TESTING A THEORETICAL MODEL TO EXAMINE THE RELATIONSHIPS AMONG
E-SOCIAL SHOPPING MOTIVATION, PERCEPTION, AND BEHAVIORAL INTENTION
By
Zee-Sun Yun

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILSOPHY
Retailing
2011

ABSTRACT
TESTING A THEORETICAL MODEL TO EXAMINE THE RELATIONSHIPS AMONG
E-SOCIAL SHOPPING MOTIVATION, PERCEPTION, AND BEHAVIORAL INTENTION
By
Zee-Sun Yun
The importance and effectiveness of Internet marketing strategies via social networking
is recognized recently. There is a growing interest in satisfying consumer demand for ConsumerGenerated Content (CGC) and strengthening the power of online Word-of-Mouth (WOM) via
social media channels because both can support consumers in making informed decisions and
can help marketers increase sales.
In the present study, the researcher proposed a comprehensive conceptual model to
explain consumers‟ e-social shopping behavior. Using the underlying tenets of Social Cognitive
Theory (SCT), specifically this study examined the relationships between 1) social shopping
motivation (i.e., information-oriented and socially-oriented motives), 2) social shopping resource
credibility (i.e., intellectual and social capital), 3) social shopping attention (i.e., perceived
utilitarian and hedonic value of social shopping), 4) social shoppers‟ memory (i.e., perceived
self-efficacy in decision making), and 5) social shoppers‟ behavior (i.e., online WOM and
purchase intentions). As a result, the proposed model investigated the effects of social shopping
motives and beliefs about social shopping resources on consumers‟ value perceptions which can
influence self-confidence in decision making and lead to online WOM buzz and purchase
decisions.
The survey instrument for the empirical study was developed and modified to reflect the
e-social shopping environment. The population was online consumers who join web
communities, such as Facebook, MySpace, etc. to search for or share shopping information.

Online survey research methods were used to recruit participants and collect data from online
consumer panels of a websurvey service company. Data were collected during May 2010; four
hundred and three usable questionnaires were analyzed.
The results of structural equation modeling indicated that perceived values were
significantly affected by credibility perceptions of informational and social resources and
moderately influenced by social shopping motives. In addition, perceived values significantly
influenced behavioral intentions. Perceived self-efficacy was more strongly and significantly
influenced by utilitarian value than hedonic value. Perceived self-efficacy was a mediator
between perceived values and behavioral intentions. In general, the findings of this study
supported the proposed theoretical model in explaining consumers‟ e-social shopping behavior.
Based on these findings, theoretical and managerial implications were discussed.

Copyright by
ZEE-SUN YUN
2011

ACKNOWLEDGEMENTS

I would never have been able to finish my dissertation without the guidance of my
committee members and support from my parents and family. I would like to express my deepest
gratitude to my advisor, Dr. Linda Good, for her excellent guidance, caring, encouragement, and
patience. I am very grateful to Dr. Brenda Sternquist, Dr. Patricia Huddleston, and Dr. Robert
LaRose for their willingness to serve on my committee and valuable inputs. I would also like to
thank my parents, twin sister, and younger brother in South Korea for their love.

v

TABLE OF CONTENTS

LIST OF TABLES ..................................................................................................................... viii
LIST OF FIGURES ..................................................................................................................... ix

CHAPTER I
INTRODUCTION ........................................................................................................................ 1
Statement of the Problem ............................................................................................... 4
Research Objectives ....................................................................................................... 4
Significance of the Study ............................................................................................... 5

CHAPTER II
LITERATURE REVIEW .............................................................................................................. 7
Consumer Uncertainty and Information Search ............................................................ 7
Online Information Seeking Behavior: Social Shopping............................................... 8
Social Shopping and Consumer Decision Support ...................................................... 10
A Theory of Decision Support: Social Learning and Social Cognitive Perspectives .. 11
MODEL DEVELOPMENT ........................................................................................................ 17
Motivation for Social Shopping and Attention to Social Shopping ............................. 17
Information-oriented motives and utilitarian value perceptions ........................ 21
Socially-oriented motives and hedonic value perceptions ................................. 24
Credibility of Social Shopping Resource and Attention to Social Shopping............... 25
Intellectual capital credibility and utilitarian value perceptions ........................ 26
Social capital credibility and hedonic value perceptions ................................... 28
Attention to Social Shopping and Memory of Social Shopper .................................... 30
Perceived values and perceived self-efficacy .................................................... 31
Attention to Social Shopper and Social Shopper‟s Behavior ...................................... 32
Perceived values and purchase intentions .......................................................... 32
Memory of Social Shopper and Social Shopper‟s Behavior ....................................... 34
Perceived self-efficacy and purchase intentions ................................................ 34

CHAPTER III
RESEARCH METHODS ........................................................................................................... 36
Data Collection and Sampling Procedures .................................................................. 36
Measures ...................................................................................................................... 38
Information-oriented motives ............................................................................ 38
Socially-oriented motives .................................................................................. 39
Perceived credibility of intellectual capital ........................................................ 40
Perceived credibility of social capital ................................................................ 41
Perceived social shopping value ........................................................................ 42
vi

Perceived self-efficacy ....................................................................................... 42
Online WOM intention ...................................................................................... 43
Purchase intention .............................................................................................. 43

CHAPTER IV
RESULTS OF ANALYSES ........................................................................................................ 45
Demographic Characteristics of the Sample ................................................................ 45
Preliminary Analyses ................................................................................................... 49
Confirmatory Factor Analysis ...................................................................................... 53
Analysis of the Structural Model for Hypothesis Testing ............................................ 61
DISCUSSION ............................................................................................................................. 66
Antecedents of Perceived Social Shopping Values ...................................................... 66
The Effects of Perceived Social Shopping Values ....................................................... 68
The Relationships among Perceived Self-Efficacy and Behavioral Intentions ........... 70

CHAPTER V
CONCLUSION AND IMPLICATIONS..................................................................................... 71
Summary of the Study ................................................................................................. 71
Theoretical Implications .............................................................................................. 75
Managerial Implications .............................................................................................. 76
LIMITATIONS AND FUTURE RESEARCH............................................................................ 80

APPENDICES
APPENDIX A. Survey Content .................................................................................................. 85
APPENDIX B. Survey Instrument ............................................................................................. 87
APPENDIX C. Frequency Statistics for Information Seeking Behavior of the Sample ............ 94
APPENDIX D. Covariance Matrix ............................................................................................. 98

BIBLIOGRAPHY ..................................................................................................................... 100

vii

LIST OF TABLES

Table 1. Classification of Social Shopping Motives ..................................................................... 20
Table 2. Demographic Characteristics of the Sample ................................................................... 47
Table 3. Measurement Properties .................................................................................................. 50
Table 4. Results of Confirmatory Factor Analysis ........................................................................ 57
Table 5. The Phi Values and Confidence Interval for Discriminant Validity ................................ 60
Table 6. Results of Path Model Analysis ...................................................................................... 64
Table 7. Summary of Hypothesis Testing ..................................................................................... 74
Table 8. Covariance Matrix for SEM Path Analysis ..................................................................... 99

viii

LIST OF FIGURES

Figure 1. Social Learning Theory ................................................................................................ 14
Figure 2. Social Cognitive Theory ............................................................................................... 15
Figure 3. Conceptual Framework of the Study ............................................................................ 16
Figure 4. Proposed Model and Hypotheses ................................................................................. 35
Figure 5. Recruitment Advertising/Web Greeting ....................................................................... 38
Figure 6. Proposed Structural Equation Model for Measurement ............................................... 44
Figure 7. Confirmatory Factor Analysis ...................................................................................... 54
Figure 8. Summary of Path Model Analysis Results ................................................................... 65
Figure 9. Information Search/Sharing Interest............................................................................. 95
Figure 10. Information Search/Sharing Activity.......................................................................... 95
Figure 11. Information Search/Sharing at a Certain Website....................................................... 96
Figure 12. Information Search/Sharing at a Certain Situation ..................................................... 96
Figure 13. Product or Service Categories .................................................................................... 97

ix

CHAPTER I

INTRODUCTION

Consumers are increasingly making decisions in computer-mediated environments since
Web 2.0 has offered a useful and essential interactive media tool and platform for
communication, information search, and shopping. Although websites offer consumers immense
amounts of information and choice and great convenience, the tasks of finding the exact products
that meet consumer needs and making a purchase decision with confidence and credibility can be
complicated. Sometimes, people experience frustrations when shopping online and thus hesitate
to make online product purchases.
Limitations in product content and communication service inhibit consumers‟ level of
confidence during decision making and can result in lost sales. Over three-quarters of online
shoppers are significantly influenced by the quality of content, relative to text, image, and tools
(e.g., ratings/reviews, product comparisons), however, shoppers reported that website content is
insufficient to complete research or purchase online „always, most often, or some of the time‟
(Freedman, 2007, 2009). Shoppers left a website without purchasing and went to a competitor or
continued their online research, due to a lack of online product information and assistance
(Freedman, 2007, 2009). Therefore, it is important to understand what type of content can
increase consumers‟ confidence when judging products and how to provide comprehensive
content that satisfies and converts shoppers into buyers.
Today‟s consumers spend significant time learning about products through ComputerMediated Communication (CMC) which provides users with a massive information resource and

1

a vehicle for social interaction (Williams & Rice, 1983). As the Internet empowers consumers
and enables CMC to accelerate the flow of information, consumers are likely to rely on robust
content, such as ratings, reviews, and recommendations (a.k.a. 3Rs of Consumer-Generated
Content (CGC)), and to research across channels, such as consumer-generated media or social
media. So, current trends show increasing consumer demand for the 3Rs and online Word-OfMouth (WOM) communication. According to a recent survey by Forrester Research, based on
5,000 online shoppers, 64% said that user reviews and ratings were the most-desired web
features to see on websites ("Online buyers seek out user reviews ", 2008). In addition, according
to data of over 1,000 online consumers surveyed from joint research by The e-tailing Group and
PowerReviews, 39% of respondents read eight and more reviews in order to be confident in
judging a product ("5 social shopping trends", 2010). Also, most of online shoppers have made a
purchase based on recommendations through web community and social media channels and
their buying behavior is significantly influenced by the following sources: customer reviews
(71%); community forums (45%); Facebook wall (31%); videos (30%); Facebook fan page
(25%) ("5 social shopping trends," 2010).
As CGC and WOM become important and, in some cases, essential in consumer
decision making, marketers are recognizing and paying attention to the 3Rs and social media
platforms (e.g., Facebook and Twitter) to assist in building social commerce and to improve the
effectiveness of Internet marketing strategies (e.g., viral marketing, social media marketing)
(Barton, 2006; Gardner, 2010; Gogoi, 2007; McCarthy, 2007). Creating and sharing CGC has
long been used by e-tailers such as Amazon and Netflix, but this practice is now spreading
rapidly to a broader array of retailers. According to MarketingSherpa research, by the end of
2005, 23% of e-commerce sites adopted features that allow for consumers to post comments,

2

reviews, ratings, and in some cases photos and videos; the percentage nearly doubled to 43% by
the end of 2006 (Gogoi, 2007). Continuing this growth trend, a recent Shop.org study reported
60% of retailers use customer reviews ("Industry statistics," 2010). Many online shopping sites
are introducing widgets that let users publish their wishlist or shopping list on their blogs and
social networking sites. Also, retailers use social websites to talk about upcoming events,
products, and problems and to stay connected to customers. Forrester Research estimates that
70% of retailers are on Facebook or MySpace, and more than half are on Twitter (Martinez,
2010).
58% of retailers who adopt customer reviews said improving the customer experience
was the most important reason for adding social commerce (selling with social media) to their
sites, followed by building customer loyalty (47%), driving sales (42%), and maintaining a
competitive advantage (37%) (Freedman, 2008). Also, a main benefit of customer reviews and
ratings is that e-tailers can improve their browsing to buying conversion rates ("Industry
statistics," 2010, "The state of retailing online 2007," 2007). The e-tailing Group study found that
11% of retailers reported that their conversion rates increased by 20% or more as a result of
adding reviews to their sites and 21% reported an 11% to 20% increase ("Industry statistics,"
2010). A UK survey found user-generated content increases conversion levels: 77% of website
owners reported web traffic increased and 42% said the average spend on site increased
("Industry statistics," 2010). Therefore, retailers should recognize the important and growing role
of CGC and WOM in social commerce in order to create places where people can collaborate
online, get advice from trusted individuals, find goods and services and then purchase them.

3

Statement of the Problem
While there is a growing interest in and evidence of the power of CGC and WOM, most
e-tailers continue to simply focus on providing producer-generated content or web-based product
recommendations via computer-based decision support systems, such as collaborative filtering
systems, reputation systems, and computational choice systems. Moreover, most e-tailers still do
not make social navigation or community-based web channels available, these tools enable
consumers to interact socially and search, share, and shop with others who have similar
preferences and interests. Even though the number of social shopping sites that allow consumers
to shop in a social networking environment is growing, most of the sites are not yet recognized
by consumers. Despite the extensive exchange of information that occurs between consumers in
community-based web environments, there is a lack of research investigating the impact of such
exchanges on consumer purchase decision-making.

Research Objectives
To successfully respond to growing social shopping market demands, retailers should
understand the underlying influences of social shopping to enhance consumer decision-making
and adjust their marketing strategies accordingly. Therefore, in our study, we specifically aim to
understand who social shoppers are and why they are likely to search for, share with others, and
shop online for products. In addition, we explore how social shopping influences consumers‟
shopping experiences, confidence in their online WOM intentions, and purchase decisions. The
objectives of this study are to develop a conceptual framework and to examine a theoretical
model based on Social Cognitive Theory (SCT) to explore consumers‟ social shopping behavior.

4

Significance of the Study
As social shopping is a key shopping process for shoppers and a key marketing factor for
retailers to manage, this study provides a rich understanding of online social shopping activity by
initiating scholarly inquiry into the potential competitive advantage of social commerce services
and by providing theoretical and conceptual foundations for further understanding online
shoppers and their shopping behavior. Thus, this study contributes to the literature in several
ways. First, it expands the application of SCT to social shopping behavior. We expect that use of
social shopping tools helps consumers acquire decision support during consumer decision
making and that these tools have a significant effect on their self-efficacy level which leads to a
purchase decision and online WOM buzz.
Second, this study formulates managerial implications by investigating key attributes
(e.g., motivations, credibility for intellectual capital and social capital, perceived values) of
social shopping that impact consumer decision-making. The results of this study will offer
insight to retailers and researchers about the role and power of the social element and the
comprehensive and credible content in a social shopping environment. CGC and social media
can be considered effective tactics for both consumers and retailers, and it can function as a
differentiation strategy designed to gain and retain customers and as a result, to gain market
share. Thus, this study will provide evidence for retailers regarding if and how social shopping
might be effective in customer decision making and how it might enhance the customer shopping
experience by adding value, and ultimately, increasing sales.
In Chapter I, we discussed the background of the study, the research problem, the
research objectives, and the significance of the study. The reminder of the dissertation is
organized as follows. In Chapter II we provide a literature review of the focal constructs of the

5

study and we propose a conceptual framework and theoretical model incorporating various
aspects of online social shopping including motivational orientation for social shopping,
credibility for social shopping resource, perceived value of the social shopping experience,
consumer shopping self-efficacy, and consumer purchase behavior. We discuss, research design,
data collection, and instrument development in Chapter III. In Chapter IV, we provide a sample
description, the results of data analysis and discussion of the findings. Finally, Chapter V is
devoted to limitations of the study, implications, and conclusions.

6

CHAPTER II

LITERATURE REVIEW

In this chapter, we review the literature relevant to the current study. First, we discuss
consumers‟ opportunities and limitations in online shopping environments and their influence on
purchase decisions. Next, we discuss consumers‟ social shopping behavior as a decision
aid/support strategy and how it enhances the shopping experience and self-efficacy of consumers
as they make their purchase decisions. Finally, we discuss SCT as an appropriate conceptual
framework.

Consumer Uncertainty and Information Search
We all make numerous decisions everyday as consumers. Specifically, we engage in a
purchase decision-making process, which leads to the selection of a course of action among
several alternatives. According to Mitchell, Walsh and Yamin (2005), consumer confusion is
related to too many, too similar, or ambiguous information stimuli; this prevents consumers from
fully understanding and being confident about products or the purchase environment because of
the complexity of the situation and the inability to process and manage all alternatives. If the
decision-making is influenced by conditions of uncertainty, risk, or complexity, consumers can
face serious decision difficulty. Consumer decision making research in traditional and online
shopping environments acknowledges that indicators of choice difficulty include deferring the
decision to buy or feeling unhappy with the choice (Haubl & Trifts, 2000; Iyenegar & Lepper,
2000; Schwartz et al., 2002). As a result, consumer confusion yields several unfavorable

7

consequences, such as negative WOM, dissatisfaction, and reduced self-confidence.
Desire for information is often a result of higher levels of uncertainty. This positive and
significant relationship between uncertainty and information seeking can be explained by
Uncertainty Reduction Theory (URT) (Berger, 1987; Weiss, Lurie, & MacInnis, 2008). Because
uncertainty is difficult to deal with, people seek information and communicate with other people
to reduce the ambiguity. As consumers‟ uncertainty about products or processes increases, they
are likely to increase information search in an effort to decrease the uncertainty (Urbany,
Dickson, & Wilkie, 1989). Moreover, when consumers have low self-efficacy and selfconfidence for problem-solving and a high degree of consumer involvement and/or a lack of time
and expertise, they are likely to rely on decision support aids (e.g., consultant, recommender) to
gather information about products/brands and they are likely to seek recommendations from
relevant others (Urbany et al., 1989).

Online Information Seeking Behavior: Social Shopping
To seek information, consumers use two methods, internal search and external search.
Internal search involves recalling stored information from memory. If the result is not
satisfactory, then people start an external search that includes collecting information from outside
sources. A consumer engages in external information search in order to gain information so that
attitudes may be formed and/or changed. The consumer may search to identify what alternative
products or brands are available or may seek to identify the various evaluative criteria on which
the products or brands may be compared (Mowen & Minor, 1997). Also, Mowen and Minor
(1997) suggest that consumers may seek information to help them decide on the relative
importance of the various evaluative criteria and form beliefs about the extent to which

8

alternatives possess the attributes they consider to be important.
In the past, people were limited to sharing information with their friends, family, or
neighbors but now people can influence the global community by sharing their experiences on
the Internet. According to Hoyer and McInnis (2001), outside sources can be either offline or
online. Some external search sources are media search (view ads or other types of marketerproduced communication) and interpersonal search (use email, chatbox, FAQ, advice from
friends, staff, and other consumers). Powered by social software and Web 2.0 tools (e.g., weblogs,
chat rooms, social network services, and instant messaging) that support social behavior to create
and recreate social conventions and social contexts, the Internet allows consumers to interact,
exchange ideas, and compare experiences with other consumers (Chen, Griffith, & Shen, 2005).
Social is a relational connection that motivates consumers to participate and contribute CGC
which becomes an essential digital asset for purchase decision-making and WOM marketing. For
example, MakeupAlley.com is the ultimate insider destination for all things related to beauty and
is the largest beauty-focused social network on the Internet (Leff, 2002, "Makeupalley About
Us," 1999); women connect to discuss beauty, fashion, and lifestyle, using message boards,
product reviews, emails, photos, and shopping diaries.
Also, YouTube and sites with blogging capabilities, such as Facebook, MySpace and
Twitter, are growing rapidly and frequently feature comments about brands and products.
According to Thomas, Peters, and Tolson‟s (2007) exploratory study of the virtual community, a
general discussion of fashion is taking place on MySpace within the forum Fashion and Style.
The four most popular discussion categories of fashion-related information are personal style,
brands and designers, tips and advice, and retailers. In user-generated content of websites like
Youtube, consumers increasingly rely on and are interested in product information and

9

recommendations (Cheong & Morrison, 2008). Recent industry data indicate that consumers are
more likely to consider blogs, Facebook, MySpace, and Twitter as an important tactics to search
for or share product information online before making a purchase ("Industry statistics," 2010).
Thus, current trends in social commerce that include the social side of activity in the
Internet business (e.g., social networking, social search, social media, social bookmarking, and
social shopping) increase research interest in marketers‟ social web applications or services that
can appeal to and deliver an integrated social experience to consumers. In other words, social
commerce provides customers with the means to virtually interact with one another in order to
make better purchase decisions.
The search for additional information to enhance decision making ease and focus is
recently embracing an added dimension related to the nature of the social networking generation,
the number of social shopping websites (e.g., ThisNext, Kaboodle, StyleHive, StyleFeeder,
Wists) is growing (Gordon, 2007; McCarthy, 2007; Tedeschi, 2006). This new type of online
shopping community combines two favorite online activities, social networking and online
shopping, by engaging in the practice of online WOM (Schneider, 2006). Through searching and
socializing in social shopping sites, consumers can learn what is popular, exchange shopping
ideas with others, and follow links to products they would not necessarily find on their own and
they can narrow product selections based on reviews and recommendations from like-minded
people with similar tastes (Gordon, 2007). Social shopping allows a consumer to access the
resources‟ accumulated information through the relationships among other members.

