Journal of Service Science and Management, 2011, 4, 243-252
doi:10.4236/jssm.2011.43029 (http://www.SciRP.org/journal/jssm)
243
Perceived Self-Efficacy and Its Effect on Online
Learning Acceptance and Student Satisfaction
Jung-Wan Lee, Samuel Mendlinger
Department of Administrative Sciences, Boston University, Boston, USA.
Received July 1st, 2011; revised August 6th, 2011; accepted August 17th, 2011.
ABSTRACT
This paper investigates the effect of perceived self-efficacy on perceptions of ease of use and usefulness of online learn-
ing systems, and its effects on behavioral intention toward online learning acceptance and student satisfaction. Eight
hundred and seventy-two samples collected from students in online classes in the United States and Korea were ana-
lyzed using factor analysis and structural equation modeling techniques. The results show that: 1) perceived self-effi-
cacy serves as an antecedent to online learning acceptance and its degree of importance is partially a function of cul-
tural background; and 2) perceived usefulness of online learning systems influences positively on online learning ac-
ceptance and student satisfaction. Significant differences were found between Korean and US students but how much of
this was due to cultural differences or degree of experience could not be determined.
Keywords: Information Technology, Online Education, Cultural Difference
1. Introduction
The Internet and information technology (IT) has been
incorporated into educational platforms to expand learn-
ing activities without depending on traditional face-to-
face classes. The flexibility of time and place for learning
may be the most important feature of online education.
Educational institutions, including many quality colleges
and universities, are using online classes to deliver course
content over the Internet. O’Donoghue, Singh, and Dor-
ward reported the merits of IT in classes [1]. Basile and
D’Aquila assessed student attitudes toward the use of the
Internet in classes [2] and Lawson reported positive ex-
periences from students with online classes [3]. However,
this educational platform has been criticized as being
based on the assumption that most students have the abil-
ity to use IT in the online educational setting [4]. It has
been argued that online education tools may be unfamil-
iar or difficult for many students, and this may result in
many students not being enthusiastic about taking online
courses [5,6]. Hong, Ridzuan and Kuek stated that in-
formation technology skills are found to be an important
aspect for students’ improvement in web-based courses
[5]. Therefore it is still arguable that students should
have basic computer skills and knowledge about IT be-
fore taking online classes.
In designing and delivering online classes, the degree
of student perceptions and satisfaction should be impor-
tant as higher education institutions often consider stu-
dent satisfaction as one of the key factors in online edu-
cation quality [7,8]. However, without knowing what
predicts student satisfaction in online classes, it is diffi-
cult to meet their needs and improve their learning out-
comes. Therefore there is a need to better understand
predictors that affect student satisfaction in online classes,
including self-efficacy, technical skills and attitudes to-
ward online learning and cultural differences that may
affect these predictors.
New challenges in online education are characterized
by the increased focus on users’ characteristics and reac-
tions and their changing needs. The problem is that peo-
ple differ across regional, linguistic, and country bounda-
ries and users’ requirements are strongly influenced by
their local cultural perspective. The increasing use and
acceptance of online learning puts forth the issue that
such use and acceptance of online learning is a function
of culture. Catering for cultural diversity seems impera-
tive for the diffusion of online courses for international
use. It has been claimed that the Internet and electronic
commerce (e-commerce) originated in Western culture as
the majority of Internet websites and e-commerce appli-
cations have been developed in Western countries [9].
The same can be said for the use and acceptance of
online learning. Subsequently, Western vs non-Western

Perceived Self-Efficacy and Its Effect on Online Learning Acceptance and Student Satisfaction 244
culture constitutes a set of parameters that may signifi-
cantly influence online learning acceptance and satisfac-
tion.
