2012 45th Hawaii International Conference on System Sciences
The Effects of Social Media on E-commerce: A Perspective of Social
Impact Theory
Kee-Young Kwahk
College of Business Administration
Kookmin University
[email protected]
Xi Ge
Graduate School of Business IT
Kookmin University
[email protected]
Abstract
In recent years social media has become more
and more popular all around the world. This study
aims to examine the influence of social media in
the e-commerce context and to find how it impacts
users’ visit intention and purchase intention.
Through a questionnaire survey, we test and
analyze the research model and its related
hypotheses by making use of structural equation
modeling. The results indicate that social media
interaction ties and social media commitment
positively affect normative social influence and
informational social influence. The last one in turn
influences visit intention and purchase intention in
e-commerce. In conclusion, we discuss the
research findings and suggest some implications
for researchers and practitioners.
Keywords: Social media, Social impact theory,
Informational social influence, Normative social
influence, Social commerce, E-commerce.
1. Introduction
The influence of social media on e-commerce
has become more and more important. If one
shares information in social media, then it could
be browsed by many people, thereby leading to
many-to-many spread of information. According
to OTX’s social media and the purchase intention
research report in 2008 [37], 70% of consumers
visit social media websites such as message boards,
social networking sites and blogs to get
information on a company, brand or product.
Further, nearly half (49%) of these consumers
made a purchase decision based on the
information they gathered from social media sites.
Despite its growing interests, however, there are
currently few studies on social media and how it
affects e-commerce.
Product links in e-commerce websites are hard
to be disseminated because there are not enough
interactions between users. Hence, most
e-commerce malls have provided other customers’
978-0-7695-4525-7/12 $26.00 © 2012 IEEE
DOI 10.1109/HICSS.2012.564
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reviews of products to help potential customers
choose the products they are looking for. But this
kind of reviews could only be found in
e-commerce malls, and there are still not enough
interactions between users because this is not
based on social relationships. Integrating social
media with e-commerce may be a possible
solution. So, our research questions are: What kind
of role does social media play in the e-commerce
context? And how shall we use social media to
enhance e-commerce?
Some e-commerce sites have already started to
provide social network services in order to
improve user interaction and increase user active
participation, expecting that user interaction can
lead to more transactions. In a social network,
users interact with and get information from each
other. When a user wants to purchase a product
online, he or she can get or ask for more
information about the product through his or her
social network. This study is based on integrating
the e-commerce mall with social media. We aim to
examine user behavior in the context of social
media and e-commerce, and we try to find how
social media affects e-commerce from a social
impact theory perspective. Specifically, from the
theoretical perspective, we examine how social
media antecedent constructs affect e-commerce
through social impact transfer. From the practical
perspective, we offer suggestions on the
development of business for both social media and
e-commerce practitioners.
2. Theoretical Background
2.1 Social media and social commerce
Kaplan and Haenlein [25] defined social media
as a group of Internet-based applications that build
on the ideological and technological foundations
of Web 2.0, which allows the creation and
exchange of user-generated content. Social media
is also named consumer-generated media and
refers to user-generated content. Social media has
a lot of different forms, including virtual
communities, weblogs, microblogs, wikis, pictures
or video sharing, social networking sites, social
bookmarking and other social applications. Studies
on social media have recently increased. Some
researchers focused on the influence of social
media on public relations [45], while others
focused on social media in the context of
travel-related environment and proved that reviews
from social media are highly trusted [56].
Although user generated contents via social media
could be faked by someone with a vested interest,
many people think that they can be trustworthy
because they are real experiences by real people
who are independent [47]. Therefore, social media
have served as a new form of word-of-mouth for
products/services or providers and have proved to
be critical to consumer decision making in
e-commerce environments [56].
The term social commerce was introduced by
Yahoo! in November 2005 to describe a set of
online collaborative shopping tools such as shared
pick lists, user ratings and other user-generated
content sharing of online product information and
advice [55]. Stephen and Toubia [47] defined
social commerce as a form of Internet-based social
media that allows people to actively participate in
the marketing and selling of products and services
in online marketplaces and communities. Marsden
[33] summarized social commerce as a subset of
electronic commerce that involves using social
media, online media that supports social
interaction and user contributions, to assist in the
online buying and selling of products and services.
