International Journal of Human-Computer Interaction
ISSN: 1044-7318 (Print) 1532-7590 (Online) Journal homepage: https://www.tandfonline.com/loi/hihc20
Applying the Technology Acceptance Model to
Social Networking Sites (SNS): Impact of Subjective
Norm and Social Capital on the Acceptance of SNS
Gilok Choi & Hyewon Chung
To cite this article: Gilok Choi & Hyewon Chung (2013) Applying the Technology Acceptance
Model to Social Networking Sites (SNS): Impact of Subjective Norm and Social Capital on the
Acceptance of SNS, International Journal of Human-Computer Interaction, 29:10, 619-628, DOI:
10.1080/10447318.2012.756333
To link to this article: https://doi.org/10.1080/10447318.2012.756333
Published online: 08 Aug 2013.
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Intl. Journal of Human–Computer Interaction, 29: 619–628, 2013
Copyright © Taylor & Francis Group, LLC
ISSN: 1044-7318 print / 1532-7590 online
DOI: 10.1080/10447318.2012.756333
Applying the Technology Acceptance Model to Social Networking
Sites (SNS): Impact of Subjective Norm and Social Capital on the
Acceptance of SNS
Gilok Choi1 and Hyewon Chung2
1
2
School of Library and Information Science, Pratt Institute, New York, New York, USA
Department of Education, Chungnam National University, Daejeon, South Korea
With their heavy traffic and technological capabilities, social
networking sites (SNS) introduced a new means of building and
maintaining perceived social capital. This study aims to identify
underlying factors and causal relationships that affect behavioral
intention to use SNS. For this purpose, this research developed
an extended technology acceptance model, incorporating subjective norm and perceived social capital for predicting SNS acceptance and usage. Exploratory correlation and path analyses were
conducted to identify the relationships between five constructs:
perceived usefulness, perceived ease of use, subjective norm, perceived social capital, and intention to use. The results showed
that perceived usefulness and perceived ease of use had robust
effects on the user’s intention to use SNS. The research findings also demonstrated that subjective norm and perceived social
capital were significant predictors of both perceived usefulness
and perceived ease of use and therefore should be considered
as potential variables for extending the technology acceptance
model.
1. INTRODUCTION
A wide variety of social networking sites (SNS), such as
Facebook, Myspace, and Twitter, have gained phenomenal
popularity in recent years. The increasing availability of wireless and mobile technology has made the use of SNS even
more ubiquitous and pervasive (Choi, Abbott, Arthur, & Hill,
2007; Petrova, 2007). With its heavy traffic and technological capabilities, SNS introduced a new means of building and
maintaining perceived social capital (Pfeil, Arjan, & Zaphiris,
2009). SNS provide various communication features, including content sharing, discussions, and organization of activities
and events, in order to facilitate interactions and enhance
perceived social capital (Cachia, Compano, & Da Costa,
2007).
Address correspondence to Hyewon Chung, Department of
Education, College of Education, Chungnam National University,
99 Daehak-ro, Yuseong-gu, Daejeon 305-764, South Korea. E-mail:
hyewonchung7@gmail.com
SNS are different from the first generation of virtual community sites because they allow formation of new connections
as well as maintenance of existing social ties (Donath & Boyd,
2004). SNS is the first successful application with which people
can articulate and capitalize their social networks and resources
(Rau, Gao, & Ding, 2008). Accordingly, the increasing popularity of SNS has profoundly influenced relationships among
people by bridging their online and offline interactions (Ellison,
Steinfield, & Lampe, 2006). Indeed, recent studies indicate
that SNS have had positive effects on community interaction,
involvement, and social capital, showing that new forms of perceived social capital building and ties can occur in online sites
(Hampton & Wellman, 2003; Kavanaugh & Patterson, 2001; J.