Social Shopping and Consumer Decision Support
CGC and social networks produce resources such as information and support from the

10

customer perspective (Lin, 2001). Intellectual capital and social capital are resources provided
through social shopping and we can expect them to play a significant role in providing decision
information and support, and improving decision quality. In other words, perceived benefits of
social shopping in consumer decision-making will depend on what consumers think about the
intellectual capital and social capital and how consumers evaluate them as to whether the
information obtained is accurate and originates from knowledgeable and trustworthy sources.
Online consumer-to-consumer communications may serve an important role in moving a
consumer closer to a positive purchase decision. Credible, informational social influence (also
called social proof, information obtained from another as evidence about reality) is likely to
reduce a consumer‟s uncertainty associated with a purchase decision and increase the consumer‟s
confidence in that decision (Kiecker & Hartman, 1993, 1994). During social shopping, the
consumer is informed about products and becomes familiar with how markets function. When
shopping information or an experience is communicated to one‟s friends and acquaintances, the
comments or opinions made by these significant others influence the consumer‟s consumptionrelated self-confidence (Folkes & Kiesler, 1991). Thus, increasing confidence through credible
information search or sharing can reduce uncertainty in consumer decision-making. If consumers
interact successfully in social shopping environments, they should have a high level of shopping
assurance and a greater likelihood of purchase intention.

A Theory of Decision Support: Social Learning and Social Cognitive Perspectives
Consumers are often influenced by other consumers. They imitate purchase and
consumption behaviors of consumers they admire, gather information from other consumers in
the process of social communication, and seek advice from others who have greater knowledge

11

and experience. When consumers collect information, through reading or talking to others or
when they simply look around (browse) and examine new or unfamiliar products, vicarious
(observational) shopping exploration occurs in the consumer decision making process (Price &
Ridgway, 1982). Because of the amount of perceived risk involved in Internet shopping,
informational social influence is important and many potential online shoppers tend to wait and
observe the experiences of others who have tried the product before they consider adopting it
(Lee, Cheung, Sia, & Lim, 2006). Consumers gain knowledge through social shopping by
referring to others‟ reviews or feedback that could be treated as experience-based sources of
comments from people who have purchased and used the product. So, searching for information
and seeking advice from others are part of a learning process in which people observe the
successful experiences of their social groups before deciding whether to buy a product.
Social Learning Theory (SLT) posits that people learn from one another, including the
process of knowledge acquisition or the concept of observational learning (vicarious condition),
imitation, and modeling (Bandura, 1977; Ormrod, 1999). Observational learning is a process by
which individuals observe how others behave in response to certain stimuli and reinforcements.
Observational learning occurs when a person watches the actions of another person and the
reinforcements that the person receives (Bandura, 1997). Bandura (1977) says “most human
behavior is learned observationally through modeling: from observing others, one forms an idea
of how new behaviors are performed, and on later occasions this coded information serves as a
guide for action” (p.22).
Miller and Dollard (1941) proposed the theory of social learning. Later, Bandura and
Walters (1963) extended the theory with the principles of observational learning and vicarious
reinforcement and originally named their contribution observational learning theory. Bandura

12

eventually renamed it as SLT because it was especially applicable to learning social behaviors
and observations that were usually focused on other people. SLT is a cognitive learning theory
that is based on mental information processing. It is often employed as a response to problem
solving, enabling individuals to gain some control over their environment. There are several
guiding principles of SLT: (1) people can learn by observing the behavior of others and
observing the outcomes of those behaviors; (2) learning may or may not result in a behavior
change; and (3) as a result of behavior being reinforced, people form expectations about the
consequences that future behaviors are likely to yield (Bandura, 1977).
According to Miller (2005), the observer can be affected by either an inhibitory effect or
a disinhibitory effect after the observations. The inhibitory effect occurs when an observer sees
the action of another involved in a situation being punished for that action. The disinhibitory
effect occurs when an individual receives positive reinforcement or is praised for an action and
the observer learns from and imitates that action (Miller, 2005). Applied to the case of
observations in social shopping, consumers are likely to avoid purchasing a product when they
read negative reviews about the product. On the other hand, consumers tend to have a motivation
to conform to the positive opinions of the normative referents and will likely form an intention to
buy the product.
Learning by observation involves the following steps in the modeling process:
motivation, attention, and memory (See Figure 1). Bandura (1977) considered motivation as
“cause” learning because observers will perform the act only if they have some reason to do so.
Attention includes modeled events (distinctiveness, affective valence, prevalence, complexity,
and functional value), which observers cannot learn unless they pay attention to what is
happening around them. Memory (retention and reproduction) explains observers‟ ability to

13

remember what they paid attention to and to replicate the behavior that the model demonstrated.

Figure 1. Social Learning Theory

Motivation
Learning by
Observation

Attention
Memory

According to SLT (Bandura, 1977), the effectiveness of behavior modeling can be
increased, depending on which component in the processes is involved. Observers are more
likely to adopt a modeled behavior if it results in outcomes they value; observers are more
likely to adopt a modeled behavior if the model is similar to the observer and has admired status,
and the behavior has functional value; and coding modeled behavior into words, labels, or
images results in better retention than simply observing.
In 1977, Bandura proposed his concept of self-efficacy, while he disproved the
traditional learning theory for understanding learning. In 1986, he renamed SLT to Social
Cognitive Theory (SCT) because cognitive processes mediate social learning. The SCT-based
framework for designing, implementing, and evaluating interactive learning includes
observational (vicarious) learning and self-efficacy. According to Bandura (1997), learning is a
function of the extent to which individuals are able to reflect upon and internalize their own
successes and failures. Self-efficacy is achieved when the learner identifies his/her ability to
perform. Learning most likely occurs if there is a close identification between the observer and
the model and if the observer also has a good deal of self-efficacy (Bandura, 1988).
14

SCT provides a framework for understanding, predicting, and changing human behavior.
SCT describes learning as the interrelation between behavioral, environmental, and personal
factors (Bandura, 1986). SCT provides the theoretical framework for interactive learning to
explain how people acquire and maintain certain behavioral patterns and how people‟s
experiences, environments, and behaviors affect how they learn. In the Bandura (1986) model
(See Figure 2), the interaction between the person and behavior involves the influences of a
person‟s thoughts and actions. The interaction between the person and the environment involves
human beliefs and cognitive competencies that are developed and modified by social influences
and structures within the environment. The third interaction, between the environment and
behavior, involves a person‟ behavior determining the aspects of their environment and in turn,
their behavior is modified by that environment. Thus, SCT is helpful for understanding and
predicting both individual and group behavior and identifying methods in which behavior can
be modified or changed.

Figure 2. Social Cognitive Theory

Personal Factors

Behavioral
Factors

Environmental
Factors

Applying SCT to our study, consumers‟ observational learning occurs when they have
some reason to search for and share information through social shopping. For example, social
shoppers can check hot deals, sales information, reviews, or recommendations before making a
15

decision and to shop with others who have similar tastes/interests. In social shopping,
consumers are likely to learn from and adopt others‟ reviews and recommendations if CGC is
relevant and social group members are trustworthy and similar to the consumers seeking the
information. Moreover, when consumers pay attention to others‟ reviews and recommendations,
learning takes place, which may become valuable and helpful in their decision making.
According to SCT, interactive learning allows learners to gain confidence through practice
(Bandura, 1977; Bandura, 1997). Likewise, social shopping activity increases consumers‟
ability to perform decision-making confidently and/or successfully and increase their behavioral
intention.
In the next section, based on SCT, we develop our conceptual model (see Figure 3) that
explains social shopping behavior and its effect on consumer‟s shopping experience, decisionmaking, and behavioral intentions. Social shopping motivation, social shopping attention, and
social shoppers‟ perceived self-efficacy and memory are the main social cognitive learning
factors affecting their shopping behavior. Social shopping resource credibility is posited as an
antecedent factor of social shopping attention.

Figure 3. Conceptual Framework of the Study

Opportunities
and Limitations
of Consumers
in Online
Shopping
Environment

Social
Shopping
Motivation
Social
Shopping
Resource
Credibility

Social
Shopping
Attention

16

Social
Shoppers‟
Memory

Social
Shopper‟s
Behavior

MODEL DEVELOPMENT

In this section, we discuss relationships between 1) social shopping motivation (i.e.,
information-oriented and socially-oriented motives), 2) social shopping resource credibility (i.e.,
credibility of intellectual capital and social capital), 3) social shopping attention (i.e., perceived
utilitarian and hedonic value of social shopping), 4) social shoppers‟ memory (i.e., perceived
self-efficacy in decision making), and 5) social shoppers‟ behavior (i.e., purchase intention and
online WOM intention). Based on proposed hypotheses, a structural equation model is presented.

Motivation for Social Shopping and Attention to Social Shopping
Shopping motives are the drivers of behavior that bring consumers to the marketplace to
satisfy their internal needs (Westbrook & Black, 1985). To identify social shopping motivations,
we reviewed literatures in offline and online shopping, communication media use, and social
networking. Since Tauber‟s (1972) seminal work on “why do people shop”, numerous shopping
motive studies were conducted in the pre-Internet shopping period to identify shoppers‟
underlying shopping motives and their relationship to shopping behavior (Bellenger &
Korgaonkar, 1980; Dawson, Bloch, & Ridgway, 1990; Eastlick & Feinberg, 1999; Westbrook &
Black, 1985). Based on in-depth interviews, Tauber (1972) hypothesized six personal motives
for shopping (e.g., diversion, learning about new trends, self-gratification, sensory stimulation,
role playing, physical activity) and five social motives (e.g., social experiences outside the home,
communication with others who have a similar interest, peer group attraction, pleasure of
bargaining, status/authority). Tauber (1972) developed these shopping motivations with the basic
principle that shoppers are motivated by a variety of psychosocial needs other than those strictly

17

related to acquiring a particular product.
McGuire (1974) identified two categories of motives or needs that affect mass
communication: internal, nonsocial motives or needs and external, social motives or needs.
Internal needs are satisfied by thinking about and exploring the world, irrespective of social
activities (e.g., consistency, causation, categorization, cues, independence, and curiosity).
External needs focus on interactions with other people (e.g., self-expression, ego-defense,
assertion, reinforcement, affiliation, and modeling). Westbrook and Black (1985) connect
Tauber‟s (1972) framework to McGuire‟s (1974) typology of human motivations and propose
that shopping behavior occurs to acquire a desired product, to provide satisfaction with nonproduct-related needs, or to mainly accomplish goals not related to product acquisition. These
shopping motives are captured in seven dimensions (e.g., anticipated utility, role enactment,
choice optimization, affiliation, stimulation, negotiation, and power/authority) and contain both
hedonic and utilitarian elements (Westbrook & Black, 1985).
Over the past decade, a number of studies examined shopping motives in the context of
online shopping. Parsons (2002) applied Tauber‟s (1972) personal and social motives to Internet
shoppers to determine how the motives influence online shoppers. Three of Tauber‟s (1972) six
personal motives (i.e., diversion, self-gratification, and learning about new trends) are strong
motives for online shopping. Four (i.e., social experiences outside the home, communication
with others who have a similar interest, peer group attraction, and status/authority) of the five
social motives are descriptive of online shopping motives; only bargaining pleasure failed to be a
shopping motive for Parson‟s (2002) sample. Role playing, physical activity, and sensory
stimulation do not describe online shopping motives. Rohm and Swaminathan (2004) classified
six motives (i.e., shopping convenience, information seeking, social interaction, immediate

18

possession, retail shopping experience, and variety seeking) for shopping online and offline and
clustered motives to identify shopping types.
As consumers use online social media (e.g., weblogs, social network services), it is also
important to understand consumer‟s media use and social communication in online shopping.
Papacharissi and Rubin (2000) examined interpersonal, media, and new technology motives and
found five computer-user CMC motives for using the Internet: interpersonal utility, passing time,
information seeking, convenience, and entertainment. Flanagin and Metzger (2001) classified ten
motive clusters for people‟s Internet use including information, learning, playing, leisure,
persuasion, social bonding, relationship maintenance, problem solving, status, and insight. Ray
(2007) investigated why people make use of CMC via social networking websites and examined
people‟s motivations of CMC; motivations include entertainment, information, and social utility.
Also, Nadjm (2007) found several reasons why people use social networking websites, such as to
be popular, make friends, exchange information, self-improvement, entertainment, belonging to
a group, etc.
Based on motivation literatures for online shopping and for social networking
(summarized in Table 1), we categorize social shopping orientations or motivations into two key
motive dimensions: information-oriented and socially-oriented motives. For example,
consumers‟ motivations to use social shopping occur when they need product information and
when they need social interaction to assist them in reducing risk and uncertainty in their purchase
decisions.

19

Table 1. Classification of Social Shopping Motives
Motives
Studies
Parsons
(2002)

Informationoriented motives
 learning about
new trends

Online
shopping
studies
Rohm and
 information
Swaminathan
seeking
(2004)

Online
social
media
use
studies

Socially-oriented
motives
 social experiences
outside the home
 communication with
others who have a
similar interest
 peer group attraction
 social interaction

Papacharissi
and Rubin
(2000)
Flanagin and
Metzger
(2001)

 information
seeking

 interpersonal utility

 information,
learning

 social bonding
 relationship
maintenance

Ray (2007)
Nadjm
(2007)

 information
 to exchange
information

 social utility
 to make friends
 to belong to a group

Other motives
diversion, selfgratification,
status/authority

shopping convenience,
immediate possession,
retail shopping
experience, variety
seeking
pass time,
convenience,
entertainment
playing, leisure,
persuasion, problem
solving, status, and
insight
entertainment
to be popular, selfimprovement,
entertainment

It is essential to know what consumers pay attention to, relative to the shopping benefits
and values, before we can understand purchase intentions; what do consumers want from their
social shopping experience? What factors are important in their judgments of perceived social
shopping value? The Theory of Value explains the value-based shopping activity of decision
makers, that is, what a consumer tries to achieve from a shopping experience (Babin, Darden, &
Griffin, 1994). Consumers are motivated and engage in on-going information search to build an
information bank and to have fun (Hirschman & Wallendorf, 1982; Punj & Staelin, 1983). The
understanding of the shopping experience from both utilitarian and hedonic shopping value is
20

emphasized by many researchers (Holbrook & Hirschman, 1982; Tauber, 1972; Westbrook &
Black, 1985). Most consumers are looking for more than utilitarian shopping value, such as
simply price savings, time savings, and convenience. Instead of defining motivation to shop only
as a function of buying, the role of hedonic and social shopping values is also recognized. In
response to the information-oriented and socially-oriented motivations, we can break down
consumer shopping value with respect to their shopping experience into utilitarian and hedonic
dimensions.
The utilitarian aspect of consumer behavior is directed toward satisfying a functional or
economic need and shopping is related to a task oriented, rational, deliberate, and efficient
activity (Babin et al., 1994). Perceived utilitarian shopping value depends on whether the
particular consumption need that stimulated the shopping trip was accomplished; this means a
product is purchased in a deliberate and efficient manner. In contrast, the hedonic aspect of
shopping is viewed as a positive experience where consumers enjoy a shopping activity,
regardless of whether or not they make a purchase (Holbrook & Hirschman, 1982). Hedonic
shopping value is more subjective and personal than its utilitarian counterpart and results from
fun and entertainment as opposed to task completion. Therefore, hedonic value reflects the
potential entertainment and emotional appeal of the shopping experience.

Information-oriented motives and utilitarian value perceptions
Purchase decision issues include how much consumers know about the product or
service when they decide to make a purchase, the reasons for seeking consumer information, and
how consumers obtain the information. The relationships between consumer motives and
information search behavior are examined comprehensively in consumer behavior literature

21

(Bloch, Sherrell, & Ridgway, 1986). According to Punj and Staelin (1983), the consumer's
motive for pre-purchase information search is to enhance the quality of the purchase outcome.
Flynn, Goldsmith, and Eastman (1996) and Goldsmith and Horowitz (2006) describe that
consumers seek information or opinions to reduce perceived risk and increase the likelihood for
success of the product choice. For example, consumers enter an online group and ask advice
about products to enhance their understanding of products. Arnold and Reynolds (2003)
considered idea shopping as a way to keep up with product information and available trends.
Idea shopping is grounded in McGuire‟s (1974) theory that explains the human need for external
guidelines and information for structure, order, and knowledge that help the individual to make
sense for himself.
Consumers‟ dependency on the Internet for information search is growing. Because the
Internet is perceived as a powerful tool for consumer information search in a manner that
minimizes the transaction costs of providing and accessing information, online information
search is a part of interactive consumer behavior (Klein, 1998; Liang & Huang, 1998; Porter,
2001). Online shopping offers an infrastructure by which the consumer is able to search,
compare, and access information much more easily and at deeper levels than within the offline
retail structure (Alba, Lynch, Weitz, & Janiszewski, 1997). This concept of information as adding
value to the retail experience is supported by Hoffman and Novak (1996), who suggest that the
hypermedia computer-mediated environment on the Internet offers not only a wide variety of
information, it offers the capability to deliver specific information tailored to the needs of the
consumer.
Due to the interactive medium of the Internet, consumers can easily obtain information
from others‟ experiences with a product or service by using information sources, such as online

22

communities and chat rooms. Consumers search for information, in order to make a judgment,
and they rely on so called „hard data‟ provided by retailers or manufacturers or so called „soft
data‟ provided by their own experiences or comments from friends and families (Bei, Chen, &
Widdows, 2004). In social shopping, obtaining the „soft data‟ from CGC is a key informationoriented motive. According to Klein (1998), consumers seek product information about search
products, for which they can acquire information prior to purchase and about experience
products, for which they cannot evaluate attributes until they purchase and use the product.
Information search related to search products can be easily obtained through product attributes
(e.g., styles, specifications, sizes, and functions), but information search related to experience
products can hardly be obtained through product attribute descriptions (e.g., taste, texture, fit).
Thus, the recommendations or reviews of others are more widely used for experience products
rather than for search products.
Consumers are more likely to pay attention to the shopping benefits and values that lead
to efficient and rational purchases, where shoppers can study what others have to say and
contribute their own thoughts. So, seeking online information, opinions, or referring to product
lists and recommendations created by other people who have purchased and used the product,
provides benefits to consumers, such as retrieval time reduction, unveiling of new preferences,
extension of recommendation lists, and interactive feedback (Smith, Menon, & Sivakumar, 2005).
According to Stell and Paden (1999), motivations of observational (vicarious) shopping
exploration are to satisfy curiosity or to get accurate product knowledge. They found that
consumers engage in online shopping activity to increase stimulation, satisfy their curiosity, or
learn about products. Thus, in social shopping, consumers are likely to access, search, identify,
and acquire information from CGC to stay/become informed about products and trends before

23

making a purchase decision. Social shopping can provide an efficient and satisfying functional
shopping experience. When the consumers are conscientious about information gathering, they
are likely to perceive utilitarian values related to social shopping. So, we propose the following
hypothesis.
Hypothesis 1: Consumers‟ information-oriented motives will positively influence their
utilitarian value perceptions in social shopping.

Socially-oriented motives and hedonic value perceptions
Studies on consumers‟ motivations for shopping identified that friends or family provide
information that helps consumers reduce risk and uncertainty and increase confidence in their
decisions (Hartman & Kiecker, 1991; Kiecker & Hartman, 1993). These studies found social
motivations for shopping with friends (e.g., for fun, enjoyment), suggesting that consumers
accompany friends on their shopping trips to assist them with their onsite purchase decisions and
enjoy a shopping activity. Stell and Paden (1999) found that consumers engage in observational
(vicarious) shopping activity to recreate and socialize with others. Arnold and Reynolds (2003)
indicate that many people treasure the time spent shopping with friends or family members, and
also enjoy social activities while shopping. Thus, shopping with friends during information
search can provide pleasurable and satisfying shopping experience that contributes to consumer
decision making (Bearden, Hardesty, & Rose, 2001).
As consumers have an increasing variety of ways to share information and feelings
about products and services (e.g., blogs, forums, online communities, newsgroups, chat rooms,
review sites, and social networking sites), the concept of offline social interaction or traditional
interpersonal communication is extended into the newer realm of cyberspace. The power of
social networking is that it offers a place for social interaction and allows social shopping that
24

moves online shopping to an online social activity. Consumers seek advice from other consumers
and place a great deal of importance on the entertainment and emotional appeal of the shopping
experience found where shoppers can browse around (Cohn & Park, 2007; Rhone, 2007).
In our social shopping study, the following motives encourage consumers to shop online
with others to share information and enjoy shopping: online social experience (e.g., seeking new
acquaintances or people watching), communication with others having similar tastes, consulting
product recommendations, and peer group or reference group influence. We expect that
consumers who are highly motivated to shop online with others are likely to perceive hedonic
values related to social shopping. Therefore, the following hypothesis is provided.
Hypothesis 2: Consumers‟ socially-oriented motives will positively influence their
hedonic value perceptions in social shopping.

