TOPCO 崇越論文大賞

論文題目：

How Behaviors on Social Network Sites and

Online Social Capital Influence Social

Commerce: The Case of Facebook

報名編號： AO0012

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Abstract

Following the fast growing of social network sites (SNS) such as Twitter, LinkedIn and

Facebook in the cyber world recently, the social commerce has become an important

emerging issue in these SNS. The aim of the study is to comprehend the antecedents for

SNS users’ social commerce intention (SCI) of giving and receiving shopping information

about products or services on SNS. According to the literature review and focus group

discussions, the study applied SNS behavior (participating and browsing), social capital

theory (bonding and bridging) and social exchange theory to investigate how these factors

influence SCI. The study conducted an empirical research on SNS After the research survey

that collected a wide range of users for one month, the research has several findings. First,

SNS behavior and social capital affect social commerce intention simultaneously, and the

effects between SNS behavior and SCI are partially mediated by the bonding and bridging

social capital. Second, both of browsing and participating behaviors have significantly

positive relationships with bonding and bridging social capitals. However, the relationship is

stronger for browsing than participating behavior. Conversely, with respect to SCI,

participating behavior has higher impact than browsing has on giving intention. ; No

significant difference can be found between participating and browsing on receiving, while

both of SNS behaviors have significantly positive impact on receiving intention.

Academic contributions and managerial implications are also discussed providing several

future research directions and suggestions to the scholars and SNS operators, respectively.

Key Words：SNS Behavior, Social Capital Theory, Social Commerce Intention

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1. Introduction

The social network sites (SNS) have emerged in the virtual world in recent years.

Increasingly more online users have become members of SNS. SNS adopt the Internet as a

platform that enables online users to connect with each other through the creation of personal

information and profiles, invite friends and colleagues to access files, and send e-mails and

instant messages including online videos between friends (Boyd and Ellison, 2007). These

personal profiles can include various types of information, including photos, videos, audio

files, and public bulletin boards (blogs) (Kaplan and Haenlein, 2010). Through the

interpersonal social activities that are based on the applications, SNS members act and

develop their social lives and accumulate social capital (Ellison et al., 2007). SNS has

become popular and an essential part of individuals’ daily life in recent years.

Social commerce (SC), a subset of e-commerce, adopts Web 2.0 technologies and

infrastructure to assist online interpersonal interactions and contributions for acquiring and

exchanging shopping experiences about products and services (Liang et al., 2011). It is

burgeoning tremendously due to the growth of SNS users’ population (Ng, 2013; Shin,

2012). According to the report of Forrester Research (Anderson et al., 2011), the social

commerce market will grow up to approximately US$30 billion in U.S. by 2015. Moreover,

according to Barria (2014), some elaborations about social commerce are observed in the

developing processes of SNS. While most users of Facebook (FB) and Twitter regularly

click the “like” or “share” function to their friends’ accounts, another function is

developing to give users the ability to “buy” products as they browse SNS. For instance,

FB added the function of the “buy” button for a few small- and medium-sized businesses

with limited use for their operations in the US (Facebook, 2014). This shows that FB tries to

promote social commerce on the website actively.

The above descriptions explain the rapid development of SNS such as FB and Twitter with

respect to more commercial applications that are still in their initial developing stage, which

may become an important discussion issue in the near future. Hence, the study chooses

social commerce as a research topic because it will become an emerging issue of the

fast-growing SNS. Based on the literature reviews, this study adopted SNS behaviors

(participating and browsing) (Casaló et al., 2010; Cotte et al., 2006; Novak et al., 2000;

Pöyry et al., 2013) and social capital (bonding and bridging) (Putnam, 2000) to evaluate

social commerce intention (SCI) which proposed by Liang et al.(2011).

The purposes of the research are as follow: 1. to comprehend the relationship between SNS

behaviors and online social capital; 2. to explore the effect of using social capital theory to

shed some light on social commerce intention; 3. to comprehend whether SNS behavior

influences SCI directly; 4. to test whether the social capital is regarded as a mediator

between SNS behaviors and social commerce intention; and 5. to measure the influences of

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SNS behaviors on SCI in order to understand whether social capital is a full or partial

mediator between SNS behaviors and SCI.

2. Theory Development and Hypothesis

2.1 Social Commerce

Following the fast growth and increasing popularity of SNS such as FB, LinkedIn, Twitter,

and YouTube in recent years, social commerce has become an important discussion issue

(also as known as social business) in the virtual world (Liang et al. 2011; Liang and Turban

2011).

The term “social commerce” was first introduced in 2005 on Yahoo! (Rubel 2005). It was

announced on November 11, 2005, that Yahoo!s Shoposphere was the earliest product to

start into social commerce (Rothberg 2005). The “Pick Lists” feature lets users comment

on and review the products lists. The user-generated content (UGC) makes Shoposphere like

a “blogosphere” (Rothberg 2005; Wang and Zhang 2012). Although the term “social

commerce” emerged in 2005, Amzon.com and eBay.com have introduced some social

commerce-related functions and features in their websites, i.e., reader rating systems and

discussion forums on the products’ browsing pages as early as the late 1990s, such as

Listmania (Amzon.com) and the feedback forum (eBay.com).

To exchange shopping information, because social commerce applied social commerce as a

platform to combine both shopping and social networking activities, eWord-of-Mouth can be

an essential element of social commerce. Hence, Dennison et al. (2009) defines SC as having

taken eWord-of-Mouth where it never existed before in online shopping. Online users are

now seeking ways to learn from each other’s expertise and experiences and comprehend

what others are purchasing and to get more information to make more accurate purchasing

decisions. Simply speaking, SC is the word of mouth utilized in e-commerce. As regards the

definition of SC, this study summarized previous studies and proposed social commerce to

utilize Web 2.0 technology such as social media to support social interaction and to receive

or give shopping information including experience, word of mouth (WOM), and

user-generated content (UGC) for transactions (Liang et al. 2011; Marsden 2010; Ng 2013b;

Zhang et al. 2014).

2.2 Online Social Capital

The concept of social capital has become increasingly popular in a wide range of social

science discipline fields (Adler and Kwon 2002). Nahapiet and Ghoshal (1998) explained

that social capital has been applied in early use to interpret a wide range of social phenomena

because researchers increasingly have concentrated their attention on the role of social

capital as an impact not only on the development of human capital (Coleman 1988; Loury

1976; Loury 1987), but also on the economic performance of firms (Baker 1990), geographic

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regions (Putnam 1993; Putnam 1995), and nations (Evans 1996; Fukuyama 1995), and on

economic development (Woolcock 1998). Therefore, social capital has been widely applied

and explained on the individual level and the country level to explain the social phenomenon

and its effects.

