3
ξ3 –0.42( γ23)
–0.54(β32)
Perceived
0.72(γ14)
Risk
Fulfillment/R η2
eliability –0.28(γ24)
ξ4
0.36(γ15)
–0.80(γ25)
Security/
Privacy
Figure 2. ξ5
Summary of results in
hypothesized structural Notes: Significant at: *p < 0.05; c2 = 721.6, df = 290 ( p-value = 0.00), GFI = 0.88,
model (standardized)
AGFI = 0.89, NFI = 0.92, CFI = 0.94, RMSEA = 0.056, RMR = 0.049

Further, the study proposed a relationship between the two constructs, perceived risk
and emotion, hypothesizing that perceived risk would have a negative influence on
consumer emotion. This study proposed a causal relationship of cognition influencing
emotion based on previous research that theoretically and empirically suggested the
relationship (Arnold, 1960; Chebat and Michon, 2003; Lazarus, 1991). In line with the
study of Chebat and Michon (2003) that found emotions follow cognition, current
research suggest that emotion will arise as a result of consumers’ evaluation of the risk
associated with shopping at the online retailer.
Besides theoretical implications, this research has several managerial implications.
The results of this study offer online retailers a better understanding of how reputation
and web site quality can contribute in reducing perceived risk and eliciting positive
emotion, which eventually leads to purchase intention. As consumer experiences are
increasingly important in the online retailing context (Fiore and Kim, 2007), it is critical to
develop high quality web site offerings as well as maintain a good reputation among
consumers. Firm reputation has been deemed important since it cannot be easily created or
traded within a short time; however, its importance is even more critical in the online
retailing context since consumers have fewer signals than they have from physical stores
(Jin et al., 2009). In this regard, online retailers need to pay particular attention to creating
and cultivating reputation and to converting reputation to a source of sustainable
competitive advantage. Especially when reputation is found to be a critical factor
influencing consumers’ perceptions of web site quality, it is important to enhance Effects of
familiarity and popularity through various promotional activities. website quality
In our study, the majority of respondents had shopped online for apparel, thus most
of their responses were made regarding online apparel retailers. This is consistent with
the online shopping behavior described by Forrester Research Report (2011), which
reported that the top products bought online by generation Y consumers were
apparel/clothing. In other earlier research, stronger effects of reputation have emerged 49
when symbolic products were examined, such as a wristwatch or clothing (Dodds et al.,
1991; Teas and Agarwal, 2000). Especially for online retailers offering products that
provide symbolic value to consumers, reputation not only affects cognitive and
affective inner states, it might also add positive value to the web site quality itself.
In addition, online retailers should invest in maintaining satisfactory web site
features in order to enhance positive emotion and reduce perceived risk. For example,
retailers should assure consumers that their personal information and credit information
will be kept secure by providing a privacy policy on the web or presenting a third party
security seal. Online retailers might want to constantly update their web site and offer
good web site design with fast, informative, uncluttered and easy-to-navigate features in
order to elicit positive emotion (Jin et al., 2009). Just like the store space in offline
retailing, web site design in online retailing creates the first impression of the retailer as
an organization, and also represents the organization.
Lastly, the result indicates the significant effect of emotion and perceived risk on
purchase intention. Perceived risk of purchasing products online is greater than in the
offline retailing context (Pires et al., 2004). Therefore, online retailers should incorporate
online features that reduce consumers’ perceived risk of shopping on the web site by
enhancing customer service, providing adequate product and security information, and
building a reputation of a reliable company. In addition to reducing perceived risk, it is
important to elicit positive emotion while consumers are shopping on the web site in
order to encourage consumers to make a purchase. For instance, emotion aroused by the
images on the web site may shape the content of beliefs about the product (Meloy, 2000;
Lee and Sternthal, 1999). Mechanisms to evoke positive emotion should be considered
when developing an online environment.

