Students’ Acceptance of E-learning Adoption in Higher
Education: An Empirical Study in Vietnam
NGUYEN VAN DUC, LUU VAN HIEU*
Hanoi Industrial Textile Garment University,
Hanoi,
VIETNAM
*Corresponding author
Abstract: - E-learning models have been dramatically spreading during the COVID-19 outbreak. The study's
objective is to investigate students’ acceptance of E-learning in the context of Vietnam's higher education. UTAUT
model was adopted to evaluate the acceptance and use of the E-learning method. The analyzed data were collected
from 531 undergraduate students in Vietnam, who currently use E-learning environments. The findings indicated
that social influence had a significant impact on behavioral intention. The behavioral intention and facilitating
conditions were two factors affecting the acceptability of E-learning for university students. Also, those findings
have enriched students' understanding of adopting E-learning. They provide suggestions and implications for
educators and institutions in the continuing implementation of e-learning at Vietnamese higher education
institutions.
Key- Words: Students’ acceptance; E-learning; higher education; UTAUT model; Vietnam.
Received: April 19, 2022. Revised: November 23, 2022. Accepted: December 22, 2022. Published: January 31, 2023.
1 Introduction
In the early 1990s, the two open universities in Hanoi
and Ho Chi Minh City, which delivered E-learning,
were opened. However, Vietnam has adopted the e-
learning mode, [1]. Educational technology, in
general, in regular university courses in Vietnam was
still limited at that time. Intrinsic motivation did not
commonly exist in Universities to utilize the new
educational technology in their daily practices. In
addition, the government did not have sufficient
policies and guidelines to support and encourage
universities to integrate new educational technology
into their regular courses, [2]. In 2020, the COVID-
19 pandemic had a considerable impact on higher
education. There was no exception in Vietnam. The
Covid-19 pandemic forced higher education
institutions (HEIs) to halt traditional in-person and
face-to-face learning, [3]. In response to an
unexpectedly long period of school closures,
Vietnamese higher education institutions have taken
to e-learning, as it is considered a promising
approach to continuing education activities during
school closures.
Electronic learning (E-learning) enables students
to take part in the educational process through a
virtual environment instead of face-to-face
communication. Several benefits of e-learning
systems include ease of access to materials content,
effortless team collaboration, and on-time mutual
discussions without the concern of time and space
limitations for interactions between student and
professor. Despite many advantages of e-learning
systems, the transformation of the educational style
arises various challenges that may significantly affect
the culture and continuing needs for the development
of technical skills of both students and educators.
More specifically, the success of e-learning depends
on users' perceptions, knowledge, and skills in
exploiting information and communication
technology (ICT) tools. Therefore, understanding the
factors affecting E-learning adoption is essential to
successfully apply the learning process in an E-
learning strategy.
Numerous previous studies focused on studying the
factors affecting e-learning applications and their
impacts. However, these factors frequently vary
because they rely on the individuals and particular
contexts. It is believed that having a specific
theoretical model is necessary. Therefore, each
student can fully understand the factors affecting
context-specific e-learning adoption. The study’s
primary purpose is twofold. First, to analyze the e-
learning research which utilized the Technology
Acceptance Model (TAM) and the Unified Theory of
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2023.20.5
Nguyen Van Duc, Luu Van Hieu
E-ISSN: 2224-3402
34
Volume 20, 2023
Acceptance and Use of Technology (UTAUT), then
identify the factors that affect student’s acceptance
into E-learning adoption in higher education in
Vietnam. Second, to empirically explore the impacts
of the factors which influence students in adopting E-
learning.
