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Article
Social Isolation and
Acceptance of the
Learning Management
System (LMS) in the
time of COVID-19
Pandemic: An
Expansion of the
UTAUT Model
Syed A. Raza
1
, Wasim Qazi
2
,
Komal Akram Khan
1
, and
Javeria Salam
1
Abstract
The COVID-19 Pandemic has led to social isolation; however, with the help of
technology, education can continue through this tough time. Therefore, this research
attempts to explore the Unified Theory of Acceptance and Use of Technology
(UTAUT) through the expansion of the model. Also, make it relevant to investigate
the influence of social isolation, and the moderating role of Corona fear on
Behavioral Intention of the Learning Management System and its Use Behavior of
Learning Management System among students. The data was analyzed using Partial
Least Square (PLS) and Structural Equation Modelling (SEM). The findings show a
positive link of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence
(SI), and Social Isolation on Behavioral Intention of LMS and, also between Behavioral
Intention of LMS and its Use behavior. Moreover, the results of the moderation
analysis show that Corona fears only moderates the link of Performance
1
Department of Management Sciences, IQRA University, Karachi, Pakistan
2
Department of Education and Learning Sciences, IQRA University, Karachi, Pakistan
Corresponding Author:
Syed A. Raza, Department of Management Sciences, IQRA University, Karachi 75300, Pakistan.
Email: syed_aliraza@hotmail.com
Journal of Educational Computing
Research
0(0) 1–26
!The Author(s) 2020
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0735633120960421
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Expectancy and Social influence with Behavioral Intention of LMS. The findings imply
the need for improving the LMS experience to increase its Behavioral Intention
among students. Finally, the author’s recommendation for future researchers is to
examine the extended model in other countries and territories to analyze
Coronavirus’s influence on e-learning acceptance.
Keywords
COVID-19, corona fear, social isolation, E-learning, learning management system
(LMS), UTAUT, behavior, higher education, Smart-PLS
Introduction
The COVID-19 emerged in the year 2019, in the Wuhan, China, and was soon
declared a pandemic as it spread worldwide due to its extremely high infectious
rate. According to the World Health Organization (2020) report, over 130
countries and territories had confirmed the presence of Coronavirus as its
cases emerged during the mid of March 2020. As the highly-infectious disease
has a relatively high mortality rate, it has caused an increase of fear among the
people naturally (Ahorsu et al., 2020), as the worry regarding the COVID-19
was based on contact with individuals who might be infected with the disease
(Lin, 2020).
As a response to the COVID-19 crisis, governments around the world have
issued public policies that include social distancing, isolation, and self-
quarantine (Anderson et al., 2020), having unprecedented economic and psy-
chosocial consequences worldwide. While millions of people around the world
stay in their homes to prevent the Coronavirus from spreading, their livelihoods
have been obstructed, and, in the students’ case, their access to education has
been impeded. However, as countries go into lockdown, the advancement in
information technology gives light to possible alternatives. The dramatic
changes caused by the evolution of Information technology in all aspects of
life, especially considering its involvement, higher education is crucial to discuss
during the COVID-19 pandemic. Technology has always helped enhance the
simplest of tasks, such as the advancement of the traditional learning process. A
technology that lies under the umbrella of e-learning has made it possible to
continue the learning process during the lockdown (Zwain, 2019). This technol-
ogy is referred to as the Learning Management System (LMS). LMS is defined
by Alias and Zainuddin (2005), as a web-based technology developed to
improve the learning process through its proper planning, application, and eval-
uation in educational institutions. Using LMS in the learning process helps
facilitate e-learning as it provides educational material without the constraint
2Journal of Educational Computing Research 0(0)
of time or place, (Ain et al., 2016), enabling students and teachers to interact via
the internet and facilitates sharing of course-related information and resources
(Al-Busaidi & Al-Shihi, 2010; Lonn et al., 2011). This indicates that the use of
this technology during the COVID-19 pandemic is the need of the hour to keep
the learning process continued. A few examples of LMS used in educational
institutions include Moodle, WebCT, Blackboard, and Desire2Learn (Iqbal,
2011; Waheed et al., 2016). Hassanzadeh et al. (2012) revealed in his study
that, with the advent of information technology, the definition of higher edu-
cation had been changed. Therefore, the area of technology acceptance was seen
by scholars as a mature area in the role of information systems in science
(Venkatesh et al., 2003). According to Teo (2011), technology acceptance is
the willingness of an individual to adopt the use of technology for facilitating
task performance based on the support it was designed to provide. In recent
times, the acceptance of e-learning systems and technologies is being investigat-
ed by researchers in different educational environments around the world, using
different models based on distinct criteria (Decman, 2015; Raza, Khan, & Rafi,
2020). Considering the context of the higher education sector, it is crucial to
investigate factors that result in e-learning technology acceptance among stu-
dents, as investing e-learning systems requires huge investment in resources and
infrastructure (Ma & Yuen, 2011). If the students do not accept the new system
of learning, the return on investment of universities would be reduced (Zwain,
2019). The existing literature revealed that the acceptance of LMS among stu-
dents in higher education varies from country to country (Zwain, 2019), as Arab
universities in the Middle Eastern region registered level of e-learning accep-
tance was low (Matar et al., 2011) while a high-acceptance rate of the e-learning
system was registered in western countries (Decman, 2015).
The present paper investigates the factors that influence the acceptance of
LMS from the perspective of students during the COVID-19 pandemic. For this
purpose, the theory of UTAUT is considered the well-developed, updated, and
relevant technology acceptance theory by researchers, as it has been merged
from existing recognized theories of technology acceptance (Decman, 2015).
The reason for the development of the UTAUT model was to explore a unified
view of Information technology (Venkatesh et al., 2003). The model has been
subsequently validated by Venkatesh et al. (2003) in a longitudinal research
study, where it was found that the model accounted for 70 percent variance
in BI to use technology and 50 percent about its actual use. Therefore, this
theory was chosen among the other theories as it is more comprehensive,
enabling higher explanatory power than the early theories used for studying
technology acceptance. Through the years, researchers have explored the
model through the incorporation of several factors to understand technology
acceptance relevant to the situational factors of the area being researched. Lin
and Anol (2008) added online social support to understand its influence on the
use of network information technology in Taiwan. Further, Raza et al. (2019)
Raza et al. 3
studied the factors which affect mobile banking (M-banking) acceptance in
Islamic banks of Pakistan by using the modified unified theory of acceptance
and use of technology (UTAUT) model. Moreover, Chao (2019) aimed to
empirically test the factors that influence student’s BI towards mobile learning
through the addition of different factors such as perceived enjoyment, satisfac-
tion, trust, risk, and mobile self-efficacy. Taiwo and Downe (2013) and Dwivedi
et al. (2011) revealed, in their recent meta-analysis of the outcomes of the
UTAUT studies, that its constructs are positively and significantly associated
with the existing literature, but stress the lack of investigation of a moderator in
several studies. Therefore, the extension of this model through the incorporation
of Social factors and the Corona Fear will help understand the user’s behavioral
intention of technology acceptance in light of the recent pandemic and its sub-
sequent behavioral use. Testing the acceptance of e-learning system using the
UTAUT model is sufficient as it is the latest, most up to date technology accep-
tance theory that is widely recognized by scholars (Decman, 2015). The findings
of the extended model will prove to be useful for understanding the acceptance
of LMS among students, in this way, educational institutions will focus on the
system’s effective implementation and invest in e-learning technology for a good
purpose.