Credibility of Social Shopping Resource and Attention to Social Shopping
"You may have heard that 'knowledge is power,' or that information, the raw material of
knowledge, is power. But the truth is that only some information is power: reliable information"
(Harris, 2007). Information serves as the basis for beliefs, decisions, and choices. For example, if
we make a purchase decision based on inaccurate or unreliable information, we lack power and
cannot avoid regret after purchase. As the web has become an increasingly rich information
resource, gaining competitive advantage through high quality web content also has become an
important topic (Flanagin & Metzger, 2007; Metzger & Flanagin, 2008; Metzger, Flanagin, Eyal,
Lemus, & McCann, 2003; Rieh & Danielson, 2007). Therefore, it is essential to understand how
to establish online information quality and to assure web users that online information is credible.
Credibility refers to the believability of a source or message (Flanagin & Metzger, 2008).
Although the concept of credibility is closely related to several concepts, including trust,
25

reliability, accuracy, reputation, quality, authority, and competence, these concepts can be
classified in two core dimensions of credibility: expertise dimension (e.g., quality, accuracy,
authority, competence) and trustworthiness dimension (e.g., reputation, reliability, trust)
(Flanagin & Metzger, 2008). In our study, we identify two social shopping information resources
that affect social shopping perceptions and attitudes: intellectual capital (expertise dimension)
and social capital (trustworthiness dimension).
The information resource credibility depends on the believability of the information
contained in the shopping resource and influences consumers‟ perceptions of the outcomes
resulting from using the resource. Chiou and Cheng (2003) found that favorable message levels
through reliable and accurate social information, consisting of comments and observations made
by other people, impacted consumers‟ brand image evaluation positively. Smith, Menon, and
Sivakumar (2003) investigated the influence of peer recommendations on decision making and
found two important factors, recommender expertise and rapport with the recommender which
explain that consumers are more likely to rely on recommenders with expertise and close/similar
tastes as a knowledgeable and trustworthy source when seeking product information. So, the
credibility of social shopping resources accumulated in CGC and social group relationships is a
fundamental driver that may reduce uncertainty and influence consumers‟ perceptions about
social shopping and subsequent decision-making.

Intellectual capital credibility and utilitarian value perceptions
Intellectual capital is an intangible asset that comprises the intellect or brain-power about
products and services and can be employed for consumer buying decisions. Stewart (1997)
defines intellectual capital as the capacity of individuals to provide solutions and states that a

26

company and its customers can jointly create a body of intellectual capital. For example, a
company creates a website which can assist its customers; also the customers can contact and
help one another. Because a considerable amount of online assistance is carried out by
consumers while social shopping, intellectual capital can be formed in an information rich
environment and can reduce uncertainty related to consumer decision making. Fogg and
colleagues examined web users to investigate the elements that affect their judgments of web
credibility (Fogg et al., 2000). They found that real-world feel, ease of use, expertise,
trustworthiness, and message tailoring positively influence web credibility. Therefore, social
shoppers can perceive credible intellectual capital when other consumers‟ reviews or
recommendations about the product provide relevant, accurate, clear, and knowledgeable product
research and shopping information.
According to Tam and Ho (2006), personalized recommender systems reduce customer‟s
search effort. They found that recommender system users exposed to relevant web content sought
less information and spent less time on decision making. Also when personalized systems
provided more accurate recommendations, user satisfaction increased (Liang, Lai, & Ku, 2007).
Thus, consumers‟ perceived increased usefulness of, trust in, and adoption of the systems (AlNatour, Benbasat, & Cenfetelli, 2008). Thus, In our study, when the consumer‟s credibility
perceptions of intellectual capital (i.e., relevance, expertise, and clarity of CGC) are positive, the
consumer‟s utilitarian value perceptions of social shopping in decision making is more likely
positive. Therefore, we hypothesize the following:
Hypothesis 3: Consumers‟ perceived credibility of intellectual capital will positively
influence their perceived utilitarian value of social shopping.

27

Social capital credibility and hedonic value perceptions
Social capital refers to social networks that may provide access to resources and social
support ("Social capital," 2003). Etzioni (1996) defines social capital as an intangible force that
helps to bind society together by transforming self-seeking individuals into members of a
community with shared interests, and shared assumptions about social relations. Social capital
may satisfy social needs and bear a social potentiality sufficient to acquire the advantages of
community assistance.
Based on Putnam‟s (2000) concepts of social capital, there are two types of relationships
within the social network that can be used to estimate the value of social capital: bridging and
bonding. First, bridging social capital relationships are inclusive and they occur when individuals
from different backgrounds make connections between social networks (Putnam, 2000). These
individuals (e.g., acquaintances and relative strangers) often have only tentative relationships,
but what they lack in depth they make up for in breadth (weak-tie networks). Consequently,
bridging may broaden social horizons or world views, or open up opportunities for information
or new resources. However, bridging relationships may provide little emotional support such as
trusting others to help solve problems, having someone to turn to for advice, and having
someone to go to with intimate personal problems (Williams, 2006).
Second, bonding social capital relationships can be exclusive and they occur when
strongly tied individuals, such as family and close friends, provide emotional support for one
another (Putnam, 2000). The individuals with bonding social capital relationships have little
diversity in their backgrounds but have stronger personal connections (strong-tie networks). The
continued reciprocity found in bonding relationships provides strong emotional support
(Williams, 2006). While weaker ties lead to more people in different life situations and thus to a

28

broader set of information and opportunities, those people in strong-tie networks are likely to
offer emotional support. Thus, consumers may receive benefit from supporting computermediated weak-tie and strong-tie connections.
According to Wang & Benbasat (2007), social presence, interaction with a social group
in an online environment, influences the way individuals perceive a medium and individuals
from whom they receive messages and communications; social media allows users to establish
personal connections with other users. Specifically, social presence is linked with intimacy and
immediacy; intimacy indicates how close a web user feels to similar users and immediacy
represents the degree of psychological distance between a user and other similar users (Wang,
Baker, Wagner, & Wakefield, 2007). Kim, Baker, and Song (2007) found that social presence
increases acceptance of and trust in recommendation systems among online consumers when
they feel more comfortable with and similar to other users. In addition, the social interaction
enhances the hedonic value of using computer-mediated social network sites (Thambusamy,
Church, Nemati, & Barrick, 2010).
In our study, social shoppers could share information and shopping experiences with
those who have the same interests online. Online experience could also become the subject of
conversations to give a social shopper pleasure. When the consumer‟s credibility perceptions of
bridging and bonding social capital in shopping knowledge sharing are positive, the consumer‟s
perceived hedonic value of social shopping in decision-making is more likely positive. Thus, we
formulate the following hypotheses.
Hypothesis 4: Consumers‟ perceived credibility of social capital will positively
influence their perceived hedonic value of social shopping.

29

Attention to Social Shopping and Memory of Social Shopper
According to SLT, memory explains an ability of people to remember and produce a
level of learning (Bandura, 1997). The consumers who consider themselves capable in and
assured of their shopping decisions have a low level of uncertainty about the decisions. If
consumers feel too much uncertainty in online transactions, they are reluctant to make purchases
from online retailers. Thus, researchers identified that self-confidence (often referred to as selfefficacy) is related to anxiety-, risk-, and uncertainty-reducing strategies (Hartman & Kiecker,
1991; Kiecker & Hartman, 1993; Mangleburg, Doney, & Bristol, 2004; Paridon, 2005).
Perceived self-efficacy is an affirmation of a capability level and the strength of one‟s
confidence level (Bandura, 1997). Barbalet (1998) refers to self-efficacy as a person‟s judgment
about a future event or a belief in a person‟s own ability (i.e., self-confidence). The feeling of
confidence in one‟s own ability is characterized as essential for any behavior to take place,
because this belief serves as a form of self-assurance. Bearden et al. (2001) defined the concept
of consumer self-confidence as “the extent to which an individual feels capable and assured with
respect to his or her marketplace decisions and behaviors” (p.122). Mallalieu and Palan (2006)
found that people perceive shopping competence as a multi-faceted construct composed of
effectively utilizing environmental resources, having and using knowledge related to shopping,
and possessing a degree of self-confidence. Thus, consumer self-confidence is an important
research stream and attempts to understand and improve consumer performance through
knowledge of how marketplace information is obtained, analyzed, and used to make purchase
decisions (Bettman, Johnson, & Payne, 1991; Park, Whan, Mothersbaugh, & Feick, 1994).
Creating and strengthening people‟s beliefs about their efficacy can be developed by the
following sources of influence: mastery experiences, the vicarious experiences provided by

30

social models, social persuasion, and positive mood (Bandura, 1986; Staples, Hulland, & Higgins,
1998). Specifically, the way of self-efficacy building through observational learning experiences
by modeling can generate expectations in observers that they can improve their own performance
by learning from what they have observed. People seek proficient models that possess the
competencies to which they aspire. Through their behavior and expressed ways of thinking,
competent models transmit knowledge and teach observers effective skills and strategies for
managing environmental demands. Self-efficacy building through social persuasion can
influence self-efficacy judgments. Through suggestion or feedback on performance, people are
led into believing that they can cope successfully with a specific task.

Perceived values and perceived self-efficacy
During social shopping, if a consumer gets decision support from other consumers‟
reviews for the product he or she intends to buy, utilitarian and/or hedonic values could be found
in that shopping experience. That is, utilitarian value is present because of the efficiency and
usefulness of the product information acquisition during social shopping, while hedonic value
comes from the excitement and enjoyment caused by social interaction and communications with
others having similar interests. Consumers can increase confidence to form and reinforce their
purchase intention when they perceive a significant shopping value from observational learning.
In our study, consumers who positively perceive the values of social shopping will perceive a
higher self-efficacy. Thus, we develop the following hypotheses.
Hypothesis 5: Consumers‟ perceived utilitarian values will positively influence their
perceived self-efficacy.
Hypothesis 6: Consumers‟ perceived hedonic values will positively influence their
perceived self-efficacy.

31

Attention to Social Shopper and Social Shopper’s Behavior
Perceived values and purchase intentions
Consumers tend to change their behavior through learning. For instance, consumers who
had a bad experience with a restaurant are likely to avoid the restaurant and they also avoid
restaurants with negative reviews or found to be very crowded (Voight, 2007). Empirical results
support such a perspective by demonstrating that perceived value leads to purchase intention
(Chu & Lu, 2007; Dodds, Monroe, & Grewal, 1991) and the spread of information via word of
mouth (Babin, Lee, Kim, & Griffin, 2005; Pihlström & Brush, 2008). Powered by email,
weblogs, chat rooms, and discussion boards, online social communication will help consumers
share interests, experiences, and ideas in the shopping and purchase process. Then, consumers
will organize their shopping behavior through social learning.
Online WOM or “Word-of-Mouse” (online version of WOM) activities are becoming
increasingly important to retailers. Compared to traditional WOM, online WOM is more
influential due to its speed, convenience, one-to-many reach, and its absence of face-to-face
human pressure (Phelps, Lewis, Mobilio, Perry, & Raman, 2004). Typical WOM communication
consists of spoken words exchanged with someone else in a face-to-face situation, while online
WOM involves transmitting personal experiences and opinions through the written word (e.g.,
text messages, web dialogue, blog, email) (Bickart & Schindler, 2001). An advantage of the
written word is that people can seek information at their own pace and within their own time
frame (Bickart & Schindler, 2001). According to Marshall McLuhan, as cited in Griffin (2003),
writing may transmit the information in a more intact manner and make the information appear
more formal.

32

A few studies examine the antecedents and consequences of consumer‟s online WOM
activities. Brown, Barry, Dacin, and Gunst (2005) found that consumers' satisfaction with
purchasing experiences impacts consumer commitment and positive WOM intentions and/or
WOM behaviors. Sun, Youn, Wu, and Kuntaraporn (2006) identified significant predictors (e.g.,
innovativeness, Internet usage, and Internet social connection) and consequences (e.g., online
forwarding and chatting) of online WOM in the context of music-related communication. Also,
people share feelings and information (i.e., opinion acceptance, interaction intentions, and WOM
intentions) after reading online forum blogs (Huang, chou, & Lin, 2008). Huang et al. (2008)
find affective exchange (e.g., socially-oriented motive) and bandwagon effect (e.g., best sellers,
top-rated products) positively influence interaction intentions (e.g., I would like to participate in
activities held by bloggers) and WOM intentions (e.g., I would recommend good blogs to my
friends and I would forward some interesting articles on blogs to my friends). Usually WOM
communication is explored in terms of the opinion leadership and opinion seeking process.
Opinion leadership and opinion seeking are integral to the construct of WOM (Flynn et al.,
1996) and signal purchase intentions (Bellman, Lohse, & Johnson, 1999; Fong & Burton, 2006).
In our study, consumers who positively perceive the values of social shopping will more
likely demonstrate behavioral purchase intentions. And an active consumer who willingly
participates in WOM activities is one who is buying. Thus, we develop the following hypotheses.
Hypothesis 7: Consumers‟ perceived utilitarian values will positively influence their (a)
online WOM intentions and (b) purchase intentions.
Hypothesis 8: Consumers‟ perceived hedonic values will positively influence their (a)
online WOM intentions and (b) purchase intentions.
Hypothesis 9: Consumers‟ online WOM intentions will positively influence purchase
intentions.

33

Memory of Social Shopper and Social Shopper’s Behavior
Perceived self-efficacy and purchase intentions
Bandura (1997) states that self-efficacy is the most important precondition for
behavioral change, since it determines the initiation of coping behavior. Ajzen (2002) proposed
that a behavior is a function of compatible intentions and perceptions of behavioral control
(conceptually related to self-efficacy) in that perceived behavioral control is expected to
moderate the effect of intention on behavior, such that a favorable intention produces the
behavior only when perceived behavioral control is strong.
Consistent with the Theory of Planned Behavior (TPB) (Ajzen, 1985), Liaw‟s (2002)
study found that Web self-efficacy (a perceived behavioral control element) predicts Internet
users‟ behavior intention. In the context of explaining consumer intention to use online shopping,
Kim and Kim (2005) refined self-efficacy to online transaction self-efficacy as the belief of an
individual in his or her ability to organize and execute certain behaviors necessary for the
consumer to achieve certain objectives in online purchasing (e.g., a successful and satisfactory
transaction) under uncertainty. Also, prior research shows that self-efficacy or self-confidence
increases one‟s WOM communication (Reynolds & Darden, 1971) and one‟s information sharing
behavior (Holbrook & Hirschman, 1982). Consumers who are highly confident about their
purchase and other shopping related decisions are likely to purchase products and initiate
discussions with other consumers and respond to requests from other consumers for market
information. Therefore, consumers who possess a high level of self-efficacy about their shopping
related decisions are likely to have purchase intentions and WOM intentions. Therefore, we
hypothesize the following:
Hypothesis 10: Consumers‟ perceived self-efficacy will positively influence (a) online
WOM intentions and (b) purchase intentions.
34

Figure 4 depicts the proposed hypotheses of the overall relationships between
information- and socially-oriented motives, perceived credibilities of intellectual and social
capital, perceived utilitarian and hedonic values, perceived self-efficacy, online WOM intention,
and purchase intention.

Figure 4. Proposed Hypotheses

Social
Shopping
Motivation
Information
-oriented
Motives

Socially
-oriented
Motives

Social
Shopping
Attention

Social
Shopper‟s
Memory

Perceived
Utilitarian
Value

Social
Shopper‟s
Behavior

+H7a

+H1

+H7b
+H5
+H10a

+H2
Perceived
Self-efficacy

Intellectual
Capital
Credibility

Social
Capital
Credibility

Online
WOM
Intention

+H9

+H3
+H6

+H10b
+H8a

Perceived
Hedonic
Value

+H8b

+H4

Social
Shopping
Resource
Credibility

35

Purchase
Intention

CHAPTER III

RESEARCH METHODS

In this section, we discuss sample selection and data collection procedures, followed by
the questionnaire items that measure each construct of the proposed model. Finally, we present
data analysis methods and procedures to test the proposed model and hypotheses.

Data Collection and Sampling Procedures
We apply online survey research methods to this study of online behavior and Internet
use. We presume the key advantages of Internet-based methodologies, which are good coverage,
good speed, and low cost (Duffy, Smith, Terhanian, & Bremer, 2005). The advantages for online
surveys can be considerable as it is possible to accumulate very large volumes of data in a short
span of time. Regarding the use of new technology, other advantages include that online surveys
allow research to be more visual, flexible, and interactive (Taylor, 2000). Of course, online
survey research methods have several disadvantages, one of which is online survey sampling
issues. Unlike face-to-face surveys, which can be sampled from reasonably comprehensive
databases, online surveys are most often conducted among respondents from a panel who have
agreed to be contacted for market research (Duffy et al., 2005). No simple database of everyone
who is online exists. According to Terhanian (2003), there are three major sampling issues
relating to coverage bias or selection error: first, online surveys can reach only those who are
online; second, they can reach only those who agree to become part of the panel; and, third, not
all those who are invited respond. Despite these limitations, we chose an online survey research

36

method after careful consideration of our research circumstances and developing approaches that
are as robust and representative as possible.
We used Zoomerang.com, a firm that specializes in online marketing research to collect
data for this study. The survey was conducted on a private website administered by the company.
A sample of respondents, drawn from the company‟s online panel, was invited by email to
participate in the survey. The panel members were recruited by online ads or posts (See Figure 5)
and participated voluntarily. Thus, the online survey panel is tailored to meet our specific needs,
offering unsurpassed profiling breadth and depth so we can quickly and easily reach our target
survey panel.
The population for this study is a social shopper who likes to shop online with family or
friends who help him/her make good decisions. Online consumers who join web communities,
such as Facebook, MySpace, etc., frequently ask questions and request information and knowhow. They are also willing to invest considerable time and effort, themselves, to provide
knowledge and valuable insights to others. Our screening process invited social shoppers or
consumers who joined a web community or online social networking to search for and share
information about a product or a service. The ads or posts included a survey invitation with a
short description of the research, information about confidentiality (See Appendix A), and a link
to the websurvey. For completing our survey, participants received an incentive (i.e., 50
ZoomPoints) from Zoomerang market research firm.

37

Figure 5. Recruitment Advertising/Web Greeting

Do you ever search for or share information on hot deals, sales info,
reviews or recommendations with people online?
You are invited to be in a research study on social shopping behavior. We need
your help to trace online consumer trends. Please respond to the survey.
Before beginning the survey, we ask that you read a consent form. Clicking the
Start Survey button confirms that you have read and agree with the consent
form and would like to continue to take the survey.
Start Surv ey !

Measures
All of the measures we used were developed and tested in previous studies. The
competing measures for each construct were carefully studied and compared. Based on the
validity and reliability of the measures in the original study and our own pretest, best appropriate
measures were selected for our study. Some adaptations were made to items to make them
applicable to social shopping environment.
The first part of the survey asked participants to respond to questions about their regular
social shopping activity. The second part of the survey asked questions relating to the
respondent‟s motivations, perceptions, and behavioral intentions of social shopping.
Demographic information was gathered in the final part of the survey.

Information-oriented motives
Research suggests that one of the primary motives for using the Internet is information
seeking (Papacharissi & Rubin, 2000). Patwardhan and Ramaprasad (2005) measured
38

information-oriented motives with eight pre-purchase search items (Cronbach‟s alpha = .93). We
adopted seven of eight pre-purchase search items from Patwardhan and Ramaprasad (2005) and
developed three items based on the literature to extend the measure to ten items applicable to
information search using other consumers‟ reviews or recommendations. Participants indicated
the likelihood that they would seek social shopping to search/share reviews and
recommendations with others about a product, a service, or a company on the Internet in the
following types of situations: to check out the best deals, to look for product information that is
specific to my requirements, etc. To be consistent with other scales used in this study, we
modified the 5-point Likert scale to a 7-point Likert scale (1=”very unlikely” to 7=”very likely”).

Socially-oriented motives
With the growing importance of social interaction on the Internet in today‟s retail
environment, researchers measure socially-oriented motives. The desire to get connected and
share ideas with others is identified as the dominant motivation for sending pass-along emails
(Phelps et al., 2004). Individuals who engage in online WOM communication would have a
strong desire to fulfill social needs and experience a sense of virtual community (LaRose, Lai,
Lange, Love, & Wu, 2005).
Arnold and Reynolds‟s (2003) measure of social shopping motives included enjoyment
of shopping with friends and family, shopping with friends and family as a social occasion, and
socializing and bonding with others while shopping. We modified four items (Cronbach‟s
alpha=.85) from Arnold and Reynolds (2003) and add six items based on Tauber‟s (1972) social
motive statements for a total of ten items, measuring social shopping motives. Participants
indicated on a 7-point Likert scale (1=”very unlikely” to 7=”very likely”) how likely it is that

39

they would seek social shopping to search/share reviews and recommendations with others about
a product, a service, or a company on the Internet in the following situations: to enjoy socializing
with others when I shop online, to communicate with other people who share similar shopping
experiences, etc.