Research limitations and suggestion for future studies

One of the limitations of this study is the use of a convenience sample, which resulted in a
majority of female respondents who identified apparel online stores as their most visited
web site. Apparel shopping is considered a form of hedonic consumption in that
consumers place importance on the enjoyment of shopping experience itself. They are
recreational shoppers who experience psychological rewards from the shopping process
itself, either in conjunction with, or independent of, the acquisition of goods and services
(Bellenger and Korgaonkar, 1980; Guiry et al., 2006). For these consumers, shopping is a
form of recreation that may be one of their favorite leisure activities (Kim and Kim, 2008),
while utilitarian shoppers seek value in a task-oriented, rational manner (Blackwell et al.,
2000). Therefore, it would be interesting to examine the effect of reputation and web site
quality on emotion and perceived risk in a utilitarian shopping context.
Another limitation is that this study did not differentiate multichannel retailers and
pure online retailers in our research model. However, there might be a significant
difference between the two types of retailers in terms of how consumers process the
JRIM two different stimuli (reputation and web site quality) in their decision making process.
7,1 Previous research suggests that reputation might be more important for pure online
retailers than for multichannel retailers because multichannel consumers form
expectations based on their offline channel experiences or knowledge (Jin et al., 2009).
Therefore, future research could be directed to examine the difference between the two
types of retailers, multichannel and pure online, in terms of consumers’ evaluation of
50 reputation, web site quality, emotion formation and perceived risk.
Finally, the current research can be extended to study Gen Y adult’s mobile shopping
behavior. Nielson reports that 66 percent of Americans ages 24-35 own a smartphone
(Nielson, 2012). 52 percent of adult cell phone owners use their devices while in a store to
get help with purchasing decisions (Smith, 2012) and more than 33.3 million US
consumers already engage in shopping-related activities on their mobile phones
(Woodward, 2011). Accordingly, the research framework can be applied to examine how
reputation earned through various online resources (e.g. social networks, review and
rating sites) influence perceived risk or emotion associated with mobile shopping.

References
Anderson, J. and Gerbing, D. (1988), “Structural equation modeling in practice: a review and
recommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-23.
Andrade, E.B., Kaltcheva, V. and Weitz, B. (2002), “Self-disclosure on the web: the impact of
privacy policy, reward, and company reputation”, Advances in Consumer Research, Vol. 29,
pp. 350-3.
Arnold, M. (1960), Emotion and Personality, Vol. 1/2, Columbia University Press, New York, NY.
Babin, B.J., Darden, W.R. and Babin, L.A. (1998), “Negative emotions in marketing research:
affect or artifact?”, Journal of Business Research, Vol. 42, pp. 271-85.
Bagozzi, R.P. and Edwards, J.R. (1998), “A general approach for representing constructs in
organizational research”, Organizational Research Methods, Vol. 1, pp. 45-87.
Bagozzi, R.P. and Yi, Y. (1988), “On the evaluation of structural equation models”, Journal of the
Academy of Marketing Science, Vol. 16 No. 1, pp. 74-94.
Baker, J., Grewal, D. and Levy, M. (1992), “An experimental approach to making retail store
environment decisions”, Journal of Retailing, Vol. 68, pp. 445-60.
Batra, R. and Ray, M.L. (1986), “Affective responses mediating acceptance of advertising”,
Journal of Consumer Research, Vol. 13, pp. 234-49.
Bellenger, D.N. and Korgaonkar, P.K. (1980), “Profiling the recreational shopper”, Journal of
Retailing, Vol. 56 No. 3, pp. 77-92.
Blackwell, R., Miniard, P. and Engel, F. (2000), Consumer Behavior, 9th ed., South-Western
College Publishing, Cincinnati, OH.
Carmines, E. and McIver, J. (1981), “Analyzing models with unobserved variables: analysis of
covariance structures”, in Bohmstedt, G. and Borgatta, E. (Eds), Social Measurement:
Current Issues, Sage, Beverly Hills, CA.
Chebat, J. and Michon, R. (2003), “Impact of ambient odors on mall shoppers’ emotions, cognition
and spending: a test of competitive causal theories”, Journal of Business Research, Vol. 56
No. 7, pp. 529-39.
Chiles, T.H. and McMackin, J.F. (1996), “Integrating variable risk preference, trust, and
transaction cost economics”, Academy of Management Review, Vol. 21, pp. 73-99.