This research paper follows the introduction with the literature review, elab-
orating on the theoretical background and the hypotheses development that is
to be tested. Then, the paper emphasizes the research methodology used for
measuring the impact of variables, and the used sampling and data collection
methods. Then, the data analysis techniques and findings have been discussed.
Lastly, the paper is concluded with the implications of the findings and future
research directions that follow the study’s limitations.
Literature Review
Theoretical Background
This paper develops an integrated model through the extension of the Unified
Theory of Acceptance and Use of Technology (UTAUT) by adding the inde-
pendent variable Social Isolation that is caused by the recent COVID-19
pandemic, and the moderating variable that is Corona Fear, to the model’s
pre-existing constructs that include Performance Expectancy (PE), Effort
Expectancy (EE), Social Influence (SI) and Facilitating Conditions (FC). The
original UTAUT was introduced by Venkatesh et al. (2003). He reviewed eight
existing theories to develop a unified model. The theories include the Theory of
Reasoned Action (TRA), Innovation Diffusion Theory (IDT), Social Cognitive
Theory (SCT), Technology Acceptance Model (TAM), Theory of Planned
Behavior (TPB), Model of PC Utilization (MPCU), Motivational Model
(MM), and, the Combined TAM and TPB (C-TAM-TPB). The integrated
4Journal of Educational Computing Research 0(0)
model enables scholars to view and show the complete picture of the predictors
of technology acceptance, according to Al-Imarah et al. (2013). Venkatesh et al.
(2003) revealed that his unified model predicts 69 percent variance in Behavioral
Intention of users, which is higher than the pre-existing models that only pre-
dicted 17 to 53 percent. Therefore, this model is a useful tool for investigating
student’s acceptance of the LMS during the COVID-19 pandemic. Thus, the
authors have deployed the model and extended it to assess the role of Social
Isolation on Behavioral Intention of LMS and the moderating effect of Corona
Fear caused by the Pandemic
Hypotheses Development
Performance Expectancy (PE). The extent of an individual’s perception regarding
technology’s usefulness to perform different tasks is called Performance
Expectancy (PE) (Venkatesh et al., 2003; Ain et al., 2016), and in the case of
evaluating the acceptance of LMS among the students, it is regarded as the
student’s belief regarding the effectiveness of the system for studying
(Decman, 2015). Lwoga and Komba (2015) defining is to what extent students
understand the system’s potential to allow them to perform better in their clas-
ses. It suggests that consumers would be able to implement the technology if
their understanding is that their efficiency benefits from it. The use of a learning
management system will enable students to use technology for their educational
activities. Several scholars have used the voluntary and mandatory setting to
evaluate the influence of a technology’s PE on the Behavioral Intention of using
it and found a significant direct effect (Casey & Wilson-Evered, 2012; Dwivedi
et al., 2011; Gupta et al., 2008;
SUmak et al., 2011; Venkatesh et al., 2003; Zhou
et al., 2010). In the area of the e-learning environment, Sumak et al. (2010)
analyzed the impact of PE on the BI of LMS and found that it is positively
significant. Thus, the existing literature shows that student’s belief that using
LMS would improve their Performance will enable them to adopt its use readily.
Therefore, based on the review of the literature, the following hypothesis is
proposed by the authors:
H1: PE positively influences BI of LMS
Effort Expectancy (EE). Yoo et al. (2012), revealed that the most influential factors
of the UTAUT model are the Effort Expectancy (EE) which is considered as an
intrinsic element, as it is the amount of effort an individual perceives to invest to
use a technology, which is low in general due to the user-friendly nature of
information technology (Decman, 2015). Researchers assess the relationship
between Effort expectancy during the early stages of adoption of technology,
where they found it had a direct impact on BI (Gupta et al., 2008; Venkatesh
Raza et al. 5
et al., 2003), while Venkatesh (2000) revealed that it becomes insignificant over
time and Gruzd et al. (2012) found a negative relationship. Raman and Don
(2013) discussed that the relationship of EE on BI was positively significant
when they tested the variables in the context of pre-school teachers’ acceptance
of LMS. It is assumed by the authors of this paper that student’s belief regarding
the low degree of effort required to use LMS that leads to a higher BI of using it.
Therefore, based on the review of the literature, the following hypothesis is
proposed by the authors:
H2: EE positively influences the BI of LMS.
Social Influence (SI). Social influence (SI) constitutes the reflection of peers,
instructors, and friends’ perceptions regarding technology on the individual’s
Behavioral intentions within a social environment (Venkatesh et al., 2003).
While evaluating the acceptance of LMS, SI is the degree of a student’s social
circle influencing their BI of LMS. As information technology has advanced and
social networking sites are emerging, the focus of this factor has shifted from
physical to virtual (Decman, 2015). Researchers have found a direct relationship
of SI on BI of individuals regarding the use of technology in both voluntary and
mandatory settings (Gruzd et al., 2012; Gupta et al., 2008; Venkatesh et al.,
2003). An international study was done by Im et al. (2011), revealed that the role
Social influence played on BI was positively significant and relatively higher in
Korean respondents compared with the US. Another scholar declared that
employees were socially influenced to adopt the use of services offered by e-
government (Al-Shafi et al., 2009). Investigating the factors concerning the area
e-learning system Fidani and Idrizi (2012) found that there was a positive influ-
ence of social influence on student’s BI to use LMS. Therefore, based on the
review of the literature, the following hypothesis is proposed by the authors:
H3: SI positively influences the BI of LMS.
Facilitating Conditions (FC). Venkatesh et al. (2003) refer to Facilitating Conditions
(FC) as the availability of adequate support and resources for the proper use of
technology. In the context of the E-learning environment, FC focuses on the
accessibility of technical and organizational infrastructure for the adoption and
use of the LMS. This includes training, technical support, and the required
infrastructure (Decman, 2015). The original model of UTAUT found that the
role FC had on an individual’s BI to use a particular technology was direct but
insignificant (Venkatesh et al., 2003). While Dwivedi et al. (2011) revealed that
the link of FC conditions was found to be the least significant with BI among the
four factors of the UTAUT model. According to Nanayakkara (2007), lack of
6Journal of Educational Computing Research 0(0)
assistance and timely support due to limited availability of resources and infor-
mation will hinder the acceptance level of web-based technology among stu-
dents, because they need their teacher’s and technical support to positively
influence their use of Learning Management System (Ain et al., 2016). The
existing literature shows that the e-learning acceptance level is positively influ-
enced by facilitating conditions (Bakar et al., 2013). This indicates student’s
perception regarding the availability of facilitating conditions is a valid predic-
tor of their BI of LMS. Therefore, based on the review of the literature, the
following hypothesis is proposed by the authors:
H4: FC positively influences the BI of LMS.