Moreover, Nahapiet and Ghoshal (1998) defined social capital as “the sum of the actual and

potential resources embedded within, available through, and derived from the network of

relationships possessed by an individual or social unit.” Lin (1999a) proposed that social

capital may be regarded as a pool of resources that should be tapped via the social ties which

allow access to and use of resources embedded in social networks.

The notion of social capital has been applied to a wide range of social science settings (Adler

and Kwon 2002). Due to the nature of its ambiguity and multi-dimensionality, a variety of

different operationalizations and variables have contributed to the different representations

of social capital (Bourdieu, 1986; Coleman, 1988). Moreover, there are various forms of

social capital involving ties with neighbors and friends that are related to indices of

psychological well-being, such as self-esteem and life satisfaction (Bargh and McKenna,

2004; Helliwell and Putnam, 2004).

Putnam (2000) extended the concept of social capital and measured social capital by the

amount of trust and “reciprocity” in a community or between individuals and separated

social capital as two different characteristics of social capital, which are bonding and

bridging social capital.

The former (bonding social capital) is more exclusive and “reinforces exclusive identities

and homogeneous groups” (Putnam, 2000). Hence, bonding social capital has a little

diversity in its background but stronger personal connections. The bonding social capital

between individuals in tightly-knit, emotionally close relationships for high recognition and

mutual goals, such as family, close friends, or even neighbors, is referred to as “strong

ties” (Baron et al., 2000; Granovetter, 1973). Such ties can be costly to maintain, requiring

much time and attention. Strong ties feature frequent contact and multiple foci and are found

in dense networks (Donath and Boyd, 2004). These strong ties provide emotional or

substantive support rather than informational quality for each member in the network.

Bonding social capital also refers to resources obtained from within-group ties (Yuan and

Gay, 2006).

The latter (bridging social capital) is linked to what network researchers refer to as “weak

ties”,which are loose or fragile connections between individuals who may provide useful or

novel information or new perspectives for one another but typically not emotional support

formed by mutual benefits; colleagues or community groups from different networks such as

heterogeneous groups lack internally cohesive or emotionally close relationships

(Granovetter, 1973). Bridging social capital occurs when individuals from different

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backgrounds make connections between social networks (Williams, 2006). It can also be

viewed as the broadening of one’s social horizons or worldviews because it lacks in depth,

but makes up for this in breadth. Bridging social capital (weak ties) can be less costly to

maintain, and a person who has many weak, heterogeneous ties has access to a wide range of

information and opportunities (Burt, 2002a; Granovetter, 1983; Granovetter, 1973), but it

provides little in the way of emotional support (Putnam, 2000).

Because SC has applied Web 2.0 technology to support social interaction and give or receive

shopping information, the study adopted social capital as an antecedent with the definition

given by Putnam (2000) for bonding and bridging social capital to predict SC.

3. Methodology

This research applied the theory based on the literature review and constructed the research

framework including SNS behaviors, social capital theory, and social commerce intention.

Therefore, the study’s purpose tries to explore the relationship between SNS behaviors and

online bonding and bridging social capital. Moreover, the study also aimed to comprehend

whether the influences of online bonding and bridging social capital affect the giving and

receiving social commerce intention separately. Adopting the research framework explored

the factors to influence social commerce intention. In addition, after constructing the

research framework, the study develops the measurement items for each construct and

introduces the research methods the study utilized. Moreover, the study also explained the

selection of the research website and sampling method. Finally, the study utilized the

developed questionnaire items for pre-testing and showed the reliability testing results from

pre-testing.

3.1 Research Framework

Following the literature review, the study applied SNS behavior (participating and browsing)

and social factors (familiarity, interaction, reciprocity) influencing online users’ attitudes to

adopting social commerce intentions. Hence, the research framework is based on the above

findings and is constructed. As to the social factors about interaction and reciprocity,

according to Putnam (2000), social capital includes a mutual feeling of reciprocity,

participation, and interpersonal trust. Moreover, Jin (2013) proposed that social capital is

comprised of resources that are retrieved through interpersonal social interactions. Moreover,

defining SC (Huang and Benyoucef, 2013; Social Commerce Today, 2011) includes applying

social media that support social interactions and users’ contributions to assist online and

offline activities in the buying and selling of products and services. Hence, from the social

factors perspective and definition of SC, the study adopted social capital theory as an

antecedent’s factor to associate with social commerce intention. Hence, the research

framework is as shown in Figure 1.

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Figure 1 Research Framework 1

Moreover, because SNS behavior and SCI are both second order constructs in this research,

the study also tries to comprehend how participating and browsing behaviors influence SCI

of giving and receiving as shown in Figure 2. Additionally, the study examined the

relationship between online bonding and bridging social capital to SCI of giving and

receiving, respectively, as shown in Figure 3.

Figure 2 Research Framework 2 Figure 3 Research Framework 3

3.2 Research Hypothesis

Valenzuela et al. (2009) proposed in their study results that social capital exists on SNS.

Their results provide stable evidence of positive associations between SNS and social capital.

In addition, their study also pointed out that the intensity of FB usage seems to be related to

personal satisfaction, greater trust, and involvement in political and civic activity among

undergraduate students. The study argued that these certain features of FB usage enable

online users to take part in behaviors that contribute to their online social capital.

In addition, FB (SNS) helps users establish and maintain communication with facilitating

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deeper, meaningful communication with a more selected group of strong ties as bonding

social capital (Aubrey and Rill, 2013). Close friends who connect through FB are likely to

find an efficient and easy way to keep in touch, and the lightweight interactions enabled by

the site are likely to benefit these more developed relationships as well (Ellison et al., 2011).

Hence, the study proposes the following:

H1: SNS behavior is positively associated with perceived bonding social capital.

The intensity of SNS behavior positively associates with bridging social capital because SNS

can lower barriers to users. Therefore, SNS may encourage the formation of weak ties

(Ellison et al., 2007; Steinfield et al., 2008). Moreover, their studies also proposed that SNS

breaks distance and time limits to let SNS users interact with other SNS users(family, close

or ordinary friends) and exchange information by its function of instant update information.