Perceived credibility of intellectual capital
There is no single appropriate indicator of credibility. To assess credibility, we
developed a second-order factor and tested whether a set of social shopping resources (i.e., CGC
relevance, clarity, and expertise) load on an overall construct that was termed “perceived
credibility of intellectual capital.” We adopted five items from Mishra, Umesh, and Stem‟s
(1993) information relevance scale (Cronbach‟s alpha=.94) to measure the perception of CGC
relevance. Originally, the scale measured the relevance of information related to alternative
products as part of a decision task, using a seven-point summated rating scale. To evaluate the
perception of CGC clarity, we used 5C criteria (i.e., clear, correct/accurate, concise, complete,
consistent) for assessing information quality of communication (Claxton & McDougall, 2000).
To measure a consumer‟s assessment of a specified person‟s expertise as a source of information
about a particular product, we used four items from Netermeyer and Bearden‟s (1992) expertise
of an information source scale (Cronbach‟s alpha=.94). The original scale used a seven-point
semantic differential scale.
To be consistent with other scales, we modified the original response format to a 7-point
Likert type scale for the fourteen intellectual capital items (1="strongly disagree" to 7="strongly
agree"). Respondents indicated their view of the product information they received when they are
in a social shopping environment. For example, one item measuring perception of CGC

40

relevance asked the respondents to indicate their level of agreement with the following
statement: I consider other consumers‟ reviews or recommendations provided in a social
shopping environment to be meaningful. One example of a CGC clarity item was the following
statement: I consider other consumers‟ reviews or recommendations provided in a social
shopping environment to be concise. An item measuring of the expertise of the CGC provider in
a social shopping environment is the following statement: I consider other consumers who
provide information to be knowledgeable.

Perceived credibility of social capital
The credibility perception of social capital was evaluated by a second-order factor. It
tested whether a set of social shopping resources (i.e., bridging and bonding) load on an overall
construct that was termed “perceived credibility of social capital.” Five of ten items from
Williams‟s (2006) online bridging scale (Cronbach‟s alpha= .90) by were used for the study. The
five items excluded were specifically designed for the original study and not applicable to our
study. We adapted the items to the social shopping context. Participants indicated the response
that reflected their view of the interaction relationship between people in a social shopping
environment. For example, they were asked to respond the following statement:
Searching/sharing product information with others gives me new people to talk to.
To measure the credibility perception of online bonding social capital, we used three
items (Cronbach‟s alpha= .84) from Williams‟s (2006) ten bonding scale items as appropriate for
our study. The concept was measured by questions about whether or not people trust others to
help them solve problems, have someone to turn to for advice, and have someone to go to with
personal problems in a social shopping environment. All items from Williams (2006) were a 5-

41

point response format. So to be consistent with other scales used in our study, we modified the 5point Likert scale to a 7-point Likert scale.

Perceived social shopping value
According to Woodruff and Gardial (1996), customer perceived values are the
customers‟ perceptions of what they want to have happen in a specific kind of use situation and
to accomplish a desired purpose or goal. To measure perceived utilitarian values of social
shopping, we used nine items of Wang and Benbasat‟s (2005) perceived usefulness of virtual
advisor measure (Cronbach‟s alpha= .90) and we used four hedonic shopping value items
(Cronbach‟s alpha= .85) from Paridon (2005). Perceived values were measured by asking the
respondents to indicate their level of agreement with items, such as the following: using other
consumers‟ reviews or recommendations as shopping advice enhance my effectiveness in finding
suitable products; using other consumers‟ reviews or recommendations as shopping advice is
truly a joy. We used a 7-point Likert scale (1="strongly disagree" to 7="strongly agree") was
used as a response format.

Perceived self-efficacy
Self-efficacy is commonly understood as being very specific; that is, one can have more
or less firm self-beliefs in different domains or particular situations. Social shopping self-efficacy
perception describes individuals' judgments of their ability to shop online to reduce
uncertainty/risk and achieve good-quality decision-making. We used Widing and Talarzyk‟s
(1993) three perceived self-efficacy items to measure perception of the decision quality (i.e.,
accuracy, certainty, and confidence) using electronic decision aids (Cronbach‟s alpha= .81). The

42

scales were modified appropriately to the social shopping situation. For example, perceived selfefficacy was measured by asking the respondents to indicate how their feeling of using other
consumers‟ reviews and recommendations as shopping advice affects the accuracy of their
decisions (1=”greatly decreased decision accuracy” to 7=”greatly improved decision accuracy”).

Online WOM intention
We examined behavioral intentions to include online WOM intention and purchase
intention as a consequence/outcome of social shopping. To measure online WOM intention, we
adapted four out of six items of an online forwarding scale (Cronbach‟s alpha= .89) and four out
of five items of an online chatting scale (Cronbach‟s alpha= .91) from Sun et al. (2006) as
appropriate for our study. Online WOM intention was measured by asking the respondents to
indicate the level of agreement or disagreement with statements about information sharing
related to interesting products or shopping experiences with others while social shopping online
(1="strongly disagree" to 7="strongly agree").

Purchase intention
To measure purchase intention as an outcome of social shopping, we used and modified
two of five willingness to buy items from Dodds et al. (1991) (Cronbach‟s alpha= .96). We
developed and included five more items: three items for in-store purchase intention and two
items for placing a product in an online shopping basket or wish list which remains there until a
consumer purchases or removes the item. Participants indicated their level of agreement or
disagreement of a purchase intention for a product that was reviewed, rated, or recommended by
other consumers while social shopping online (1="strongly disagree" to 7="strongly agree").

43

Based on the measurement specifications, the measurement model was proposed (See
Figure 6).

Figure 6. Proposed Structural Equation Model for Measurement

d1
Information
-oriented
Motives

Perceived
Utilitarian
Value

Socially
-oriented
Motives

d3

Expertise
Bridging
Bonding

Intellectual
Capital
Credibility
Social
Capital
Credibility

Online
WOM
Intention

1
Perceived
Self-efficacy

Relevance
Clarity

d4

Perceived
Hedonic
Value
d2

44

Purchase
Intention
d5

CHAPTER IV

RESULTS OF ANALYSES

We present research findings from statistical analysis of the collected data and to
interpret and discuss the findings in this chapter. First, we discuss sample characteristics and
preliminary analyses. We conducted factor analysis in preliminary analyses before assessing the
reliabilities of constructs. Following the recommendations by Anderson and Gerbing (1988), we
used a two-step approach to structural equation modeling. The first step, CFA was employed to
evaluate the reliability and validity of the measures. In the second step, we tested the proposed
hypotheses with structural equation modeling methods. Finally, a discussion of findings
concludes this chapter.

Demographic Characteristics of the Sample
Online consumer panel members from Zoomerang market research were invited to
participate in the research survey. Six hundred twenty five panel members participated and were
asked to answer a pre-screening question (“Have you ever searched for or shared hot deals, sales
information, reviews or recommendations with others about a product, a service, or a company
on the Internet?”). Four hundred three respondents answered “Yes” to the screening question and
completed the survey, yielding a response rate of 64.5%.
Frequency statistics provided sample characteristics of the study (See Table 2). Total
sample demographics indicate that the majority (73.8%) of the respondents are aged 31 to 60.
More males (51.2%) than females responded and over half of the sample was married (55.4%).

45

About 76% of the respondents were White/Caucasian, followed by Asian/Pacific Islander (7.4%),
Black/African-American (6.0%), and Hispanic/Latino(a) (4.2%). 35% of the respondents hold a
bachelor‟s degree and nearly 70% had a college/university degree or higher educational
background; respondents with a high school degree or below constituted only about 10% of the
sample. 44% of the sample reported that their annual household income was $50,000 or below
and 56% had an annual income of $50,001 and above.
According to U.S. Census Bureau‟s national statistics for Internet usage, 45-to-64-yearolds made up the largest group of affluent Internet users, in terms of age ("Current Population
Survey", 2009). Next highest in number were 18-to-34-year-olds, followed by older users. For
the Internet usage by race and ethnicity, Whites and Asians led all other groups, followed by a
grouping of Black, Native Americans, and Hispanics. About 55% of the users over 25 years had
some college degree of higher education. Users‟ income ranged from the highest percentages by
those persons who are most affluent (with annual households income of at least $150,000) to the
lowest percentages by those with $15,000 annual households income or less ("Current
Population Survey", 2009). Thus, compared to the general population of Internet use in the U.S.,
our sample has a slightly higher education level and a lower income.

46

Table 2. Demographic Characteristics of the Sample
Characteristics
Age

Gender
Marital Status

Ethnicity

Education

Income

Frequency Valid %
18~20
21~30
31~40
41~50
51~60
61~70
≥71
Male
Female
Single
Married
Divorced/Separated
Widowed
Other
White/Caucasian
Black/African-American
Hispanic/Latino(a)
Asian/Pacific Islander
Other
Less than high school
High school/GED
Some college
2-year college degree (Associates)
4-year college degree (BA, BS)
Master‟s degree
Doctoral degree
Professional degree (MD, JD)
Other
≤ $10,000
$10,001 ~$ 20,000
$20,001 ~$ 30,000
$30,001 ~$ 40,000
$40,001 ~$ 50,000
$50,001 ~$ 60,000
$60,001 ~$ 70,000
$70,001 ~$ 80,000
$80,001 ~$ 90,000
$90,001 ~$ 100,000
≥ $100,001

47

1
62
118
85
77
26
10
197
188
103
216
52
11
8
306
24
17
30
9
4
36
79
45
138
55
14
15
4
12
18
33
50
41
24
26
38
17
33
59

0.3
16.4
31.1
22.4
20.3
6.9
2.6
51.2
48.8
26.4
55.4
13.3
2.8
2.1
75.9
6.0
4.2
7.4
2.2
1.0
9.2
20.3
11.5
35.4
14.1
3.6
3.8
1.0
3.4
5.1
9.4
14.2
11.7
6.8
7.4
10.8
4.8
9.4
16.8

In addition to the sample‟s demographic characteristics, frequency statistics provide an
overview of participants‟ information seeking behavior including the sample‟s involvement and
activity in searching for or sharing information with others on hot deals, sales info, reviews or
recommendations about a product, a service, or a company on the Internet (See Appendix C).
From these findings, we can describe social shoppers and how they behave when shopping. Their
average interest in searching/sharing information was 5.4 (most of the time) and the mean of
their information search/sharing activity was 5.5 (often or most often searched). About 94% of
respondents said they „often, most often, or regularly‟ search for or share information at online
retailers‟ sites (e.g., Amazon.com, Drugstore.com), followed by consumer review sites (e.g.,
Epinions.com) (69%), social networking weblogs (e.g., Facebook.com, Twitter.com) (63%), and
social shopping sites (e.g., Kaboodle.com) (51%). When participants shop online, they „often,
most often, or regularly‟ search for or search reviews or recommendations posted by others at the
following situations: shopping for themselves (64%) and shopping for another person (67%). For
the question regarding product or service categories, three-quarters of respondents searched for
or shared information for electronics, followed by books (60%), computer/computer hardware
(58%), restaurants (58%), travel (57%), clothing (54%), computer software (47%), automotives
(33%), information goods (e.g., data, newspaper) (25%), insurance (25%), and financial (23%).

48

Preliminary Analyses
Most of measures in this study have a large number of observed variables for each latent
(unobservable) variable. Before conducting Anderson and Gerbing‟s (1988) two-step approach,
we used SPSS 16 to conduct factor analysis to identify a small number of factors that explain
most of the variance observed in a much larger number of manifest (observed) variables (See
Table 3). In the first factor analysis, all measure latent variables (except two variables: perceived
credibility of intellectual capital and perceived credibility of social capital) loaded highly
(between .659 and .943) onto one and only one factor. Factor analysis entered fourteen items of
perceived credibility of intellectual capital and extracted four factors (five items of relevance,
five items of clarity, three items of expertise, and one item (competent) of expertise); the
competent item was excluded from further analysis because of the low factor loading. Factor
analysis entered eight items of perceived credibility of social capital and extracted two factors
(five items of bridging and three items of bonding). These factor extraction results were expected
on the basis of pre-established theory as discussed in Chapter 3. As all items had significant
loadings, we proceeded to complete the analysis with these factors.
After performing factor analysis, the reliability of each multi-item scale was tested using
Cronbach‟s alpha coefficient (See Table 3). The results show that high reliabilities (s ranged
from .90 to .97) are achieved for all scales. As the widely-accepted social science cut-off for
Cronbach‟s alpha is .70 or higher for a set of items in a scale (Nunnally & Bernstein, 1994), the
results indicate satisfactory internal consistency. We submitted a total of 72 observed variables to
the next step of analysis, CFA.

49

Table 3. Measurement Properties
Factor
% of
α
Loading Variance

Items

Information-oriented Motives
I engage in online social shopping…
IM1. to check out the best deals.
IM2. to search for detailed information about the product/brand
category.
IM3. to compare several products/brands online before making a
decision.
IM4. to check out websites for sales and service information.
IM5. to look for product information that is specific to my
requirements.
IM6. to check out company information online for products I
would like to buy.
IM7. to look for online discounts and bargains.
IM8. to hear about something new and learn more about it.
IM9. to seek advice and solutions for my problems with a product.
IM10. to get a variety of information from people who have
positive and negative opinions.
Socially-oriented Motives
I engage in online social shopping…
SM1. to shop online with others as a way to socialize.
SM2. to enjoy socializing with others when I shop online.
SM3. to shop online with others having a social occasion.
SM4. to shop online with others as a way to have a bonding
experience.
SM5. to shop online with others who have similar tastes/interests.
SM6. to shop online with my peer group or reference group.
SM7. to communicate with other people who share similar
shopping experiences.
SM8. to achieve a sense of belonging by shopping for the same
products and brands that others purchase.
SM9. to observe what others are buying and using.
SM10. to purchase those products or brands that I think others will
approve of.
Perceived Credibility of Intellectual Capital
Relevance
I consider other consumers‟ reviews or recommendations
provided in a social shopping environment to be ________.
ICR1. relevant
ICR2. meaningful
ICR3. important
50

Mean
(Std.
Dev.)

69.607

.95

5.49
(1.190)

79.523

.97

3.91
(1.506)

82.722 .95

5.33
(1.150)

.812
.872
.880
.873
.870
.837
.813
.825
.772
.783

.913
.914
.935
.925
.874
.913
.846
.918
.789
.880

.795
.691
.763

Table 3 (Cont‟d). Measurement Properties
ICR4. useful
ICR5. helpful
Clarity
I consider other consumers‟ reviews or recommendations
provided in a social shopping environment to be ________.
ICC1. clear
ICC2. correct/accurate
ICC3. concise
ICC4. complete
ICC5. consistent
Expertise
I consider other consumers who provide information in a
social shopping environment to be ________.
ICE1. knowledgeable
ICE3. expert
ICE4. experienced
Excluded item:
ICE2. competent
Perceived Credibility of Social Capital
Bridging
In a social shopping environment, searching/sharing
product information with others…
SCR1. makes me feel like part of a larger community.
SCR2. makes me feel connected to the bigger picture.
SCR3. reminds me that everyone in the world is connected.
SCR4. gives me new people to talk to.
SCR5. gives me new people to contact with all the time.
Bonding
In a social shopping environment,
SCO1. there are several people online I trust to help solve my
problems.
SCO2. there is someone online I can turn to for advice about
making very important decisions.
SCO3. there is someone online that I feel comfortable talking to
about personal problems.
Perceived Utilitarian Value
Social shopping that provides interactive product
information and uses other consumers‟ reviews or
recommendations as shopping advice…
UV1. enables me to find suitable products more quickly.
UV2. improves the quality of analysis and searching I perform to
find suitable products.
51

.876
.839

82.155

.95

4.78
(1.228)

83.645

.90

4.85
(1.194)

84.913

.96

4.22
(1.506)

87.413

.93

4.17
(1.524)

79.799

.97

5.13
(1.170)

.659
.715
.802
.757
.769

.671
.748
.711

.818
.862
.825
.672
.715

.824
.804
.838

.911
.911

Table 3 (Cont‟d). Measurement Properties
UV3.
UV4.
UV5.
UV6.

makes the search task for products easier to complete.
enhances my effectiveness in finding suitable products.
gives me more control over the product search task.
allows me to accomplish more analysis than would
otherwise have been possible.
UV7. greatly enhances the quality of my judgments.
UV8. conveniently supports all the various types of analysis
needed to find suitable products.
UV9. overall, I find reviews or recommendations by other
consumers useful in finding suitable products.
Perceived Hedonic Value
Social shopping that provides interactive product
information and uses other consumers‟ reviews or
recommendations as shopping advice…
HV1. is truly a joy.
HV2. truly feels like an escape.
HV3. is enjoyable for its own sake, not just for the items I may
have purchased.
HV4. is a good time because I am able to act on „the spur-of-themoment.‟
Perceived Self-Efficacy
SE1. How do you feel the use of other consumers‟ reviews and
recommendations as shopping advice affects the
accuracy of your decisions?
SE2. How do you feel the use of other consumers‟ reviews and
recommendations as shopping advice affects the degree
of certainty of your decisions?
SE3. How do you feel the use of other consumers‟ reviews and
recommendations as shopping advice affects the amount
of confidence of your decisions?
Online Word-of-Mouth Intention
While social shopping online,
WOM1. I would like to share some information about interesting
products or shopping experiences with others.
WOM2. I would like to share my favorite products or shopping
experiences with others.
WOM3. People ask me for my opinions about products and
shopping.
WOM4. I would like to post reviews or recommendations to
persuade others to buy the products/services that I like.
WOM5. I would like to use the “share this” or “buzz” function in a
website when I find interesting products.
52

.920
.912
.881
.878
.874
.876
.875

84.564

.94

4.33
(1.520)

88.292

.93

5.16
(1.060)

75.142

.95

4.75
(1.413)

.898
.934
.925
.921

.940

.943

.935

.900
.901
.787
.868
.887

Table 3 (Cont‟d). Measurement Properties
WOM6. I would like to forward information about interesting
products or shopping experiences to my friends.
WOM7. I would like to forward positive reviews on a product to
my friends.
WOM8. I would like to forward negative reviews on a product to
my friends.
Purchase Intention
When social shopping online, I would…
PI1. consider buying the product.
PI2. buy the product online.
PI3. add the product to my wishlist.
PI4. add the product to shopping basket.
PI5. try the product in a store.
PI6. buy the product if I happened to see it in a store.
PI7. actively seek out the product in a store in order to purchase it.

.879
.894
.811

71.601

.93

.868
.854
.790
.888
.810
.864
.845

5.11
(1.130)

Confirmatory Factor Analysis
The proposed measurement model in our study involves fourteen multi-item constructs:
two of the constructs developed by a second-order factor representing perceived credibility of
intellectual capital from three first-order factors (relevance, clarity, and expertise) and perceived
credibility of social capital from two first-order factors (bridging and bonding) and seven firstorder factors (information-oriented motives, socially-oriented motives, perceived utilitarian value,
perceived hedonic value, perceived self-efficacy, online word-of-mouth intention, and purchase
intention). According to the guidelines offered by Anderson and Gerbing (1988), CFA was
performed for fourteen constructs using AMOS 4 (See Figure 6) to assess the measurement fit of
the constructs with multiple indicators. The CFA results were reported in Table 4. All estimated
factor loadings in the measurement model showed high factor loading coefficients and
significant t-values (all loadings >.705, p<0.001).

53

The overall fit of the measurement model was assessed. Even though the chi-square test
2

2

was significant (χ = 4368.268, df= 2379, p<0.001, χ /df=1.836), indicating that the proposed
model failed to fit the data, other fit indices were considered because chi-square test tends to be
sensitive to sample size (Bollen, 1989). Comparative Fit Index (CFI = .94), Bentler-Bonett NonNormed Fit Index (NNFI = .94), and Root Mean Square Error of Approximation (RMSEA
=.046) indicated a good fit of the model to the data.