Social Isolation. De Jong Gierveld et al. (2016) define social isolation as an indi-
vidual’s absence or the low number of meaningful ties with other people, thus
making them socially isolated. The COVID-19 Pandemic has forced countries to
go into lockdown, and drastically reduced social gatherings, through the encour-
agement of social distancing as it is required to eradicate the spread of
Coronavirus. Due to the closing of classrooms, public markets and the post-
ponement and cancellation of activities and meeting, social distancing decreases
social contact between people in the group, leading to isolation around the
world (Wilder-Smith & Freedman, 2020), the authors predict socially isolated
students will be positively stimulated to take online classes through Learning
Management System. Therefore, based on this assumption, the following
hypothesis is proposed by the authors:
H5: Social Isolation positively influences the BI of LMS.
Behavioural Intention (BI) of LMS. A person’s intention to adopt the use of a specific
technology for performing various tasks is referred to have his or her Behavioral
Intention (BI) (Ain et al., 2016). Ngai et al. (2007) defined BI as the level of
commitment a person shows to engage in a specific behavior, which in the
context of this paper is the student’s commitment level for accepting the use
of LMS to fulfill their educational course objectives. Several scholars have ana-
lyzed the role of BI of technology on its actual use behavior and found that there
is a direct and significant link (Davis, 1989; Motaghian et al., 2013; Raman &
Don, 2013; Wang & Wang, 2009). Nicholas-Omoregbe et al. (2017) revealed in a
paper that the BI of students regarding the adoption of the e-learning system has
a positive link with their use behavior, which ultimately results in better grades.
Use Behavior is the extent of the actual use of technology by an individual to
perform various tasks (Bagozzi, 1981). In harmony with the existing literature,
the authors of this study, expect a positive association between Behavioral
Raza et al. 7
intention of Learning Management System and its Use Behavior. Therefore,
based on the review of the literature, the following hypothesis is proposed by
the authors:
H6: BI of LMS positively affects the Use Behavior of LMS.
Moderating Effect of Corona Fear. Mertens et al. (2020) define fear as an adaptive
emotion that mobilizes energy in an individual to deal with a potential threat.
Pakpour and Griffiths (2020) revealed that unexpected and extraordinary sit-
uations such as disease outbreaks could cause fear among people, and therefore
it is one of the psychological aspects of the COVID-19 pandemic. This indicates
the need to determine its effect on students, especially concerning acceptance
and use of LMS that is being deployed by educational institutions, to continue
the learning process. So, the authors of this paper propose the following hypoth-
eses for analyzing the moderating role of Corona Fear:
H7: Corona Fear moderates the relationship between PE and the BI of LMS.
H8: Corona Fear moderates the relationship between EE and the BI of LMS.
H9: Corona Fear moderates the relationship between SI and the BI of LMS.
H10: Corona Fear moderates the relationship between FC and the BI of LMS.
H11: Corona Fear moderates the relationship between Social Isolation and the BI
of LMS.
Research Methodology
Research Model
Since the original UTAUT model by Venkatesh et al. (2003) offered relevant
factors for determining student’s behavioral intention towards LMS and its Use
behavior, they were used for fulfilling the purpose of this research. However, the
model needed to be extended to explore the acceptance of LMS among students
enrolled in higher-education institutions, during the COVID-19 pandemic. For
this reason, social isolation was added as an independent variable, while
Corona’s fear was included as a moderating variable (Figure 1).
Data Collection and Instrumentation
The sample used in this research included students enrolled in the Universities of
Karachi, Pakistan. For the development of the scale for data collection, items
8Journal of Educational Computing Research 0(0)
were adapted from the existing literature. The items for measuring variables
were adapted from Venkatesh et al. (2003), Zwain (2019), and Garone et al.
(2019). The scale for measuring constructs was based on a five-point Likert scale
design and consisted of a total of 35 items. Responses for analysis were collected
from students by distributing the questionnaire online. The sample size selected
for the data was based on the guidelines presented by Raza and Hanif (2013),
Comrey and Lee (2013), Raza et al. (2020), Sharif and Raza (2017) that the
sample of 50 is considered as poor, 300 as good, 500 as very good and 1000 was
considered as an excellent sample with respect to factor analysis. Hence, we
gathered a total of 516 responses.
Demographics
The demographic analysis showed the following description of the respondent’s
profiles, as depicted in Table 1. The gender distribution of the respondents
showed that male respondents at 54.8 percent comprised the majority, while
female respondents totaled 45.2 percent. While concerning the ages of the
respondents, the analysis showed that 5.6 percent were less than or equal to
19 years old, 30.8 percent were between 20-24, 38.4 percent were between 25-29,
18.4 percent were between 30-34, 5.8 percent were between 35-39, and lastly only
1 percent were more than and equal to 40 years old. Moreover, analyzing the
level of education of the respondents, it was found that Graduates constituted
Table 1. Respondent’s Profile (N ¼516).
Demographic items Frequency Percentile
Gender
Male 283 54.8%
Female 233 45.2%
Age
Less than and equal to 19 29 5.6%
20–24 159 30.8%
25–29 198 38.4%
30–34 95 18.4%
35–39 30 5.8%
More than and equal to 40 5 1.0%
Education
Undergraduate 171 33.1%
Graduate 297 57.6%
Post graduates 47 9.1%
Other 1 0.2%
Source: Author’s estimation.
Raza et al. 9
the majority totaling 57.6 percent, 33.1 percent were undergraduates, 9.1 percent
were postgraduates, and lastly, only 0.2 percent of respondents marked other.
Data Analysis and Results
In the present study, the partial least square structural equation modeling (PLS-
SEM) technique was applied to the data, using Smart PLS version 3.2.3 (Ringle
et al., 2015). The criteria of Raza et al. (2020) has been followed in the present
research. Hence, we applied a bootstrapping method with 5000 subsamples to
determine the significance value for each path coefficient. PLS-SEM was per-
formed in two steps. The first step involves the evaluation of the measurement
model; the second step involves the evaluation of the structural model. In the
measurement model, we assessed the construct validity and discriminant validity
criteria, whereas, in the structural model, we assessed the R2 and the significance
of the path coefficients.
Ringle et al. (2005) revealed that Structural Equation Modeling (SEM) is a
valid statistical technique that helps analyze the validity of a study’s theory
using statistical facts and figures. The current paper deploys a variance-based
method for analyzing the hypothetical model. The execution of PLS-SEM using
Smart PLS software is a suitable method for analyzing and examining several
integrated models in various contexts for research (Chin, 1998; Henseler et al.,
2009).
Measurement Model
A measurement model assesses the scale’s competency that is used for research
purposes, which can be determined through obtaining Composite Reliability
(CR), Individual Item Reliability (IIR), Convergent Validity (CV), and the
Average Variance Extracted (AVE).
The value that determines the scale’s reliability is referred to as the
Cronbach’s alpha, the criteria of which is given by Tabachnick and Fidell
(2007) that is greater than 0.55. Since the values meet the prescribed criteria,
the scale is determined to be reliable. Straub (1989) said that the value of
Composite reliability should be greater than 0.7, and the values in Table 2
show that the said criteria have also been met.