Kim, Kim, & Nam, 2010). Certainly, SNS is a very useful way
to improve perceived social capital and interactions because
technology is well suited to maintaining social networks with
reasonable effort and cost (Donath & Boyd, 2004).
For this reason, SNS have been a subject of growing interest in both scholarly and practitioner worlds; it is imperative
to understand such important drivers of technology adoption
and usage (Qin, Kim, Hsu, & Tan, 2011). In this respect, this
study aims to identify underlying factors and causal relationships that affect behavioral intention to use SNS. This research
will not only deepen our understanding of SNS but also provide an appropriate model to explain user acceptance and usage
of SNS.
Although several theoretical models were introduced to
explain the acceptance, adoption, and usage of new technology,
technology acceptance model (TAM) is the most widely applied
and validated model in various contexts and across a variety
of technologies (Venkatesh, 2000). Therefore, the current study
proposes TAM as the main theoretical framework to describe
the determinants of SNS and an extended version of TAM with
two external variables, that is, subjective norm and perceived
social capital, to provide a better understanding of SNS acceptance and usage. This study focuses on Facebook, Myspace,
and Twitter because they are currently the most popular and
influential SNS.
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G. CHOI AND H. CHUNG
2. RESEARCH BACKGROUND
2.1. Technology Acceptance Model
TAM was introduced by Davis in 1989 as an application
of the “theory of reasoned action” (TRA). According to TAM
(Davis, 1989), users’ attitudes toward technology are critical
factors in their accepting and using new technologies. Perceived
usefulness and perceived ease of use are the most fundamental
determinants for formulating positive attitudes toward technology and behavioral intention to use technology and, therefore,
ultimately define actual use.
Perceived ease of use refers to “the extent to which a person believes that using a particular system would be free of
effort” (Lee & Tsai, 2010, p. 603). Perceived ease of use measures a person’s subjective assessment of efforts needed to use
the system (Davis, 1989). Perceived ease of use is an important
construct in TAM for two reasons (Davis, 1989). First, perceived ease of use influences intention directly and indirectly
via perceived usefulness. Second, to accept and use any type of
technology, users have to overcome an initial barrier related to
perceived ease of use.
Perceived usefulness, on the other hand, is defined as “the
degree to which a person believes that using a certain system
(e.g., computers) enhances his/her productivity” (Teo, 2009,
p. 90). TAM suggests that perceived ease of use constitutes a
significant influence on perceived usefulness because, if other
conditions are equal, the easier a technology is to use, the more
useful it can be (Davis, Bagozzi, & Warshaw, 1989).
Figure 1 illustrates key constructs of TAM and their relationships. It shows that behavioral intention is jointly determined
by perceived usefulness and perceived ease of use. Then, the
TAM posits that behavioral intention is critical to determine
actual usage of the system. The figure also represents that perceived ease of use affects attitudes toward computer use not only
directly but also indirectly via perceived usefulness. TAM also
theorizes that the effects of external variables are mediated by
perceived usefulness and perceived ease of use.
The TAM has been extensively examined through validations, applications, and replications, and, as a result of such
extensive research, it is considered one of the most robust models in explaining the adoption and usage of new technology
across time, settings, populations, and technologies (Venkatesh,
2000). One of the key limitations of TAM is its parsimony and
generality (Legris, Ingham, & Collerette, 2003). That is, in the
original model, TAM attempts to explain technology acceptance
with only a limited number of variables. Accordingly, previous
research points out that future studies need to take into account
human and social factors to better explain technology adoption
(Legris et al., 2003). One of the external factors that have not
been widely examined is social influence and relationship that
enhance attitudes toward new technology. In this respect, the
current research suggests that subjective norm and perceived
social capital are significant determinants of SNS acceptance
and use.
2.2. Subjective Norm and Social Influence
Subjective norm refers to “an individual’s perception of how
important others in his or her social environment wish or expect
him or her to behave in a certain way” (Moan & Rise, 2006,
p. 719). In this study, subjective norm means the degree to
which a person perceives the demands of important or referent
others on him or her to use SNS.