2 Literature Review
Several studies have been conducted to examine
which factors influence the acceptability of students
toward E-learning. The Technology Acceptance
Model (TAM) [4], derived from the Theory of
Reasoned Action [5], has been the most widely used
conceptual model in the field of research. The core
elements of TAM are perceived usefulness and
perceived ease of use. Perceived usefulness can be
defined as “the degree to which a person believes that
using a particular system would enhance his or her
job performance”. Whereas, perceived ease of use is
“the degree to which a person believes that using a
particular system would be free of effort”. The TAM
model indicates that perceived usefulness and
perceived ease of use are predictors of the behavioral
intention to use an information system. The original
Technology Acceptance Model (TAM) has been
extended and modified to identify the most widely
used external factors concerning e-learning
acceptance [6]. The research findings show that
system quality, computer self-efficacy, and computer
playfulness have a significant impact on the
perceived ease of use of e-learning systems.
Moreover, information quality, perceived enjoyment,
and accessibility were proved to have a positive
influence on the perceived ease of use and perceived
usefulness of the e-learning systems. [7] also used the
TAM model to predict the acceptance of e-learning
by Jordanian students. The study indicated that in
order to motivate students’ intentions to use
technology in their learning environment, it is
necessary to deliver a positive perception of
technology advantages. An extended Technology
Acceptance Model (TAM) was used to investigate
the importance of factors in technology adoption and
use in the Lebanese context. The findings showed
that perceived usefulness, perceived ease of use,
social norms, and quality of work life are significant
determinants of students’ behavioral intention, [8].
Based on the TAM model, [9] developed the
UTAUT model to focus on the intent to use and the
use behavior of users towards information
technologies, placing emphasis on four main
determinants of the intention to use and use behavior.
A study by [10] evaluated the acceptance and use of
a virtual learning environment in higher education by
using the UTAUT model. The study finding
indicated that the behavioral intention and use
behavior regarding the utilization of a virtual learning
environment in higher education differed between
Turkey and UK, and that the level of impact of the
factors that form behavioral intention and use
behavior also differed from one factor to another.
Another study, using the UTAUT model, by [11]
explored student readiness for online learning in the
Northeast of Thailand. The study explored students’
self-regulation, computing devices ownership, and
level of familiarity with education-related
technologies. The findings indicate that students have
a slightly positive perception of e-learning. The study
also shows that students use mobile technologies
extensively, and have experience using social media,
but are unfamiliar with other collaborative e-learning
tools. The study by [3] used the adjusted TAM to
investigate the relationships of the elements in the
model. The results show that computer self-efficacy
has a positive impact on perceived ease of use. It is
also indicated that the social factor has a direct effect
on the student’s attitudes.
Based on the above discussions, the proposed
hypotheses are as follows:
H1: Performance expectancy has a significant
effect on behavioral intention.
H2: Effort expectancy has a significant effect on
behavioral intention.
H3: Social influence has a significant effect on
behavioral intention.
H4: Facilitating conditions have a significant
effect on e-learning acceptance.
H5: The users’ behavioral intention has a
significant effect on e-learning acceptance.
The research framework (as in Figure 1) captures
both practical and psychological implications in
regard of the acceptance of E-learning.
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2023.20.5
Nguyen Van Duc, Luu Van Hieu
E-ISSN: 2224-3402
35
Performance
Expectancy
Effort
expectancy
Social
influence
Behavioral
intention
H1
H2
H3
Facilitating
conditions
E-learning
acceptance
H4
H5
Fig. 1: The research framework [9]
3 Methodology
An online questionnaire survey was utilized in this
study and distributed among undergraduate students.
It mainly consisted of closed-form questions. The
first part included questions aimed at collecting data
on the demographic characteristics of the
respondents, such as age, gender, and education. In
the second part, to identify the factors affecting
students’ acceptance of E-learning adoption in higher
education, a sample questionnaire was listed in the
sheet. This survey was conducted online between 28
May and 10 June 2021, when the COVID-19
pandemic struck Vietnam and the world. The survey
questionnaire was distributed using social networks
to reach as many students in Vietnam as possible.
The survey link was delivered to all the targeted
students via email and other social networks (Zalo
and Facebook). The data collection was supervised
and monitored by the research group.