Moreover, the benchmark for individual reliability is that it should be greater
than 0.7 (Churchill, 1979), which can be seen in Table 2. Since all the loadings
have a value that is greater than the benchmark, hence it is deemed reliable for
research. Furthermore, the analysis shows that every variable has a higher than
0.5 Average Variance Extract (AVE), which meets the criteria given by Fornell
and Larker (1981). Hence, the scale’s convergent validity is confirmed.
Moving on to the analysis of discriminant validity, the values of which are
displayed in Table 3, it can be seen that the discriminant validity has been
10 Journal of Educational Computing Research 0(0)
Table 2. Measurement Model Results.
Items Loadings Mean
Standard
deviations
Cronbach’s
alpha
Composite
reliability
Average
variance
extracted
BI BI1 0.756 3.840 1.007
BI2 0.844 3.740 0.964 0.808 0.874 0.635
BI3 0.817 3.920 1.020
BI4 0.768 3.860 0.944
CF CF1 0.824 3.980 0.794
CF2 0.845 3.940 0.860
CF3 0.809 3.910 0.847 0.912 0.932 0.696
CF4 0.872 3.900 0.867
CF5 0.822 3.990 0.844
CF6 0.830 3.934 0.928
EE EE1 0.877 3.770 1.094
EE2 0.855 3.800 1.016 0.826 0.896 0.742
EE3 0.852 3.970 1.035
FC FC1 0.785 3.850 0.992
FC2 0.887 3.910 0.980 0.874 0.914 0.726
FC3 0.869 3.870 0.949
FC4 0.866 3.860 0.980
PE PE1 0.821 4.160 0.923
PE2 0.851 4.100 0.998 0.846 0.897 0.685
PE3 0.856 4.270 0.901
PE4 0.780 4.140 0.939
SI SI1 0.746 3.840 0.981
SI2 0.826 3.710 1.001 0.814 0.878 0.643
SI3 0.801 3.820 0.972
SI4 0.830 3.820 1.023
SIS SIS1 0.881 4.050 0.761
SIS2 0.880 4.230 0.666
SIS3 0.934 4.210 0.654 0.933 0.949 0.788
SIS4 0.882 4.200 0.692
SIS5 0.859 4.250 0.651
UB UB1 0.758 3.840 1.066
UB2 0.858 3.840 1.041
UB3 0.827 3.770 1.045 0.863 0.901 0.647
UB4 0.826 3.780 1.034
UB5 0.747 3.930 0.909
Notes: BI ¼Behavioral Intention of LMS , CF ¼Corona Fear, EE ¼Effort Expectancy, FC ¼Facilitating Conditions,
PE ¼Performance Expectancy, SI ¼Social Influence, SIS ¼Social Isolation, UB ¼Use Behavior of LMS.
Raza et al. 11
measured by performing a cross-loading analysis and extracting the AVE. Its
criteria have been determined by Fornell and Larker (1981) that says that the
value of AVE should be higher than the correlation of the variables, and it can
be seen in Table 3 that it has been met as the diagonally represented values of the
square root of AVE satisfy the given criteria.
Furthermore, Table 4 depicts the cross-loadings of the items, and results
reveal that all the items are loaded higher in their relevant construct in compar-
ison with the corresponding variable. Moreover, the cross-loading difference is
also higher than the suggested threshold of 0.1(Gefen & Straub, 2005).
Finally, Table 5 shows the heterotrait-monotrait ratio of correlations
(HTMT), the values of which are less than 0.85, confirming the validity, as
per the criteria given (Henseler et al., 2015; Raza et al., 2018, 2020).
Ultimately, as the scale’s reliability and validity have been established
through analyzing the measurement model, the distinctiveness of the framework
has been confirmed, deeming it reliable and valid for moving forward to the
analysis of the structural model.
Structural Model
The structural model analysis was done using standardized paths to get the
results. Each path that has been tested in the structural model corresponds to
the hypotheses developed by the authors of this research. The First-order anal-
ysis results are shown in Table 6, while the results of the moderating variable
that have been tested are shown in Table 6. Moreover, Figure 2 depicts the
results of Standardized Regression Weight (SRW). Also, the value of R-squared
is mentioned in the model. R-squared is a goodness-of-fit measure for linear
regression models. It is also termed as the coefficient of determination. This
Table 3. Fornell-Larcker Criterion.
BI CF EE FC PE SI SIS UB
BI 0.797
CF 0.561 0.834
EE 0.558 0.609 0.861
FC 0.410 0.474 0.328 0.852
PE 0.375 0.266 0.327 0.306 0.828
SI 0.634 0.627 0.683 0.437 0.396 0.802
SIS 0.279 0.402 0.231 0.425 0.041 0.243 0.888
UB 0.537 0.566 0.431 0.763 0.390 0.551 0.247 0.804
Notes: BI ¼Behavioral Intention of LMS, CF ¼Corona Fear, EE ¼Effort Expectancy, FC ¼Facilitating
Conditions, PE ¼Performance Expectancy, SI ¼Social Influence, SIS ¼Social Isolation, UB ¼Use
Behavior of LMS. The diagonal elements (bold) represent the square root of average variance extracted
(AVE).
12 Journal of Educational Computing Research 0(0)
Table 4. Loadings and Cross Loadings.
BI CF EE FC PE SI SIS UB
BI1 I intend to continue using LMS 0.756 0.459 0.387 0.278 0.221 0.470 0.197 0.406
BI2 For my studies, I would use LMS 0.844 0.480 0.492 0.328 0.262 0.521 0.236 0.442
BI3 I will continue to use LMS on a
regular basis
0.817 0.444 0.418 0.371 0.407 0.521 0.245 0.454
BI4 Because of the possibilities that
LMS offers, I plan to approach
my next course more
effectively
0.768 0.408 0.481 0.327 0.296 0.506 0.209 0.410
CF1 I do not want to leave the house
because of the risk of getting
infected by COVID 19
pandemic
0.485 0.824 0.506 0.409 0.238 0.549 0.314 0.469
CF2 I am concerned that I may get
sick from COVID-19 pandem-
ic during the next 6 months
0.468 0.845 0.524 0.411 0.240 0.528 0.347 0.499
CF3 I am feeling anxious about
COVID-19 pandemic
0.434 0.809 0.514 0.409 0.185 0.506 0.279 0.462
CF4 I am concerned that someone in
my immediate family may get
sick from COVID-19 pandem-
ic during the next 6 months
0.492 0.872 0.526 0.407 0.185 0.526 0.371 0.474
CF5 I am scared about getting infected
by COVID-19 pandemic
0.409 0.822 0.481 0.351 0.236 0.498 0.311 0.439
CF6 I see the possibility that Covid-19
pandemic will break out in the
area where
0.507 0.830 0.496 0.379 0.248 0.529 0.377 0.484
EE1 I live and work 0.461 0.487 0.877 0.247 0.224 0.569 0.196 0.339
EE2 Learning how to use LMS is easy
for me.