Numerous studies in psychology have identified subjective
norm as a significant factor in determining perceived usefulness
and thus behavioral intention (H. Kim, Kim, & Shin, 2009).
Subjective norm was included as one of the determinants of
behavioral intention in the TRA, which gave rise to the development of TAM. However, Davis et al. (1989) omitted subjective
norm from their original TAM, expressing the need for additional research on the conditions and mechanisms that define
social influences on usage behavior.
Subjective norm is the perceived social pressures to perform a given behavior and the motivation to comply with those
pressures (Hyde & White, 2009). According to social identity theory and self-categorization theory (Hogg & Abrams,
1988; Tajfel & Turner, 1979), individuals evaluate themselves
based on their memberships in social groups and categories.
Accordingly, an important part of the self-concept is derived
from self-inclusive social groups, which define the appropriate
attitudes and behaviors of group members (Hogg & Abrams,
1988; Tajfel & Turner, 1979). Based on these theories, the rationale for subjective norm is that people may choose to perform a
certain behavior if they believe one or more important referents
Perceived
Usefulness
External
Variables
Attitude toward
Computer Use
Intention
to Use
Perceived
Ease of Use
FIG. 1. Technology acceptance model (Davis et al., 1989). Adopted with permission from the Institute for Operations Research and the Management Sciences.
TECHNOLOGY ACCEPTANCE MODEL
think they should, even if they do not favor the behavior or its
consequences (Venkatesh & Davis, 2000).
Along with this line of research, Teo (2009) reported that perceived social pressure was significantly correlated with a person’s behavioral intentions. As a related study, Marcinkiewicz
and Regstad (1996) examined the impacts of subjective norm
on technology use and found that subjective norm was the
most predictive factor of computer use, in parallel to selfcompetence, perceived relevance, and innovativeness. In a similar study, Sugar, Crawley, and Fine (2004) also identified the
subjective norm as a key driver in determining the use of technology among teachers, school administrators, and students.
Unlike many other TAM study settings in which a given
behavior is largely dependent upon people’s internal motivations, accepting and using SNS is inherently related to other
people. For those who use SNS, managing social networks with
SNS will be considered a universal trend; thus, the popularity
of SNS is expected to encourage the users to participate in the
same activity. In this respect, the current study examines the role
of subjective norm in accepting and using SNS; it is expected
that a person who perceives SNS use to be normative will have
stronger intentions to engage in SNS. TAM asserts that subjective norm has an impact on behavioral intention, which in
turn affects how people perform the action, that is, accept and
use SNS.
2.3. Perceived Social Capital
Social capital means “the sum of the resources, actual or
virtual, that accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition” (Bourdieu &
Wacquant, 1992, p. 14). As an alternative, Huysman and Wulf
(2004) defined it as “network ties of goodwill, mutual support, shared language, shared norms, social trust, and a sense
of mutual obligation that people can derive value from” (p. 1).
To summarize, social capital is the resources that are created
through interactions in social relationships and that provide
benefits to participants in a network (Coleman, 1988).
Social capital offers special value to members who draw
on resources from other participants and leverage connections
from multiple social contexts (Ellison et al., 2006). The form of
resources can be important information, personal relationships,
or the opportunity to organize groups and activities (Paxton,
1999). In a large network, there are members who act as hubs or
brokering agents, and they tie subnetworks together by increasing the efficiency of information flows (Granovetter, 1982).
These structural holes support the exchange of new information
and ideas between subgroups, thus allowing networks to operate
more efficiently (Burt, 2000). The value of information changes
while information moves from one group where it is common
and mundane to another in which it is new and more valuable
(Ellison et al., 2006).
Putnam (2000) distinguished between “bridging” and
“bonding” social capital. Bridging social capital describes
621
“loose connections” between individuals by which people can
exchange useful information or new perspectives without personal or emotional experience. In contrast, bonding social
capital refers to close relationships people might have with
friends or family in which emotional support is exchanged.