Choi, J. and Lee, K.H. (2003), “Risk perception and e-shopping: a cross cultural study”, Journal of Effects of
Fashion Marketing & Management, Vol. 7 No. 1, pp. 49-64.
website quality
Chu, W., Choi, B. and Song, M.R. (2005), “The role of on-line retailer brand and infomediary
reputation in increasing consumer purchase intention”, International Journal of Electronic
Commerce, Vol. 9 No. 3, pp. 115-27.
Cox, D.F. and Rich, S.J. (1964), “Perceived risk and consumer decision making”, Journal of
Marketing Research, Vol. 1, pp. 32-9. 51
Dodds, W.B., Monroe, K.B. and Grewal, D. (1991), “Effects of price, brand, and store information
on buyers’ product evaluations”, Journal of Marketing Research, Vol. 28 No. 3, pp. 307-19.
Doll, W.J. and Torkzadeh, G. (1988), “The measurement of end-user computing satisfaction”, MIS
Quarterly, Vol. 12 No. 2, pp. 259-74.
Doney, P.M. and Cannon, J.P. (1997), “An examination of the nature of trust in buyer-seller
relationships”, Journal of Marketing, Vol. 61, pp. 33-51.
Donovan, R.J. and Rossiter, J.R. (1982), “Store atmosphere: an environmental psychology
approach”, Journal of Retailing, Vol. 58, pp. 34-57.
Dowling, G.R. and Staelin, R. (1994), “A model of perceived risk and intended risk-handling
activity”, Journal of Consumer Research, Vol. 21 No. 1, pp. 119-34.
Eroglu, S.A., Machleit, K.A. and Davis, L.M. (2001), “Atmospheric qualities of online retailing: a
conceptual model and implications”, Journal of Business Research, Vol. 54, pp. 177-84.
Eroglu, S.A., Machleit, K.A. and Davis, L.M. (2003), “Empirical testing of a model of online store
atmospherics and shopper responses”, Psychology & Marketing, Vol. 20 No. 2, pp. 139-50.
Estelami, H., Grewal, D. and Roggeveen, A.L. (2004), “The effect of retailer reputation and
response on post purchase consumer reactions to price-matching guarantees”, MSI
Reports Working Paper No. 3 (04-003), pp. 27-47.
Fiore, A.M. and Kim, J. (2007), “An integrative framework capturing experimental and utilitarian
shopping experience”, International Journal of Retail & Distribution Management, Vol. 35
No. 6, pp. 421-42.
Folkes, V.S. (1984), “Consumer reactions and product failure: an attributional approach”, Journal
of Consumer Research, Vol. 10 No. 4, pp. 398-409.
Forrester Research Report (2010), “North American techographics benchmark survey, US,
Canada Q2 2010”, Forrester.
Forrester Research Report (2011), “The date of consumers and technology: benchmark 2011, US”,
Forrester, November.
Forsythe, S.M. and Shi, B. (2003), “Consumer patronage and risk perceptions in internet
shopping”, Journal of Business Research, Vol. 56 No. 2, pp. 867-75.
Frijda, N.H. (1989), The Emotions, Cambridge University Press, Cambridge, MA.
Frijda, N.H. (1993), “Appraisals and beyond”, Cognition and Emotion, Vol. 7, May-July,
pp. 225-31.
Ganesan, S. (1994), “Determinants of long-term orientation in buyer-seller relationships”, Journal
of Marketing, Vol. 58, pp. 1-19.
Gregg, D. (2009), “Outline reputation scores: how well are they understood?”, Journal of
Computer Information Systems, Vol. 50 No. 1, pp. 90-7.
Grewal, D., Iyer, G.R., Gotlieb, J. and Levy, M. (2007), “Developing a deeper understanding of
post-purchase perceived risk and behavioral intentions in a service setting”, Journal of the
Academy of Marketing Science, Vol. 35, pp. 250-8.
JRIM Griffith, D.A. and Krampf, R.F. (1998), “An examination of the web-based strategies of the top
100 US retailers”, Journal of Marketing Theory and Practice, Vol. 6 No. 3, pp. 12-23.
7,1
Guiry, M., Magi, A.W. and Lutz, R.J. (2006), “Defining and measuring recreational shopper
identity”, Journal of the Academy of Marketing Science, Vol. 34 No. 1, pp. 74-83.