From this definition, we note that there are two
key aspects - social interaction and user
contributions - for social commerce, which are
derived from social media characteristics.
Social interaction can be interpreted as ties [53]
or social interaction ties [50]. It comes from the
social capital theory and has been manifested as
the structural dimension of social capital [36].
Social interaction refers to a link established via
reciprocity behavior between two actors [52]. It
describes the linkages between people or units,
and this is facilitated by social media [16]. In the
social media context, all activities that actors
participate in are based on the social media
interaction ties. They have an impact on
knowledge transfer because social media
interaction ties provide channels for information
exchange and facilitate them [53]. This can be also
applied to the context of social commerce. Thus,
social media interaction ties can play an important
role in encouraging users to interact more and
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thereby making the transaction more social.
User contributions refer to users’ participation
in social media. Commitment is defined as a
psychological construct representing the desire
and resolve to continue participation [42].
Considering that a lot of people keep using social
media, we can expect that the stronger the users’
commitment is, the more the users’ contributions
are. In the social capital theory, commitment has
been manifested as the relational dimension of
social capital [53]. It has been shown to affect both
attitudes [57] and future purchase intention [54].
Commitment to a website has been found as a key
construct to impact online purchase attitude [43]
and virtual community commitment was also
considered to be important to brand loyalty and
consumer behavior [24].
2.2 User behavior between different contexts
Many studies on user behavior have been
conducted on user adoption and acceptance, based
on such theories and models as the theory of
reasoned action [44], the theory of planned
behavior [1], the technology acceptance model [13]
and the unified theory of acceptance and use of
technology [51]. Most researches have focused on
the usage behavior in isolation, while some studies
have examined the relationship of system usage
between different contexts, demonstrating that
user behavior in one context could affect user
behavior in another related context by transferring
attitude toward systems from one context to
another [32]. Some research also found that early
usage of an IT system may influence post usage
intention of others in the future [26]. Considering
that systems and platforms are increasingly
integrated, it is necessary to pay attention to the
relationship of user behavior between different
contexts.
Stewart [48] used entitativity to explain trust
transfer between different targets. Song et al. [46]
applied entitativity to illuminate the usage
behavior transfer between technologies in different
contexts. Entitativity refers to the degree to which
a collection of individual entities is perceived as
being bonded together in a coherent group [8]. It is
considered to be an important dimension based on
which groups are compared, and perceptions of
entitativity strongly influence one’s impression
formation towards information from individuals or
groups [34], which in turn determines the
information-processing mechanism [46]. Since a
group has been considered as a collection of
entities [30], social media users in a social
network can also be thought of as a group.
According to Lickel et al. [30], social interaction
in a group can cause perceptions of entitativity.
This means that a social network group with
high-level of social interaction will also have
strong entitativity that influences group members’
impression formation and information processing,
which in turn has an impact on their perception
and attitude.
2.3 Social impact theory and social influence
The social impact theory suggests that an
individual’s feelings, attitudes and behaviors can
be influenced by the presence of others [29].
Social impact means any of the great variety of
changes in physiological states and subjective
feelings, motives and emotions, cognitions and
beliefs, values and behavior, that occur in an
individual, human or animal, as a result of the real,
implied, or imagined presence or actions of the
other individuals [29]. In this study, we briefly
define the social impact as any kind of subjective
attitude or behavior changes by the impact of
others. Latane [28] updated it to the dynamic
social impact theory and viewed society as a
self-organizing complex system in which
individuals interact and impact each others’ beliefs.
In the social impact theory, the impact of any
information source is a function of three factors:
strength, immediacy and number [29]. Strength
refers to the importance or social position of the
source. Immediacy refers to time or closeness
between source and target. Number refers to the
quantity of sources. These three dimensions were all
proposed to positively affect individual’s attitudes
and behaviors in the offline environment [35].