These two types of social capitals have been widely investigated in the context of SNS. As an example, Ellison et al. (2006)
conducted a survey on the role of SNS and found that SNS
help users build and maintain social capital with higher bridging and bonding. In a similar study, Boyd (2008) focused on
teenage users and reported that SNS was used as an indispensable means to build identity, socialize with peers, and negotiate
status among others. All these studies support that the notion
that SNS can be a useful to facilitate the generation of social
capital.
3. RESEARCH QUESTIONS
The current study was designed to extend TAM to form
a composite model, including subjective norm and perceived
social capital, to explore participant acceptance of SNS. The
direct and indirect effects of each construct (shown in Figure 2
were hypothesized and examined. Each hypothesis is described
as follows.
3.1. Perceived Ease of Use
Perceived ease of use indicates “the degree to which the
prospective user expected the target system to be free of effort”
(Yuen & Ma, 2008, p. 232). This was hypothesized to be a
fundamental determinant of intention to use as follows:
H1a: An individual’s perceived ease of use has a positive effect
on his or her perceived usefulness of SNS.
H1b: An individual’s perceived ease of use has a positive effect
on his or her intention to use SNS.
3.2. Perceived Usefulness
Perceived usefulness refers to “the prospective user’s subjective probability that using a specific application system would
increase his or her job performance within a context” (Yuen &
Ma, 2008, p. 232). This was hypothesized to be a fundamental
determinant of intention:
H2: An individual’s perceived usefulness has a positive effect
on his or her intention to use SNS.
3.3. Subjective Norm
An individual’s subjective norm is defined as “a person’s
perception that most people who are important to him or her
think he should or should not perform the behavior in question”
(Teo, 2009, p. 93). A person perceives that the more others (who
are important to him or her) think she or he should perform a
behavior, the more that person is willing to do so (Yuen & Ma,
2008). Thus, the proposed hypothesis is as follows:
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G. CHOI AND H. CHUNG
FIG. 2. A hypothesized model: A path model of individual’s perceived usefulness and perceived ease of use on his or her intent to use (Davis et al., 1989).
Adopted with permission from the Institute for Operations Research and the Management Sciences.
H3a: An individual’s perception of subjective norm has a
positive effect on his or her intention to use SNS.
We also argue that from the learner’s point of view, the community in which she or he resides might be important others in
using SNS.
H3b: An individual’s perception of subjective norm has a
positive effect on his or her perceived usefulness of SNS.
H3c: An individual’s perception of subjective norm has a
positive effect on his or her perceived ease of use of SNS.
3.4. Perceived Social Capital
Social capital is the resources that are created through social
interactions and relationships, which provide special values and
benefits to participants of a network (Coleman, 1988). Based
on previous research, it was hypothesized that perceived social
capital has positive impacts on two fundamental constructs of
TAM, that is, perceived usefulness and perceived ease of use.
H4a: An individual’s perceived social capital has a positive
effect on his or her perceived usefulness of SNS.
H4b: An individual’s perceived social capital has a positive
effect on his or her perceived ease of use of SNS.
4. METHOD
4.1. Participants
A total of 179 graduate students were recruited from a college on the East Coast. Participation was voluntary, and data
were collected through a survey questionnaire from one of
the required courses at the School of Library and Information
Science program. The sample included 29 male and 150 female
students, ranging in age from 22 to 64 (demographics in
Table 1).