Gummerus, J., Liljander, V., Pura, M. and Van Riel, A. (2004), “Customer loyalty to content-based
websites: the case of an online health-care service”, Journal of Service Marketing, Vol. 18
52 Nos 2/3, pp. 175-86.
GVU (1998), GVU’s 10th WWW User Surveys, Graphic, Visualization, & Usability Center,
October-December, available at: www.gvu.gatech.edu/user_surveys (accessed 6 June
2011).
Ha, Y. and Lennon, S.J. (2010), “Effects of site design on consumer emotions: role of product
involvement”, Journal of Research in Interactive Marketing., Vol. 4 No. 2, pp. 80-96.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006), Multivariate Data
Analysis, 6th ed., Prentice-Hall, Upper Saddle River, NJ.
Herbig, P. and Milewicz, J. (1995), “The relationship of reputation and credibility to brand
success”, Journal of Consumer Marketing, Vol. 12 No. 4, pp. 5-10.
Holbrook, M. and Hirschman, E. (1982), “The experiential aspects of consumption: consumer
fantasies, feelings, and fun”, Journal of Consumer Research, Vol. 9, pp. 132-40.
Hui, K.-L., Teo, H.H. and Lee, S.-Y. (2007), “The value of privacy assurance: an exploratory field
experiment”, MIS Quarterly, Vol. 31 No. 1, pp. 19-33.
Izard, C.E. (1977), Human Emotions, Plenum, New York, NY.
Jang, S.C. and Namkung, Y. (2009), “Perceived quality, emotions, and behavioral intentions:
application of an extended Mehrabian-Russell model to restaurants”, Journal of Business
Research, Vol. 62, pp. 451-60.
Jin, B., Park, J. and Kim, J. (2008), “Cross-cultural examination of the relationships among firm
reputation, e-satisfaction, e-trust and e-loyalty”, International Marketing Review, Vol. 21
No. 3, pp. 324-37.
Jin, B., Park, J. and Kim, J. (2009), “Joint influence of online store attributes and offline operations
on performance of multichannel retailers”, Behaviour & Information Technology, Vol. 29
No. 1, pp. 85-96.
Joo, J. (2007), “An empirical study on the relationship between customer value and repurchase
intention in Korean internet shopping malls”, Journal of Computer Information Systems,
Vol. 48 No. 1, pp. 53-62.
Kim, A. and Ahn, J. (2006), “Comparison of trust sources of an online market-maker in the
e-marketplace: buyer’s and seller’s perspectives”, Journal of Computer Information
Systems, Vol. 47 No. 1, pp. 84-94.
Kim, H. and Niehm, L.S. (2009), “The impact of website quality on information quality, value, and
loyalty intentions in apparel retailing”, Journal of Interactive Marketing, Vol. 23, pp. 221-33.
Kim, H.Y. and Kim, Y.K. (2008), “Shopping enjoyment and store shopping modes: the moderating
influence of chronic time pressure”, Journal of Retailing and Consumer Services, Vol. 15,
pp. 410-19.
Kim, M. and Lennon, S.J. (2008), “The effects of visual and verbal information on attitudes and
purchase intentions in internet shopping”, Psychology & Marketing, Vol. 25 No. 2,
pp. 146-78.
Kim, S. and Stoel, L. (2004a), “Apparel retailers: website quality dimensions and satisfaction”,
Journal of Retailing and Consumer Services, Vol. 11 No. 2, pp. 109-17.
Kim, S. and Stoel, L. (2004b), “Dimensional hierarchy of retail website quality”, Information Effects of
& Management, Vol. 41 No. 5, pp. 619-33.
website quality
Kotha, S., Rajgopal, S. and Rindova, V. (2001), “Reputation building and performance: an
empirical analysis of the top-50 pure internet firms”, European Management Journal,
Vol. 19 No. 6, pp. 571-86.
Kumar, A. and Dillon, W. (1987), “Some further remarks on measurement-structure interaction
and the unidimensionality of constructs”, Journal of Marketing Research, Vol. 24, 53
pp. 438-44.