Overall, the survey had 531 valid responses, and
thus, it was included in the data analysis. The
collected sample size (N=531) is greater than the
minimum sample size requirement. Therefore, the
sample size is regarded to be acceptable. These 531
respondents were from various universities across
Vietnam. Among respondents, there were more
males (78.8%) than females (21.2%). Most of the
respondents (77.9%) were third-year students.
Regarding the ease of Internet access, most students
(95.2%) had no difficulty accessing the Internet and
using the network devices.
4 Results and Discussions
Main devices and platforms in E-learning
adoption
The study shows that most students own notebook
computers and smartphones, with only 20.9% of
those who own desktop computers. Moreover, the
smartphone is the top device students use to connect
to the Internet. The consistency of the findings
consolidates the high ownership rate of smartphone
devices in Vietnam. It seems that Vietnamese
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2023.20.5
Nguyen Van Duc, Luu Van Hieu
E-ISSN: 2224-3402
36
students tend to only use the computer at school and
do not have desktop computers at home.
Providing effective tools within blended and online
learning environments is necessary. Highly regarded
tools like Course Management Systems (CMS) and
Learning Management Systems (LMS) are not
commonly used in schools. Systems (LMS) are not
frequently used in schools. The survey indicates that
Zoom (71.56%), Microsoft Team (10.55%), and
Google Meet (17.89%) are the three main streaming
platforms that universities are using. With regard to
using social networks and online application software
platforms, the study shows that the students are very
familiar with Facebook, search engines (Google
Search), video sharing platforms (YouTube and
Tiktok), and text chat. It can be concluded that
almost students can use basic software tools and
utilize the Internet, mostly for web browsing,
connecting with friends via chatting or Facebook,
and watching YouTube and Tiktok videos. Students
who are familiar with computers and technology are
expected to easily accept e-learning.
Testing the validity and reliability of the scales
Table 1 summarizes the factor analysis
performed on the dataset and also shows the factor
loads of the questionnaire items and Cronbach’s
alpha coefficients of the variables. As can be seen,
Cronbach’s alpha coefficients are in the range of
0.881 and 0.932, confirming that the factor can be
acceptable. Table 2 shows the correlation coefficients
between the variables in the data sample. As shown
in Table 2, strong positive correlations were found
among constructs within the acceptance scale. Within
the acceptance scale, the relationship between
performance expectancy and effort expectancy was
the strongest ( 𝑟 = .860, 𝑝 < .01 ). The weakest
relationship was between facilitating conditions and
performance expectancy (𝑟 = .685, 𝑝 < .01).
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2023.20.5
Nguyen Van Duc, Luu Van Hieu
E-ISSN: 2224-3402
37
Table 1. Factor analysis results and Cronbach’s alpha coefficients
Measurement Items
Factor loads
Cronbach’s alpha
Performance Expectancy (PE)
.932
E-learning would help me improve my academic
performance (PE1).
.699
E-learning would allow me to do more work in less
time (PE2).
.728
E-learning would make it easier to do my
schoolwork (PE3).
.743
E-learning will be useful for my career (PE4).
.771
Effort expectancy (EE)
.883
Learning to use e-learning would be easy for me
(EE1).
.791
I need support when using e-learning (EE2).
.692
It would be easy for me to become skillful at using
e-learning (EE3).
.763
Social influence (SI)
.881
My parents will agree on it if I choose to enroll in
an online class (SI1).
.613
My classmates are willing to use e-learning (SI2).
.739
My lecturers have been helpful in the use of e-
learning (SI3).
.586
In general, my university has supported the use of
e-learning (SI4).
.688
Facilitating conditions (FC)
.900
I have the necessary resources (the Internet and
access devices) to use e-learning (FC1).
.651
I have the knowledge necessary to use e-learning
(FC2).
.625
Behavioral intention
I intend to use e-learning in future modules.