TABLE 1
Demographics
Age (M, SD)
Age group (n, %)
20–29
30–39
40–49
50+
Gender (n, %)
Male
Female
Ethnicity (n, %)
White/Caucasian
African American
Hispanic/Latino
Asian
Others
30.7
7.35
98
60
16
5
54.75%
33.52%
8.94%
2.79%
29
150
16.20%
83.80%
150
6
11
11
1
83.80%
3.35%
6.15%
6.15%
0.56%
Three items were administered specifically asking respondents to report their SNS usage behaviors: (a) “When did you
start using SNS?” (b) “How often do you check SNS?” and (c)
“On average, how long do you use SNS per session?” Among
179 participants, (a) approximately 39.66% started using SNS
3 years ago, (b) about 38.55% checked SNS up to five times per
day, and (c) approximately 60.34% used SNS less than 10 min
per session. Detailed results with the frequency of each item
were summarized in Table 2.
4.2. Measures
The purpose of this study was to investigate the relationship between participant intent to use SNS and a set
of predictors. The four predictors included perceived social
TECHNOLOGY ACCEPTANCE MODEL
TABLE 2
Summary of SNS Usage Behavior Survey Items
Item
n
When did you start using SNS?
Less than 6 months ago
21
6 months–1 year ago
21
1–2 years ago
33
2–3 years ago
29
More than 3 years ago
71
Missing
4
How often do you check SNS?
Less than once a week
26
A few times a week
46
1–5 times per day
69
6–10 times per day
17
More than 10 times per day
18
Missing
3
On average, how long do you use SNS per
session?
Less than 10 min
108
Between 10 min and 30 min
53
Between 30 min and 1 hr
10
Between 1 hr and 2 hr
2
More than 2 hr
3
Missing
3
%
11.73%
11.73%
18.44%
16.20%
39.66%
2.23%
14.53%
25.70%
38.55%
9.50%
10.06%
1.68%
60.34%
29.61%
5.59%
1.12%
1.68%
1.68%
Note. SNS = social networking sites.
capital, perceived usefulness, perceived ease of use, and subjective norm. We adopted measures validated by previous studies with revised wording. More specifically, the questionnaire
included five items for perceived social capital (PSC1-5), four
items for perceived usefulness (PU1-4), five items for perceived
ease of use (PEOU1-5), five items for subjective norm (SN1-5),
and three items for intention to use (IU 1-3.) Table 3 summarizes the list of items measured for all the constructs and their
reference sources. All items used a 7-point Likert scale, from 1
(strongly disagree) to 7 (strongly agree).
4.3. Analyses
Prior to conducting a series of path analyses, exploratory correlation analyses were conducted at the bivariate level among all
five measures using SPSS 19.0. Reliability and validity for each
measure were also assessed. Next, a set of path analyses was
conducted to test our hypotheses. The hypothesized path model
(Figure 2) was submitted to path analysis using Mplus software
6.1 (Muthén & Muthén, 1998–2010). Path model fits were evaluated using chi-square, the comparative fit index (CFI; Bentler,
1980), the standardized root mean square residual (SRMR;
Bentler, 1980), and the root mean square error of approximation (RMSEA; Steiger & Lind, 1980). In the current study, CFI
values above .95 are preferable (Hu & Bentler, 1999). RMSEA
623
values of less than .08 are considered to be a preferable fit
(Browne & Cudeck, 1993), but values less than .10 are acceptable (Kline, 2005). SRMR values of less than .08 are acceptable
(Hu & Bentler, 1999).
5. RESULTS
5.1. Descriptive Analysis
We calculated the average of item scores within each measure and used the average scores as exogenous and endogenous
variables in the path analysis. Table 4 shows the means, standard
deviations, and zero-order bivariate correlations among the five
measures. For further investigation, we used a path analysis to
decompose correlations into different pieces for interpretation
of effects (i.e., direct and/or indirect effects) among the five
measures.