Figure 7. Confirmatory Factor Analysis

e1

IM1

e2

IM2

e3

IM3

e4

IM4

e5

IM5

e6

IM6

e7

IM7

e8

IM8

e9

IM9

e10

IM10

e11

SM1

e12

SM2

e13

SM3

e14

SM4

e15

SM5

e16

SM6

e17

SM7

e18

SM8

e19

SM9

e20

SM10

Information
-oriented
Motives

Socially-oriented
Motives

54

Figure 7 (cont‟d). Confirmatory Factor Analysis

e21

ICR1

e22

ICR2

e23

ICR3

e24

ICR4

e25

ICR5

e26

ICC1

e27

ICC2

e28

ICC3

e29

ICC4

e30

ICC5

e31

ICE1

e32

ICE3

e33

ICE4

e34

SCR1

e35

SCR2

e36

SCR3

e37

SCR4

e38

SCR5

e39

SCO1

e40

SCO2

e41

SCO3

e42

UV1

e43

UV2

e44

UV3

e45

UV4

e46

UV5

e47

UV6

e48

UV7

e49

UV8

e50

d1

UV9

Relevance

d2

Clarity

Intellectual
Capital
Credibility

d3
Expertise

Bridging

d4

Social
Capital
Credibility

Bonding

d5

Perceived
Utilitarian Value

55

Figure 7 (cont‟d). Confirmatory Factor Analysis

e51

HV1

e52

HV2

e53

HV3

e54

HV4

e55

SE1

e56

SE2

e57

SE3

e58

WOM1

e59

WOM2

e60

WOM3

e61

WOM4

e62

WOM5

e63

WOM6

e64

WOM7

e65

WOM8

e66

PI 1

e67

PI 2

e68

PI 3

e69

PI 4

e70

PI 5

e71

PI 6

e72

PI 7

Perceived
Hedonic Value

Perceived
Self-Efficacy

Online WOM
Intention

Purchase
Intention

56

Table 4. Results of Confirmatory Factor Analysis
Parameters
IM1  Info-oriented Motives
IM2  Info-oriented Motives
IM3  Info-oriented Motives
IM4  Info-oriented Motives
IM5  Info-oriented Motives
IM6  Info-oriented Motives
IM7  Info-oriented Motives
IM8  Info-oriented Motives
IM9  Info-oriented Motives
IM10  Info-oriented Motives
SM1  Soc-oriented Motives
SM2  Soc-oriented Motives
SM3  Soc-oriented Motives
SM4  Soc-oriented Motives
SM5  Soc-oriented Motives
SM6  Soc-oriented Motives
SM7  Soc-oriented Motives
SM8  Soc-oriented Motives
SM9  Soc-oriented Motives
SM10  Soc-oriented Motives
ICR1  Relevance
ICR2  Relevance
ICR3  Relevance
ICR4  Relevance
ICR5  Relevance
ICC1  Clarity
ICC2  Clarity
ICC3  Clarity
ICC4  Clarity
ICC5  Clarity
ICE1  Expertise
ICE3  Expertise
ICE4  Expertise
Relevance  Intellectual Capital
Clarity  Intellectual Capital
Expertise  Intellectual Capital
SCR1  Bridging
SCR2  Bridging
SCR3  Bridging
SCR4  Bridging
SCR5  Bridging

Std.
Std.
Estimates Error
0.784
0.858
0.880
0.866
0.856
0.814
0.778
0.801
0.705
0.738
0.900
0.900
0.916
0.916
0.846
0.900
0.818
0.915
0.756
0.868
0.897
0.920
0.908
0.859
0.833
0.897
0.920
0.806
0.921
0.875
0.907
0.818
0.880
0.895
0.934
0.952
0.922
0.904
0.871
0.914
0.923

0.054
0.052
0.050
0.051
0.056
0.042
0.056
0.058
0.055
0.025
0.026
0.032
0.037
0.032
0.039
0.032
0.039
0.037
0.036
0.038
0.039
0.039
0.038
0.043
0.043
0.045
0.046
0.039
0.055
0.050
0.026
0.034
0.036
0.038
57

T-value P-value
19.441
20.071
19.774
19.427
17.429
20.915
17.763
15.143
16.074
38.528
37.455
29.902
23.701
28.533
21.377
29.702
19.809
26.043
29.260
28.932
24.736
23.128
27.206
21.020
25.026
23.517
22.638
26.474
20.036
20.963
36.925
26.422
27.892
26.434

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

Construct Variance
Reliability Extracted
.993

.933

.996

.962

.992

.963

.991

.959

.988

,964

.987

.961

.994

.969

Figure 4 (cont‟d). Results of Confirmatory Factor Analysis
SCO1  Bonding
0.882
SCO2  Bonding
0.917
0.033
SCO3  Bonding
0.874
0.044
Bridging Social Capital
0.962
Bonding Social Capital
0.952
0.039
UV1  Utilitarian Value
0.900
UV2  Utilitarian Value
0.897
0.029
UV3  Utilitarian Value
0.904
0.035
UV4  Utilitarian Value
0.895
0.036
UV5  Utilitarian Value
0.858
0.041
UV6  Utilitarian Value
0.841
0.041
UV7  Utilitarian Value
0.849
0.040
UV8  Utilitarian Value
0.862
0.039
UV9  Utilitarian Value
0.860
0.039
HV1  Hedonic Value
0.869
HV2  Hedonic Value
0.907
0.043
HV3  Hedonic Value
0.896
0.043
HV4  Hedonic Value
0.896
0.042
SE1  Self-Efficacy
0.917
SE2  Self-Efficacy
0.905
0.034
SE3  Self-Efficacy
0.901
0.035
WOM1  WOM Intention
0.891
WOM2  WOM Intention
0.881
0.028
WOM3  WOM Intention
0.742
0.044
WOM4  WOM Intention
0.861
0.038
WOM5  WOM Intention
0.877
0.038
WOM6  WOM Intention
0.850
0.038
WOM7  WOM Intention
0.854
0.039
WOM8  WOM Intention
0.752
0.042
PI1  Purchase Intention
0.875
PI2  Purchase Intention
0.867
0.035
PI3  Purchase Intention
0.778
0.051
PI4  Purchase Intention
0.869
0.040
PI5  Purchase Intention
0.714
0.058
PI6  Purchase Intention
0.784
0.053
PI7  Purchase Intention
0.745
0.057
d1
0.248
0.029
d2
0.176
0.031
d3
0.114
0.025
d4
0.170
0.045
d5
0.183
0.040
χ2= 4368.268, df=2379, p<0.001, χ2/df=1.836
CFI=.94, NNFI=.94, RMSEA=.046
58

31.666
23.979
23.184
33.808
28.769
27.998
25.374
24.237
24.738
25.664
25.538
26.170
25.522
25.492
29.233
28.938
36.223
18.562
24.422
25.469
23.509
23.186
18.665
28.703
19.097
25.846
15.220
17.458
16.693
8.578
5.744
4.656
3.819
4.589

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

.989

.969

.990

.979

.995

.958

.990

.961

.991

.973

.994

.955

.991

.939

-

-

The quality of data was evaluated in terms of convergent validity, discriminant validity,
and reliability (Kline, 1998). Referring to Table 4, all the indicators were statistically significant
for the proposed constructs. Because no indicators had loadings so low that they should be
deleted and the model re-estimated, the SEM-based composite reliability and variance extracted
estimates for each construct need to be computed. Table 4 contains the computations for both the
composite reliability and the variance extracted estimates. In terms of reliability, all constructs
exceed the acceptable level of .70. In terms of variance extracted, all constructs exceed the
threshold value of .50 (Fornell & Larcker, 1981). Thus, all scales achieved satisfactory internal
consistency.
Convergent validity was assessed by the significance of the factor loadings (Anderson &
Gerbing, 1988). All factor loading values for each individual indicator to its respective latent
variable were higher than .70 and significant at the .001 level. These results support that the
measured items robustly represented the underlying constructs, showing strong evidence of
convergent validity (Bollen, 1989; Kline, 1998).
Discriminant validity refers to the degree to which extracted factors measured by
different sets of indicators falling within the same latent construct are distinguished from one
another (Bollen, 1989; Kline, 1998). One test which is useful to determine whether constructs are
significantly different is Bagozzi, Yi, and Phillips‟s (1991) discriminant validity test (Confidence
Interval = [parameter estimate (phi value) ± 1.96] * standard error). Discriminant validity is
achieved when the confidence interval (CI) of the parameter estimate is less than 1.0 (Anderson
& Gerbing, 1988). Since the CIs of the parameter estimates of the nine latent variables are less
than 1.0, the discriminant validity is achieved (See Table 5).

59

Table 5. The Phi Values and Confidence Interval for Discriminant Validity

Covariances

Parameter
Estimate

Std.
Error

Info Motives ↔ Soc Motives
Info Motives ↔ Intellectual Capital
Info Motives ↔ Social Capital
Info Motives ↔ Hedonic Value
Info Motives ↔ Utilitarian Value
Info Motives ↔ Self-Efficacy
Info Motives ↔ Purchase Intention
Info Motives ↔ WOM Intention
Soc Motives ↔ Intellectual Capital
Soc Motives ↔ Social Capital
Soc Motives ↔ Hedonic Value
Soc Motives ↔ Utilitarian Value
Soc Motives ↔ Self-Efficacy
Soc Motives ↔ Purchase Intention
Soc Motives ↔ WOM Intention
Intellectual Capital ↔ Social Capital
Hedonic Value ↔ Intellectual Capital
Hedonic Value ↔ Social Capital
Hedonic Value ↔ Self-Efficacy
Hedonic Value ↔ Purchase Intention
Hedonic Value ↔ WOM Intention
Utilitarian Value ↔ Intellectual Capital
Utilitarian Value ↔ Social Capital
Utilitarian Value ↔ Hedonic Value
Utilitarian Value ↔ Self-Efficacy
Utilitarian Value ↔ Purchase Intention
Utilitarian Value ↔ WOM Intention
Self-Efficacy ↔ Intellectual Capital
Self-Efficacy ↔ Social Capital
Self-Efficacy ↔ Purchase Intention
Self-Efficacy ↔ WOM Intention
WOM Intention ↔ Intel_cap
WOM Intention ↔ Social Capital
WOM Intention ↔ Purchase Intention
Purchase Intention ↔ Intellectual Capital
Purchase Intention ↔ Social Capital

0.781
0.725
0.712
0.716
0.948
0.714
0.934
1.014
1.083
2.052
1.698
1.017
0.894
1.033
1.614
1.117
0.998
1.752
0.828
1.017
1.502
0.955
1.078
1.059
0.928
1.061
1.265
0.820
0.931
0.891
1.125
1.177
1.676
1.286
0.922
1.059

0.112
0.082
0.104
0.099
0.095
0.080
0.095
0.116
0.109
0.173
0.155
0.112
0.100
0.113
0.158
0.106
0.098
0.151
0.089
0.103
0.143
0.086
0.109
0.105
0.081
0.093
0.117
0.075
0.096
0.081
0.104
0.110
0.154
0.119
0.085
0.109

60

Confidence
Interval
Low
-0.132
-0.101
-0.130
-0.123
-0.096
-0.100
-0.097
-0.110
-0.096
0.016
-0.041
-0.106
-0.107
-0.105
-0.055
-0.089
-0.094
-0.031
-0.101
-0.097
-0.065
-0.086
-0.096
-0.095
-0.084
-0.084
-0.081
-0.086
-0.099
-0.087
-0.087
-0.086
-0.044
-0.080
-0.088
-0.098

Confidence
Interval
High
0.307
0.220
0.278
0.265
0.276
0.214
0.275
0.345
0.332
0.694
0.567
0.333
0.285
0.338
0.565
0.326
0.290
0.561
0.248
0.307
0.495
0.251
0.331
0.317
0.234
0.281
0.377
0.209
0.278
0.231
0.321
0.345
0.560
0.386
0.245
0.329

Analysis of the Structural Model for Hypothesis Testing
Using a maximum likelihood estimation technique, we conducted SEM analysis to test
the causal relationship between constructs in the proposed model. The results of path analysis
2

2

indicated that the model had a significant chi-square value (χ = 292.612, df= 41, p<0.001, χ /df=
7.137). Although the RMSEA= .124 was slightly higher than would be desirable (RMSEA ≤0.1),
other fit indices met the recommended criteria and showed a satisfactory fit to the data (CFI= .95,
NNFI= .94). Based on these measures, we can conclude that the model is marginally acceptable
due to high RMSEA value. The standardized parameter estimates, standard errors, t-values, and
fit indices are reported in Table 6, hypothesis test results are summarized in Figure 8, and the
covariance matrix for the path analysis is reported in Appendix D.

Relationship between Social Shopping Motivation and Social Shopping Attention
As predicted in H1, the test results indicate that consumers‟ information-oriented
motives positively affect their perceived utilitarian value of social shopping (γ=.27, p<0.001). H2
proposed that consumers‟ socially-oriented motives would have a positive effect on their
perceived hedonic value of social shopping and the results show no significant effect of
consumers‟ socially-oriented motives on their perceived hedonic value (γ=.03, p=0.591, n.s.).
Thus, testing results for the relationship between social shopping motivations and value
perceptions show that hypothesis 1 is supported but hypothesis 2 is not accepted.
H1: Consumers’ information-oriented motives will positively influence their utilitarian
value perceptions in social shopping. [Supported]
H2: Consumers’ socially-oriented motives will positively influence their hedonic value
perceptions in social shopping. [Not Supported]

61

Relationship between Social Shopping Resource Credibility and Social Shopping Attention
H3 addressed a positive relationship between consumers‟ perceived credibility of
intellectual capital and perceived utilitarian value of social shopping. The results show a
significant and positive effect of consumers‟ perceived credibility of intellectual capital on their
perceived utilitarian value (γ=.66, p<0.001). As proposed in H4, consumers‟ credibility
perceptions of social capital have a significant and positive effect on their hedonic value
perceptions (γ=.82, p<0.001). Therefore, two hypotheses regarding the relationships between
credibility perceptions of social shopping resource and perception values are supported.
H3: Consumers’ perceived credibility of intellectual capital will positively influence their
perceived utilitarian value of social shopping. [Supported]
H4: Consumers’ perceived credibility social capital will positively influence their perceived
hedonic value of social shopping. [Supported]

Relationship between Social Shopping Attention and Social Shopper‟s Memory
As predicted in H5, the results in the path analysis show that consumers‟ perceive that
utilitarian values positively affect their self-efficacy (β=.68, p<0.001). Regarding the effect of
perceived hedonic values on self-efficacy, the analysis results show that consumers‟ perceive
that hedonic values positively affect their self-efficacy (β=.11, p<0.05). Thus, both hypothesis 5
and 6, examining the relationships between value perceptions and self-efficacy perception are
supported.
H5: Consumers’ perceived utilitarian values will have a positive influence their perceived
self-efficacy. [Supported]
H6: Consumers’ perceived hedonic values will have a positive influence their perceived
self-efficacy. [Supported]

Relationship between Social Shopping Attention and Social Shopper‟s Behavior

62

H7 proposed that consumers‟ perceived utilitarian values would have a positive effect on
online WOM and purchase intentions. The results indicate that online WOM intentions (H7a:
β=.27, p<0.001) and purchase intentions (H7b: β=.40, p<0.001) are influenced by utilitarian
values. Thus, hypotheses 7a and 7b are supported. Regarding the relationships between
consumers‟ perceived hedonic values and shopping intensions, the significant impacts of
perceived hedonic values on online WOM intentions (H8a: β=.35, p<0.001) and purchase
intentions (H8b: β=.13, p<0.001) are found, and therefore H8a and H8b are supported.
H7: Consumers’ perceived utilitarian values will positively influence their (a) online WOM
intentions [Supported] and (b) purchase intentions. [Supported]
H8: Consumers’ perceived hedonic values will positively influence their (a) online WOM
intentions [Supported] and (b) purchase intentions. [Supported]

Relationship in Social Shopper‟s Behavior
H9 predicted a positive relationship between consumers‟ online WOM intentions and
purchase intentions. The result reveals that a significant and positive impact of online WOM
intentions on purchase intentions (β=.23, p<0.001), supporting hypothesis 9.
H9: Consumers’ online WOM intentions will positively influence purchase intentions.
[Supported]

Relationship between Social Shopping Memory and Shopper‟s Behavior
As predicted in H10, the analysis results show that consumers‟ perceived self-efficacy
positively affects online WOM intentions (H10a: β=.29, p<0.001) and purchase intentions
(H10b: β=.18, p<0.001). Therefore, regarding the effect of perceived self-efficacy on shopping
intentions, hypothesis 10a and 10b are supported.
H10: Consumers’ perceived self-efficacy will positively influence (a) online WOM
intentions [Supported] and (b) purchase intentions. [Supported]

63

Table 6. Results of Path Model Analysis
Parameters
Utilitarian Value  Info-oriented Motives
Hedonic Value  Soc-oriented Motives
Utilitarian Value  Intellectual Capital
Hedonic Value  Social Capital
Self-Efficacy  Utilitarian Value
Self-Efficacy  Hedonic Value
WOM Intention  Utilitarian Value
Purchase IntentionUtilitarian Value
WOM Intention  Hedonic Value
Purchase IntentionHedonic Value
Purchase Intention WOM Intention
WOM Intention  Self-Efficacy
Purchase Intention Self-Efficacy
Measurement Relevance  Intellectual Capital
Model
Clarity  Intellectual Capital
Expertise  Intellectual Capital
Bridging Social Capital
Bonding Social Capital
Covariance
Info Motives ↔ Soc Motives
Info Motives ↔ Intellectual Capital
Info Motives ↔ Social Capital
Soc Motives ↔ Intellectual Capital
Soc Motives ↔ Social Capital
Intellectual Capital ↔ Social Capital
d1 ↔ d2
Disturbance
d1
Variance
d2
d3
d4
d5
χ2= 292.612, df=41, p<0.001, χ2/df=7.137
CFI=.95, NNFI=.94, RMSEA=.124
Structural
Model

64

Std.
Estimates
0.274
0.034
0.655
0.824
0.681
0.113
0.271
0.399
0.352
0.127
0.234
0.288
0.184
0.850
0.919
0.904
0.948
0.908
0.429
0.592
0.429
0.681
0.862
0.787
0.233
0.377
0.625
0.473
0.738
0.386

Std.
Error
0.034
0.063
0.049
0.070
0.037
0.028
0.060
0.045
0.036
0.028
0.036
0.062
0.047
0.046
0.045
0.030
0.097
0.072
0.095
0.095
0.146
0.098
0.028
0.030
0.052
0.033
0.052
0.027

T-value
7.857
0.537
15.922
12.435
16.572
2.739
5.410
8.505
9.002
3.273
5.184
6.139
4.160
25.188
24.448
32.802
7.904
9.539
7.667
10.491
12.704
11.143
4.081
12.767
12.008
14.177
14.177
14.177

Figure 8. Summary of Path Model Analysis Results

d1
Information
-oriented
Motives
Socially
-oriented
Motives

.27***

.66***

Perceived
Utilitarian
Value

Clarity
Expertise
Bridging
Bonding

Social
Capital
Credibility

d4

.40***

.68***

.29***

d3
1
Perceived
Self-efficacy

Relevance
Intellectual
Capital
Credibility

.27***

.03 n.s.

.82***

.11**
Perceived
Hedonic
Value

.35***

.23***

.18***

Purchase
Intention

.13***

d2

*** p<.001, ** p<.01, * p<.05, n.s.=not significant (p>.05)

65

Online
WOM
Intention

d5

DISCUSSION

This study raises questions about the role of social shopping in decision-making and
examines the effects of social shopping motivations and perceptions on consumers‟ shopping
intentions. SLT, SCT, self-efficacy, and social media effect provide theoretical support for the
research. Perceived values influenced by the two factors of social shopping motivation and
credibility perception of the social shopping resource are proposed as antecedents of perceived
self-efficacy. Consumers‟ shopping intentions (online WOM and purchase intentions) are
modeled as consequences of perceived values and self-efficacy.
The results indicate that social shopping motives have a moderately positive effect on
perceived values, while credibility perceptions of social shopping resources have a strong and
positive effect on perceived values. Perceived self-efficacy mediated perceived values and
behavioral intentions. Also we found that social shopper‟s behavioral intentions are directly
affected by perceived values. Overall, the findings support the proposed model in explaining
social shopping behavior and its effect on consumer‟s shopping experience, decision-making,
and behavioral intentions. In the following section, research findings are discussed more
specifically.

Antecedents of Perceived Social Shopping Values
This study proposed a conceptual model of social shopping motivation (informationoriented and socially-oriented motives) and resource credibility (credibility perceptions of
intellectual capital and social capital) as antecedents to social shopping attention (perceived
utilitarian and hedonic values). Based upon the literature, an underlying assumption was that

66

consumers‟ utilitarian values are likely perceived by information-oriented motives and perceived
credibility of intellectual capital; their hedonic values are likely perceived by socially-oriented
motives and perceived credibility of social capital.
Our results indicate that information-oriented motives significantly influence utilitarian
value. Social shopping sites as an information resource help consumers to discover and learn
about products. Consumers are likely to achieve a useful shopping experience when they can
search and acquire information from CGC to become informed about products and trends before
making a purchase decision. Therefore, our findings are consistent with prior studies in the
literature on the relationship between consumers‟ information search motives and value-based
shopping activity (Bei et al., 2004; Bloch et al., 1986; Hirschman & Wallendorf, 1982; Klein,
1998). Furthermore, the empirical results indicate that social shoppers‟ utilitarian values are
influenced by not only information-oriented motives but also perceived credibility of intellectual
capital; Information-oriented motives have a moderately positive effect on perceived utilitarian
value, while perceived credibility of intellectual capital has a stronger positive effect on
perceived utilitarian value. In a social shopping environment, social shoppers are more likely to
perceive usefulness when other consumers‟ reviews or recommendations are relevant and
accurate and when other consumers are knowledgeable and expert.
Unexpectedly, our findings show that perceived hedonic value is not significantly
affected by socially-oriented motives. Consumer‟s motivation to shop with others does not
increase the perceived enjoyment in a social shopping environment. This finding is not consistent
with the literature which reports a positive relationship between consumers‟ social interaction
and communication motives and value-based shopping activity (a fun and entertainment activity)
(Bei et al., 2004; Cohn & Park, 2007; Holbrook & Hirschman, 1982; Kiecker & Hartman, 1993;

67

Rhone, 2007). Although socially-oriented motives have no effect on perceived hedonic value,
perceived credibility of social capital has a significant and positive effect on perceived hedonic
value.
These findings demonstrate that, most importantly, credibility perceptions of social
shopping resources are stronger drivers of perceived social shopping values than social shopping
motivations. This implies that believable CGC information and a trustworthy interaction
relationship in a social shopping environment increase the benefits of social shopping. This is
consistent with the notion that consumers perceive shopping benefits and values when they have
a positive experience with shopping resources, such as useful product information and fun social
interaction (Babin et al., 1994; Hirschman & Wallendorf, 1982; Holbrook & Hirschman, 1982).
This also suggests that the perceived value literature can be applied to a specific social shopping
situation.