In this regard, in order to better understand the diverse
user populations and their preferences toward online
learning acceptance, this study examines how perceived
self-efficacy affects online learning acceptance and how
perceived self-efficacy influences student satisfaction in
online classes in two culturally different group of stu-
dents: 1) Koreans who learn in a culture that is defined as
group orientated or collectivism and thus more likely to
work in a group; and 2) US students who learn in a cul-
ture defined as individualism who are more likely to
work alone. This difference, collectivism vs. individual-
ism may affect a student’s sense of self-efficacy with
individualistic cultures having students with high self-
efficacy versus cultures that are more collective having
students with a wider range of self-efficacy. Path analysis
is used to determine the extent to which key elements
explain online learning acceptance and student satisfac-
tion in online classes. This paper will examine to what
extent does perceived self-efficacy influence online
learning acceptance and student satisfaction in collective
vs. individualistic cultures?
2. Literature Review and Hypothesis
2.1. Online Education and Student Satisfaction
Online education is the most widely used term to describe
information technology based learning in all its forms,
although “e-learning”, “distance learning” or “distance
education”, and “online learning” have also been used.
Rosenberg defined online education as delivering course
content to the end-user via computer using Internet tech-
nology [10]. This definition is endorsed by others who
describe learning through a networked computer system
using web-based software [11]. Studies on online educa-
tion demonstrated its positive impact and potential on stu-
dents due to its flexibility and convenience offered by
online classes [12]. In this paper, online education is de-
fined as a form of education facilitated by information
technology, and promoted by the form of social learning
that creates connectivity and interaction between instruc-
tors and students. This paper will use the terms online
education, online learning, and online classes interchange-
ably.
Student satisfaction and appreciation of online educa-
tion was found in much literature [13]. The literature sup-
ports that a higher level of computer experience is linked
to greater satisfaction in online learning [14]. Liu et al.
reported that the Internet-enabled and tangible user inter-
face helped build students’ positive perception toward on-
line learning [15]. Changchit reported that WebCT, mes-
sage boards, and chat rooms were the most useful tools
that were linked to greater satisfaction in online learning
[16]. Hammoud, Love, Baldwin, and Chen reported that
students often have a positive attitude toward WebCT, and
the use of WebCT has a positive influence on students’
achievements and their outcomes [17].
2.2. Self-Efficacy and Online Learning
Acceptance
General self-efficacy refers to an individual’s belief that
one has the ability to perform a particular behavior.
Bandura defined self-efficacy as an individual’s judg-
ment of the individual’s capabilities to organize and
execute courses of action required to attain designated
types of performances [18]. He further stated that peo-
ple’s beliefs in their efficacy influenced their choices,
their aspirations, and how much effort they mobilized in
a given endeavor. Self-efficacy should not be considered
as a measure of a specific skill because it concerns the
extent to which individuals believe they can perform by
using their skills [19]. Thus, self-efficacy could be un-
derstood as a key mechanism that accounts for the inter-
active relationship between internal forces and external
stimuli that affect human behavior. Individuals who per-
ceive themselves as highly self-efficacious tend to initi-
ate a sufficient effort that may produce successful out-
comes, whereas those who perceive low self-efficacy are
likely to cease their efforts prematurely and fail in the
task.
To the same extent, self-efficacy toward online learn-
ing, which is a situation-specific form of efficacy, refers
to individuals’ judgment of their capabilities to use
online learning systems (including computers, the Inter-
net, and web-based instructional and learning tools).
Marakas, Yi, and Johnson pointed out that there is a dif-
ference between task-specific and general self-efficacy
[20]. Marakas et al. suggested that individuals who have
high technology self-efficacy were more likely to report
higher perceptions of usefulness and ease of use [20].
Even for users with general self-efficacy, there may be a
lack of task-specific self-efficacy. Agarwal, Samba-
murthy, and Stair found that technology self-efficacy
affected the perceived ease of use toward new systems
[21]. Therefore, it seems that familiarity with the tech-
nology is important when taking an online course. Tech-
nology can provide a better online learning experience by
enhancing interaction between students and instructors.