0.480 0.554 0.855 0.262 0.327 0.606 0.162 0.363
EE3 I find the system to be flexible to
interact with.
0.499 0.530 0.852 0.335 0.291 0.588 0.236 0.409
FC1 I have resources to use LMS 0.328 0.509 0.342 0.785 0.263 0.389 0.244 0.665
FC2 I have the knowledge to use LMS 0.359 0.399 0.272 0.887 0.303 0.365 0.411 0.667
FC3 A specific person (or group) is
available to assist when diffi-
culties arise with LMS
0.337 0.347 0.252 0.869 0.228 0.346 0.391 0.629
FC4 Using the system fits into my
study styles.
0.373 0.367 0.259 0.866 0.249 0.390 0.395 0.642
PE1 I find LMS useful for studies. 0.295 0.182 0.333 0.234 0.821 0.310 0.038 0.308
PE2 LMS allows me to accomplish
class activities more quickly
0.339 0.204 0.222 0.286 0.851 0.335 0.032 0.354
PE3 LMS increases learning
productivity
0.288 0.215 0.284 0.227 0.856 0.335 0.024 0.295
PE4 Using the system would make it
easier to do my studies
0.312 0.279 0.251 0.259 0.780 0.328 0.039 0.326
(continued)
Raza et al. 13
statistic indicates the percentage of the variance in the dependent variable that
the independent variables explain collectively. The R
2
for “Behavioral Intention
of LMS” is 0.502, implying that 50.2% of the behavioral intention to use LMS is
due to the latent variable in the model. Similarly, R
2
for “Use Behavior of
LMS” is 0.289, implying that 28.9% of the use behavior of LMS is because
of the behavioral intention to use LMS.
Table 4. Continued
BI CF EE FC PE SI SIS UB
SI1 My peers who influence my
behavior think that I should
use LMS.
0.473 0.512 0.444 0.332 0.203 0.746 0.175 0.460
SI2 My friends who are important to
me think that I should use
LMS.
0.518 0.477 0.507 0.353 0.322 0.826 0.171 0.433
SI3 Instructors whose opinions that I
value prefer that I should use
LMS.
0.487 0.499 0.579 0.383 0.383 0.801 0.247 0.477
SI4 I use the system because of the
proportion of classmates who
use the system
0.550 0.525 0.648 0.337 0.354 0.830 0.189 0.405
SIS1 I felt alone and friendless. 0.239 0.349 0.197 0.397 0.075 0.203 0.881 0.237
SIS2 I felt isolated from other people 0.230 0.326 0.181 0.371 0.000 0.192 0.880 0.180
SIS3 I have someone to share my
feelings with
0.251 0.377 0.193 0.395 0.006 0.212 0.934 0.220
SIS4 I found it easy to get in touch
with others when I needed
others to felt they had to help
me.
0.223 0.348 0.171 0.340 0.026 0.193 0.882 0.189
SIS5 When with other people, I feel
separate from them.
0.284 0.376 0.266 0.378 0.067 0.265 0.859 0.260
UB1 I use LMS frequently during my
academic period
0.362 0.449 0.323 0.671 0.332 0.398 0.197 0.758
UB2 I use many functions of LMS (e.g.,
discussion forum, chat session,
messaging, download course
contents, upload assignments,
etc.
0.446 0.463 0.342 0.709 0.387 0.458 0.217 0.858
UB3 I depend on LMS 0.404 0.403 0.331 0.651 0.312 0.426 0.173 0.827
UB4 Use of LMS by our university is a
good idea
0.406 0.400 0.333 0.669 0.332 0.456 0.159 0.826
UB5 LMS makes learning more inter-
esting for the students
0.509 0.532 0.387 0.410 0.220 0.461 0.235 0.747
Notes: BI ¼Behavioral Intention of LMS, CF ¼Corona Fear, EE ¼Effort Expectancy, FC ¼Facilitating
Conditions, PE ¼Performance Expectancy, SI ¼Social Influence, SIS ¼Social Isolation, UB ¼Use Behavior
of LMS. All self-loading is significant (bold).
14 Journal of Educational Computing Research 0(0)
Discussion
Table 6 depicts the findings of the first-order analysis. The hypotheses tested
have shown that the relationship between PE (B¼0.112 p <0.1), EE (B¼0.143
p<0.01), and SI (B¼0.321 p <0.01) is found to be positively significant with BI
of LMS. Therefore, the H1, H2, and H3 have been accepted. This means that
students’ BI to use LMS in Pakistani Universities is influenced by the expecta-
tion of its usefulness, the effort required to invest in its use, and also social
Table 5. Heterotrait-Monotrait Ratio (HTMT).
BI CF EE FC PE SI SIS UB
BI
CF 0.652
EE 0.682 0.700
FC 0.487 0.533 0.387
PE 0.448 0.303 0.393 0.354
SI 0.780 0.728 0.828 0.520 0.474
SIS 0.318 0.432 0.258 0.467 0.046 0.276
UB 0.633 0.629 0.504 0.892 0.458 0.656 0.269
Notes: BI ¼Behavioral Intention of LMS, CF ¼Corona Fear, EE ¼Effort Expectancy, FC ¼Facilitating
Conditions, PE ¼Performance Expectancy, SI ¼Social Influence, SIS ¼Social Isolation, UB ¼Use
Behavior of LMS.
Table 6. Results of Path Analysis-First Order and Moderating Role of Corona Fear.
Hypothesis Regression path Effect type SRW Remarks
A: Results of path analysis-first order
H1 PE >BI Direct effect 0.112* Supported
H2 EE >BI Direct effect 0.143*** Supported
H3 SI >BI Direct effect 0.321*** Supported
H4 FC >BI Direct effect 0.050 Not supported
H5 SIS >BI Direct effect 0.082** Supported
H6 BI >UB Direct effect 0.538*** Supported
B: Moderating role of corona fear
H7 PE >BI Indirect effect 0.088** Supported
H8 EE >BI Indirect effect 0.063 Not supported
H9 SI >BI Indirect effect 0.125** Supported
H10 FC >BI Indirect effect 0.020 Not supported
H11 SIS >BI Indirect effect 0.019 Not supported
Notes: BI ¼Behavioral Intention of LMS, CF ¼Corona Fear, EE ¼Effort Expectancy, FC ¼Facilitating
Conditions, PE ¼Performance Expectancy, SI ¼Social Influence, SIS ¼Social Isolation, UB ¼Use Behavior
of LMS, SRW=Standardized regression weight.
***p <0.01, **p <0.05, *p <0.10.
Raza et al. 15
Use Behavior
of LMS
Performance
Expectancy
Behavioral
Intention of
LMS
Effort
Expectancy
Social
Influence
Facilitating
Condition
Social
Isolation
Corona Fear
Figure 1. Conceptual Model.
Use Behavior of
LMS (R
2
= 0.289)
Performance
Expectancy
Behavioral
Intention of LMS
(R
2
= 0.502)
Effort
Expectancy
Social
Influence
Facilitating
Condition
Social
Isolation
Corona Fear
0.538***
-0.088**
-0.063
0.125**
-0.020
0.019
Figure 2. Results of Path Analysis.