Ladhari, R., Brun, I. and Morales, M. (2008), “Determinants of dining satisfaction and post-dining
behavioral intentions”, International Journal of Hospitality Management, Vol. 27 No. 4,
pp. 563-73.
Lazarus, R.S. (1991), Emotion and Adaptation, Oxford University Press, New York, NY.
Lee, A.Y. and Sternthal, B. (1999), “The effect of positive mood on memory”, Journal of Consumer
Research, Vol. 26, pp. 115-27.
Lee, K. and Shavitt, S. (2006), “The use of cues depends on goals: store reputation affects product
judgments when social identity goals are salient”, Journal of Consumer Psychology, Vol. 16
No. 3, pp. 260-71.
Luo, W. and Cook, D. (2007), “An empirical study of trust of third party rating services”, Journal
of Computer Information Systems, Vol. 48 No. 2, pp. 66-73.
Lusch, R. and Brown, J. (1996), “Interdependency, contracting, and relational behavior in
marketing channels”, Journal of Marketing, Vol. 60 No. 4, pp. 19-38.
Lwin, M.O. and Williams, J.D. (2006), “Promises, promises: how consumers respond to warranties
in internet retailing”, Journal of Consumer Affairs, Vol. 40 No. 2, pp. 236-60.
Lynch, J.G., Marmorstein, H. and Weigold, M.F. (1988), “Choices from sets including remembered
brands: use of recalled attributes and prior overall evaluations”, Journal of Consumer
Research, Vol. 15, September, pp. 169-84.
Machleit, K.A. and Eroglu, S.A. (1998), “Describing and measuring emotional responses to
shopping experience”, Journal of Business Research, Vol. 54 No. 2, pp. 177-84.
Macintosh, G. and Lockshin, L.S. (1997), “Retail relationships and store loyalty: a multi-level
experience”, International Journal of Research in Marketing, Vol. 14 No. 5, pp. 487-97.
MacKenzie, S.B. and Lutz, R.J. (1989), “An empirical examination of the structural antecedents of
attitude toward the ad in an advertising presenting context”, Journal of Marketing
Research, Vol. 23, pp. 130-43.
Mehrabian, A. and Russell, J.A. (1974), An Approach to Environmental Psychology, MIT Press,
Cambridge, MA.
Meloy, M.G. (2000), “Mood-driven distortion of product information”, Journal of Consumer
Research, Vol. 27, pp. 345-59.
Menon, S. and Kahn, B. (2002), “Cross-category effects of induced arousal and pleasure on the
internet shopping experience”, Journal of Retailing, Vol. 78, pp. 31-40.
Mitchell, V. (1999), “Consumer perceived risk: conceptualisations and models”, European Journal
of Marketing, Vol. 33 Nos 1/2, pp. 163-95.
Mulpuru, S., Sehgal, V., Evans, P.F. and Roberge, D. (2011), “US online retail forecast, 2010 to
2015”, available at: www.forrester.com (accessed 23 November 2011).
Mummalaneni, V. (2005), “An empirical investigation of website characteristics, consumer
emotional states and on-line shopping behavior”, Journal of Business Research, Vol. 58,
pp. 526-32.
JRIM Murray, K.B. (1991), “A test of services marketing theory: consumer information acquisition
activities”, Journal of Marketing, Vol. 55 No. 1, pp. 10-25.
7,1
Nielson (2012), Survey: New US Smartphone Growth by Age and Income, Nielson Media
Research, February, available at: http://blog.nielsen.com/nielsenwire/?p¼30950
Nunnally, J. (1978), Psychometric Theory, McGraw-Hill, New York, NY.
Nyer, P.U. (1997), “A study of the relationships between cognitive appraisals and consumption
54 emotions”, Journal of the Academy of Marketing Science, Vol. 25 No. 4, pp. 296-304.
Olson, J.C. (1977), “Price as an informational cue: effects on product evaluations”,
in Woodside, A.G., Sheth, J.N. and Bennet, P.D. (Eds), Consumer and Industrial Buying
Behavior, Holland, New York, NY, pp. 267-86.