Aubrey and Rill (2013) adopted FB habit defined as part of participants’ regular Internet

routine. Their results found that FB habits positively predicted online bridging social capital.

The extent to which FB was habitual for users was associated with an increase in online

bridging social capital. Therefore, this leads to the following hypothesis:

H2: SNS behavior is positively associated with perceived online bridging social capital.

The strong ties (bonding social capital) are imperative for transferring sophisticated

knowledge across department boundaries in an organization (Hansen, 1999). Moreover, the

strong tie facilitates the transfer of tacit knowledge. According to the definition of SC

(Huang and Benyoucef, 2013; Liang et al., 2011), the study adopted that SC utilizes Web 2.0

social media technologies and infrastructures to support online interpersonal interactions and

online user contributions to assist in acquiring or giving shopping information about

products and services. Shopping information may include various forms of information, even

specific or complex knowledge like 3C (computer, communication, and consumer electronic)

products or general information about daily usage products in ordinary life. Therefore, the

study proposes the following:

H3: Individuals’ perceived online bonding social capital is positively associated with

SCI.

According to the weak-tie theory originally proposed by Granovetter (1983; 1973), distant

and infrequent relationships (i.e., weak ties) are efficient for knowledge sharing because

those friendships provide access to novel information by bridging otherwise disconnected

groups and individuals in an organization (Wasko and Faraj, 2005). Moreover, bridging

social capital (weak tie) more easily obtains novel information than bonding social capital

(strong tie) because weak ties are more likely to link an information seeker with sources in

disparate parts of a social network such that information seekers may not previously know

that the information is circulating in the social network (Cross and Borgatti, 2004). Some

previous studies have demonstrated the strength of weak ties (Levin and Cross, 2004). Hence,

the bridging social capital depends on the altruism of strangers who may be experts so that

SNS users may receive their opinions and suggestions. Hence, bridging social capital

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positively associates with giving and SCI of receiving. Hence, the study hypothesizes the

following:

H4: Individuals’ perceived online bridging social capital is positively associated with

SCI.

According to past studies’ definition of SC, these studies involve adopting Web 2.0 social

media technologies and infrastructures to support online interpersonal interactions and online

users’ contributions to assist in acquiring shopping information about products and services

(Hwang et al. 2014; Liang et al. 2011; Marsden 2010; Noh et al. 2013). Hence, the usage of

behaviors in social media (SNS) is positively associated with SC because social media (SNS)

is a platform assisting online and offline activities in the buying and selling of products and

services to adopt SC. Therefore, the study postulates the following hypothesis:

H5: SNS behavior is positively associated with SCI.

If H1 and H3 are supported simultaneously, online bonding social capital is the mediator

between SNS behavior and SCI. Similarly, if H2 and H4 are also supported instantaneously,

online bridging social capital is the mediator between SNS behavior and SCI. Hence, the

study proposes the following hypotheses:

H6a: Online bonding social capital is the mediator between SNS behavior and SCI.

H6b: Online bridging social capital is the mediator between SNS behavior and SCI.

H6: Online social capital is the mediator between SNS behavior and SCI.

With respect to SC (Liang et al., 2011; Marsden, 2010), SNS are regarded as a platform for

users to exchange shopping information and experiences to accomplish their giving and

receiving information behavior and finally help SNS members to buy the products and

services that will suit their needs. This study is different from Pöyry et al. (2013) and

explored participating and browsing behaviors toward SCI.

In addition, Casaló et al. (2010) proposed the possible effects of information obtained in the

network (e.g., posts and comments by other members) on consumer decisions and they used

members of online travel communities as survey samples. Their results showed that

participating behaviors in online travel communities have a positive effect on consumer

intention to use online traveling community products. Participation on SNS means in-depth

knowledge or information exchange about the value of the products and services offered by

the SNS users. More habitual participation increases awareness of SNS users that may

broaden the shopping information sources about products and services. Moreover,

relationships with familiar online users who may have similar interests and support create a

comfortable atmosphere that helps SNS users to give or refer the information acquisition of

products and services offered by other SNS friends. Because the members with more

participating behaviors will interact with others frequently in the SNS, they will be more

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willing to buy the products of online traveling community.

Wasko and Faraj (2005) proposed that the ability to have extrinsic rewards may become

more effective than intrinsic returns to motivate knowledge contribution when electronic

networks of practice are used to support professional activities. In professional SNS, users

will contribute their knowledge to gain their professional reputation. Hence, participating

behavior may be more effective to acquire professional knowledge than browsing on SNS.

Comparing with participating behavior, browsing behavior on SNS tends to see and read

substantial amounts of information, and fulfilling the information needs of the members may

have a positive effect on brand (SNS) loyalty that leads to purchase intentions (Pöyry et al.,

2013). In other words, the more the user browse an SNS, the more likely the user are

exposed to information and marketing messages. Therefore, useful or interesting information

will sometimes be forwarded to other SNS users as users are motivated to help others and

enhance their self-worth in their Word-Of-Mouth behavior (Hennig-Thurau et al., 2003).

However, the browsing behavior is unlike participating behavior having more interactions

with other SNS users. More interactive behaviors on SNS also imply higher familiarity

with each other and increased social capital (Nahapiet and Ghoshal, 1998; Tsai and Ghoshal,

1998).

Hence, the study intended not only to search for the evidence that participating and browsing

behavior are positively associated with SCI, but also to prove that participating behavior is

more significantly associated with SCI than browsing behavior. Consequently, the study

proposes the following hypotheses:

H7: The positive relationship with SCI is stronger for participating behavior than

browsing behaviors

H7a: The positive relationship with SCI of giving is stronger for participating behavior

than browsing behavior

H7b: The positive relationship with SCI of receiving is stronger for participating behavior

than browsing behavior

Putnam (2000) mentioned that bonding social capital provides emotional support with

homogenous group on a dense network (Baron et al., 2000; Granovetter, 1973). On the other

hand, bridging social capital provides information support and has many weak

heterogeneous ties to access a wide range of information and opportunities (Burt, 2002;

Granovetter, 1973, 1983; Lin, 1999b). Hence, both of the emotional and information

supports are important factors for SNS users to achieve higher SCI (Hajli, 2014).The

previous literature regarding bonding and bridging social capital indicated that online users

may rely on the information support to give and receive the shopping information to and

from other SNS users, respectively. Users with bridging social capital may be more willing

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to give or receive shopping information than users with bonding social capital.