According to the social impact theory, the more
important a group is, the closer the distance is
between the group and oneself; therefore, the more
likely it is for one to conform to the group’s
normative pressures [29]. This kind of social
pressure is considered as normative social
influence [31]. Normative social influence refers
to conformity to the expectations of another
person or group and creates social pressure for
people to adopt a product or a service because
people not adopting the product may be treated as
‘old fashioned’ regardless of the individual’s
preference toward the product [27]. The normative
social influence is reflected in individuals’
attempts to comply with the expectations of others
to achieve rewards or avoid punishments, and it
operates through the process of compliance [4].
For example, the greatest normative social
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influence is usually exerted with primary reference
groups like family [12].
However, not all social influences occur due to
group normative pressures. In contrast to the
normative social influence, an influence would
also be internalized if it were perceived as
enhancing an individual’s knowledge about his or
her environment or improving his or her ability to
cope with some aspect of it. This is called
informational social influence [14]. Informational
social influence means influence to accept
information obtained from another as evidence
about reality [14]. Informational social influence
may occur in two ways. Individuals may either
search for information and knowledge from others
or make inferences based upon the observation of
the behavior of others [38]. It has been found to
affect the consumer decision processes regarding
product evaluations [7]. In the online environment,
the informational social influence is also found to
affect consumer decision making and can be
considered as a learning process through which
people observe the experience of early adopters in
their social network and decide whether or not to
buy a new product [27].
These two social influences have different goal
orientations. The normative social influence
relates to self-maintenance and compliance, while
the informational social influence is associated
with knowledge [7]. In other words, the normative
social influence means that people are influenced
by group compliance and the informational social
influence indicates that people are influenced by
knowledge and evidence. These two kinds of
social influences have been proved to affect
consumer behavior both offline [7] and online [27].
Compared with past times, where individuals’
influence is limited due to their narrow social
circle, in the Internet with social media,
individuals’ influence is broader and stronger,
making it more important to their lives.
3.
Research model and hypotheses
To examine user behavior in the context of
social media and e-commerce, we next develop
our research model and hypotheses.
Normative social influence is the influence of
other people that leads us to conform in order to be
liked and accepted by them [2]. It exists in any
kind of groups and has been proved that normative
social
influence
can
be
amplified
in
computer-mediated communication [41]. In a
social network group, social media interaction ties,
the links between people, provide channels to help
transfer the normative social influence to group
members. Social media facilitates social
interaction, thereby enhancing this channel [16].
The more channels there are, the greater the
influence that people can get from the social
network group. In other words, more social media
interaction ties can bring higher group pressure
and lead one to conform to the group. Thus, social
media interaction ties can be proposed to affect
normative social influence, so we propose:
information or knowledge, and thereby users with
a high level of social media commitment are more
likely to get informational social influence. Thus,
we propose:
H2b: Social media commitment will positively
affect informational social influence.
Normative social influence could affect one’s
behavior intention [27]. Visiting a website, which
also belongs to a kind of online behavior, will also
be affected by the normative social influence. For
example, if most of your friends have a Facebook
account, then you will also search and visit the
Facebook website to find what it is to avoid being
called ‘old fashioned’. Especially website links
could be conveniently shared or found on the
Internet and what one needs to do is just a click
away. It is easy for one to be compelled by others
to visit a website. In the e-commerce context, if
most of your friends have visited a website to look
for or buy a product, then you will also intend to
visit that website to see the same product. Thus,
the following hypothesis is proposed.
H1a: Social media interaction ties will
positively affect normative social influence.
As discussed before, informational social
influence relates to knowledge [7] that means
people are influenced by knowledge and evidence.
Social media interaction ties have been proved to
positively affect knowledge exchange and sharing
[50]. Similar to the normative social influence,
social media interaction ties provide information
exchange channels, and knowledge can be spread
by this channel. In a social network group, people
with more links are easier to get more knowledge,
causing informational social influence. Therefore,
we propose:
H3a: Normative social influence will
positively affect visit intention of e-commerce.
H1b: Social media interaction ties will
positively affect informational social influence.