Olson, J.C. and Jacoby, J. (1972), “Cue utilization in the quality perception process”, in
Venkatesan, M. (Ed.), Proceedings of the Third Annual Conference of the Association for
Consumer Research, Iowa City, IA, pp. 167-79.
Ortony, A., Clore, L. and Collins, A. (1988), The Cognitive Structure of Emotions, Cambridge
University Press, New York, NY.
Park, J., Lennon, S.J. and Stoel, L. (2005), “On-line product presentation: effects on mood,
perceived risk, and purchase intention”, Psychology & Marketing, Vol. 22 No. 9, pp. 695-719.
Pavlou, P.A., Liang, H. and Xue, Y. (2007), “Understanding and mitigating uncertainty in online
exchange relationships: a principal-agent perspective”, MIS Quarterly, Vol. 31 No. 1,
pp. 105-36.
Pires, G., Stanton, J. and Eckford, A. (2004), “Influences on the perceived risk of purchasing
online”, Journal of Consumer Behaviour, Vol. 4 No. 2, pp. 118-31.
Purohit, D. and Srivastava, J. (2001), “Effect of manufacturer reputation, retailer reputation, and
product warranty on consumer judgments of product quality: a cue diagnosticity
framework”, Journal of Consumer Psychology, Vol. 10 No. 3, pp. 123-34.
Richard, M.O. (2005), “Modeling the impact of internet atmospherics on surfer behavior”, Journal
of Business Research, Vol. 58, pp. 1632-42.
Richard, M.O. and Chandra, R. (2005), “A model of consumer web navigational behavior:
conceptual development and application”, Journal of Business Research, Vol. 58,
pp. 1019-29.
Richard, M.O., Chebat, J.C., Yang, Z. and Putrevu, S. (2009), “A proposed model of online
consumer behavior”, Journal of Business Research, Vol. 63 Nos 9/10, pp. 926-34.
Richardson, P., Dick, A. and Jain, A. (1994), “Extrinsic versus intrinsic cue effects of perception of
store brand quality”, Journal of Marketing, Vol. 58 No. 4, pp. 28-36.
Roseman, I.J. (1984), “Cognitive determinants of emotions: a structural theory”, in Shaver, P. (Ed.),
Review of Personality and Social Psychology, Vol. 5, Sage, Beverly Hills, CA, pp. 11-36.
Roseman, I.J., Antoniou, A.A. and Jose, P.E. (1996), “Appraisal determinants of emotions:
constructing a more accurate and comprehensive theory”, Cognition and Emotion, Vol. 10,
May, pp. 241-77.
Roseman, I.J., Dhawan, N., Rettek, S.I., Naidu, R.K. and Thapa, K. (1990), “Cultural differences
and cross-cultural similarities in appraisals and emotional responses”, Journal of
Cross-Cultural Psychology, Vol. 26, January, pp. 23-48.
Russell, J.A. (1983), “Pancultural aspects of the human conceptual organization of emotions”,
Journal of Personality and Social Psychology, Vol. 45, pp. 1281-8.
Ruth, J.A., Brunel, F.F. and Otnes, C.C. (2002), “Linking thoughts to feelings: investigating Effects of
cognitive appraisals and consumption emotions in a mixed-emotions context”, Journal of
the Academy of Marketing Science, Vol. 30 No. 1, pp. 44-58. website quality
Scherer, K.R. (1993), “Studying the emotion-antecedent appraisal process: an expert system
approach”, Cognition and Emotion, Vol. 7, May-July, pp. 325-55.
Smith, A. (2012), “The rise of in-store mobile commerce”, Pew Research Center American
& Internet Life Project, January, available at: http://pewinternet.org/Reports/2012/In-store- 55
mobile-commerce.aspx
Steenkamp, J. and Trijp, H. (1991), “The use of LISREL in validating marketing constructs”,
International Journal of Research in Marketing, Vol. 8, pp. 283-99.
Tabachnick, B.G. and Fidell, L.S. (2007), Using Multivariate Statistics, 5th ed., Allyn and Bacon,
New York, NY.
Teas, R.K. and Agarwal, S. (2000), “The effects of extrinsic product cues on consumers’
perceptions of quality, sacrifice, and value”, Journal of the Academy of Marketing Science,
Vol. 28 No. 2, pp. 278-90.