When SNS users have more bonding social capital, they may not have the needed

information to share with other SNS users. It can be depicted in detail as the following.

First, bonding social capital involves close friends or family members, and the SNS users

may give their suggestions of shopping information to them to increase bonding social

capital. However, the information may be limited and have only little effect on their purchase

decision. Second, bridging social capital involves ordinary friends of SNS users, and the

SNS users may prefer the recommendations from ordinary friends because they have a wide

range of shopping information. Hence, bridging social capital will be more effective than

bonding capital for the social commerce intention that can be further decomposed into giving

or receiving shopping information. Following the above statements, the study proposed:

H8: Bridging social capital has a higher impact on SCI than bonding social capital

H8a: Bridging social capital has a higher impact on SCI of giving than bonding social

capital

H8b: Bridging social capital has a higher impact on SCI of receiving than bonding

social capital

The construct measurements, including items, questions, and sources, are shown in Table 1.

3.3 Research Method

The study adopted the partial least squares structural equation modeling (PLS-SEM) method

to test the research model (Ranganathan et al. 2004; Wold 1989), and SmartPls 2.0 is used to

analyze the data.

The study uses FB as the target of this study following the research by Valenzuela et al.

(2009). At the time of the study, Facebook is the most popular SNS in term of active

members. In order to capture a diversifying sample and age distribution pattern of FB users

reported by Socialbakers (2014), which is 18–55 years old, this study surveyed a wide

range of participants through two different approaches, online survey and paper-based

survey.

For the online survey, an announcement was posted on Taiwan’s largest bulletin board

system (PTT) in which only FB users responding the survey were collected. As to the

paper-based survey, the study administrated the survey to collect samples from college

students (undergraduate, graduate, and executive degrees).

Three MBA classes were chosen for the pre-test. Out of 79 samples, 67 are valid for further

analysis. As a result, three items, Bon3, Bon9, and BRI10 were deleted because of low factor

loadings. A second pre-test was conducted to delete further four items of Bon5, Bon8, Bon10,

and Bri9 with low factor loadings.

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4. Data Analysis

4.1 Pre-test and data collection

The data collection process lasted for approximately one month, collecting data of

paper-based and online surveys from 2014/10/15 to 2014/11/15. A total of 1020 samples

were collected in which 492 samples are from an online survey and 528 from a paper-based

survey. Because of incomplete data, 29 paper-based samples and 50 online samples are

invalid resulting in an effective sample collection rate of 95%. The online and paper surveys

have 471 and 499 samples, respectively. The number of male and female samples are 367

and 603, respectively. To make sure the generalization of samples, a two sample t-test was

conducted to test the differences between two data collection approaches against all items.

No significant problems were found and the total of 970 samples are used for further

analysis.

4.2 Reliability and validity tests

To test the reliability of the items, composite reliability is calculated ranging from 0.862 to

0.905, which are all higher than the acceptable value of 0.7 (Cronbach 1951).Fornell and

Larcker (1981) suggested that average variance extracted (AVE) can also be interpreted as a

measure of reliability for the latent variable component score, and it tends to be more

conservative than composite reliability. A minimum of 0.5 forAVE is required. (Chin 2010).

The results for reliability and validity test are shown in Table 1.

Table 1 First-Order Construct Reliability and Validity

Constructs Item Contents Loadings CR/AVE Source

Participating

Par1 I participate actively in FB

activities. 0.72***

0.91/0.68 (Casaló et

al., 2010)

Par2 I use to contribute to FB. 0.89***

Par3 I usually provide useful information

to other FB members. 0.76***

Par4 I post messages and responses on

FB frequently. 0.88***

Par5 I post messages and responses on

FB with great excitement. 0.83***

Browsing

Bro1

I like to browse FB to see what

friends shared through the real

timeline.

0.71***

0.91/0.63 (Novak et

al., 2000) Bro2

I like to browse FB to see what’s

new (either directly on the

community page or through

newsfeed).

0.79***

Bro3 I like to browse FB seeking new

ideas and thoughts. 0.85***

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Constructs Item Contents Loadings CR/AVE Source

Bro4 I like to browse FB to help me to

generate new ideas and thoughts. 0.83***

Bro5 I like to browse FB to absorb new

knowledge. 0.83***

Bro6 I browse the contents of FB

frequently. 0.74***

Online Bonding

Social Capital

Bon1 There are several SNS friends I trust

to help solve my problems. 0.82***

0.89/0.61 (Williams,

2006)

Bon2

There is someone on FB I can turn

to for advice about making very

important decisions.

0.83***

Bon4 When I feel lonely, there are several

FB friends I can talk to. 0.79***

Bon6

The people I interact with on FB

would put their reputation on the

line for me.

0.72***

Bon7 The people I interact on FB would

be good job references for me. 0.73***

Online Bridging

Social Capital

Bri1

Interacting with FB friends makes

me interested in things that happen

outside of my town.

0.79***

0.93/0.62 (Williams,

2006)

Bri2 Interacting with FB friends makes

me want to try new things. 0.83***

Bri3

Interacting with FB friends makes

me interested in what people unlike

me are thinking.

0.85***

Bri4

Talking with FB friends makes me

curious about other places in the

world.

0.85***

Bri5

Interacting with FB friends makes

me feel like part of a larger

community.

0.74***

Bri6

Interacting with FB friends makes

me feel connected to the bigger

picture.

0.78***

Bri7

Interacting with FB friends reminds

me that everyone in the world is

connected.

0.72***

Bri8 I am willing to spend time to

support general FB activities. 0.71***

Social

Commerce

Intention

(giving)

Giv1

I am willing to provide my

experiences and suggestions when

my friends on the FB want my

advice on buying something.

0.86***

0.93/0.81 (Liang et al.,

2011) Giv2

I am willing to share my own

shopping experience with my

friends on FB.

0.93***

Giv3

I am willing to recommend a

product that is worth buying to my

friends on FB.

0.90***

Social

Commerce

Intention

(receiving)

Rec1

I will consider the shopping

experiences of my friends on FB

when I want to shop.

0.89***

0.92/0.79 (Liang et al.,

2011) Rec2

I will ask my friends on FB to

provide me with their suggestions

before I go shopping.