People tend to follow the choices of others
rather than making their judgment by themselves
when they face overwhelming information online
[6]. It is easy for people to find others’ reviews
about a product on the Internet. Especially in a
social network group, people can find what others
are doing and the choices of the majority. As
members of the same group, they can be easily
affected by the others’ choices because of the
trustworthiness of social media members.
Moreover, the normative social influence occurs
when individuals make decisions to gain approval
from other group members. Users can comply with
the opinions of group members to purchase a
product, which means normative social influence
has a direct influence on purchase intention. In
addition, subjective norm, similar to normative
social influence, has been considered as an
important factor affecting consumer behavior
according to the theory of reasoned action [1][31].
Thus, we propose:
Normative social influence is one form of
conformity, which is related to the group’s social
norms [4]. In an online environment, the premise
of getting the social norms of a group is to keep
participating in online activities. We define social
media commitment in this study as the users’
desire to continue one’s association with social
media. If one keeps on using social media, which
means a high commitment to social media, then
one is more likely to be influenced by the group’s
social norms. Hence, we propose:
H2a: Social media commitment will positively
affect normative social influence.
A user’s commitment to social media means
that he or she frequently participates in interacting
through social media. Because social media has
been a powerful information source [25], one with
higher social media commitment can receive an
amount of information that can be considered as
evidence or knowledge when he or she needs to
make a decision. That is to say, social media
commitment brings more opportunities to obtain
H3b: Normative social
positively
affect
purchase
e-commerce.
influence will
intention
of
Informational social influence is based on
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China, and Paipai.com, a popular e-commerce site
that has the largest instant messenger users in
China, have both provided social network services
as well, which include blog, microblog, forums,
image and video sharing, online social game and
other applications. In these sites, the social
network service is integrated with an e-commerce
system. Users can select whether or not to share a
transaction when they engage in the transaction. If
they choose to share it, then the transaction record,
product link and also the reviews of that product
are shown on the social network homepage and
friends can see and comment on the transaction.
Blog, microblog and variety of web applications
are provided to enhance the users’ interaction, and
the transaction records and reviews are transferred
within this social network as a link. Users are
encouraged to share their transactions with points
and coupons. In this process, the users’ online
shopping activities become more social. It
indicates that social media might play an
important role in the online shopping behaviors of
consumers.
reality and evidence [4]. It has been proved to
influence people’s attitudes and behavior in the
online environment [27]. Whether or not to visit a
website can be affected by friends or information
online. For example, if one gets a positive
comment about a website and there are enough
reasons to believe that it is really a good site worth
visiting, then he or she may also have the intention
to visit it. Thus, we propose the following
hypothesis:
H4a: Informational social influence will
positively affect visit intention of e-commerce.
Informational social influence occurs when
individuals make decisions to reach the best
possible decision. It was also found to have a
positive influence on buyer behavior [7]. When
people try to find the best choice, they will also try
to get more information or evidence. There is a lot
of information about products like reviews or
other consumer-generated contents via social
media. It has been proved that such information
affect the consumer’s purchasing behavior [56].
Consumers take the information as evidence for
purchase decision, which means that the
informational social influence can affect one’s
purchase intention. Thus, we propose:
4.2 Measure
As the proposed research model shows, in this
paper, the following six constructs were involved:
social media interaction ties, social media
commitment,
normative
social
influence,
informational social influence, visit intention of
e-commerce
and
purchase
intention
of
e-commerce. The social media interaction ties
were adopted from Chiu [10], and the social media
commitment was developed from Garbarino and
Johnson [18]. For normative social influence and
informational social influence, the measures were
adopted from Bearden and Netemeyer [4]. We
developed visit intention and purchase intention
measures from Pavlou [39]. The questionnaires
used five-point Likert scales and consisted of 35
items that measured the respondents’ perceptions.
H4b: Informational social influence will
positively affect purchase intention of e-commerce.