The Effects of Perceived Social Shopping Values
The study investigates the effects of consumers‟ perceived values on their self-efficacy
and online WOM and purchase intentions. The results show that perceived utilitarian and
hedonic values significantly influence perceived self-efficacy, online WOM and purchase
intentions. These findings support the important role of perceived values in influencing
perceived self-efficacy and behavioral intentions (Zeithaml, 1988). When consumers effectively
utilize social shopping environment resources and pleasantly receive social supports, their
uncertainty is reduced and they are likely to consider themselves capable of and assured of their
shopping decisions.
Both utilitarian value and hedonic value influence self-efficacy, but utilitarian value is
more influential. A possible explanation of this finding is that social shoppers are highly
68

confident about their shopping related decisions when they heavily rely on the efficiency and
usefulness of the product information rather than the excitement and enjoyment achieved through
social interactions. Utilitarian value is more important than hedonic value in terms of influencing
consumers‟ self-efficacy. Hence, this finding implies that utilitarian values, established by
credibility perceptions of intellectual capital, increase shopper confidence in making purchase
decisions.
Our research shows that there are significant effects of perceived values on online WOM
intentions. Relatively, hedonic value has a stronger influence on online WOM intentions than
utilitarian value. Consumers with high perceived hedonic value are willing to get involved in
WOM activities (online forwarding or online chatting) and are interested in sharing information
on their networks when making a decision. We can interpret this result as indicating that
consumers consider sharing shopping experience to a lesser degree with regard to a utilitarian
value than with a hedonic value.
Our hypotheses about the effect of perceived values on purchase intentions are supported.
Both perceived utilitarian value and perceived hedonic value significantly influence purchase
intentions. These findings are supported by the previous literature, which describes a positive
relationship between consumers‟ perceived value and shopping intention. Utilitarian value has a
relatively stronger effect on purchase intentions than hedonic value. It is consistent with several
studies that demonstrate that Internet shopping intention is mostly driven by utilitarian values
(Alba et al., 1997; Blake, Neuendorf, & Valdiserri, 2005; Keeney, 1999; To, Liao, & Lin, 2007).
Our result reveals that social shoppers‟ purchase intentions are more likely to be determined by
perceived utilitarian value than by perceived hedonic value.
Further investigation reveals that there is an indirect effect of perceived hedonic value on

69

purchase intentions through online WOM intentions. Perceived hedonic value triggers online
WOM intentions and indirectly causes a purchase intention. In other words, shopping enjoyment
generates WOM intention, which in turn generates purchase intention. Taken together, such
findings imply that online forwarding and chatting are the behavioral consequences of perceived
social shopping values and increase purchase intention of social shoppers.

The Relationships among Perceived Self-Efficacy and Behavioral Intentions
The study empirically supports the mediating role of consumers‟ self-efficacy between
their perceived values and behavioral intentions. Consumers‟ self-efficacy built by perceived
values has significant and positive effects on online WOM and purchase intentions. This finding
is consistent with the notion proposed by SCT and TPB (Ajzen, 1985; Bandura, 1997) that selfefficacy predicts people‟s behavioral intention. Thus, the more social shoppers are assertive and
confident online, and they are more likely to share information through online
forwarding/chatting and buy a product than less social shoppers.
In addition, the result indicates that purchase intentions are significantly affected by
online WOM intentions. This finding is supported by prior research examining the effect of
WOM communication on purchase intentions (Bellman et al., 1999; Fong & Burton, 2006). Also
further investigations about the role of online WOM intentions reveal that there are indirect
effects of perceived values and self-efficacy on purchase intentions through online WOM
intentions. In other words, there is a mediating variable (i.e., online WOM intentions) in the
model between perceived values and purchase intentions and between perceived self-efficacy
and purchase intentions. These imply that online WOM behaviors play an important and
influential role in determining purchase behavioral intentions.

70

CHAPTER V

CONCLUSION AND IMPLICATIONS

The purpose of this chapter is to summarize the study discussed in Chapter I through
Chapter IV and report implications from the research findings. We summarize the objectives of
the study, development of conceptual model, research design and methods, and empirical study
findings in the next section. In the following section, we discuss the theoretical and managerial
implications of our findings. Finally, we conclude with a discussion of the study‟s limitations and
directions for future research.

Summary of the Study
With the advent of Web 2.0 which builds on openness, participation, and sharing,
consumers‟ activities and marketers‟ practices have changed significantly as they search for ways
to buy, sell, and communicate with consumers through cyberspace. The role of CGC and online
WOM via social media channels has received much attention recently and it is important because
it can support consumers in making informed decisions and can help marketers increase sales.
Although Internet media use and online shopping studies emphasize that satisfying consumer
demand for CGC and strengthening the power of online WOM are beneficial for both consumers
and marketers, there is still a lack of research in respect to online social shopping. The objectives
of this study are to examine consumers‟ social shopping behavior to assess 1) who social
shoppers are and why they are likely to search for and share CGC with others and 2) how social
shopping motivation and resource credibility influence consumers‟ value perceptions and

71

confidence level in their purchase decision.
Using the underlying tenets of SCT, we developed a conceptual framework to explain
social shopping behavior (See Figure 3). In the model, social shopping motivation and resource
credibility were conceptualized as antecedents to consumers‟ social shopping attention. And
consumers‟ memory was conceptualized as an important mediating variable between their social
shopping attention and behavior. Specifically in Chapter II, we discussed and hypothesized
relationships between 1) social shopping motivation (i.e., information-oriented and sociallyoriented motives), 2) social shopping resource credibility (i.e., intellectual and social capital), 3)
social shopping attention (i.e., perceived utilitarian and hedonic value of social shopping), 4)
social shoppers‟ memory (i.e., perceived self-efficacy in decision making), and 5) social
shoppers‟ behavior (i.e., online WOM and purchase intentions). As a result, the proposed model
investigated the effects of social shopping motives and beliefs about social shopping resources
on consumers‟ value perceptions which can influence self-confidence in decision making and
lead to online WOM buzz and purchase decisions.
Incorporated with the literature study, the survey instrument for the empirical study was
developed and modified to reflect the social shopping environment. The population for this study
was online consumers who join web communities, such as Facebook, MySpace, etc. to search for
or share shopping information. Online survey research methods were used to recruit participants
and collect data from online consumer panels of a websurvey service company. 625 participants
participated and during pre-screening and 403 were qualified to continue the survey; we
achieved a 64.5% response rate.
According to Anderson and Gerbing (1988) guidelines, CFA was conducted for all multi2

item constructs used in the model. The resulting model fit was acceptable (χ = 4368.268, df=

72

2379, p<0.001, CFI= .94, NNFI= .94, RMSEA= .046). To test hypotheses, path analysis was
2

performed and the results of model fit indices indicated a satisfactory fit to the data (χ = 292.612,
df= 41, p<0.001, CFI= .95, NNFI= .94, RMSEA= .124). 12 out of 13 hypotheses were supported
in the proposed model. The results of hypothesis testing are summarized in Table 7. The analysis
results indicated that perceived values were moderately influenced by social shopping motives
and significantly affected by credibility perceptions of informational and social resources. In
addition, the study found that consumers‟ perceived values significantly influenced behavioral
intentions. Perceived self-efficacy was more strongly and significantly influenced by utilitarian
value than hedonic value. The mediating role of perceived self-efficacy between perceived
values and behavioral intentions was found. In general, the findings of this study support the
proposed theoretical model in explaining consumers‟ social shopping behavior. Research
findings were specifically discussed in Chapter IV.

73

Table 7. Summary of Hypothesis Testing
Hypotheses
H1
H2
H3
H4
H5
H6
H7a
H7b
H8a

Consumers’ information-oriented motives will positively influence
their utilitarian value perceptions in social shopping.
Consumers’ socially-oriented motives will positively influence their
hedonic value perceptions in social shopping.
Consumers’ perceived credibility of intellectual capital will positively
influence their perceived utilitarian value of social shopping.
Consumers’ perceived credibility social capital will positively
influence their perceived hedonic value of social shopping.
Consumers’ perceived utilitarian values will have a positive impact on
their perceived self-efficacy.
Consumers’ perceived hedonic values will have a positive impact on
their perceived self-efficacy.
Consumers’ perceived utilitarian values will have a positive impact on
their online WOM intentions.
Consumers’ perceived utilitarian values will have a positive impact on
their purchase intentions.
Consumers’ perceived hedonic values will have a positive impact on
their online WOM intentions.

Results
Supported
Not
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported

H8b

Consumers’ perceived hedonic values will have a positive impact on
their purchase intentions.

Supported

H9

Consumers’ online WOM intentions will positively influence purchase
intentions.

Supported

H10a

Consumers’ perceived self-efficacy will positively influence online
WOM intentions.

Supported

H10b

Consumers’ perceived self-efficacy will positively influence purchase
intentions.

Supported

74

Theoretical Implications
With the growing importance of the role of CGC and WOM in the consumer decisionmaking process, our social shopping behavior study will contribute by extending the most recent
body of the literature in the online consumer behavior and retailing field.
Firstly, by applying Bandura‟s (1988) SCT, this study provides an interesting framework
for understanding social learning, social media, and social shopping. SCT, derived from the
social learning perspective, explains that consumers‟ behaviors are socially learned through
observational learning and vicarious reinforcement. Also it emphasizes the role of self-efficacy
(beliefs in one‟s ability to affect change) in the process of social learning (Bandura, 1988) and
uncertainty reduction (Berger, 1987). Therefore, our study develops the theoretical and
conceptual framework for consumers‟ social shopping behavior that they interactively seek
information with others and achieve self-confidence in purchase decision-making.
Secondly, this study extends the understanding of social shopping behavior regarding
resource credibility developed by intellectual and social capital in a social shopping environment.
Our findings show a strong influence of resource credibility on social shopping attention (i.e.,
perceived values) that lead to an increase social shopping memory (i.e., self-efficacy) and social
shopper‟s behavior (i.e., behavioral intentions). Thus, our study finds that such resource
credibility is a more dominant determinant of social shopping attention relative to social
shopping motivation in the process of social learning. It supports the SCT major view that
consumers are more likely to adopt a modeled behavior if it results in outcomes they value and if
the modeled behavior is convincing.
Thirdly, based on the empirical investigation of online social shoppers, this study
supports that social shopping motivations and credibility perceptions are the key antecedents of

75

perceived values; perceived self-efficacy is a mediator in the effect of perceived values on
behavioral intentions. Our findings indicate that perceived values and perceived self-efficacy
significantly increase online WOM intentions as well as purchase intentions. Overall, the
proposed model ensures the theoretical soundness and coherence of the conceptual model.
Finally, our study advances online shopping literature and social media literature, an
underdeveloped but important research area. By linking social shopping behavior, this study
contributes to the literatures by discovering a powerful factor that makes consumer‟s shopping
more efficient, enjoyable, and confident in a social shopping environment.

Managerial Implications
This study also provides useful implications for practitioners in developing and
implementing marketing strategies of social shopping applications to boost sales, improve
customer loyalty or keep customers engaged by enhancing user experience, activate WOM value,
and differentiate them from the competition. We found that information-oriented motives and
credibility perceptions of informational and social resources ultimately affect consumers‟ value
perceptions that result in self-efficacy and behavioral intentions. These findings imply that
retailers should recognize and utilize social shopping sites to go beyond traditional consumer
reviews. By embracing the inherently social nature of shopping, social shopping can enhance the
consumer shopping experience and thereby drive purchase behavior. Social shopping could
satisfy consumers‟ utilitarian needs in ways similar to traditional web reviews, but go beyond the
traditional realm by satisfying much more hedonic needs from social interactions. By leveraging
social shopping applications, retailers can attract more social shoppers, create credible social
shopping experiences, and maximize their WOM marketing efforts.

76

Retailers should strive to enhance the level of all aspects of information quality and trust
in a social shopping environment. To improve information quality, for example, managers can
introduce and invigorate a CGC rating system that allows virtual community members to
evaluate a CGC provider as an expert or value CGC as knowledgeable or experienced
information. Also, consumers can search relevant product information easily by adding tags that
are carried out to perform functions such as aiding in classification. Based on ratings or tags,
other consumers can employ a highly-rated or highly-relevant CGC as believable information in
maximizing the reduction of pre-purchase uncertainty and the perceived social shopping values.
To build trust in a social shopping environment, retailers should focus on how to improve
shoppers‟ bridging and bonding relationships through the collective voice from friends (people
shoppers know), brand or staff experts, people like me (people who have similar tastes/interests),
unknown users, etc. According to the Nielsen global online consumer survey, 90% of online
consumers trust recommendations from people they know and 70% trust virtual stranger
opinions (The Nielsen Company, 2009). Therefore, it is important for retailers to identify verified
users and present balanced perspectives, opinions, thoughts, and experiences. Also, retailers
should consider a successful connection with a powerful online community/social media/social
networking site that has rich CGC and a trustworthy consumer community, and make the site
link available to consumers.
Retailers need to stimulate and manage CGC and WOM. They should consider the right
product/service reviews and recommendations, regardless of positive and negative opinions, and
use them in improving product/service quality and increasing positive CGC and WOM.
Communication strategists should be interested in online WOM since it has the potential to
activate shoppers‟ interests, choices, and actions. To facilitate the uses of online WOM,

77

strategists need to enhance trust in information and ideas that are shared among web community
members, under the assumption that WOM messages are perceived to have utilitarian and
hedonic value and increase self-efficacy.
We found utilitarian and hedonic value to be significant determinants of self-efficacy
and behavioral intention, therefore practitioners should devote resources to creating websites that
provide the most compelling experience to their current and potential consumers. So, retailer
websites should be designed to include not only elements that help consumers complete their
tasks but also elements that offer fun or playfulness. It is necessarily about making interaction
rewarding and enjoyable to the shopper. For instance, practitioners can develop and test a
website element (e.g., online social cue, such as text, video, picture) that may contribute to a
positive social shopping experience. Practices that add value for shoppers include being
conversational about topics shoppers care about, facilitating communities, offering recipes or
how-tos, and providing education about relevant product or service information.
Retail shopping sites should contain blog communities or forums for discussion of
particular topics. Shoppers can form groups based on similar interests for forum discussions and
to track product discoveries and reviews. Community members can share trends and great
product finds and seek out specific information. These user profiles and group memberships
enable shoppers to receive personalized product recommendations or reviews tailored to their
particular tastes. Also, retailers should manage a technique that can make product-specific
discussion forums more effective. This technique should increase the value of community
members‟ intellectual capital and social capital building within a specific domain. With the
advancements of Internet technology, shopping websites should be able to provide a more
comprehensive Internet shopping environment and bring more enjoyment and convenience to

78

shoppers. Thus, retailers have to test and explore the potential of social commerce (selling with
social media) and integrate it into their online strategy. Also, they should constantly evolve and
adapt social shopping applications and social commerce tools (e.g., reviews/ratings,
forums/communities, purchase sharing, group buy, social apps) to exceed shopper expectations
and bring joy of use with new insights and technology.
Advertising or promoting the expertise or competence of a discussion forum member
can increase the relationship between retailers and their customers and encourage consumers to
purchase. Managers can use their social medium to influence opinion leaders and indirectly sway
consumers who look to opinion leaders for details like product recommendations. In addition,
marketers should know social shopping is about people (not technology) influencing people. So,
focusing on people, particularly „prosumers‟ who have been become the voices of products, can
significantly impact the success or failure of companies, products, particularly through their
involvement in the social network environment. The term „prosumer‟ has transformed from
professional- or producer-consumer to product advocate since prosumers (e.g., bloggers, forum
posters, social networking participants) are the members of the social network sites, who spread
messages, influence people around the world, and drive demand (Tapscott, 1997). Thus,
prosumers are the online influencers that marketers must identify and develop relationships with
in order for their products to thrive.

79

LIMITATIONS AND FUTURE RESEARCH

Before drawing generalizations from these results, we identified several limitations of
our study. In response to the limitations, we suggest several directions for future research.
First, the omission of important variables might have introduced some bias. Social
shopping behavior may be influenced by other factors, such as consumer personality, preference,
and lifestyle, that might influence uncertainty reduction and confidence level, and ultimate
purchase behaviors not accounted for in our study. For example, Arnold and Reynolds (2003)
indicate that females emphasize hedonic value more than males and have stronger hedonic
shopping motivations. In view of that, consumers with different backgrounds (i.e. gender,
education, age, and Internet shopping history) should have different perspectives on utilitarian
and hedonic values. Future research could investigate the differences of shopping motivations
based on different consumer market segments.
Secondly, the sample for the study was collected from online consumer panels whose
members are likely to participate in online surveys. They might be active online users or are
predisposed to garnering positive attitudes toward online activities. It is possible that the majority
of the respondents who participated in this survey had a more positive attitude toward social
shopping or a more pleasant shopping experience than those who did not participate. Hence, the
sample characteristics (e.g., Internet usage) should be taken into consideration when selecting a
sample. A direction for future research could be a comparison of social shoppers and non-social
shoppers. It would be interesting to investigate what the major drivers of shopping value and
self-efficacy for non-social shoppers are and how they are different from social shoppers.

80

Another related future research area is Net Generation behavior. There is significant
potential for growth in the social shopping sphere given that individuals, and particularly the
younger generation, have become increasingly comfortable with the use of technology.
According to Tapscott (1998), Net Geners (aged 20 and under) are surrounded by interactive
digital media (e.g., Internet) and for the most part, are fluent with computers. They are
exceptionally curious, self-reliant, able to adapt, high in self-esteem, and have a global
orientation. Their power is at the heart of the culture of interaction online. It would be interesting
to compare behaviors of Net Geners to those of other generations (e.g., Gen Xers and Baby
Boomers) regarding decision supports and decision making in a social shopping environment.
For example, with respect to interactive learning, whether the Net Geners learn differently than
most Baby Boomers. There might be a generation gap between web-savvy kids and their far less
literate baby boomer parents. The finding may provide significant implications for retailers to
understand and target customers. Thus, the customer segmentation issue provides a promising
focus for further research. In summary, future research on social shopping should identify
specifically 1) who is going to use the decision support through learning from an informational
and social shopping resource, 2) why, when, and how they will use it, and 3) what it is that they
will use exactly.
Thirdly, our respondents were asked to indicate their broad social shopping perceptions
and experiences as opposed to focusing on a specific website. Because participants are exposed
to many and various consumer review sites, online retailers‟ sites, or social media sites, it is
likely that respondents who used several sites may have a less clear or mixed memory about their
shopping experiences. Therefore, the findings cannot be generalized to explain a particular
shopping site experience. Depending on product/service types, information search/seeking

81

behavior and social shopping behavior may differ. In our study, the product/service categories
were not designated. Thus, the findings of this research cannot detect the influence of a specific
product shopping category on behavior. Regarding the limitations, the empirical research design
and method used in the study serve to increase the external validity and generalizability of the
findings, but at the same time, the data analysis could suffer from higher error variances caused
by various shopping categories or site choices. So, it would be interesting to conduct a study that
samples consumers shopping for a specific product type at a specific type of social shopping site
as a moderating factor to examine their shopping experiences.
Lastly, future research needs to investigate alternative explanation for the uncertainty
reduction and decision support. This study focuses on value perception and self-efficacy as key
predictors of behavioral intentions. As discussed previously, value perception and self-efficacy
may be affected by a number of other factors. Some factors could be the effect of familiarity,
prior positive experience, and involvement. Hill and Beatty (2011) found that more involved
adolescent online consumers can be differentiated from less involved online consumers on
shopping motivation, self-efficacy, and behavior. Thus, we can test a moderating effect of
consumers‟ involvement on self-efficacy and social shopping behavior. Incorporating additional
exogenous variables of shopping value and self-efficacy would increase the explanatory power
of these constructs that account for variance in behavioral intentions.
In addition to the call for extending research on the antecedents of social shopping value
and self-efficacy, future research should especially devote attention to the outcomes of social
shopping value and self-efficacy. Our study was limited to considering online WOM and
purchase intentions as the social shopping outcomes, but further research should be conducted to
investigate additional outcomes, such as repatronage intention and impulse buying intention.