5.2. Measurement Items
Before applying a path model, we evaluated the instruments
by assessing reliability and validity. We first evaluated the internal reliability of the items for each measure. As seen in Table 5,
all reliabilities were satisfactory; Cronbach’s alphas ranged
from .818 to .962. In light of the reliability results, we conducted
a principal component analysis to assess convergent and discriminant validity. The preliminary explanatory factor analysis,
using a Varimax with Kaiser Normalization, was conducted for
all 22 items. This produced five factors with eigenvalues greater
than 1. This model was then evaluated based on the following
criteria:
1. The factor-loading value of an item in the rotated component
matrix should be at least 0.5 for the corresponding factor, and
2. The value should not be greater than 0.5 for two or more
common factors.
Table 5 shows the instruments made up of the five factors.
Approximately 75.49% of total variance was explained by the
five factors.
5.3. Path Analysis
When the first hypothesized model was submitted to path
analysis (see Figure 2), the chi-square test statistic and other
fit indices were plausible except RMSEA, χ 2 (1, N = 179) =
4.210, p = .04, CFI = 0.973, SRMR = 0.021, RMSEA = 0.134
(from .023 to .276).
As seen in Table 6 the path model showed that participant’s perceived usefulness played a mediating role between
perceived social capital (β = 0.424, p < .001) and intent
to use (β = 0.336, p < .001). Perceived social capital predicted individual perceived usefulness directly and individual
intent to use Facebook/Twitter indirectly. In other words, as
we hypothesized, perceived social capital predicted intention to
use, mediated by perceived usefulness. In addition, perceived
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G. CHOI AND H. CHUNG
TABLE 3
Measures of Constructs
Construct
Perceived
Social Capital
Sources of Measured
Items
Lau & Woods (2009)a
Measures
PSC_1
PSC_2
PSC_3
PSC_4
PSC_5
Perceived
Usefulness
Lau & Woods (2009)
PU_1
PU_2
PU_3
PU_4
Perceived Ease
of Use
Venkatesh & Davis (2000)
Yuen & Ma (2008)
PEOU_1
PEOU_2
PEOU_3
PEOU_4
PEOU_5
Subjective
Norm
Charng, Piliavin, &
Callero (1988)
SN_1
SN_2
SN_3
SN_4
SN_5
Intent to Use
Davis (1989)
Davis, Bagozzi &
Warshaw (1989)
IU_1
IU_2
IU_3
Twitter or Facebook make it easier to develop social
relationship (networking).
Twitter or Facebook improve my social relationship
(networking).
Twitter or Facebook enhance my effectiveness in
building social relationship.
Twitter or Facebook help me to build social
relationship more quickly.
I find Twitter or Facebook useful in my social
relationship.
Twitter or Facebook makes it easier to find
information.
Twitter or Facebook improves my
information-seeking.
Twitter or Facebook help me to find information more
quickly.
I find Twitter or Facebook useful in my information
seeking.
My interaction with Twitter or Facebook is clear and
understandable.
Interacting with Twitter or Facebook does not require
a lot of my mental effort.
It was easy for me to become skillful in using Twitter
or Facebook.
I find Twitter or Facebook to be easy to use.
I find it easy to get Twitter or Facebook to do what I
want it to do.
Other people think that using Twitter or Facebook is
important to me.
It really would not matter to most people I know if I
decided to give up using Twitter or Facebook
(reversed).
Many of the people that I know expect me to
continuously use Twitter or Facebook
No one would really be surprised if I just stopped
using Twitter or Facebook (reversed).
Others would probably make me feel guilty if I quit
using Twitter or Facebook.
I will continue to use Twitter or Facebook in the future
I will continue to increase my use of Twitter or
Facebook.
I will continue to use Twitter or Facebook whenever
possible.
Note. PSC = perceived social capital; PU = perceived usefulness; PEOU = perceived ease of use; SN = subjective norm; IU = intent to use.
a
Items were modified based on this reference.