Visit intention here means one’s intention to
visit an e-commerce website. This is a kind of
behavior to get more information. According to the
buyer’s decision making model [15], getting
information is associated with purchasing. Visiting
an e-commerce website will probably cause a
consumer to be interested in some products. It is
the first step of online shopping and important to
e-commerce transactions. Pavlou and Fygenson
[40] have proved that getting information from a
website will positively have an influence on
purchasing a product from that web vendor.
Therefore, we propose:
4.3 Data collection
After the instrument was completed, we
conducted an online survey at a professional
survey website. The questionnaire was only aimed
at the one who had experiences in shopping on
Taobao.com or Paipai.com, which have social
network services as well. Finally, 321 responses
were collected in one week. We performed an
initial screening for usability and reliability.
Respondents that filled out the questionnaire
H5: Visit intention of e-commerce will
positively affect purchase intention of e-commerce.
4. Research methodology
4.1 Research context
Taobao.com, the largest e-commerce site in
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within less than 3 minutes were removed because
completing the questionnaire needed at least 5
minutes. Respondents with zero times of shopping
frequency within a month and some responses
with obvious mistakes were also removed. In the
end, 233 responses were found to be complete and
usable.
82.4% of the respondents were between 20 and
30 years old. This is consistent with the fact that
young people are the most active Internet users in
China [11]. 40.4% of the respondents are company
employees and most of them are at a
non-management level. On average, they have a
working experience of 4 years. University
education level takes 80.7% of the respondents,
and the respondents have 7 years of Internet
experience and 2.5 years of online shopping
experience on average. Nearly all the respondents
are from Taobao.com. This is also consistent with
the report that Taobao.com took over 80% of the
market share and Paiapai.com took only 5% of the
e-commerce market share in China [23].
Table 1. Results of convergent validity testing
Construct
Item
Social Media
Interaction
Ties
Social Media
Commitment
Normative
Social
Influence
Informational
Social
Influence
Visit Intention
of
E-commerce
Purchase
Intention of
E-commerce
SMI2
SMI3
SMI4
SMI5
SMC1
SMC3
SMC4
SMC5
NSI2
NSI4
NSI5
NSI7
ISI1
ISI2
ISI3
ISI4
ISI6
VI1
VI2
VI3
PI1
PI2
PI3
Factor
loading
0.71
0.71
0.77
0.79
0.81
0.78
0.93
0.87
0.69
0.75
0.78
0.78
0.78
0.90
0.74
0.79
0.70
0.86
0.88
0.92
0.83
0.75
0.78
CR
AVE
Cronbach’s
α
0.833
0.556
0.774
0.905
0.712
0.883
0.838
0.564
0.793
0.889
0.617
0.847
0.918
0.789
0.875
0.830
0.619
0.736
Furthermore, the square root of the AVE for
each construct and the correlations between that
construct and other constructs were compared, as
shown in Table 2, to test discriminant validity [17].
The square root of the AVE for each construct was
greater than the correlations between that construct
and the other constructs, which meant we had
established discriminant validity for the
measurement model. Hence, we could proceed to
the structural model analysis.
5. Data analysis
Collected data were analyzed by a two-step
methodology using LISREL [3]. We first
examined the validity of the constructs and then
tested the structural model based on the cleansed
measurement model.
5.1 Measurement model
Table 2. Results of discriminant validity testing
Construct
We conducted confirmatory factor analysis to
evaluate the measurement model. In order to
assess the reliability and convergent validity of our
measurement model, we needed to check the
factor loadings, the Cronbach’s α, the composite
reliability (CR) and average variance extracted
(AVE), as shown in Table 1 [20]. We removed
items whose loading values were lower than 0.7
[9]. Hence, SMI1, SMC2, ISI5, and NSI1 were
eliminated. Then, we also sequentially removed
three items whose standardized residuals values
were too large [19]. The Cronbach’s α ranged from
0.736 to 0.883 and the composite reliability of our
measurement ranged from 0.833 to 0.918. These
were all larger than 0.7, which means being
acceptable [20]. The average variance extracted of
our measurement ranged from 0.556 to 0.789,
which was above the recommended threshold of
0.5 [17]. Therefore, we had established convergent
validity for the measurement model.