16 Journal of Educational Computing Research 0(0)
influence. These results are inconsistent with the study of Zwain (2019), who
examined the variables in the University of Kufa in Iraq by collecting data from
faculty and respondents and found that PE and EE did not have a positive
influence, while SI did on the BI to use LMS. While Decman (2015) revealed
that the link between PE and SI on BI to use e-learning technology was signif-
icantly positive; however, his research did not support the positive influence of
EE. Meanwhile, other past studies in the literature show positive relationships
among the variables making the results consistent with their findings (Casey &
Wilson-Evered, 2012; Dwivedi et al., 2011; Gupta et al., 2008;
SUmak et al.,
2011; Venkatesh et al., 2003; Zhou et al., 2010)
Social Isolation (B ¼0.082 p <0.05), the extended variable in the UTAUT
model, has been found to have a positive and significant relationship with BI of
LMS. Thus, H5 has also been accepted, indicating that socially isolated students
would be inclined towards using LMS for gaining knowledge and completed
course objectives.
In contrast, FC (B ¼0.050 p >0.1) were found to have a positive but insig-
nificant relationship with BI of LMS. Hence, H4 has been rejected. This means
that the availability of timely assistance and the necessary infrastructure does
not affect the intention of the student regarding BI to use LMS for completion
of their course work. The existing literature showed that there was a direct but
insignificant relationship between FC and BI to use LMS (Dwivedi et al., 2011;
Venkatesh et al., 2003). It indicates students aren’t pleased with the support.
This can also be argued that students are reluctant to embrace technology and,
therefore, not satisfied with the assistance because their crucial concern is weak
internet connectivity.
Moreover, the hypothesis of BI of LMS (B ¼0.538 p <0.01) was also found
to be positively linked with the Use Behavior of LMS. Therefore, H6 has been
accepted. This means that students who have a higher level of BI to use LMS
will be positively influenced towards actually using LMS. This finding is con-
sistent with the studies of Davis (1989), Wang and Wang (2009), Motaghian
et al. (2013) and Raman and Don (2013), they concluded that there is a signif-
icantly positive link between BI and actual use.
Moving on to the results of the moderating variable, shown in Table 6, the
hypotheses concerning the Corona fear as a moderator was found to be mod-
erating the link of PE and SI on BI of LMS was found to be positive. Thus, H7
and H9 were supported. This means that the presence of Corona fears among
students during the pandemic does strengthen the association of PE and SI with
BI to use LMS. However, the findings showed that Corona’s fear did not have a
moderating role in the relationship of EE, FC, and Social Isolation with BI of
LMS. Hence, H8, H10, and H11 are rejected. This means that the presence of
Corona’s fear among students does not strengthen the relationship among the
variables.
Raza et al. 17
Interpretation of Tables 7, 8 and 9
Lastly, Tables 7, 8, and 9 represent the results of Stone-Geisser or O2, SRMR
indicator, and f2 coefficients. The Stone-Geisser or O2 evaluates the predictive
relevance of each of the model’s endogenous variables. The next table displays
the results of the SRMR indicator, and it estimates the goodness of fit of the
structural model. The last table depicts the results of f2 coefficients that analyze
the effect size of the relationships between variables.
Conclusion, Recommendations, and Limitations
The purpose of conducting this study was to explore factors influencing the
acceptance of e-learning systems in higher-educational institutions and its use
during the current COVID-19 pandemic. The original constructs from the
UTAUT model were used, and the model was extended to measure if Social
Isolation influences student’s BI of LMS. Furthermore, the extended model also
investigated the effect of Corona Fear on the relationship of PE, EE, SI, FC,
and Social Isolation with BI of LMS, to analyze how students respond to the
technology during the unfortunate emergence of Coronavirus, a highly infec-
tious disease.
The findings show that social isolation, PE, SI, and EE are crucial factors
influencing students in Pakistan to pursue the use of LMS while FC does not
affect. These results indicate that students will willingly use LMS for successful
completion of their courses due to their perception of the benefits provided by
the e-learning system, during social distancing. The results concerning modera-
tion of Corona fear revealed that the rise in fear among students regarding the
Coronavirus would moderate the relationship of PE and SI on BI of LMS,
indicating that students will expect an improved performance by using LMS
and will be socially influenced by their friends and family to do so. Moreover,
students are not satisfied with the assistance as they consider it difficult to take
online classes with poor internet connectivity.
Table 7. Stone-Geisser or O2.
Q
2
(¼1-SSE/SSO)
BI 0.293
CF
EE
FC
PE
SI
SISO
UB 0.170
18 Journal of Educational Computing Research 0(0)
The findings of this study revealed several implications. The first being the
extension of the UTAUT model for making it relevant to the current situation
caused by the pandemic, and its application in the higher-education sector to
investigate the acceptance of e-learning systems. Universities in Pakistan will
concentrate on improving student success by enhancing the interface and the
learning management system functionality that they introduce. As student’s
efficiency in learning is increased, they would be motivated to achieve their
study goals through the use of LMS, especially when they are socially isolated
due to the coronavirus pandemic. Moreover, improving the e-learning system
with respect to the effort needed to be invested in using LMS should be a
priority as students would be more inclined towards adopting the technology
if they perceive it as easy and beneficial to use. Hence, the advantage of using
LMS in pandemic when institutions are closed is that it will make students
flexible in the future as well. Students will adopt the concept of online education,
even when educational institutions will be opened. Therefore, it will provide a
great opportunity to educational institutions in terms of earnings and high rev-
enue as they might initiate online courses along with regular classes. So, it is
recommended to the management of higher education to establish a strong
online portal through which teachers can teach students without any hurdles.
Also, the facilitating unit, such as the IT team and student affairs department,
has to be efficient enough that they can attract students towards online educa-
tion for a long term period. Thus, it is suggested to start online diplomas, short-
courses, international courses, and regular courses as well. Many students
Table 8. SRMR Indicator.
Saturated model Estimated model
SRMR 0.060 0.104
Table 9. F-square.
BI CF EE FC PE SI SISO UB
BI 0.406
CF 0.031
EE 0.016
FC 0.003
PE 0.017
SI 0.090
SISO 0.008
UB
Raza et al. 19
pursue studies with full-time jobs so, it is recommended to initiate online courses
for such students. The other advantage is that it enables a centralized pool of
information. LMS allows us to keep all information in one single location, and
students can access them anytime, anywhere from different locations using com-
patible devices. This cuts down administrative hassles associated with maintain-
ing learning materials in multiple places. Hence, it saves the cost of educational
institutions. It is recommended to work on promoting the use of LMS through
successful strategy implementation that will help students analyze the benefits of
the technology rather than being intimidated by the change. Furthermore, the
adoption of LMS in education is evidence that other educational activities can
be done through an online platform. Hence, it is suggested to spread the roots of
the online environment and start practicing other activities as well. The world is
rapidly shifting towards artificial intelligence, so it’s high time to adopt the
online environment in our education system.