82

Discussing a purchase, and sharing/searching information with friends or other consumers who
are present online at the time of purchase is one way an online shopper can reduce uncertainty in
the make or buy decision. While some social shoppers can take advantage of social comparisons
with other people, other social shoppers may struggle for accurate self-observation. LaRose
(2001) examined unregulated online buying, including impulsive and compulsive buying, and
found that ecommerce site features have an impact on self-regulation; product descriptions using
fantasy images, the excitement generated by email alerts of sales, buying circles, on-site chat
rooms, and email exchanges with sales consultants may discourage or encourage self-regulation.

83

APPENDICES

84

APPENDIX A

Survey Consent Form

85

You are invited to be in a research study on social shopping behavior. We ask that you read this form
and ask any questions you may have before agreeing to be in the study.
Purpose of research:
You have been selected as a possible participant in this study because you are an online shopper. The
purpose of this study is to understand how consumers interact, search, share, and shop with others on the
Internet. Based on your responses, this research will help us extend the body of knowledge on online
consumer behavior. The survey will take approximately 20 minutes to complete. Please note that in order
to be eligible to participate in this study, you need to be older than 18 years old.
What you will do:
All you have to do is thinking about your online shopping patterns and responding to the survey.
Potential benefits:
You will not directly benefit from your participation in this study. However, your participation in this
study may contribute to understand online social shopping activity and enhance the customer shopping
experience.
Potential risks:
You are not expected to participate in any treatments that would incur the risk of physical or mental injury
during your participation in this study.
Privacy and confidentiality:
The data for this project are being collected anonymously. All responses will be kept strictly confidential
and reported together with those of other participants so that no individual will be identified in anyway.
Your privacy will be protected to the maximum extent as allowable by law. Data will be analyzed only in
the aggregate so that your name will not be associated with the answers you provide.
Your rights to participate, say no, or withdraw:
Participation is completely voluntary. You have the right to say no. Therefore, you may choose not to
participate and may discontinue participation at any time. Moreover, you can withdraw or refuse to
answer any particular question.
Costs and compensation for being in the study:
For completing the survey, you will receive an incentive from Zoomerang market research firm that you
agree to participate in online surveys.
Contact information for questions and concerns:
If you have any questions about this study, please contact the researchers: Dr. Linda Good (517-355-1282,
goodL@msu.edu, 309 Communication Arts and Science, Michigan State University, East Lansing, MI
48824) or Zee-Sun Yun (517-290-3817, yunzeesu@msu.edu).
If you have questions or concerns regarding your rights as a study participant, or are dissatisfied at any
time with any aspect of this study, you may contact anonymously if you wish, the Michigan State
University‟s Human Research Protection Programs at 517-355-2180, Fax 517- 432-4503, or email
irb@msu.edu or regular mail at 202 Olds Hall, East Lansing, MI 48824.
Documentation of informed consent:
By completing and submitting this online survey you consent to participate in this study. We greatly
appreciate your participation.

86

APPENDIX B

Survey Instrument

87

Social Shopping

Conducting research on consumer behavior toward online social shopping, we seek those who
have searched/shared information of hot deals, sales information, reviews, or
recommendations with others about a product, a service, or a company on the Internet.
Have you ever searched for or shared hot deals, sales information, reviews or recommendations
with others about a product, a service, or a company on the Internet? __Yes __No

If you say “Yes” to the above question, we would like you to complete our survey. This survey
will take approximately 20 minutes to complete. You indicate your voluntary agreement to
participate by completing and submitting this survey. You may discontinue participation at any
time without penalty.
Section I. Social Shopping Activity
Please answer each of the following questions.
How interested are you in searching for or sharing information on
hot deals, sales info, reviews or recommendations with others about a
product, a service, or a company on the Internet?

When you shop online, how often do you search for or share
information on hot deals, sales info, reviews or recommendations
with others about a product, a service, or a company on the Internet?
When you shop online, how often do you search for or share
information on hot deals, sales info, reviews or recommendations
with others about a product, a service, or a company at the following
websites?







All of the
Time

1 2 3 4 5 6 7

Never
Searched

Regularly
Searched

1 2 3 4 5 6 7

Never
Searched

Regularly
Searched

Consumer review sites (e.g., Epinions.com)

1 2 3 4 5 6 7

Online retailers‟ sites (e.g., Amazon.com, Drugstore.com)

1 2 3 4 5 6 7

Social networking weblogs (e.g., Facebook.com, Twitter.com)

1 2 3 4 5 6 7

Social shopping sites (e.g., Kaboodle.com)

1 2 3 4 5 6 7

Others (please specify :) ________________________

1 2 3 4 5 6 7

When you shop online, how often do you search for or share
reviews or recommendations with others about a product, a service,
or a company at the following situations?




None of
the Time

Shopping for yourself
Shopping for another person

Never
Searched

Regularly
Searched

1 2 3 4 5 6 7
1 2 3 4 5 6 7

88

For what product or service categories have you searched for or
shared information on the Internet? (Check all that apply)















__ Computer/Computer hardware
__ Computer software
__ Electronics
__ Automotives
__ Books
__ Clothing
__ Music CDs
__ Information goods (e.g., data, newspaper)
__ Travel
__ Financial
__ Restaurants
__ Insurance
__ Other (specific: _____________________ )

Section II. Social Shopping Motivation
Please indicate the likelihood that you would engage in social shopping to search/share hot deals,
reviews or recommendations with others about a product, a service, or a company on the Internet
in the following situations.
Very
Unlikely

I engage in online social shopping …
















Very
Likely

To check out the best deals.

1 2 3 4 5 6 7

To search for detailed information about the product/brand category.

1 2 3 4 5 6 7

To compare several products/brands online before making a
decision.

1 2 3 4 5 6 7

To check out websites for sales and service information.

1 2 3 4 5 6 7

To look for product information that is specific to my requirements.

1 2 3 4 5 6 7

To check out company information online for products I would like
to buy.

1 2 3 4 5 6 7

To look for online discounts and bargains.

1 2 3 4 5 6 7

To hear about something new and learn more about it.

1 2 3 4 5 6 7

To seek advice and solutions for my problems with a product.

1 2 3 4 5 6 7

To get a variety of information from people who have positive and
negative opinions.

1 2 3 4 5 6 7

To shop online with others as a way to socialize.

1 2 3 4 5 6 7

To enjoy socializing with others when I shop online.

1 2 3 4 5 6 7

To shop online with others having a social occasion.

1 2 3 4 5 6 7

To shop online with others as a way to have a bonding experience.

1 2 3 4 5 6 7

89








To shop online with others who have similar tastes/interests.

1 2 3 4 5 6 7

To shop online with my peer group or reference group.

1 2 3 4 5 6 7

To communicate with other people who share similar shopping
experiences.

1 2 3 4 5 6 7

To achieve a sense of belonging by shopping for the same products
and brands that others purchase.

1 2 3 4 5 6 7

To observe what others are buying and using.

1 2 3 4 5 6 7

To purchase those products or brands that I think others will
approve of.

1 2 3 4 5 6 7

Section III. Social Shopping Resources Credibility
For each item below, please indicate the response that reflects your view of the product
information you receive when you are in a social shopping environment.
I consider other consumers' reviews or recommendations
provided in a social shopping environment to be _______.












Strongly
Agree

relevant

1 2 3 4 5 6 7

meaningful

1 2 3 4 5 6 7

important

1 2 3 4 5 6 7

useful

1 2 3 4 5 6 7

helpful

1 2 3 4 5 6 7

clear

1 2 3 4 5 6 7

correct/accurate

1 2 3 4 5 6 7

concise

1 2 3 4 5 6 7

complete

1 2 3 4 5 6 7

consistent

1 2 3 4 5 6 7

I consider other consumers who provide information in a social
shopping environment to be _____________.






Strongly
Disagree

Strongly
Disagree

Strongly
Agree

knowledgeable

1 2 3 4 5 6 7

competent

1 2 3 4 5 6 7

expert

1 2 3 4 5 6 7

experienced

1 2 3 4 5 6 7

For each item below, please indicate the response that reflects your view of the relationships
between people in an online social shopping environment.
In a social shopping environment, searching/sharing product
information with others…

90

Strongly
Disagree

Strongly
Agree







makes me feel like part of a larger community.

1 2 3 4 5 6 7

makes me feel connected to the bigger picture.

1 2 3 4 5 6 7

reminds me that everyone in the world is connected.

1 2 3 4 5 6 7

gives me new people to talk to.

1 2 3 4 5 6 7

gives me new people to contact with all the time.

1 2 3 4 5 6 7
Strongly
Disagree

In a social shopping environment,





Strongly
Agree

there are several people online I trust to help solve my problems.

1 2 3 4 5 6 7

there is someone online I can turn to for advice about making very
important decisions.

1 2 3 4 5 6 7

there is someone online that I feel comfortable talking to about
personal problems.

1 2 3 4 5 6 7

Section IV. Perceived Social Shopping Value
Please indicate your level of agreement/disagreement with each of the following items.
Social shopping that provides interactive product information
and uses other consumers’ reviews or recommendations as
shopping advice…















Strongly
Disagree

Strongly
Agree

enables me to find suitable products more quickly.

1 2 3 4 5 6 7

improves the quality of analysis and searching I perform to find
suitable products.

1 2 3 4 5 6 7

makes the search task for products easier to complete.

1 2 3 4 5 6 7

enhances my effectiveness in finding suitable products.

1 2 3 4 5 6 7

gives me more control over the product search task.

1 2 3 4 5 6 7

allows me to accomplish more analysis than would otherwise have
been possible.

1 2 3 4 5 6 7

greatly enhances the quality of my judgments.

1 2 3 4 5 6 7

conveniently supports all the various types of analysis needed to
find suitable products.

1 2 3 4 5 6 7

overall, I find reviews or recommendations by other consumers
useful in finding suitable products.

1 2 3 4 5 6 7

is truly a joy.

1 2 3 4 5 6 7

truly feels like an escape.

1 2 3 4 5 6 7

is enjoyable for its own sake, not just for the items I may have
purchased.

1 2 3 4 5 6 7

is a good time because I am able to act on „the spur-of-themoment.‟

1 2 3 4 5 6 7

91

Section V. Perceived Self-Efficacy
Please indicate how you think social shopping affects your decision making by responding to the
following items.



How do you feel the use of other consumers‟ reviews and
recommendations as shopping advice affects the accuracy of your
decisions?

Greatly
decreased
decision
accuracy

Greatly
improved
decision
accuracy

1 2 3 4 5 6 7



How do you feel the use of other consumers‟ reviews and
recommendations as shopping advice affects the degree of
certainty of your decisions?

Greatly
decreased
decision
certainty

Greatly
increased
decision
certainty

1 2 3 4 5 6 7



How do you feel the use of other consumers‟ reviews and
recommendations as shopping advice affects the amount of
confidence of your decisions?

Greatly
decreased
decision
confidence

Greatly
increased
decision
confidence

1 2 3 4 5 6 7

Section VI. Social Shopper’s Behavior
Please think about social shopping and indicate your level of agreement/disagreement with each
of the following items.
Strongly
Disagree

While social shopping online,










Strongly
Agree

I would like to share some information about interesting products
or shopping experiences with others.

1 2 3 4 5 6 7

I would like to share my favorite products or shopping experiences
with others.

1 2 3 4 5 6 7

People ask me for my opinions about products and shopping.

1 2 3 4 5 6 7

I would like to post reviews or recommendations to persuade
others to buy the products/services that I like.

1 2 3 4 5 6 7

I would like to use the “share this” or “buzz” function in a website
when I find interesting products.

1 2 3 4 5 6 7

I would like to forward information about interesting products or
shopping experiences to my friends.

1 2 3 4 5 6 7

I would like to forward positive reviews on a product to my
friends.

1 2 3 4 5 6 7

I would like to forward negative reviews on a product to my
friends.

1 2 3 4 5 6 7

92

Please think about products for which you social shop with others and indicate your level of
agreement/disagreement with each of the following items.
Strongly
Disagree

While social shopping online,









Strongly
Agree

consider buying the product.

1 2 3 4 5 6 7

buy the product online.

1 2 3 4 5 6 7

add the product to my wishlist.

1 2 3 4 5 6 7

add the product to shopping basket.

1 2 3 4 5 6 7

try the product in a store.

1 2 3 4 5 6 7

buy the product if I happened to see it in a store.

1 2 3 4 5 6 7

actively seek out the product in a store in order to purchase it.

1 2 3 4 5 6 7

Section VII. Demographic Information
Please fill in the blank or check the appropriate response for each question about yourself.



Please indicate your gender:
__Male

__ Female



Please state your age (in years): _______



Please check your present marital status:
__ Single, never married
__ Married
__ Divorced/Separated
__ Widowed
__ Other (please specify: ___________ )



Please check the category that most accurately identifies your ethnicity:
__ White/Caucasian
__ Black/African-American
__ Hispanic/Latino(a)
__ Asian/Pacific Islander
__ Other (please clarify: ____________ )

 Please check the category that most accurately identifies your education:
__ Less than high school
__ High school/GED
__ Some college
__ 2-year college degree (Associates)
__ 4-year college degree (BA, BS)

__ Master‟s degree
__ Doctoral degree
__ Professional degree (MD, JD)
__ Other (please specify: __________ )

 Please write approximate total annual household income, before taxes:
$ ___________ per year

93

APPENDIX C

Frequency Statistics for Information Seeking Behavior of the Sample

94

Figure 9. Information Search/Sharing Interest

7=All of the time

6

5

4

3

2

1=None of the
time

Frequency

140
120
100
80
60
40
20
0

Figure 10. Information Search/Sharing Activity
120
80
60
40
20

95

7=Regularly searched

6

5

4

3

2

0

1=Never searched

Frequency

100

Figure 11. Information Search/Sharing at a Certain Website
160
140
120

Consumer review sites

Frequency

100
80

Online retailer sites

60

Social networking weblogs

40

Social shopping sites

20

7=Regularly
searched

6

5

4

3

2

1=Never
searched

0

Figure 12. Information Search/Sharing at a Certain Situation
160
140
Shopping for yourself

100
80
60

Shopping for another person

40
20

96

7=Regularly
searched

6

5

4

3

2

0

1=Never searched

Frequency

120

97
Financial

Insurance

Information goods

Automotives

Computer software

Clothing

Travel

Restaurants

Computer/computer
hardware

Books

Electronics

Frequency

Figure 13. Product or Service Categories

300

250

200

150

100

50

0

APPENDIX D

Covariance Matrix

98

Table 8. Covariance Matrix for SEM Path Analysis
V1

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V1

1.382

V2

.749

2.135

V3

.810

.837

1.308

V4

.729

1.108

1.082

1.495

V5

.712

1.103

1.022

1.197

1.416

V6

.661

1.699

.864

1.119

1.130

2.225

V7

.657

1.664

.853

1.107

1.141

1.889

2.239

V8

.930

.943

1.004

.994

.988

.983

.967

1.363

V9

.773

1.605

.895

1.190

1.137

1.721

1.603

1.119

2.235

V10

.712

.842

.888

.876

.869

.838

.820

.945

.881

1.138

V11

.939

1.325

1.016

1.104

1.163

1.351

1.323

1.163

1.428

1.016

1.989

V12

.858

.965

.903

.928

.944

1.000

.955

1.012

1.076

.851

1.159

V12

1.253

V1= Information-oriented motives; V2= Socially-oriented motives; V3= Relevance; V4= Clarity; V5=
Expertise; V6= Bridging; V7= Bonding; V8= Utilitarian value; V9= Hedonic value; V10= Self-efficacy;
V11= WOM intention; V12= Purchase intention

99

BIBLIOGRAPHY

100

BIBLIOGRAPHY

5 social shopping trends shaping the future of ecommerce. (2010). PowerReviews. Retrieved
January 10, 2011, from http://www.powerreviews.com/case-studies.php
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J.
Beckmann (Eds.), Action Control: From Cognition to Behavior (pp. 11-39). New York:
Springer-Verlag.
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of
planned behavior. Journal of Applied Social Psychology, 32, 665-683.
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior.
Englewood Cliffs, NJ: Prentice-Hall.
Alba, J., Lynch, J., Weitz, B., & Janiszewski, C. (1997). Interactive home shopping: consumer,
retailer, and manufacturer incentive to participate in electronic marketplaces. Journal of
Marketing, 61(3), 38-54.
Al-Natour, S., Benbasat, I., & Cenfetelli, R.T. (2008). The Effects of process and outcome
similarity on users‟ evaluations of decision aids. Decision Sciences, 39(2), 175-211.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review
and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
Arnold, M. J., & Reynolds, K. E. (2003). Hedonic shopping motivations. Journal of Retailing, 79,
77-95.
Babin, B., Darden, W., & Griffin, M. (1994). Work and/or fun: measuring hedonic and utilitarian
shopping value. Journal of Consumer Research, 20, 644-656.
Babin, B. J., Lee, Y.-K., Kim, E.-J., & Griffin, M. (2005). Modeling consumer satisfaction and
word-of-mouth: Restaurant patronage in Korea. Journal of Services Marketing, 19(3),
133-139.
Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational
research. Administrative Science Quarterly, 36(3), 421-459.
Bandura, A. (1977). Social Learning Theory. New York: General Learning Press.

101

Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory.
Englewood Cliffs, NJ: Prentice Hall.
Bandura, A. (1988). Organizational application of social cognitive theory. Australian Journal of
Management, 13(2), 275-302.
Bandura, A. (1997). Self-Efficacy: The Exercise of Control. New York: W.H. Freeman.
Bandura, A., & Walters, R. H. (1963). Social Learning and Personality Development. New York:
Holt: Rinehart & Winston.
Barbalet, J. M. (1998). Emotions, Social Theory, and Social Structure: A Macrosociological
Approach. Cambridge: Cambridge University Press.
Barton, B. (2006). Ratings, reviews, and ROI: How leading retailers use customer word of mouth
in marketing and merchandising. Journal of Interactive Advertising, 7(1), 1-7.
Bearden, W. O., Hardesty, D. M., & Rose, R. L. (2001). Consumer self-confidence: Refinements
in conceptualization and measurement. Journal of Consumer Research, 28, 121-134.
Bei, L., Chen, E. Y. I., & Widdows, R. (2004). Consumers' online information search behavior
and the phenomenon of search vs. experience products. Journal of Family and Economic
Issues, 25(4), 449-467.
Bellenger, D. N., & Korgaonkar, P. K. (1980). Profiling the recreational shopper. Journal of
Retailing, 56, 77-92.
Bellman, S., Lohse, G., & Johnson, E. (1999). Predictors of online buying behavior.
Communications of the ACM, 42(12), 32-38.
Berger. C.R. (1987).Communicating under uncertainty. In M.E. Rolloff & G.R. Miller (Eds.),
Interpersonal Processes: New Directions in Communication Research (pp. 39-62).
Newbury Park: Sage.
Bettman, J. R., Johnson, E. J., & Payne, J. W. (1991). Consumer decision-making. In T. S.
Robertson & H. H. Kassarjian (Eds.), Handbook of Consumer Behavior (pp. 54-80).
Engelwood Cliffs, NJ: Prentice Hall.
Bickart, B., & Schindler, R. M. (2001). Internet forum as influential sources of consumer
information. Journal of Interactive Marketing, 1(3), 31-40.
Blake, B.F., Neuendorf, K.A., & Valdiserri, C.M. (2005). Tailoring new websites to appeal to
those most likely to shop online, Technovation, 25, 1205-1214.
102

Bloch, P. H., Sherrell, D. L., & Ridgway, N. M. (1986). Consumer search: an extended
framework. Journal of Consumer Research, 13, 119-126.
Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: John Wiley & Sons.
Brown, T. J., Barry, T. E., Dacin, P. A., & Gunst, R. F. (2005). Spreading the word: Investigating
the antecedents of consumers' positive Word-of-Mouth intentions and behaviors in a
retailing context. Journal of the Academy of Marketing Science, 33(2), 123-138.
Chen, Q., Griffith, D. A., & Shen, F. (2005). The effects of interactivity on cross-channel
communication effectiveness. Journal of Interactive Advertising, 5(2), 30-44.
Cheong, H. J., & Morrison, M. A. (2008). Consumers' reliance on product information and
recommendations found in UGC. Journal of Interactive Advertising, 8(2), 38-49.
Chiou, J.-S., & Cheng, C. (2003). Should a company have message boards on its web site?
Journal of Interactive Marketing, 17, 50-61.
Chu, C.-W., & Lu, H.-P. (2007). Factors influencing online music purchase intention in Taiwan:
An empirical study based on the value-intention framework. Internet Research, 17(2),
139-155.
Claxton, J., & McDougall, P. A. (2000, October 1). Measuring the quality of models.
October 28, 2008, from http://www.tdan.com/view-articles/4877/

Retrieved

Cohn, A., & Park, J. (2007). Social shopping: How technology is reshaping the consumer
experience in apparel retailing. Tuck School of Business at Dartmouth. Retrieved October
6, 2008, from
http://mba.tuck.dartmouth.edu/digital/Research/ResearchProjects/ResearchSocialShoppin
g.pdf
Current Population Survey - Internet Usage. (2009). Washington, DC: U.S. Census Bureau.
Retrieved July 20, 2010, from
http://www.census.gov/population/socdemo/computer/2009/tab02.xls
Dawson, S., Bloch, P. H., & Ridgway, N. M. (1990). Shopping motives, emotional states, and
retail outcomes. Journal of Retailing, 66, 408-427.
Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store
information on buyers' product evaluation. Journal of Marketing Research, 28(3), 307319.