Once students become familiar with the technology, they
should be more in favor of online learning. Inadequate or
incomplete skills and knowledge inevitably compromises
to poor quality of learning experiences. Students’ online
class readiness and motivation are keys for success of
any online program. Hence, students who use informa-

Perceived Self-Efficacy and Its Effect on Online Learning Acceptance and Student Satisfaction245
tion technology in their personal and professional lives
should be more comfortable and familiar with online
learning environments.
2.3. Technology Acceptance and Student
Satisfaction of Online Learning
Based on the theory of reasoned action, the technology
acceptance model (TAM) suggests that user acceptance
of technology is driven by users’ beliefs about the con-
sequences of that usage [22,23]. According to Davis,
perceived ease of use and perceived usefulness are the
two main factors affecting users’ acceptance behaviors.
Davis defined perceived ease of use as the degree to
which an individual believes that using a particular sys-
tem would be free from physical and mental efforts and
defined perceived usefulness as the degree to which an
individual believes that using a particular system will
enhance his or her job performance. In particular, TAM
predicts that users embrace new technology when their
perceptions of the ease of use and the usefulness of the
technology are positive.
Adapting TAM to examine student satisfaction and
technology adoption in online classes, Lin found that
student intention to use technology affected their learning
outcome in the online class environment [24]. Previous
studies recognized that students’ familiarity with tech-
nology usage and perceptions of how they are supported
by online learning systems influenced student satisfac-
tion [15-17]. Therefore, the technology acceptance be-
havior of students may influence satisfaction with online
learning because technology and communication tools
play deterministic roles.
2.4. Culture and Online Learning Acceptance
Culture refers to values, traits, beliefs, and behavioral
patterns that may characterize a group of people. Hof-
stede suggests that culture reflects a composite of human
nature and personality (i.e., values and traits inherited or
learned by individuals) [25]. Cross-cultural research has
identified an array of cultural values including individu-
alism/collectivism, power distance, time orientation, and
uncertainty avoidance [25], in which that may affect stu-
dent satisfaction when taking online courses. Societies
and their culture differ in their emphasis on individual
rights and obligation to society. Individualism/collec-
tivism refers to the extent to which individuals’ emphasis
and identity is centered on the self or the group. Indi-
vidualism describes societies in which the ties between
individuals are loose, such as the US, the UK, and Can-
ada, and people are expected to both take greater initia-
tive and work on their own. In collectivism, people are
integrated into strong and cohesive groups that work to-
ward a common goal, and tend to focus on the needs of
the group over their personal needs, such as Korea, China,
and Japan.
Researchers have started to employ cultural parame-
ters in their studies of online education. Srite, Thatcher,
and Galy suggested that cultural values influence tech-
nology acceptance and use [26], and specifically indi-
vidualism/collectivism directly influences use of com-
puter-based learning. Zaharias reported that participants
from Greece, Bulgaria, Romania, and Turkey where
found to be statistically different in their attitudes toward
several online learning dimensions including accessibil-
ity, instructional feedback and learner guidance and sup-
port [27]. Studies have examined both technology accep-
tance and national culture [28,29]. Most studies in this
field have employed Hofstede’s framework and cultural
dimensions. Graff, Davies, and McNorton found indi-
vidual differences in terms of attitudes toward computer-
based learning and differences between UK and Chinese
students [30]. Downey, Wentling, Wentling, and Wads-
worth measured the relationship between national culture
and the usability of an online learning system and re-
ported that individuals from cultures with low power
distance indicators rated the system’s usability higher
than individuals from high power distance cultures [31].
It may be necessary for recognizing cultural difference in
accepting online courses and understanding student sat-
isfaction.