Temkin, N. (2009), “Engage Gen Y online with social interactivity”, Forrester, June.
Thorndike, E.L. (1920), “A constant error in psychological ratings”, Journal of Applied
Psychology, Vol. 4, pp. 25-9.
Van den Poel, D. and Leunis, J. (1995), “The impact of price, branding and money-back guarantee
on store choice”, Proceedings of the 8th International Conference on Research in the
Distributive Trades, Università Bocconi, Milan, Italy, pp. B4.21-9.
Vijayasarathy, L.R. and Jones, J.M. (2000), “Intentions to shop using internet catalogues:
exploring the effects of product types, shopping orientations, and attitudes towards
computers”, Electronic Markets, Vol. 10 No. 1, pp. 29-38.
Wang, Y.J., Hernandez, M.D. and Minor, M.S. (2009), “Web aesthetics effects on perceived online
service quality and satisfaction in an e-tail environment: the moderating role of purchase
task”, Journal of Business Research, Vol. 63 Nos 9/10, pp. 935-42.
Westbrook, R.A. (1987), “Product/consumption-based affective responses and post purchase
processes”, Journal of Service Marketing, Vol. 24, pp. 258-70.
Westbrook, R.A. and Oliver, R.L. (1991), “The dimensionality of consumption emotion patterns
and consumer satisfaction”, Journal of Consumer Research, Vol. 18, pp. 84-91.
Wheaton, B., Muthen, B., Alwin, D.F. and Summers, G. (1977), “Assessing reliability and stability
in panel models”, Sociological Methodology, Vol. 8 No. 1, pp. 84-136.
Wolfinbarger, M. and Gilly, M.C. (2003), “etailQ: dimensionalizing, measuring and predicting
etail quality”, Journal of Retailing, Vol. 79 No. 3, pp. 193-8.
Woodward, K. (2011), “33 million consumers shop with a mobile phone”, Internet Retailer,
August, available at: wwwinternetretailercom/2011/08/02/33-wmillion-consumers-shop-
mobile-phone
Wu, C., Cheng, F. and Yen, D.C. (2008), “The atmospheric factors of online storefront
environment design: an empirical experiment in Taiwan”, Information & Management,
Vol. 45, pp. 493-8.
Yoo, B. and Donthu, N. (2001), “Developing a scale to measure the perceived quality of an internet
shopping site (SITEQUAL)”, Quarterly Journal of Electronic Commerce, Vol. 2 No. 1,
pp. 31-47.
Yoo, C., Park, J. and Macinnis, D.J. (1998), “Effects of store characteristics and in-store emotional
experiences on store attitude”, Journal of Business Research, Vol. 42, pp. 253-63.
JRIM Zeithaml, V.A. (1988), “Consumer perceptions of price, quality, and value: a means-end model
and synthesis of evidence”, Journal of Marketing, Vol. 52 No. 3, pp. 2-22.
7,1 Zeithaml, V.A. and Bitner, M.J. (1997), Services Marketing, McGraw-Hill, Singapore.

Further reading
Dizén, M. and Berenbaum, H. (2008), “Extreme outcome expectations and affect intensity”,
56 Cognition & Emotion, Vol. 22 No. 6, pp. 130-1148.

About the authors

Jiyoung Kim is an Assistant Professor of Merchandising in the Department of Merchandising at
the University of North Texas. She received her PhD from The Ohio State University and
conducts research primarily on consumer behavior in various retailing settings, including online
and mobile, as well as their attitudes and intentions toward socially responsible activities.
Jiyoung Kim is the corresponding author and can be contacted at: Jiyoung.kim@unt.edu
Dr Sharron J. Lennon is the Irma Ayers Professor in the Department of Fashion and Apparel
Studies at the University of Delaware. She received her PhD from Purdue University and has
published more than 100 research articles and book chapters. Her research interests include
consumer misbehavior on Black Friday, online visual merchandising, online shopping,
extreme consumption, consumption of fashion counterfeit products, and customer service in the
multi-channel context.

To purchase reprints of this article please e-mail: reprints@emeraldinsight.com

Or visit our web site for further details: www.emeraldinsight.com/reprints