0.88***

Rec3 I am willing to buy the products

recommended by my friends on FB. 0.89***

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Note: CR=Composite Reliability AVE=Average Variance Extracted *** p<0.01

4.3 Discriminant Validity

Fornell and Larcker (1981) suggested that discriminant validity is established if a latent

variable accounts for more variance in its associated indicator variables than it shares with

other constructs in the same model. Each construct to satisfy this requirement for

discriminant validity, each construct’s square root of the AVE, must compare with its

correlations with other constructs in the research model. Hence, the discriminant validity was

assessed by the square root of AVE values for each construct, which should be greater than

the correlation estimates involving the construct (Fornell and Larcker 1981). As shown in

Table 2, all square roots of AVE values (shaded numbers) met this criterion (greater than the

off-diagonal numbers) showing an acceptable discriminant validity. (1) The square root of

each construct’s AVE is higher than its correlation with another construct, and (2) each

item loads highest on its associated construct.

Table 2 Correlation Between Constructs and Square Roots of AVE

Participating Browsing

Bonding social

capital

Bridging

social capital Giving Receiving

Participating 0.82

Browsing 0.46 0.79

Bonding social

capital 0.39 0.47 0.78

Bridging social

capital 0.41 0.59 0.57 0.78

SCI of giving 0.46 0.39 0.39 0.48 0.90

SCI of receiving 0.39 0.45 0.50 0.52 0.60 0.89

Notes: Numbers on the diagonal (in boldface) are the square root of the average variance

extracted (AVE). Other numbers are the constructs correlation.

4.3 CMV (Common Method Variance) Test

The collected data from respondents in the study is self-reported and it may cause a potential

for common method biases resulting from multiple sources such as consistency motif and

social desirability (Podsakoff et al., 2003; Podsakoff and Organ 1986). According to

Harmon’s testing for measuring CMV, all of the indicators are measured by factor analysis

and set to one factor. If the extraction sums of squared variance are more than 50%, it may

have CMV problems. Hence, the extraction sums of squared variance for all of the items of

constructs in the study is 24.69%, which is lower than 50% and fittable for Harmon’s

testing, indicating that common method biases are not a likely contaminant of our results.

In addition, the study also applied a common method factor whose indicators included all the

principal constructs’ indicators and calculated each indicator’s variances substantively

explained by the principal construct and by the method (Huigang et al., 2007) in the PLS

model. The results demonstrate that the average substantively explained the variance of the

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indicators is 0.602, while the average method-based variance is 0.016. The ratio of

substantive variance to method variance is about 57:1. Additionally, most factor loadings are

insignificant. As to the small magnitude and insignificance of method variance suggested by

Huigang et al. (2007), the CMV is unlikely to be a serious concern for this study.

4.4 Model Fit Evaluation

Hair et al. (2014) proposed that the most widely applied way to measure the structural model

is the coefficient of determination (R2 value). It is a measure of the research model’s

predictive accuracy and is calculated as the squared correlation between a specific

endogenous construct’s actual and predicted values. Chin (1998) describes R2 values of

0.19, 0.33, and 0.67 in PLS path models as weak, moderate, and, substantial levels,

respectively. Furthermore, if inner path model structures explain an endogenous latent

variable by only a few (e.g., one or two) exogenous latent variables,“moderate” R2 may be

acceptable. As Figure 4 indicates the R2 of each construct in the study, all of the values are at

a moderate to substantial level for the research model. Although the R2 of SCI is 0.41 and at

the moderate level for the study, it is acceptable by the criterion (Henseler et al., 2009).

Figure 5 and Figure 6 are the results of SNS behavior to SCI and social capital to SCI,

respectively.

The study has tested the second order constructs for SNS behavior, social capital and SCI.

The relationship among three second order constructs are positively significant.

4.6 Hypotheses Testing

Figure 4 Results of the PLS Analysis 1

15

Figure 5 Results of the PLS Analysis 2 Figure 6 Results of the PLS Analysis 3

*p < 0.05; ** p < 0.01; *** p < 0.001

Additionally, about the hypotheses testing of H7a and H7b, the study tested the relationship

that is participating and browsing behavior both have significant effects on social commerce

intention has sustained. Moreover, the study also adopted heterogeneity test that proposed

by (Altman and Bland, 2003) to test whether participating behavior are significantly

positively associated with giving and SCI of receiving than browsing behavior is related to

giving and SCI of receiving separately. The testing results revealed that Z value is 4.77 for

the significant difference between the participating to giving and browsing to SCI of giving

after heterogeneity test. The coefficient of participating to giving is 0.29 which is more

than the coefficient of browsing to giving is 0.04. Hence, the H7a is supported. However,

as to the significant difference between the participating behavior to receiving and browsing

behavior to receiving that z value is 0.43 which is not significant. The testing results

revealed that the coefficient of participating behavior to receiving is 0.14 which is an

insignificant difference with the coefficient of browsing to receiving is 0.12. Hence, the

H7b is not supported.

Moreover, with respect to the hypotheses testing of H8a and H8b, the study also tested the

relationship that bonding and bridging social capital both have significant effects on social

commerce intention has sustained. Moreover, the study adopted heterogeneity test that

proposed by (Altman and Bland, 2003) to test whether bridging social capital are

significantly positively associated with giving and SCI of receiving than bonding social

capital is related to giving and SCI of receiving respectively. The testing results revealed

that the Z value is -2.89 for the significant difference between the bonding social capital to

giving and bridging social capital to SCI of giving after heterogeneity test. The coefficient

of participating to giving is 0.11 which is less than the coefficient of browsing to giving is

0.28. Hence, the H8a is supported. However, as to the significant difference between the

bonding social capital to receiving and browsing to receiving that z value is 0.10 which is

not significant. The testing results revealed that the coefficient of bonding social capital to

receiving is 0.25 which is no significant difference than the coefficient of browsing to

receiving is also 0.25. Hence, the H8b is not supported.

16

4.7 Mediation Testing of Online Social Capital Between SNS Behavior and SCI

Table 3 Mediation Testing of Online Social Capital on SCI

*VAF= Variance Accounted For

Table 3 also illustrates the mediation effects of bonding and bridging social capital on SCI.

With respect to the mediating effects, the research testified the influence of SNS behavior on

bonding and bridging social capital (H1 and H2) and the influence of bonding and bridging

social capital on SCI (H3 and H4), respectively. Similarly, all the path coefficients are

significant and positive, which indicates that all four hypotheses are supported.