625
TECHNOLOGY ACCEPTANCE MODEL
TABLE 4
Mean Standard Deviation and Zero Order Bivariate
Correlation for Each Construct
Construct
Perceived
usefulness
Perceived ease
of use
Subjective
norm
Perceived
social capital
Intent to use
M
SD
PU
PEOU
SN
TABLE 5
Results of the Reliability and Principal Component Analysis
for Each Construct
PSC
Component
4.57 1.398
Items
4.46 1.553 .410∗∗
Perceived
social capital
PSC_1
PSC_2
PSC_3
PSC_4
PSC_5
Perceived
usefulness
PU_1
PU_2
PU_3
PU_4
Perceived ease
of use
PEOU_1
PEOU_2
PEOU_3
PEOU_4
PEOU_5
Subjective
norm
SN_1
SN_2
SN_3
SN_4
SN_5
Intent to use
IU_1
IU_2
IU_3
5.54 1.056 .263∗∗ .280∗∗
3.37 1.414 .606∗∗ .192∗
.216∗∗
4.43 1.188 .467∗∗ .433∗∗ .282∗∗ .431∗∗
Note. PU = perceived usefulness; PEOU = perceived ease of use;
SN = subjective norm; PSC = perceived social capital.
∗
p < .05. ∗∗ p < .01.
social capital predicted individual perceived ease of use directly
(β = 0.208, p = .02) and intent to use (β = 0.114, p = .078) indirectly. Individual perceived ease of use predicted perceived
usefulness (β = 0.191, p = .005) directly and intent to use (β =
0.336, p < .001) indirectly.
Subjective norm showed significant direct effect on intention to use (β = 0.342, p < .001). The significant path loading
implies that the social influence in Twitter/Facebook is crucial
among participants. However, subjective norm was not substantially associated with either perceived usefulness (β = –0.107,
p = .202) or perceived ease of use (β = 0.090, p = .317). This
implies that subjective norm did not have significant indirect
predictions on intent to use. All the parameter estimates and
associated p values are summarized in Table 6. The explanatory
power of the proposed models was evaluated using R2 for perceived usefulness = 20.7%, perceived ease of use = 7.4%, and
intent to use = 32.5%.
After testing the first hypothesis, we deleted the insignificant
path loadings to finalize the TAM for use of SNS. When the final
hypothesized model was submitted to path analysis (Figure 3),
the chi-square test statistic and other fit indices were acceptable including RMSEA, χ 2 (3, N = 179) = 6.813, p = .04,
CFI = 0.968, SRMR = 0.029, RMSEA = 0.084 (from .000 to
.170). All standardized direct effects were (marginally) significant. The model explained 19.9 % of perceived usefulness, and
6.9 %of perceived ease of use, and 33.1 % of intention to use.
6. CONCLUSION
The purpose of this study was to examine the validity of an
extended TAM for predicting SNS acceptance and usage incorporating subjective norm and perceived social capital. From the
TAM perspective, it was expected that subjective norm and perceived social capital would serve as the two most significant
perception anchors of the fundamental constructs in TAM; the
α
1
2
3
4
5
.941
.816
.842
.856
.835
.759
.962
.865
.923
.928
.908
.838
.683
.648
.812
.897
.794
.856
.666
.783
.762
.755
.658
.818
.683
.844
.783
results showed that both of these hypotheses involved in the
TAM constructs were supported.
Perceived usefulness had robust effects on users’ intention to
use SNS. Perceived ease of use also exerted substantial effects
on behavioral intention of SNS use both directly and indirectly;
perceived ease of use was a fundamental determinant to perceived usefulness and, hence intention to use, as theorized in
the original TAM (Davis et al., 1989).