Limitations of this paper are important to discuss as they hold ground for
future research. First of all, a limitation of this paper is the sample size used that
can be enlarged to achieve more generalizable results. The sample only consisted
of students enrolled in Pakistani universities, which can be further explored by
researchers concerning the acceptance of LMS by targeting the faculty of the
university, or even through the implementation of a gender-specific study that
analyzes and compares behaviors of male and female respondents. Moreover,
the authors suggest the need to investigate the extended UTAUT model in other
developed and developing countries during the pandemic, to analyze what fac-
tors influence acceptance of LMS and use of e-learning systems, to better pro-
vide course material and assistance to students in pursuit of education. Other
than that, moderating and mediating variables can also be added to extend the
model further and evaluate mechanisms relevant to the current situation.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The authors received no financial support for the research, authorship, and/or publica-
tion of this article.
ORCID iD
Syed A. Raza https://orcid.org/0000-0002-2455-6922
20 Journal of Educational Computing Research 0(0)
References
Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., & Pakpour, A. H.
(2020). The fear of COVID-19 scale: Development and initial validation. International
Journal of Mental Health and Addiction,18, 1–9. https://doi.org/10.1007/s11469-020-
00270-8
Ain, N., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning
management system use: An extension of UTAUT2. Information Development,32(5),
1306–1321. https://doi.org/10.1177/0266666915597546
Al-Busaidi, K. A., & Al-Shihi, H. (2010). Instructors’ acceptance of learning management
systems: A theoretical framework. Communications of the IBIMA,2010(2010), 1–10.
Alias, N. A., & Zainuddin, A. M. (2005). Innovation for better teaching and learning:
Adopting the learning management system. Malaysian Online Journal of Instructional
Technology,2(2), 27–40.
Al-Imarah, A., Zwain, A., & Al-Hakim, L. (2013). The adoption of e-government services
in the Iraqi Higher Education Context: An application of the UTAUT model in the
University of Kufa. Journal of Information Engineering and Applications,3(10), 77–84.
Al-Shafi, S., Weerakkody, V., & Janssen, M. (2009). Investigating the adoption of
eGovernment services in Qatar using the UTAUT model. AMCIS 2009
Proceedings,260, 1–11.
Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020).
How will country-based mitigation measures influence the course of the COVID-19
epidemic? The Lancet,395(10228), 931–934. https://doi.org/10.1016/S0140-6736(20)
30567-5
Bagozzi, R. P. (1981). Attitudes, intentions, and behavior: A test of some key hypotheses.
Journal of Personality and Social Psychology,41(4), 607. https://doi.org/10.1037/0022-
3514.41.4.607
Bakar, A. A., Razak, F. Z. A., & Abdullah, W. S. W. (2013). Assessing the effects of
UTAUT and self-determination predictor on students continuance intention to use
student portal. World Applied Sciences Journal,21(10), 1484–1489. https://doi.org/10.
5829/idosi.wasj.2013.21.10.2920
Casey, T., & Wilson-Evered, E. (2012). Predicting uptake of technology innovations in
online family dispute resolution services: An application and extension of the
UTAUT. Computers in Human Behavior,28(6), 2034–2045. https://doi.org/10.1016/
j.chb.2012.05.022
Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning:
An application and extension of the UTAUT model. Frontiers in psychology,10, 1652.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling.
Modern Methods for Business Research,295(2), 295–336.
Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing
constructs. Journal of Marketing Research,16(1), 64–73. https://doi.org/10.1177/
002224377901600110
Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Psychology Press.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly,13, 319–340. https://doi.org/10.2307/249008
Raza et al. 21
De
cman, M. (2015). Modeling the acceptance of e-learning in mandatory environments
of higher education: The influence of previous education and gender. Computers in
Human Behavior,49, 272–281. https://doi.org/10.1016/j.chb.2015.03.022
De Jong Gierveld, J., Van Tilburg, T., & Dykstra, P. (2016). Loneliness and social iso-
lation. In V. Anita & P. Daniel (Eds.), The Cambridge handbook of personal relation-
ships, 1–30. Cambridge University Press.
Dwivedi, Y. K., Rana, N. P., Chen, H., & Williams, M. D. (2011, September). A meta-
analysis of the unified theory of acceptance and use of technology (UTAUT). In M.
Nu
¨ttgens, A. Gadatsch, K. Kautz, I. Schirmer, & N. Blinn (Eds.) IFIP international
working conference on governance and sustainability in information systems-managing
the transfer and diffusion of it (pp. 155–170). Springer. https://doi.org/10.1007/978-3-
642-24148-2_10
Fidani, A., & Idrizi, F. (2012). Investigating students’ acceptance of a learning manage-
ment system in university education: A structural equation modeling approach. ICT
Innovations 2012 Web Proceedings,2(23), 311–320.
Fornell, C., & Larker, D. (1981). Structural equation modeling and regression:
Guidelines for research practice. Journal of Marketing Research,18(1), 39–50.
https://doi.org/10.1177/002224378101800313
Garone, A., Pynoo, B., Tondeur, J., Cocquyt, C., Vanslambrouck, S., Bruggeman, B., &
Struyven, K. (2019). Clustering university teaching staff through UTAUT:
Implications for the acceptance of a new learning management system. British
Journal of Educational Technology,50(5), 2466–2483. https://doi.org/10.1111/bjet.
12867
Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-Graph:
Tutorial and annotated example. Communications of the Association for Information
systems,16(1), 5.
Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of
social media in research practices of faculty using the UTAUT model. Computers in
Human Behavior,28(6), 2340–2350. https://doi.org/10.1016/j.chb.2012.07.004
Gupta, B., Dasgupta, S., & Gupta, A. (2008). Adoption of ICT in a government orga-
nization in a developing country: An empirical study. The Journal of Strategic
Information Systems,17(2), 140–154. https://doi.org/10.1016/j.jsis.2007.12.004
Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning
systems success in universities. Expert Systems with Applications,39(12), 10959–10966.
https://doi.org/10.1016/j.eswa.2012.03.028
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discrim-
inant validity in variance-based structural equation modeling. Journal of the Academy
of Marketing Science,43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares
path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Ed.)
New challenges to international marketing (Advances in international marketing) (Vol.
20, pp. 277–319). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-
7979(2009)0000020014
Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology
adoption: Testing the UTAUT model. Information & Management,48(1), 1–8.
https://doi.org/10.1016/j.im.2010.09.001
22 Journal of Educational Computing Research 0(0)
Iqbal, S. (2011). Learning management systems (LMS): Inside matters. Information
Management and Business Review,3(4), 206–216. https://ssrn.com/abstract = 3331024
Lin, C. Y. (2020). Social reaction toward the 2019 novel coronavirus (COVID-19). Social
Health and Behavior,3(1), 1. https://doi.org/10.4103/SHB.SHB_11_20
Lin, C. P., & Anol, B. (2008). Learning online social support: an investigation of network
information technology based on UTAUT. CyberPsychology & behavior, 11(3), 268–
272.