The results showed that participating behavior is significantly more positively associated

with SCI of giving than browsing behavior is associated with SCI of giving. Moreover, To

assess the statistical validity of mediation, the significance of indirect effects is further

examined (MacKinnon et al., 2004). Hence, the research adopted the Sobel test for

validating the moderation effects because Baron and Kenny (1986) suggested an

approximate significance test for the indirect impact of the independent variable on the

dependent variable via the mediator (Sobel, 1982). Hence, the study applied the Sobel test to

measure bonding and bridging social capital as mediators between SNS behavior and SCI in

the model. As the Sobel testing results indicate, the Z value of the Sobel test is 5.09 and 6.84

at α=0.05. The results are significant for bonding and bridging social capital as mediators

between SNS behavior and SCI. According to the above mediation analyzing results, H6a,

H6b, and H6 are all supported in the study.

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4.8 Hypotheses Testing Results

From the above section of data analysis for hypotheses testing, the results are listed in Table

4.

Table 4 Research Hypotheses Testing Results

Hypothesis Results

H1 SNS behavior is positively associated with individuals’ perceived bonding social capital. Supported

H2 SNS behavior is positively associated with individuals’ perceived online bridging social capital. Supported

H3 Individuals’ perceived online bonding social capital is positively associated with SCI. Supported

H4 Individuals perceived online bridging social capital is positively associated with SCI. Supported

H5 SNS behavior is positively associated with SCI. Supported

H6a Online bonding social capital is the mediator between SNS behavior and SCI. Supported

H6b Online bridging social capital is the mediator between SNS behavior and SCI. Supported

H6 Online social capital is the mediator between SNS behavior and SCI. Supported

H7a Participating behavior is positively significant associated with SCI of giving than browsing

behavior is associated with SCI of giving. Supported

H7b Participating behavior is positively significant associated with receiving than browsing behavior

is associated with SCI of receiving. Not Supported

H7 Participating behavior is positively significant associated with SCI than browsing behaviors is

associated with SCI.

Partly

Supported

H8a Bridging social capital is more positively significantly associated with SCI of giving than

bonding social capital is associated with SCI of giving. Supported

H8b Bridging social capital is more positively significant associated with receiving than bonding

social capital is associated with SCI of receiving. Not Supported

H8 Bridging social capital is positively significant associated with SCI than bonding social capital is

associated with SCI.

Partly

Supported

5. Discussion

The research framework can be discussed in several parts. As the first part of the relationship

between SNS behavior (participating and browsing) and social capital, i.e., H1 and H2,

Ellison et al. (2007), Tomai et al. (2010), and Ellison et al. (2011) continued to focus on the

users’ participation in SNS behavior influencing online bonding and bridging social capital.

As for their study results, the relationship between SNS behavior and bridging social capital

is positively significant. However, the relationship between SNS behavior and bonding

social capital is insignificant in their study. The study found SNS behavior’s influence on

bonding social capital is positively significant. The results may indicate that adopting SNS

became one part of daily life gradually, and SNS users communicate with not only ordinary

friends but also close friends to exchange their feelings with each other. It may give SNS

operators more opportunity to raise more topics or hot issues to their close friends for their

mutual interest and increase their stickiness and reliability on SNS.

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The second part is online social capital and SCI, i.e., H3 and H4. Based on the previous

literature review, the study adopted social capital theory, including bonding and bridging

social capital to evaluate the impacts of two constructs on SCI giving and receiving. Putnam

(2000) mentioned that bonding social capital is like emotional support (Baron et al. 2000;

Granovetter, 1973) and bridging social capital is like information support (Burt, 2002b;

Granovetter, 1983). Hence, emotional support and information support are also both

important factors to SCI. As the study assumes in the results that bridging social capital is

information support, online users relied on the information to give and receive shopping

information to and from other SNS users.

The third part is SNS behavior to SCI as the H5. The hypothesis is similar to that Pöyry et al.

(2013) proposed. The dependent variable in their study is the performance outcome that

separates as purchase intention and referral intention. As to the definition of social commerce

(Liang et al., 2011), the social commerce is based on SNS as a platform and lets SNS users

give and receive shopping information. Hence, their research construct of referral intention is

similar to the SCI of giving in our study. Moreover, in their study, participation behavior has

no significant influence on purchase intention or referral intention. On the contrary, browsing

behavior has positive impacts on purchase intention and referral intention. As to our study,

the questionnaire items were adopted from Liang et al. (2011), which includes giving and

receiving shopping information of SCI.

In this study, the questionnaire items were adopted from (Liang et al., 2011) which includes

giving and receiving shopping information of SCI. According to research findings,

participating behavior both influences giving and receiving (0.28 vs. 0.13). However,

browsing behavior has no influences SCI of giving but has significant influence on SCI of

receiving (0.04 vs. 0.12) AS to H7a, the influences between participating behavior on

giving (H7a1 is 0.28) is stronger than influences between browsing behavior on giving

(H7a2 is 0.04). Hence, the H7a is sustained. Study results is consistent with Casaló et al.

(2010) found that active participation was a strong indicator of the intention to use

community host’s products in the setting of an online travel community. It infers when SNS

users give shopping information, participating to post, reply and give comments is necessary

for giving shopping information. Conversely, the browsing behavior is unable to provide

shopping information. Therefore, the hypothesis testing is insignificant (0.04). As for

H7b, both of the impacts of participating behavior on SCI of receiving (H7b1) and impacts

of browsing behaviors of SCI of receiving (H7b2) are both positively significant (0.13 and

0.12). However, the difference of participating behavior and browsing behavior on SCI of

receiving is insignificant which H7b is not supported. According to the research findings,

it inferred when users are on SNS, they may receive some shopping information on SNS

while they interact with other users or they may just browse to acquire the shopping

information that is posted by other SNS users. Both participating and browsing behavior

influence users SCI receiving intention. Therefore, participating and browsing behavior are

both significant on SCI of receiving but there is no significant difference between

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participating and browsing behavior on SCI of receiving.

Hence,for further research findings, as the H8a for the relationship between influences of

bonding (H8a1) and bridging social capital (H8a2) on SCI of giving, the results of research

indicated that both of bonding and bridging social capital to SCI of giving are positively

significant which value is (0.10 vs. 0.27) separately. After heterogeneity test, the research

findings appears there are significant differences between bonding and bridging social

capital on SCI of giving Z=-3.00145.