SMI
SMC
NSI
ISI
VI
PI
Mean
(SD)
3.273
(0.695)
3.697
(0.864)
3.449
(0.823)
3.972
(0.686)
4.235
(0.658)
3.614
(0.567)
SMI
SMC
NSI
ISI
VI
PI
0.746
0.641
0.853
0.529.
0.471
0.781
0.466
0.470
0.517
0.802
0.367
0.528
0.363
0.658
0.874
0.208
0.109
0.006
0.432
0.513
0.792
Note: Leading diagonal shows the square root of AVE of
each variable. Others are correlations.
5.2 Structural model
The structural model, including the research
hypotheses and the causal paths, was examined
using the cleansed measurement model. Figure 1
shows the standardized LISREL path coefficients
of the research model.
The normed χ 2(χ 2 to degree of freedom) was
3.156, which was not good but could be mediocre
1819
accepted [5]. The root mean squared
approximation of error (RMSEA) was 0.085 lower
than 0.1, which was also mediocre fitted [21].
Root mean square residual (RMR) was 0.059
lower than 0.08, which still could be accepted [5].
Goodness of fit index (GFI) was 0.824 that was
above the recommended threshold of 0.8 [20]. The
other fit indices were all satisfactory that normed
fit index (NFI) was 0.917, nonnormed fit index
(NNFI) was 0.931 and comparative fit index (CFI)
was 0.941. These results suggest that the structural
model fitted the data adequately. Social media
interaction ties and social media commitment
positively influenced normative social influence
and informational social influence and explained
33.1% and 29.8% of each variance respectively.
Normative and informational social influence were
positively related to visit intention of e-commerce,
which explained 44.4% of its variance in visit
intention of e-commerce. Informational social
influence and visit intention were positively
associated with purchase intention of e-commerce
and explained 33.6% of its variance in purchase
intention. However, normative social influence did
not have a positive impact on purchase intention of
e-commerce (path coefficient= -0.240, t-value=
-3.157).
social influence) and e-commerce factors (visit
intention of e-commerce and purchase intention of
e-commerce), which sequentially have causal
relationships. The empirical results showed that all
hypotheses were supported except for H3b
(normative social influence → purchase intention
of e-commerce).
There are two questions to be discussed about
the findings. First, how does informational social
influence transfer in different context? As
discussed before, the informational social
influence means influence by knowledge and
information as evidence [7]. Users in the online
social network share and receive information by
online links. In other words, information and
knowledge are constantly transferred in social
networks; this means that social media users could
get more information and knowledge. This
indicates that the possibility of being influenced is
also higher. Just as Pavlou and Fygenson [40] have
proved, getting information is the first step of
purchase and could positively influence the
purchase intention. Therefore, social media affects
getting information and knowledge transfer, which
in turn enhances the influence of the information
or knowledge (i.e., informational social influence),
and finally influences online shopping behavior.
That is to say, informational social influence is
transferred from a social media context to an
e-commerce context. Hence, social media has a
high informational social influence, which affects
the users’ online behavior such as visit intention
and purchase intention in e-commerce.
The other question is why normative social
influence does not affect purchase intention in
e-commerce but informational social influence
does? Normative social influence relates to the
influence of group compliance [7]. Compliance
only occurs when a user conforms to the
expectations of another to receive a reward or
avoid rejection and hostility [22]. Because the
social network of users is built on the users’ own
social relationships, there are none strict group
norms or rewards or punishment. For example,
when a user finds that most of his or her friends in
social media have purchased a product and have
posted a good comment about it, it does not mean
that he or she has to purchase that product for the
reason of group compliance. One can be attracted to
purchase the product but not comply to purchase it.
Users are rational to accept information as evidence.
Therefore, the informational social influence is
more important to determine the users’ behavior in
e-commerce context compared with the normative
social influence.
Note: **Significant at p < 0.01
Figure 1. LISREL Results
6. Discussion
This study attempts to investigate how social
media affects e-commerce. For the purpose of
doing this, we proposed a theoretical research
model that consisted of social media factors
(social media interaction ties and social media
commitment), social impact transfer factors
(normative social influence and informational
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7. Implication
focused on transferring roles in different contexts.