2.5. Research Framework and Hypotheses
Building on the above arguments, it would be useful to
understand how perceived self-efficacy (PSE) and cultural
differences influence acceptance of online classes and
student satisfaction. In addition, individualism/collecti-
vism may influence perceptions of self-efficacy, accep-
tance of online learning and student satisfaction. Accep-
tance of online learning and satisfaction may also differ in
individuals’ experience and confidence of their abilities
and capabilities. It is likely that groups who score high on
individualism are more likely to accept online learning
and achieve better outcomes in online class activities as
compared to their more collectivist groups. The research
framework is developed in Figure 1.
Accordingly this study proposes five hypotheses:
H1: PSE will have a positive effect on perception of
ease of use (PEOU) toward online learning;
H2: PSE will have a positive effect on perception of
usefulness (PU) toward online learning;
H3: PSE will have a positive effect on behavioral in-
tention to accept online learning and student satisfaction;
H4: PEOU will have a positive effect on behavioral
intention to accept online learning;
H5: PU will have a positive effect on behavioral inten-
ion to accept online learning. t

Perceived Self-Efficacy and Its Effect on Online Learning Acceptance and Student Satisfaction 246
Figure 1. Research Framework and Structural Model
3. Research Methodology Table 1. Survey and sample characteristics.
3.1. Survey and Sample Characteristics Country Korea The US
Number of respondents N = 582 N = 290
Sample characteristics Frequency
(percentage)
Frequency
(percentage)
Male 340 (58.4) 152 (52.4)
Gender
Female 242 (41.6) 138 (47.6)
23 - 29 310 (53.3) 151 (52.1)
Age
30 - 39 272 (46.7) 139 (47.9)
Secondary
(high school) 160 (27.5) -
Junior college 206 (35.4) -
University 178 (30.6) 215 (74.1)
Education
Graduate 38 (6.5) 75 (25.9)
A questionnaire was developed for determining student
perceptions toward PSE, PEOU, PU, behavioral intention
to accept online class and degrees of student satisfaction.
Two surveys were conducted. The first survey examined
students from a Korean university through a web-based
survey method during spring 2009 semester (March-June
2009). Of approximately one thousand participants in the
survey, five hundred eighty-two useful responses that
were between 23 to 39 years old were chosen for in-
depth analysis (Table 1). The second survey examined
students from a US university through a web-based sur-
vey during summer 2009 semester (May-July 2009). Of
approximately four hundred participants in the survey,
two hundred ninety useful responses that were between
23 and 39 years old were chosen for in-depth analysis.
Though this study has employed the samples of compa-
rable ages, the authors realize the weakness of examining
undergraduates from one culture versus graduates from
the other culture. More than 84% of the respondents had
been enrolled at their university for one year or more.
tions were used to measure student perceptions toward
ease of use and usefulness of online classes. Those ques-
tions were borrowed from the TAM model and modified
for online education research.
3.2. Summary of Descriptive Statistics and
Survey Questions
To measure behavioral intention to accept online
learning and satisfaction variables, this study used four
survey questions: “intent to register for online classes,”
“likelihood of recommending online classes”, “intent to
continue taking online classes,” with including response
options ranging from “least likely = 1” to “most likely =
5”, and “overall satisfaction with online classes”. Per-
ceived self-efficacy was measured by using four survey
questions developed by the authors. Nine survey ques-
There were significant differences found between the
Korean and the US students in their answers to the sur-
vey questions (Table 2). While in 16 of the 17 questions
the US students scored higher than the Korean students
in satisfaction (p < 0.01, G-test), the results differed
among categories. For perceived ease of use (PEOU) the
difference was significant in only one question, PEOU3,
“It is easy for me to become skillful at using the online
learning system”. For perceived usefulness (PU), only

Perceived Self-Efficacy and Its Effect on Online Learning Acceptance and Student Satisfaction247
one question, PU5 “I find online learning system useful
in my study completion” was significant. For behavioral
intention to accept online learning, no significant dif-
ferences were found. However for perceived self-efficacy
(PSE), all four questions were very significant between
the Korean and the US students with the US students
scoring significantly higher.