Regarding bridging social capital that are ordinary friends to SNS users, they also give the

shopping information if they are regarded as experts by others (H8a2). The SNS users may

also like to give their recommendation for the shopping information when other SNS users

are just ordinary friends. Also, as to the coefficient that bridging social capital has more

impact on SCI of giving than bonding social capital on SCI of giving is significant different

according to heterogeneity test, the results indicates bridging social capital (information

support) is more important than bonding social capital (emotional support) because

possessing information is important than willingness to give important.

With relation to bonding social capital which are close or family members, hence, the SNS

users may like to give their suggestion for the shopping information because they are

willingness to give shopping information to their close or family members. Regarding

Putnam (2000) mentioned, bonding social capital is like the emotional support (Baron et al.,

2000; Granovetter, 1973) and bridging social capital is like information support (Burt, 2002;

Granovetter, 1973, 1983; Lin, 1999b). With respect to bonding social capital (emotional

support), family and close friends provide deeper and specific knowledge to SNS users, they

may not provide general shopping information. As to bridging social capital (information

support), they are ordinary friends and provide general, broad shopping information for SNS

users. Therefore, bridging social capital may be more useful than bonding social capital for

SCI of giving.

Additionally, as the H8b for the relationship between influences of bonding and bridging

social capital on SCI of receiving, the research findings indicated that both of bonding (H8b1)

and bridging social capital (H8b2) to SCI of receiving are positively significant which value

is (0.25 vs. 0.25) separately. However, the research findings appear there is no significant

difference (Z value is 0) between bonding and bridging social capital on SCI of receiving

after heterogeneity test. The implication can be also separated from two different

discussions. First, as to bonding social capital which are close or family members, hence,

the SNS users may receive their suggestion of close friends for the specific and deeper

shopping information even knowledge because they trust the information that provided by

their close or family members. As for bridging social capital that are ordinary friends.

Nevertheless, the SNS users may also like to receive their recommendation for the general

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and wider shopping information from their ordinary friends.

The fourth part is that online bonding and bridging are the mediators between SNS behavior

and SCI, as in H6a, H6b, and H6, although they are partial mediators because SNS behavior

as related to SCI is also significant. Hence, online social capital is playing a partial

mediating role between SNS behavior and SCI. It implies that users with bonding social

capital (strong tie) and bridging social capital (weak tie) are helping SCI in SNS. As for

bonding social capital, it is emotional support (Baron et al., 2000). Hence, close friends may

give SNS users deeper and more specific range information to other SNS users. Conversely,

as for bridging social capital, it is information support (Burt, 2002a; Granovetter, 1973;

Wellman and Gulia, 1999). Therefore, ordinary friends may give general and wide-range

information for SNS as needed.

6. Conclusion

6.1 Academic Contribution

Seldom research adopted social capital theory to evaluate the influence on SCI. The study

adopted online social capital (bonding and bridging) to predict SCI. A lot previous research

tried to explore factors influence online social capital (Ellison et al., 2007; Ellison et al.,

2011; Lee et al., 2013). However seldom researchers have explored social capital from

SNS behavior with participating and browsing behavior. As to the relationship between SNS

behavior and bridging social capital, the study findings is consistent with Ellison et al. (2007)

and Steinfield et al.’s (2008) results that SNS behavior has more positively stronger effects

than bridging social capital. As to the relationship between SNS behavior and bonding

behavior, the study supported the relationship, while it was not supported in the previous

literature (Ellison et al., 2007).

Moreover, Ellison et al. (2011) also suggested they would not expect Facebook-enabled

interaction with close friends to have a large impact on either form of social capital, as these

social resources are available with or without Facebook. However, the study proposed that

following the fast-growing and maturity of SNS, SNS behaviors also impact bonding social

capital. In other words, SNS behavior also affects users’ relationship with their close friends

and classmates. Hence, according to research findings, SNS behavior affects not only

bridging social capital but also bonding social capital.

The seldom research adopted social capital theory to evaluate the influences on SCI. The

study adopted online social capital (bonding and bridging) to measure SCI suggested by

Liang and Turban (2011). The result findings appear bonding social capital significantly

affects SCI including giving and receiving. It indicates that SNS users will not only give

shopping information but also receive shopping information from their close friends.

Additionally, bridging social capital also affects SCI including giving and receiving. It

indicates that SNS users will give and receive shopping information to and from their

21

ordinary friends on SNS because they or their ordinary friends may be regarded as experts or

opinion leaders for the products SNS users are interested in. The shopping information is

provided by their ordinary friends can be valuable and reduce search cost of transaction cost

because the shopping information is without commercial implication.

Bridging social capital is more positively significantly associated with SCI of giving than

bonding social capital is associated with SCI of giving. The study results indicate that SNS

users are likely to become experts or opinion leaders to give other SNS users shopping

information more than to giving familiar or close friends shopping information. However,

bridging social capital is not more positively significantly associated with SCI of receiving

than bonding social capital is associated with SCI of receiving. It implies SNS users both

receiving information from familiar, close friends and product experts or opinion leaders on

SNS. With relation to academic contribution, the research findings also throw much light on

the previous study may not apply social capital theory to study SCI, especially the study also

explore bonding and bridging social capital to SCI of giving and receiving in detail. The

study also implies bonding and bridging social capital is a suitable point of view as an

important factor to explore SCI by quantitative research.

Moreover, the relationship between SNS behavior and SCI is supported. It may be

interesting to comprehend users’ behaviors to adopt SCI gradually. The research sheds much

light on studying the relationship between participating behavior and the SCI giving and

receiving. Their findings are in line with previous studies, although no previous study has

explored the relationship between SNS behavior and SCI of giving and receiving in detail,

respectively.

With respect to the influence of SNS behavior on SCI, Pöyry et al. (2013) proposed a

research model to analyze the participating and browsing to purchase intention and referral

intention. According to their study, their referral intention is like SCI of giving in the study.

Their study results appeared that participating behavior have no significant influences on

purchase intention and SCI of giving. Conversely, the browsing behavior has both

positively significant impacts on purchase intention and referral (giving) intention.

Compared with our study and their study, the research findings appeared that participating

behavior do have positively affects for SCI of giving and receiving. What makes the

change maybe the SNS has become one part of users’ life (Ng, 2013; Shin, 2012), users like

to share and receive the shopping information to and from other users consequently. As to

browsing behavior, the study also appeared it influences SCI of receiving although browsing

does not influence SCI of giving significantly.