This study indicated that social media affect
e-commerce by means of transferring the social
impact. Considering that information systems are
increasingly integrated now, we suggest that
studies on users’ behavior in different contexts are
more needed in the future, and this study provides
a good starting point to the researchers who are
interested in those topics.
This research also provides some practical
implications. First of all, for e-commerce
managers, according to our findings, we suggest
that e-commerce managers need to directly
develop social media based applications or
collaborate with other popular social media
websites to enhance the users’ social interactions
and make the transactions more social. In doing so,
e-commerce websites should try to encourage
users, especially expert users, to share information
about their shopping experiences and knowledge.
Users’ interactions with each other will make
social media richer and help diffuse the product
information. Because of the informational social
influence was found to be more powerful, our
results also suggest that e-commerce managers
provide more information and knowledge about
products to help users select products.
Second, for social media managers, as we have
discovered, this study proposes that the
informational social influence embedded in social
media is very powerful in affecting one’s online
behavior. We suggest that social media managers
provide more interesting applications and hold
more activities to attract users in order to keep
participating in social media. Managers should
also try to help users find more good friends to
widen their social relationships. Meanwhile, they
should also encourage users to share their
experiences or interact with others to deepen their
relationships. Because the users’ abundance of
social relationships and commitment to social
media tend to have a great influence on their
behaviors, we suggest that managers should
explore more business value in the field of social
media. For example, they might want to
collaborate with other e-commerce websites or
companies.
Third, the results also reveal implications for
other practitioners. Because social media play an
important role in providing valued information and
helping users interact with each other, its business
values can be further explored. Our study suggests
that companies take social media as a tool to
enhance the connection of staffs and enhance
internal knowledge sharing. It is efficient but has
The results of this study offer several
implications for both researchers and practitioners.
For researchers, first of all, we developed a
research model in terms of both social media and
e-commerce. It can be extended into a research
model of social commerce as well. We connected
social media context and e-commerce context
from the social impact transfer perspective, which
helps explain how social media affects
e-commerce. Considering that social media and
social commerce have recently been hot issues and
more and more researches are paying attention to
them, our research model provides a theoretical
starting point for researchers who are interested in
further studying social media and social
commerce.
Second, this study developed and proposed
social media interaction ties and social media
commitment as two antecedent measurements of
social impact. People’s relationships in social
media are all built on their social networks. Social
media interaction ties focus on how many
relationships there are in one’s social network and
describe the width of the social network. Social
media commitment focuses on how strong one’s
social network relationships are and describes the
depth of the social network. With both width and
depth, we can fully reflect the characteristics of a
social network.
Third, the findings also give some theoretical
insights to social commerce studies. In our studies,
users’ behaviors were found to be more affected by
group information but not group norms in the
combined environment of social media and
e-commerce. Because social media tends to play a
role as a many-to-many information channel
enhancing information spread, the users in social
media and its related e-commerce website may be
easily influenced by that information. This means
that researches on users’ behavior in the combined
context of social media and e-commerce should
pay more attention to the informational aspects
that help users make the right decisions, rather
than the normative aspects that have been
emphasized in the prior researches with a single
view of social media context.
Fourth, our research also revealed factors that
can be transferred between the social media
context and e-commerce context from the social
impact theory perspective. Although previous
researches have noted the importance of both
normative social influence and informational
social influence, there were few studies that were
1821
low-cost to build and maintain. We also suggest
that companies use social media to communicate
with their customers and provide more information
and knowledge about their products. Moreover,
companies can also encourage their customers to
interact with each other through social media. In
this process, companies can not only diffuse their
information easily but also get users’ feedback
information instantly.
206-215.
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8. Conclusions
Considering that social media has been widely
used in recent years, this paper investigated the
influence of social media based on normative
social influence and informational social influence
in the context of Chinese e-commerce from the
social impact theory perspective. We reviewed
some former studies and proposed hypotheses
based on them. Through an online questionnaire
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e-commerce websites with social network services
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social media in the e-commerce environment.
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