These results support the argument that more US stu-
dents believe that they have the skills and learning ex-
perience needed and are ready for online learning than
Korean students. In fact the US students score statisti-
cally higher in the PSE questions than in the others
(ANOVA, 0.05 < p < 0.01). Interestingly, when asked if
they would recommend the online learning as an ideal
learning platform, the US students gave this question the
lowest score and the Korean students the second lowest.
It appears that even though US students show more con-
fidence and are more ready to accept online learning than
Korean students, both groups have reservations about
online education.
3.3. Factor Analysis and Reliability Test
Factor analysis with a varimax rotation procedure was
employed to identify underlying predictors of behavioral
intention toward online learning acceptance and student
satisfaction with online classes. Afterwards, a statistical
reliability test was used to test internal consistency for
the survey items. Factor analysis yielded three factors
based on an eigenvalue cut-off of one (Table 3). The
sums of squared loadings from the three factors, “per-
ceived ease of use (PEOU)”, “perceived usefulness (PU)”
and “perceived self-efficacy (PSE),” have the cumulative
value of 81% in explaining the total variance of the data.
To test the appropriateness of factor analysis, two
measures were used. The Kaiser-Meyer-Olkin (KMO)
overall measure of sampling adequacy (MSA) was 0.946,
which falls within the acceptable level, significance at p <
0.001. The Bartlett’s test of sphericity was 10160.774
(degree of freedom = 78), significance at p < 0.001,
which showed a highly significant correlation among the
survey questions. Further scale refinement was done by
examining item-to-total correlation. This led to the reten-
tion of 13 items, which represented the three components:
PEOU (4 items, α = 0.893), PU (5 items, α = 0.940), and
PSE (4 items, α = 0.926) (Table 3).
The analysis of moment structures was used for an
empirical test of the structural model [32]. The maximum
likelihood estimation was applied to estimate numerical
values for the components in the model. Confirmatory
factor analysis was applied to test the validity of the
Table 2. Summary of descriptive statistics and survey questions.
Survey Questions (5-point scale) Mean of Korea Mean of The US
PEOU:
1. I find it easy to use the online learning system to do what I want it to do 3.921 4.005
2. I find the online learning system is clear and understandable for me 3.957 3.879
3. It is easy for me to become skillful at using the online learning system 3.921 4.177*
4. I find the online learning system easy to use 4.008 4.094
PU: Using online learning system
1. enables me to accomplish programs more quickly. 3.723 3.941
2. improves my ability to accomplish academic tasks. 3.743 3.863
3. increases my productivity in accomplishing academic tasks. 3.729 3.903
4. enhances my effectiveness in accomplishing academic tasks 3.804 3.831
5. I find online learning system useful in my study completion. 3.767 4.185**
PSE:
1. I have skills necessary to use the online learning system 3.726 4.552***
2. I have Internet connection fast enough to use the online learning system 3.982 4.464***
3. I have the knowledge necessary to use the online learning system 3.862 4.324***
4. Overall, I am ready to use the online learning system 3.973 4.445***
Behavioral Intention to Online Learning and Satisfaction:
1. If I need to study for advanced degrees (programs), I would expect to use the online learning system 3.643 3.761
2. If asked, I would likely recommend the online learning system as an ideal learning platform 3.672 3.692
3. For future advanced degrees (programs/certificates), I would probably use the online learning system 3.703 3.817
4. Overall, I am satisfied with the online learning system 3.851 3.901
*0.05 < p < 0.01; **0.01 < p < 0.001; ***p < 0.001.

Perceived Self-Efficacy and Its Effect on Online Learning Acceptance and Student Satisfaction 248
Table 3. Results of factor analysis for survey questions.