It also shows participating behavior will like to SCI of giving than browsing behavior to SCI

of giving. The study proposes once SNS browsing behavior may not like to give shopping

information to other SNS while participating on SNS like to give the shopping information.

22

These results are quite different with those of (Pöyry et al., 2013) which found the

relationship between browsing behavior and SCI of giving. Moreover, participating

behavior is not positively significant associated with receiving than browsing behavior is

associated with SCI of receiving because participating and browsing behavior are both

significant influence SCI of receiving. The results infers the influences of participating

behavior including posting, clicking and checking into places to receive shopping

information is similar to browsing behavior to receive shopping information because

participating and browsing behavior are both significant on SCI of receiving. The

relationship between SNS behavior and SCI is supported. Hence, not only browsing but

also participating behavior have influences on SCI. It may become an interesting findings

for us to comprehend users’ behaviors to adopt SCI. The research shed much light on

studying the relationship between participating behavior and SCI giving and receiving that

needed to explore the relationships. These findings are in line with previous studies,

although no previous study has explored the relationship between SNS behavior and SCI of

giving and receiving in detail.

Previous research has studied social capital as antecedents (Wasko and Faraj, 2005) or

outcome variables (Ellison et al., 2007; Ellison et al., 2011). However, this study analyzes

social capital as a mediator between SNS behavior and SCI. Both online bonding and

bridging social capital have partial mediating effects because the SNS behavior to SCI is also

positively significant. It also may shed light on other scholarship about social capital to

analyze it as a mediator for SNS behavior or even consumer behavior because SNS is fast

growing and become mature.

6.2 Managerial Implication

According to the study result, SNS behavior are mainly influence SCI while online bonding

and bridging social capital is partial mediation between SNS behavior and SCI. Moreover,

regarding SNS behavior, participating behavior on SCI of giving is more positively

significant than browsing behavior on SCI of receiving, however; there is no difference

between SCI of receiving for participating or browsing behavior while both of relationships

are significant on SCI of receiving. Hence, how to motivate SNS users to participate on

SNS may become an essential issue for increasing SCI.

With respect to the users’ behavior to adopt SC, the users may adopt SC according to their

behavior. Hence, SNS operators may think about how to let SNS users not only buy products

but also have the willingness to promote the products to others voluntarily. According to this

research finding, social commerce gradually becomes mature as one form of e-commerce

following the rapid growing of SNS. Hence, SC will also grow with users behavior.

Moreover, SNS operators notice the potential market value of SC. However, the function for

SNS to apply social commerce is still in the initial stage (Facebook, 2014). SNS operators

may recommend the suitable fan pages or clubs to users according to their behaviors on SNS

23

following SNS past behaviors like posting contents, chatting topics and clicking “like” pages

SNS may learn from Google AdWords to predict what contents are suitable for users. SNS

may acquire more information from every SNS user’s online behavior and provide more

attracting information by different users for customization. Now, FB can provide content

for users to browse to increase their stickiness to browsers by the algorithm; it is more

capable of providing such forms of shopping information to its users individually. Hence,

SNS operators may try to give SNS users for their favorite topics or content about shopping

information further.

As for SNS behavior on bonding social capital, SNS may provide more interactive functions

for familiar friends to let SNS users have deeper and more sustainable relationships because

SNS builds more bonding social capital among social networks. It may result from the fast

growth and gradual maturation of the SNS.

With relation to SNS behavior on bridging social capital, SNS may provide functions to let

SNS users increase connections more and more with other SNS users from offline to become

online friends. As for unknown SNS users, SNS may also introduces SNS users to join fan

pages, clubs in SNS. Members in the fan pages or clubs are with the same interests. SNS

should provide users more similar shopping topics to SNS users of close friends who are

homogeneous to discuss by sending private messages or leaving comments in friends’

timeline. Users are interested in their private or open discussion groups and share the same

shopping information and experience because SNS users with bonding social capital may

have the same interests while they are homogeneous, and they are reliable and trust each

other. Adopting the above suitable shopping information promotes SNS users who have the

same interest in shopping information to discuss together and facilitate their online bonding

social capital. Consequently, doing so also promotes SNS users’ social commerce intention

toward shopping experiences.

Conversely, with respect to bridging social capital, SNS should provide more variety of

shopping topics to SNS users with bridging social capital to discuss in the different public

groups to raise their interests because they are from different backgrounds, with

heterogeneity that allows them to know each other’s interests because SNS users with

bridging social capital may have different interests.

SNS may also evaluate the relationships between each user and their SNS friends. SNS may

find what they both are interested in and give SNS users shopping information

simultaneously to attract their attention and to discuss and share the information with other

SNS users voluntarily. In addition, online bonding and bridging social capital are

mediators between SNS behavior and SCI. Hence, when SNS operators decide to

implement SC, they may consider users’ close and ordinary friends. With different

relationship of friends on SNS gives them different content and shopping information

between SNS users to raise their discussion topics and interests to promote SC in the SNS.

24

6.3 Conclusion

The study applied SNS behavior and social capital theory based on the literature review to

analyze social commerce intention. The study collected data from online and paper-based

surveys that college students were the major samples. From the total of 970 samples, after

reliability, validity, and partial least square structural model analysis, the study found that

SNS behaviors positively influence online bonding and bridging social capital, respectively.

Online bonding and bridging social capital also positively affect social commerce intention.

For the combined influence of participating and browsing behavior on SCI, the study also

found that SNS behavior significantly affects it.

6.4 Research Limitation

Although the research findings are interesting, the study is not without limitations. First, the

empirical study used for evaluating our theoretical model was conducted on the SNS,

Facebook, that has the most SNS members in the cyber world. As there are a variety of

forms of SNS mentioned in literature review, the result may not be directly applicable to

other SNS which may apply to social commerce sites such as Twitter and LinkedIn because

each SNS has its features and characteristics.

In addition, the study was done in one country with undergraduate students as a major survey

group, a relatively homogeneous population. Hence, generalization to other different cultures

or countries of SNS and social commerce needs to be further investigated.

6.5 Future Research

Liang and Turban (2011) provided a research framework to study SC with commercial

activities, social media, research theme, research methods, theories, and outcomes as

dependent variables. Hence, the future study may combine empirical questions and issues

from the qualitative findings of the study and the research framework proposed Hence, it

will raise more potential research questions about social commerce following the fast growth

and maturity of SNS.

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