.650
Table 2. Pearson correlations between key variables
PE
EE
SI
FC
PE
1
EE
.860**
1
SI
.828**
.786**
1
FC
.685**
.718**
.755**
1
**. Significant at p<.01, two-tailed
Testing the hypotheses
The regression analysis was made to identify the
effects of Performance Expectancy (PE), Effort
Expectancy (EE), and Social Influence (SI) on
Behavioral intention. The obtained F value of
51.489 with 𝑝 < 0.01 indicates that the
regression model was statistically significant. An
evaluation of the adjusted 𝑅2 value showed that
the regression model 60.7% of the variance in
the sample. Table 3, and Table 4 present the beta
coefficient and significance levels concerning
the effect of the variables. The effects of social
influence on behavioral intention were
significant. This finding revealed that the H3
hypothesis is supported. Also, according to the
analysis result, the evaluation of the coefficients
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2023.20.5
Nguyen Van Duc, Luu Van Hieu
E-ISSN: 2224-3402
38
related to the effects of performance expectancy
and effort expectancy on behavioral intention
was not significant (𝑝 > 0.5). For this reason,
the H1 and H2 hypotheses are not supported.
Table 3. Regression analysis results
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
(Constant)
-.064
.307
-.209
.835
PE
.040
.181
.031
.223
.824
EE
.154
.175
.112
.878
.382
SI
.840
.147
.661
5.710
.000
Dependent variable: Behavioral intention
R=.779; Adjusted 𝑅2=.607; F=51.489; p=.000
In the following step, a multi-linear
regression was also used to test the H4 and H5
hypotheses. In the model, facilitating conditions
and the users’ behavioral intention were included
as independent variables, and the dependent
variable is E-learning acceptance. The data set
had an 𝐹 value of 106.454 and a 𝑝 value < 0.01.
This showed that the model was statistically
significant. An evaluation of the adjusted 𝑅2
value demonstrated that the regression model
made up 67.2% of the variance in the sample. As
a result, the coefficients’ assessment related to
the effects of Behavioral intention and
Facilitating conditions on E-learning acceptance
was significant. For this reason, the H4 and H5
hypotheses are supported.
Table 4. Regression analysis results
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
(Constant)
.514
.232
2.213
.029
BI
.641
.074
.700
8.625
.000
FC
.168
.085
.161
1.985
.050
Dependent variable: E-learning acceptance
R=.824; Adjusted 𝑅2=.672; F=106.454; p=.000
According to obtained analysis results, three
out of five hypotheses were supported. As in the
previous studies [10], [11], social influence has
had the most positive effect on behavioral
intention. Social influence can be defined as “the
degree to which an individual perceives that he
or she should use the new system." As in the
aforementioned studies, social influence is the
variable that has had the most significant
influence on the intention to use a new system.
Additionally, it can be known as the approval
from parents. It was reported that student’s
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2023.20.5
Nguyen Van Duc, Luu Van Hieu
E-ISSN: 2224-3402
39
parents would be pleased when they enrolled in
an online course. Also, being closely aligned
with the earlier studies, it was found that the
effects of the facilitating conditions and
behavioral intention variables on the E-learning
acceptance were significant. It was identified
that behavioral intention and facilitating
conditions were two direct determinants of
adopting behavior [9]. Students with good
facilitating conditions, such as computers and
Internet access, tend to think that e-learning
brings advantages and is easy to accept.
Contrary to some previous studies,
performance expectancy and effort expectancy
were found to have no effect on behavioral
intention. The difference may come from the fact
that COVID-19 was an unexpected intermission
for students in Vietnam, and since then, schools
and universities have been asked to roll out their
teaching to online mode, [12]. This no-impact
result is reasonable given the fact that students
had little choice at that time.
5 Conclusions
In light of the global trend towards e-learning,
higher education institutions in Vietnam have
experienced radical changes. The transition from
traditional learning to an online learning model
in late 2020 due to the COVID-19 pandemic was
unprecedented in Vietnamese history. The
COVID-19 outbreak was crucial in bringing
online learning to the mainstream in Vietnam. E-
learning adoption is an essential subject for the
education sector. Various studies have been
conducted on this subject, and several models
that mainly attempt to describe e-learning
adoption on an individual basis have been
proposed.
Some results of the current studies are
different from the prior research. It might be due
to a lack of information about online education.