Items Factor loadings Eigenvalue Extracted varianceComponent name Corrected item-total correlationα
PEOU1 0.558 2.674 20.569 Ease of Use (PEOU) 0.635 0.893
PEOU2 0.667 0.812
PEOU3 0.717 0.824
PEOU4 0.723 0.796
PU1 0.804 4.327 33.288 Usefulness (PU) 0.804 0.940
PU2 0.824 0.846
PU3 0.849 0.858
PU4 0.872 0.859
PU5 0.779 0.820
PSE1 0.760 3.481 26.774 Self-efficacy (PSE) 0.788 0.926
PSE2 0.840 0.822
PSE3 0.873 0.862
PSE4 0.776 0.844
Total 80.631
The Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.946. Bartlett’s Test of Sphericity, Chi-square = 10160.774, Significance p = 0.000. Extraction:
Principal Component Analysis, Rotation: Varimax with Kaiser Normalization.
scales in measuring specific constructs of the measure-
ment model. The degree of freedom with large standard
error variances was evaluated to diagnose possible iden-
tification problems. The identification problem was re-
medied in accordance with Hayduk’s guidelines [33].
The criteria of Bollen were applied to evaluate the over-
all goodness-of-fit of the proposed model [34]. Good-
ness-of-fit measures were selectively assessed as follows:
Chi-square statistic (CMIN), degrees of freedom (DF),
CMIN divided by DF (CMIN/DF), root mean square
residual (RMR), root mean square of approximation
(RMSEA), goodness of fit index (GFI), adjusted good-
ness of fit index (AGFI), normed fit index (NFI), and
parsimony ratio (PRATIO).
4. Results of Hypothesis Test
The results of the data analysis generally achieved ac-
ceptable goodness-of-fit measures (Table 4). The index
of GFI (0.884) indicates that the fit of the proposed
model is about 88% of the saturated model (the perfectly
fitting model). The index of NFI (0.932) indicates that
the fit of the proposed model is about 93% over the null
model. Many fit measures represent an attempt to bal-
ance between parsimonious and well fitting model, that is,
two conflicting objectives―simplicity and goodness of
fit [35]. This study prefers a simple and parsimonious
model over complex one.
Null hypothesis 1, “There is no relationship between
PSE and PEOU” and null hypothesis 2, “There is no re-
lationship between PSE and PU” were empirically tested
by the data. The results showed that there are positive
relationships between PSE and PEOU, and between PSE
and PU, which are statistically significant (p < 0.001) at a
95% confidence level, for Korean and US students (Ta-
ble 4). This suggests that perceived self-efficacy has a
positive and significant effect on both perceived ease of
use and perceived usefulness of online classes.
Null hypothesis 3. This study empirically tested the
hypothesis “There is no relationship between PSE and
behavioral intention toward online learning acceptance
and satisfaction.” Korean students had a statistically sig-
nificantly positive relationship (p < 0.05) at a 95% con-
fidence level (Table 4). However, US students had no
statistically significant positive relationship (p > 0.05) at
a 95% confidence level. This suggests that perceived
self-efficacy has different effects on behavioral intention
toward online learning acceptance and satisfaction.
Null hypothesis 4. This study empirically tested the
hypothesis “There is no relationship between PEOU and
behavioral intention toward online learning acceptance
and satisfaction.” Korean students had no statistical sig-
nificant positive relationship (p > 0.05) at a 95% confi-
dence level (Table 4). However, US students had a sta-
tistically significant positive relationship (p < 0.001) at a
95% confidence level. This suggests that perceived ease
of use has different influences on behavioral intention
toward online learning acceptance and satisfaction.
Null hypothesis 5. This study empirically tested the
hypothesis “There is no relationship between PU and
behavioral intention toward online learning acceptance

Perceived Self-Efficacy and Its Effect on Online Learning Acceptance and Student Satisfaction249
Table 4. Outputs of structural equation model (SEM) estimate.
Path diagram Korean students (N = 582) US students (N = 290)
Independent
variables Dependent variables Estimate (S.E) Estimate (S.E.)