The research findings also demonstrated that subjective norm
was a significant predictor of both perceived usefulness and perceived ease of use, which implies that perceived social pressure
plays a critical role in SNS use. In other words, SNS users
believe they are expected to use SNS by one or more important referents. Finally, perceived social capital was identified as
626
G. CHOI AND H. CHUNG
TABLE 6
Standardized Effects Decomposition of the Path Model (Figure 2)
Hypotheses
H1a
H1b
H2
H3a
H3b
H3c
H4a
H4b
Path
Standardized Path
Coefficient
p value
Result
PEOU → PU
PEOU → IU
PU → IU
SN → IU
SN → PU
SN → PEOU
PSC → PU
PSC→ PEOU
0.191∗∗
0.114
0.336∗∗∗
0.342∗∗∗
−0.107
0.090
0.424∗∗∗
0.208∗
.005
.078
<.001
<.001
.202
.317
<.001
.020
Significant
Significant
Significant
Significant
Not significant
Not significant
Significant
Significant
Note. PEOU = Perceived Ease of Use; PU = Perceived Usefulness; IU =Intent to Use; SN =
Subjective Norm; PSC = Perceived Social Capital.
∗
p < .05. ∗∗ p < .01. ∗∗∗ p < .001.
FIG. 3. Final path model. Note. R2 for the path predicting intention to use and the interim R2 for each step were listed under each criterion. ∗ p < .05. ∗∗ p < .01.
∗∗∗ p < .001.
a key factor in accepting and using SNS and therefore should be
considered as a potential variable for extending TAM.
After taking into account subjective norm and perceived
social capital, individual differences such as gender, age, and
race were found to have no significant relationship with behavioral intention to use SNS. One possible explanation that may
account for this finding is that the respondents in this study were
all graduate students with similar demographic backgrounds.
From a theoretical standpoint, the current study elaborated
TAM with additional factors influencing behavioral intention
toward new technology. The current study supports the robustness of TAM as a powerful model to understand and predict user
acceptance of SNS. Also, this study attempted to go beyond the
TAM’s original key constructs to explain user acceptance and
usage of SNS. Two new determinants were identified and empirically validated: subjective norm and perceived social capital.
Extending TAM with these two constructs provides a more indepth understanding of the dynamics underlying the formation
and change of SNS user acceptance.
One of the primary limitations of the current study is the
sampling method. The volunteer participants were all graduate
students and therefore may not represent the overall characteristics of SNS users. Sampling methods certainly limited the
generalizability (i.e., external validity) of the current study;
future research should be performed to examine the model with
a different sample to increase generalizability of findings.
Another potential direction for future research is to investigate the impacts of additional constructs that might influence
behavioral intention or attitude toward SNS within the framework of TAM. Many researchers have highlighted the importance of external factors in the expansion of TAM and the acquisition of a greater understanding of technology acceptance. For
TECHNOLOGY ACCEPTANCE MODEL
example, knowing how people access SNS sites (e.g., smartphone or computer) and what percentage of time they spend on
each might help explain their SNS usage behavior. Researchers
should attempt to include external factors to more accurately
assess the adoption and usage of new technology as well
as SNS.
According to TAM (Davis, 1989), intention to use defines
actual use, but there are differences between these two constructs. Unlike an objective measure of actual use, intention
to use is a subjective measure, serving as a mediator between
other determinants (e.g. social norm, perceived social capital)
and actual use. The current study models intention to use SNS
as a criterion measure following the previous study. Future studies, however, need to address an objective measure of actual use
of SNSs to propose a more complete model.
Finally, the proposed TAM has been developed on the
basis of Facebook and Twitter. Although Facebook and Twitter
are both SNSs, there are differences between Facebook and
Twitter in terms of motivation and intention, usage pattern
and performance, and interaction styles (Java, Song, Finin, &
Tseng, 2007). It is therefore, strongly recommended that future
research examine Facebook and Twitter separately to account
more specifically for the impacts of various TAM constructs and
propose more accurate models.
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ABOUT THE AUTHORS
Gilok Choi is an Assistant Professor of School of Information
and Library Science at Pratt Institute. Her research areas
include human–computer interaction, interface design, information architecture, and usability.
Hyewon Chung is an Assistant Professor in the Department
of Education of Chungnam National University, South Korea.
Her research interests are interface design, human–computer
interaction, and social networking.