103

Duffy, B., Smith, K., Terhanian, G., & Bremer, J. (2005). Comparing data from online and faceto-face surveys, International Journal of Market Research. 47(6), 615-639.
Eastlick, M. A., & Feinberg, R. A. (1999). Shopping motives for mail catalog shopping. Journal
of Business Research, 45(3), 281-290.
Etzioni, A. (1996). The responsive community: A communitarian perspective. American
Sociological Review, 61(1), 1-11.
Flanagin, A. J., & Metzger, M. J. (2001). Internet use in the contemporary media environment.
Human Communication Research, 27, 153-181.
Flanagin, A. J., & Metzger, M. J. (2007). The role of site features, user attributes, and
information verification behaviors on the perceived credibility of web-based information.
New Media & Society, 9(2), 319-342.
Flanagin, A. J., & Metzger, M. J. (2008). Digital media and youth: Unparalleled opportunity and
unprecedented responsibility. In M. J. Metzger & A. J. Flanagin (Ed.), Digital Media,
Youth, and Credibility (pp. 5-28). Cambridge, MA: MIT Press.
Flynn, L. R., Goldsmith, R. E., & Eastman, J. K. (1996). Opinion leaders and opinion seekers:
two new measurement scales. Journal of the Academy of Marketing Science, 24(2), 137147.
Fogg, B.J., Marshall, J., Laraki, O., Osipovich, A., Varma, C., Fang, N., Paul, J., Rangnekar, A.,
Shon, J., Swani, P., & Treinen, M. (2000). Elements that affect web credibility: Early
results from a self-report study. Proceedings of ACM CHI 2000 Conference on Human
Factors in Computing Systems. Extended Abstracts (pp. 295-296). New York: ACM Press.
Folkes, V. S., & Kiesler, T. (1991). Social cognition: Consumers' inferences about the self and
others. In T. S. Robertson & H. H. Kassarjian (Eds.), Handbook of Consumer Behavior
(pp. 281-315). Engelwood Cliffs, NJ: Rentice-Hall.
Fong, J., & Burton, S. (2006). Electronic Word-of-Mouth: A comparison of stated and revealed
behavior on electronic discussion boards. Journal of Interactive Advertising, 6(2), 61-70.
Fornell, C., & Larcker, D. (1981). Structural equation models with unobservable variables and
measurement error. Journal of Marketing Research, 18(1), 39-50.
Freedman, L. (2007). The content dilemma - consumer insights and merchant execution: Online
content impact survey. The e-tailing Group/ARS commerce. Retrieved March 3, 2008,
from http://www.e-tailing.com/research/whitepapers/index.html

104

Freedman, L. (2008, February). Merchant and customer perspectives on customer reviews and
user-generated content. The e-tailing Group and PowerReviews. Retrieved July 30, 2008,
from http://www.powerreviews.com/socialshopping/solutions/whitepaper/2008_WhitePaper_0204_4.pdf
Freedman, L. (2009). The content dilemma - consumer insights and merchant execution: Onsite
product content: A pipeline to profitability. The e-tailing Group and ARS Interactive.
Retrieved July 25, 2010, from
http://www.nrf.com/modules.php?name=Documents&op=viewlive&sp_id=3908
Gardner, W. D. (2010, July 27). Facebook, Twitter influence purchases. InformationWeek.
Retrieved July 31, 2010, from
http://www.informationweek.com/news/software/web_services/showArticle.jhtml?article
ID=226300075
Gogoi, P. (2007, February 13). Retailers take a tip from MySpace. Top News: BusinessWeek.com.
Retrieved March 5, 2007, from http://BusinessWeek.com
Goldsmith, R. E., & Horowitz, D. (2006). Measuring motivations for online opinion seeking.
Journal of Interactive Advertising, 6(2), 1-16.
Gordon, K. T. (2007, March). The power of social shopping networks. Entrepreneur Magazine.
Retrieved March 6, 2007, from
http://www.entrepreneur.com/marketing/onlinemarketing/article174746.html
Griffin, E. (2003). A First Look at Communication Theory. Boston, MA: McGraw Hill.
Harris, R. (2007, June 15, 2007). Evaluating Internet research sources. VirtualSalt. Retrieved
August 1, 2010, from http://www.virtualsalt.com/evalu8it.htm
Hartman, C. L., & Kiecker, P. L. (1991). Marketplace influencers at the point of purchase: The
role of purchase pals in consumer decision making. Paper presented at the AMA summer
educators, Chicago.
Haubl, G., & Trifts, V. (2000). Consumer decision making in online environments: The effects of
interactive decision aids. Marketing Science, 1, 4-21.
Hill, W. W., & Beatty, S. E. (2011). A model of adolescents' online consumer self-efficacy
(OCSE), Journal of Business Research. In Press.
Hirschman, E. C., & Wallendorf, M. R. (1982). Motive underlying marketing information
acquisition and transfer. Journal of Advertising, 11(2), 25-31.

105

Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated
environments: Conceptual foundations. Journal of Marketing, 60(3), 50-68.
Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption:
Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2), 132-140.
Hoyer, W. D., & McInnis, D. (2001). Consumer Behavior (2 ed.). Boston, MA: Houghton Mifflin
Company.
Huang, L.-S., Chou, Y.-J., & Lin, C.-H. (2008). The influence of reading motives on the
responses after reading blogs. Cyber Psychology & Behavior, 11(3), 351-355.
Industry statistics. (2010). Bazaarvoice. Retrieved July 30, 2010, from
http://www.bazaarvoice.com/resources/stats
Iyenegar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much
of a good thing? Journal of Personality and Social Psychology, 79(6), 995-1006.
Keeney, R.L. (1999). The value of Internet commerce to the customer, Management Science,
45(4), 533-542.
Kiecker, P., & Hartman, C. L. (1993). Purchase pal use: why buyers choose to shop with other.
Paper presented at the 1993 AMA Winter Educators, American Marketing Association.
Kiecker, P., & Hartman, C. L. (1994). Predicting buyers' selection of interpersonal sources: the
role of strong tie and weak ties. Paper presented at the Advances in Consumer Research,
Association for Consumer Research, Provo, UT.
Kim, Y. H., & Kim, D. (2005). A study of online transaction self-efficacy, consumer trust, and
uncertainty reduction in electronic commerce transaction. Paper presented at The 38th
Hawaii International Conference on System Sciences, Hawaii.
Kim, Y.J., Baker, J., & Song, J. (2007). An exploratory study of social factors influencing virtual
community members‟ satisfaction with Avatars. Communications of the Association for
Information Systems, 20, 567- 593.
Klein, L. R. (1998). Evaluating the potential of interactive media through a new lens: search
versus experience goods. Journal of Business Research, 41, 195-203.
Kline, R. B. (1998). Principles and Practice of Structural Equation Modeling. New York: The
Guilford Press.

106

LaRose, R. (2001). On the negative effects of e-commerce: A sociocognitive exploration of
unregulated on-line buying. Journal of Computer Mediated Communication, 16(3). doi:
10.1111/j.1083-6101.2001.tb00120.x
LaRose, R., Lai, Y., Lange, R., Love, B., & Wu, Y. (2005). Comments on sharing or stealing? An
exploration of downloading behavior. Journal of Computer-Mediated Communication,
11(1), Article 1. Retrieved from http://jcmc.indiana.edu/vol11/issue1/larose.html
Lee, M. K. O., Cheung, C. M. K., Sia, C. L., & Lim, K. H. (2006). How positive informational
social influence affects consumers' decision of Internet shopping? Paper presented at The
39th Hawaii International Conference on System Sciences.
Leff, L. (2002, June 23). Hoping their makeup takes a powder. LATimes.com. Retrieved
December 12, 2007, from http://articles.latimes.com/2002/jun/23/news/lv-makeup23
Liang, T., & Huang, T. (1998). An empirical study on consumer acceptance of products in
electronic marketing: A transaction cost model. Decision Support Systems,24, 29-43.
Liang, T.-P., Lai, H.-J., & Ku, Y.-C. (2007). Personalized content recommendation and
user satisfaction: Theoretical synthesis and empirical findings. Journal of Management
Information Systems,23(3), 45-70.
Liaw, S. S. (2002). Understanding user perceptions of World Wide Web environments. Journal of
Computer Assisted Learning, 18(2), 137-148.
Lin, N. (2001). Building a network theory of social capital. In N. Lin, K. Cook & R. S. Burt
(Eds.), Social Capital: Theory and Research (pp. 3-30). New York: Aldine de Gruyter.
Makeupalley About Us. (1999). MakeupAlley.com. Retrieved December 12, 2007, from
http://www.makeupalley.com/content/content.asp/s=0/c=AboutUs/
Mallalieu, L., & Palan, K. M. (2006). How good a shopper am I? Conceptualizing teenage girls'
shopping competition. Academy of Marketing Science Review. Retrieved September 12,
2007, from http://www.amsreview.org/articles/mallalieu05-2006.pdf
Mangleburg, T. T., Doney, P. M., & Bristol, T. (2004). Shopping with friends and teens'
susceptibility to peer influence. Journal of Retailing, 80, 101-116.
Martinez, A. (2010, April 7). Retailers jump on social media bandwagon with Facebook, Twitter.
Seattle Times. Retrieved July 31, 2010, from
http://seattletimes.nwsource.com/html/businesstechnology/2011539429_socialmedia07.ht
ml

107

McCarthy, R. (2007, February). The power of suggestion: social shopping sites turn online
shopping into a group activity. Inc. Magazine, 29, 48-49.
McGuire, W. J. (1974). Psychological motives and communication gratification. In J. G. Blumler
& E. E. Katz (Eds.), The Uses of Mass Communication: Current Perspectives on
Gratification Research (pp. 106-167). Beverly Hills, CA: Sage.
Metzger, M. J., & Flanagin, A. J. (2008). Digital Media, Youth, and Credibility. Cambridge, MA:
MIT Press.
Metzger, M. J., Flanagin, A. J., Eyal, K., Lemus, D. R., & McCann, R. (2003). Credibility in the
21st century: Integrating perspectives on source, message, and media credibility in the
contemporary media environment. In P. Kalbfleisch (Ed.), Communication Yearbook (Vol.
27, pp. 293-335). Mahwah, NJ: Lawrence Erlbaum.
Miller, K. (2005). Communication Theories: Perspectives, Processes, and Contexts (2nd ed.).
New York: McGraw-Hill.
Miller, N. E., & Dollard, J. (1941). Social Learning and Imitation. New Haven, CT: Yale
University Press.
Mishra, S., Umesh, U. N., & Donald E. Stem, J. (1993). Antecedents of the attraction effect: An
information processing approach. Journal of Marketing Research, 30, 331-349.
Mitchell, V.-W., Walsh, G., & Yamin, M. (2005). Towards a conceptual model of consumer
confusion. Advances in Consumer Research, 32, 143-150.
Mowen, J. C., & Minor, M. (1997). Consumer Behavior (5th ed.). Upper Saddle River, NJ:
Prentice H.
Nadjm, M. (2007). Security recommendations for social network communities. Retrieved April
1, 2008, from
http://www.enisa.europa.eu/doc/pdf/workshop/june2007/presentations/sn_maznadjm_rare
face.pdf
Netermeyer, R. G., & Bearden, W. O. (1992). A comparative analysis of two models of
behavioral intention. Journal of the Academy of Marketing Science, 20, 49-59.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3 ed.). New York: McGraw-Hill.
Online buyers seek out user reviews. (2008, February 15). Retrieved February 28, 2008, from
http://www.emarketer.com/Article.aspx?id=1005943
108

Ormrod, J. E. (1999). Human Learning (3rd ed.). Upper Saddle River, NJ: Prentice-Hall.
Papacharissi, Z., & Rubin, A. M. (2000). Predictors of Internet use. Journal of Broadcasting &
Electronic Media, 44(2), 175-196.
Paridon, T. J. (2005). The effects of personal shopping values in consumer self-confidence and
retail information sharing research. Central Business Review, 24(1-2), 20-25.
Park, C., Whan, D. L., Mothersbaugh, L., & Feick, L. (1994). Consumer knowledge assessment.
Journal of Consumer Research, 21, 71-82.
Parsons, A. G. (2002). Non-functional motives for online shoppers: why we click. Journal of
Consumer Marketing, 19(5), 380-392.
Patwardhan, P., & Ramaprasad, J. (2005). A rational integrative model of online consumer
decision making, Journal of Interactive Advertising, 6(1), 3-18.
Phelps, J. E., Lewis, R., Mobilio, L., Perry, D., & Raman, N. (2004). Viral marketing or
electronic word-of-mouth advertising: Examining consumer responses and motivations to
pass along email. Journal of Advertising Research, 45(4), 333-348.
Pihlström, M., & Brush, G. J. (2008). Comparing the perceived value of information and
entertainment mobile services. Psychology and Marketing, 25(8), 732-755.
Porter, M. E. (2001). Strategy and the Internet. Harvard Business Review, 63-78.
Price, L. L., & Ridgway, N. M. (1982). Use innovativeness, vicarious exploration and purchase
exploration: Three facets of consumer varied behavior. Assessment of Marketing Thought
and Practice, American Marketing Association, 56-60.
Punj, G. N., & Staelin, R. (1983). A model of consumer information search for new automobiles.
Journal of Consumer Research, 9, 366-380.
Putnam, R. D. (2000). Bowling Alone: The Collapse and Revival of American Community. New
York: Simon & Schuster.
Ray, M. (2007). Needs, motives, and behaviors in computer-mediated communication: An
inductive exploration of social networking websites. Paper presented at The International
Communication Association. San Francisco, CA.
Reynolds, F. D., & Darden, W. R. (1971). Mutually adaptive effects of interpersonal
communication. Journal of Marketing Research, 8(4), 449-454.
109

Rhone, N. (2007, May 8). Fashion feedback: Technology streamlines advice from friends, family,
fashionistas for savvy consumers. The Atlanta Journal-Constitution.
Rieh, S. Y., & Danielson, D. R. (2007). Credibility: A multidisciplinary framework. Annual
Review of Information Science and Technology, 41, 307-364.
Rohm, A. J., & Swaminathan, V. (2004). A typology of online shoppers based on shopping
motivations. Journal of Business Research, 57(7), 748-757.
Schneider, K. (2006, November 1). Word of mouth helps them fly off the shelves. The New York
Times, p. 6.
Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002).
Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality
and Social Psychology, 83(5), 1178-1197.
Smith, D., Menon, S., & Sivakumar, K. (2003). Trust me, would I steer you wrong? The
influence of peer recommendations within virtual communities. Paper presented at the
Academy of Marketing Science Annual Conference, Florida.
Smith, D., Menon, S., & Sivakumar, K. (2005). Online peer and editorial recommendations, trust,
and choice in virtual markets. Journal of Interactive Marketing, 19(3), 15-37.
Social capital: Building on a network-based approach. (2003). Policy Research Initiative:
Government of Canada. Retrieved October 25, 2008, from
http://www.policyresearch.gc.ca/doclib/Social_Capital_BNBA_e.pdf
Staples, D. S., Hulland, J. S., & Higgins, C. A. (1998). A self-efficacy theory explanation for the
management of remote workers in virtual organizations, Journal of Computer-Mediated
Communication, 3(4), Retrieved from
http://www.ascusc.org/jcmc/vo13/issue4/wiesenfeld.html
Stell, R., & Paden, N. (1999). Vicarious exploration and catalog shopping: a preliminary
investigation. Journal of consumer Marketing, 16(4), 332-344.
Stewart, T. A. (1997). Intellectual capital: the new wealth of organizations. London: Nicholas
Brealey.
Sun, T., Youn, S., Wu, G., & Kuntaraporn, M. (2006). Online word-of-mouth (or mouse): An
exploration of its antecedents and consequences, Journal of Computer-Mediated
Communication, 11(4), Retrieved March 30, 2008, from
http://jcmc.indiana.edu/vol11/issue4/sun.html

110

Tam, K.Y. & Ho, S.Y. (2006). Understanding the impact of Web personalization on user
information processing and decision outcome. MIS Quarterly, 30(4), 865-890.
Tapscott, D. (1997). The Digital Economy: Promise and Peril In The Age of Networked
Intelligence: McGraw-Hill.
Tapscott, D. (1998). Growing Up Digital: The Rise of the Net Generation: McGraw-Hill.
Tauber, E. M. (1972). Why do people shop. Journal of Marketing, 36, 46-59.
Taylor, H. (2000). Does internet research work? International Journal of Market
Research,42(1), 51-63.
Tedeschi, B. (2006, September 11). Like shopping? Social networking? Try social shopping. The
New York Times, p. 6.
Terhanian, G. (2003). The unfulfilled promise of internet research, MRS Conference
Paper 37.
Thambusamy, R., Church, M., Nemati, H., & Barrick, J. (2010). Socially exchanging privacy for
pleasure: Hedonic use of computer-mediated social networks. ICIS 2010
Proceedings. Paper 253.
The Nielsen Company. (2009 July). Personal recommendations and consumer opinions posted
online are the most trusted forms of advertising globally, Nielsen Global Online
Consumer Survey press release. Retrieved March 1, 2011 from
http://blog.nielsen.com/nielsenwire/wp-content/uploads/2009/07/pr_globalstudy_07709.pdf
The state of retailing online 2007: Online marketing, merchandising, & multichannel trends.
(2007). The 10th annual of Shop.org study by Forrester Research: Shop.org & Forrester
Research. Retrieved July 1, 2008, from http://www.scribd.com/doc/324360/Shop-orgState-of-Retailing-Online-Part-2-of2?query2=sears%2022%25%20online%20store%20survey
Thomas, J. B., Peters, C. O., & Tolson, H. (2007). An exploratory investigation of the virtual
community MySpace.com: What are consumers saying about fashion? Journal of
Fashion Marketing and Management, 11(4), 587-603.
To, P., Liao, C., & Lin, T. (2007). Shopping motivations on Internet: A study based on utilitarian
and hedonic value. Technovation, 27(12), 774-787.
Urbany, J. E., Dickson, P. R., & Wilkie, W. L. (1989). Buyer uncertainty and information search.
Journal of Consumer Research, 16(2), 208-215.

111

Voight, J. (2007). Getting a handle on customer reviews. Adweek, 48, 16-17.
Wang, L.C., Baker, J., Wagner, J.A., & Wakefield, K. (2007). Can a retail web site be social?.
Journal of Marketing, 71(3), 143-157.
Wang, W., & Benbasat, I. (2005). Trust in and adoption of online recommendation agents.
Journal of the Association for Information Systems, 6(3), 72-101.
Wang, W., & Benbasat, I. (2007). Recommendation agents for electronic commerce: Effects of
explanation facilities on trusting beliefs. Journal of Management Information Systems,
23(4), 217-246.
Weiss, A.M., Lurie, N.H., & MacInnis, D.J. (2008). Listening to strangers: whose responses are
valuable, how valuable are they, and why? Journal of Marketing Research, 45(4), 425436.
Westbrook, R. A., & Black, W. C. (1985). A motivation-based shopper typology. Journal of
Retailing, 61(1), 78-103.
Widing, R. E., & Talarzyk, W. (1993). Electronic information systems for consumers: An
evaluation of computer-assisted formats in multiple decision environments. Journal of
Marketing Research, 30, 125-141.
Williams, D. (2006). On and off the 'net: Scales for social capital in an online era. Journal of
Computer-Mediated Communication, 11(2). Retrieved January 10, 2009, from
http://jcmc.indiana.edu/vol11/issue2/williams.html
Williams, F., & Rice, R. E. (1983). Communication research and the new media technologies.
Communication Yearbook, 7, 200-224.
Woodruff, R. B., & Gardial, S. F. (1996). Know Your Customer: New Approaches to
Understanding Customer Value and Satisfaction. Cambridge, MA: Blackwell.
Zeithaml, V. A. (1988). Consumer perceptions of price, quality and value: A means-end model
and synthesis of evidence. Journal of Marketing, 52, 2-22.

112