H1: Perceived
Self-efficacy  Perceived ease of use 0.889 (0.033)*** 0.943 (0.077)***
H2: Perceived
Self-efficacy  Perceived usefulness 0.666 (0.037)*** 0.620 (0.072)***
H3: Perceived
Self-efficacy  Online learning acceptance and satisfaction0.246 (0.072)** 0.000 (0.085)
H4: Perceived Ease of use  Online learning acceptance and satisfaction0.014 (0.068) 0.389 (0.056)***
H5: Perceived Usefulness  Online learning acceptance and satisfaction0.613 (0.034)*** 0.500 (0.058)***
**p < 0.05; ***p < 0.001 statistically significant at a 95% confidence level. Korean students: CMIN = 885.988, DF = 114, Probability level = 0.000, CMIN/DF =
8.341, RMR = 0.040, RMSEA = 0.051, GFI = 0.884, Adjusted GFI = 0.844, NFI = 0.932, PRATIO = 0.838. US students: CMIN = 659.833, DF = 114, Prob-
ability level = 0.000, CMIN/DF = 4.868, RMR = 0.046, RMSEA = 0.054, GFI = 0.862, Adjusted GFI = 0.815, NFI = 0.883, PRATIO = 0.838.
and satisfaction”. The result was a significant statistical
positive relationship between PU and behavioral intention
toward online learning acceptance and satisfaction (p <
0.001) at a 95% confidence level, for both Korean and US
students (Table 4). This suggests that perceived usefulness
has a positive and direct effect on behavioral intention
toward online learning acceptance and satisfaction.
Overall, the results of the hypothesis test suggest that:
1) Perceived self-efficacy can serve as a predictor of be-
havioral intention toward online learning acceptance; and
2) Perceived usefulness has a positive and direct influ-
ence on behavioral intention toward online learning ac-
ceptance. However, the effects of perceived self-efficacy
and perceived ease of use on behavioral intention toward
online learning acceptance and satisfaction are different
in the two groups, though the reasons may be cultural,
degree of experience, academic degree or a combination
of the three.
5. Discussions and Managerial Implica tions
This study investigated the effect of perceived self-effi-
cacy on perceptions of ease of use and usefulness toward
online classes and its effect on behavioral intention to-
ward online learning acceptance in Korea and the US
The results showed: 1) there was a significant difference
between Korean and US students, i.e. US students scored
significantly higher in perception of self-efficacy than
Korean students; 2) perceived self-efficacy was a sig-
nificant predictor of online learning acceptance for both
Korean and US students; and 3) perceived usefulness of
online learning was a significant predictor of positive
behavioral intention toward online learning acceptance
for both Korean and US students. It appears that most
students in the two countries, regardless of their differ-
ences, feel that the acceptance of online learning would
be useful and beneficial to them.
On the other hand, the results showed that: 1) percei-
ved self-efficacy was a significant predictor of positive
behavioral intention toward online learning acceptance
and satisfaction for Korean but not US students; and 2)
perceived ease of use toward online learning systems was
a significant predictor of positive behavioral intention
toward online learning acceptance and satisfaction for
US but not Korean students. It appears that Korean stu-
dents, compared to US students, feel that self-efficacy
motivates and promotes positive behavioral intention
toward online learning acceptance and satisfaction, and
the ease of use of online learning systems has an insig-
nificant relation to behavioral intention toward online
learning acceptance and satisfaction.
For US students most had a high perception of self-
efficacy and so this was not a useful predictor but per-
ceived ease of use, especially survey question PEOU#3,
was a better predictor. A predictor is only useful if there
are significant differences among the group of students
being tested. As Korean students, who are from a more
collectivistic society than US students, are more de-
pendent on their social group, their individual confidence
level and their self-efficacy may be more varied than US
students. On the other hand, US students, who have a
high level of self-efficacy, feel that the ease of use of
online learning systems, which may differ from one stu-
dent to another, motivates and promotes their positive
behavioral intention toward online learning acceptance
and satisfaction. That is, US students feel that the diffi-
culty level of learning how to use online learning systems
is a major factor that influences their behavioral intention
toward online learning acceptance and satisfaction.