With the shift to exclusive online learning during
the COVID-19 pandemic, many universities had
to provide online courses to adapt social
distancing guidelines. Institutions, lecturers, and
students needed to be well-prepared to
participate in this kind of learning mode through
training and other support. This includes
developing good curriculums and facilities for
solving technical problems and difficulties both
lecturers and students might face during e-
learning classes.
According to the study findings, there was a
positive impact of social influence on students’
behavioral intention of e-learning systems. In
addition, behavioral intention and facilitating
conditions have positively influenced the
acceptability of e-learning in higher education
The findings of this research are based on
empirical evidence, which examines factors that
influence the acceptance of e-learning systems
among university students. Policymakers,
designers, and developers can be benefited from
the study's results. Thus, they have made a
remarkable contribution to reviewing and
utilizing the successful usage of e-learning
systems in higher education. Fundamentally, this
study has shed light on the importance of e-
learning adoption in higher education
institutions, especially for the younger
generation.
References:
[1] M. Kang and A. Duong, “Student Perceptions
of First-time Online Learning During the
COVID-19 Pandemic in Vietnam,” ie Inq.
Educ., vol. 13, no. 1, p. 8, 2021.
[2] H.-H. Pham and, T. T. H. Ho, “Toward a
‘new normal’ with e-learning in Vietnamese
higher education during the post COVID-19
pandemic,” High. Educ. Res. Dev., vol. 39,
no. 7, pp. 1327–1331, 2020.
[3] N. T. T. Ho, S. Sivapalan, H. H. Pham, L. T.
M. Nguyen, A. T. Van Pham, and H. V. Dinh,
“Students’ adoption of e-learning in
emergency situation: the case of a Vietnamese
university during COVID-19,” Interact.
Technol. Smart Educ., 2020.
[4] F. D. Davis, “Perceived usefulness, perceived
ease of use, and user acceptance of
information technology,” MIS Q., pp. 319–
340, 1989.
[5] M. Fishbein, and I. Ajzen, “Belief, attitude,
intention, and behavior: An introduction to
theory and research,” 1977.
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2023.20.5
Nguyen Van Duc, Luu Van Hieu
E-ISSN: 2224-3402
40
[6] S. A. Salloum, A. Q. M. Alhamad, M. Al-
Emran, A. A. Monem, and K. Shaalan,
“Exploring students’ acceptance of e-learning
through the development of a comprehensive
technology acceptance model,” IEEE Access,
vol. 7, pp. 128445–128462, 2019.
[7] A. Al-Adwan, A. Al-Adwan, and J. Smedley,
“Exploring students acceptance of e-learning
using Technology Acceptance Model in
Jordanian universities,” Int. J. Educ. Dev.
using ICT, vol. 9, no. 2, 2013.
[8] A. Tarhini, K. S. Hone, and X. Liu, “Factors
affecting students’ acceptance of e-learning
environments in developing countries: a
structural equation modeling approach,” 2013.
[9] V. Venkatesh, M. G. Morris, G. B. Davis, and
F. D. Davis, “User acceptance of information
technology: Toward a unified view,” MIS Q.,
pp. 425–478, 2003.
[10] Ö. E. Kurt, and Ö. Tingöy, “The acceptance
and use of a virtual learning environment in
higher education: an empirical study in
Turkey, and the UK,” Int. J. Educ. Technol.
High. Educ., vol. 14, no. 1, pp. 1–15, 2017.
[11] A. Ngampornchai, and J. Adams, “Students’
acceptance and readiness for E-learning in
Northeastern Thailand,” Int. J. Educ. Technol.
High. Educ., vol. 13, no. 1, pp. 1–13, 2016.
[12] G. Maheshwari, “Factors affecting students’
intentions to undertake online learning: An
empirical study in Vietnam,” Educ. Inf.
Technol., pp. 1–21, 2021.
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2023.20.5
Nguyen Van Duc, Luu Van Hieu
E-ISSN: 2224-3402
41
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US