The results of this study show online learning self-ef-
ficacy positively influences online learning acceptance.
This means that students with higher self-efficacy (both
Korean and US) are more likely to perceive online
learning systems as easier to use and more useful. When
students believe in their capability of taking online
classes successfully, they perceive online learning sys-
tems easier to use and more useful. This finding supports

Perceived Self-Efficacy and Its Effect on Online Learning Acceptance and Student Satisfaction 250
that task-specific (online learning) self-efficacy plays a
significant role when an individual has to make a choice
and behaves under uncertainty.
Perceived self-efficacy has a strong positive impact on
behavioral intention toward online learning acceptance
and satisfaction. It is desirable that students deem online
learning systems easy to use, and have control over the
system. Therefore, it is recommended that online learn-
ing system suppliers, adopting institutions and online
education marketers should provide adequate training
and information about online learning systems to stu-
dents to build skills and increase confidence in taking
online classes. In this regard, institutions and educators
should devote resources to students to develop the skills
and knowledge of online learning management systems.
The instructor should make sure that students have basic
computer skills and IT knowledge before taking online
classes, so that the student will not be frustrated and dis-
couraged by using tools and environments of online
learning. If necessary, at the beginning of an online pro-
gram, students who have a low level of online learning
proficiency should be provided with a training program
or an informative orientation to assure the student gain-
ing computer and web-communication skills and knowl-
edge required for online classes.
This study highlights the critical role of self-efficacy in
perceived ease of use and perceived usefulness of online
learning systems. The findings clearly show that as long as
students have the skills and knowledge to use online
learning systems, they perceive online education is a use-
ful learning format and an easy way of learning. Students
will be more likely to enjoy online classes; if they believe
in they have a reasonable level of competence to use
online learning systems. While self-efficacy has been in-
troduced and utilized in studies in information systems and
social sciences, it is the first attempt to anchor perceived
self-efficacy in the domain of online education.
Due to the limitation of the data, i.e. comparing un-
dergraduate Korean students with graduate US students,
people must be careful in interpretation these results as
differences could be due to cultural differences between
the two groups; US students are more used to working
and solving problems on their own than Korean students
may be more attuned to the needs of the online platform
than Korean students. The authors believe that much of
these results support this conclusion (see PSE questions).
However, the result cannot rule out that some of these
differences may be due to the US students being graduate
students and thus more experienced both in education
(having had more courses and learned how to manage
their educational needs) and possibly in the work force.
This study tried to overcome this limitation by having
both groups of students at about the same age.
6. Conclusions and Research Limitations
The results showed: 1) the positive relationship between
perceived self-efficacy and perceptions of ease of use
and usefulness toward online learning systems; and 2)
the positive relationship between perceived usefulness of
online learning systems and behavioral intention toward
online learning acceptance and satisfaction. Although
limited, this study provides empirical evidence that cul-
tural dimensions may influence online learning accep-
tance and satisfaction due to its different approaches and
characteristics. The finding suggests that many aspects of
broadly defined culture influence situation-specific per-
ceptions and behaviors.
In light of the findings, the authors suggest that future
research should examine additional cultural values and/or
many cultures as potential sources of variation in online
learning acceptance and satisfaction. This effort provides
a series of hypotheses that integrate cultural dimensions
into an extended online learning acceptance model. This
integration is particularly relevant given the growing
importance of global information communication tech-
nologies (ICT), such as the Internet, Wi-Fi, and the
fourth generation of cellular wireless communication
networks, across several countries and cultures.
There are still some personal variables (e.g., experi-
ence using ICT and academic levels) and course vari-
ables (course content, instructional designs, and instruc-
tor experience) not addressed by this study. Addressing
these limitations should increase the generalization of the
findings to online learning formats.
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