Lonn, S., Teasley, S. D., & Krumm, A. E. (2011). Who needs to do what where?: Using
learning management systems on residential vs. commuter campuses. Computers &
Education,56(3), 642–649. https://doi.org/10.1016/j.compedu.2010.10.006
Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-
based learning management system in Tanzania. Education þTraining,57, 738–756.
https://doi.org/10.1108/ET-02-2014-0014
Ma, W., & Yuen, A. (2011). E-learning system acceptance and usage pattern. In T. Teo
(Eds.), Technology acceptance in education (pp. 201–216). Brill Sense.
Matar, N., Hunaiti, Z., Halling, S., & Matar,
S. (2011). E-Learning acceptance and
challenges in the Arab region. In S. Abdallah, & A. Fayez Ahmad (Eds.), ICT accep-
tance, investment and organization: Cultural practices and values in the Arab world (pp.
184–200). IGI Global. https://doi.org/10.4018/978-1-60960-048-8
Mertens, G., Gerritsen, L., Duijndam, S., Salemink, E., & Engelhard, I. M. (2020). Fear
of the coronavirus (COVID-19): Predictors in an online study conducted in March
2020. Journal of Anxiety Disorders, 74, 102258. https://doi.org/10.1016/j.janxdis.2020.
102258
Motaghian, H., Hassanzadeh, A., & Moghadam, D. K. (2013). Factors affecting univer-
sity instructors’ adoption of web-based learning systems: Case study of Iran.
Computers & Education,61, 158–167. https://doi.org/10.1016/j.compedu.2012.09.016
Nanayakkara, C. (2007). A model of user acceptance of learning management systems: A
study within tertiary institutions in New Zealand. The International Journal of
Learning,13(12), 223–232.
Ngai, E. W., Poon, J. K. L., & Chan, Y. H. (2007). Empirical examination of the adop-
tion of WebCT using TAM. Computers & Education,48(2), 250–267. https://doi.org/
10.1016/j.compedu.2004.11.007
Nicholas-Omoregbe, O. S., Azeta, A. A., Chiazor, I. A., & Omoregbe, N. (2017).
Predicting the adoption of e-learning management system: A case of selected private
universities in Nigeria. Turkish Online Journal of Distance Education,18(2), 106–121.
Pakpour, A. H., & Griffiths, M. D. (2020). The fear of CoVId-19 and its role in preven-
tive behaviors. Journal of Concurrent Disorders. Advance online publication.
Raman, A., & Don, Y. (2013). Preservice teachers’ acceptance of learning management
software: An application of the UTAUT2 model. International Education Studies,
6(7), 157–164.
Raza, S. A., & Hanif, N. (2013). Factors affecting internet banking adoption among
internal and external customers: A case of Pakistan. International Journal of Electronic
Finance,7(1), 82–96. https://doi.org/10.1504/IJEF.2013.051746
Raza, S. A., Khan, K. A., & Rafi, S. T. (2020). Online education & MOOCs:
Teacher self-disclosure in online education and a mediating role of social presence.
Raza et al. 23
South Asian Journal of Management,14(1), 142–158. https://doi.org/10.21621/sajms.
2020141.08
Raza, S. A., Khan, K. A., Rafi, S. T., & Javaid, S. T. (2020). Factors affecting the
academic performance through cloud computing adoption. Social Sciences,8(2),
1–15. https://doi.org/10.20547/jess0822008201
Raza, S. A., Najmi, A., & Shah, N. (2018). Transferring knowledge from universities to
organizations by business students: Findings from a developing country. Journal of
Workplace Learning,30(3), 199–215. https://doi.org/10.1108/JWL-06-2016-0054
Raza, S. A., Qazi, W., Umer, B., & Khan, K. A. (2020). Influence of social networking
sites on life satisfaction among university students: A mediating role of social benefit
and social overload. Health Education,120(2), 141–164. https://doi.org/10.1108/HE-
07-2019-0034
Raza, S. A., Shah, N., & Ali, M. (2019). Acceptance of mobile banking in Islamic banks:
Evidence from modified UTAUT model. Journal of Islamic Marketing,10(1),
357–376. https://doi.org/10.1108/JIMA-04-2017-0038
Raza, S. A., Umer, A., Qureshi, M. A., & Dahri, A. S. (2020). Internet banking service
quality, e-customer satisfaction and loyalty: The modified e-SERVQUAL model. The
TQM Journal. Advance online publication. https://doi.org/10.1108/TQM-02-2020-
0019
Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3.2. 7. SmartPLS GmbH.
Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 M3 beta. SmartPLS GmbH.
Sharif, A., & Raza, S. A. (2017). The influence of hedonic motivation, self-efficacy, trust
and habit on adoption of internet banking: A case of developing country.
International Journal of Electronic Customer Relationship Management,11(1), 1–22.
https://doi.org/10.1504/IJECRM.2017.086750
Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly,13(2),
147–169.
Sumak, B., Hericko, M., Polancic, G., & Pusnik, M. (2010). Investigation of e-learning
system acceptance using UTAUT. International Journal of Engineering Education,
26(6), 1327–1342.
SUmak, B., Heri
cKo, M., & Pu
sNik, M. (2011). A meta-analysis of e-learning technology
acceptance: The role of user types and e-learning technology types. Computers in
Human Behavior,27(6), 2067–2077. https://doi.org/10.1016/j.chb.2011.08.005
Tabachnick, B. G., & Fidell, L. S. (2007). Experimental designs using ANOVA (p. 724).
Thomson/Brooks/Cole.
Taiwo, A. A., & Downe, A. G. (2013). The theory of user acceptance and use of tech-
nology (UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical
& Applied Information Technology,49(1), 48–58.
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model devel-
opment and test. Computers & Education, 57(4), 2432–2440.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic
motivation, and emotion into the technology acceptance model. Information Systems
Research,11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of
information technology: Toward a unified view. MIS Quarterly, 27, 425–478. https://
www.jstor.org/stable/30036540
24 Journal of Educational Computing Research 0(0)
Waheed, M., Kaur, K., Ain, N., & Hussain, N. (2016). Perceived learning outcomes
from Moodle: An empirical study of intrinsic and extrinsic motivating
factors. Information Development,32(4), 1001–1013. https://doi.org/10.1177/
0266666915581719
Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-
based learning systems. Computers & Education,53(3), 761–774. https://doi.org/10.
1016/j.compedu.2009.02.021
Wilder-Smith, A., & Freedman, D. O. (2020). Isolation, quarantine, social distancing and
community containment: Pivotal role for old-style public health measures in the novel
Coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine,27(2), 1–4. https://
doi.org/10.1093/jtm/taaa020
World Health Organization. (2020). Coronavirus disease 2019 (COVID-19): Situation
report.
Yoo, S. J., Han, S. H., & Huang, W. (2012). The roles of intrinsic motivators and
extrinsic motivators in promoting e-learning in the workplace: A case from South
Korea. Computers in Human Behavior,28(3), 942–950. https://doi.org/10.1016/j.chb.
2011.12.015
Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile
banking user adoption. Computers in Human Behavior,26(4), 760–767. https://doi.org/
10.1016/j.chb.2010.01.013
Zwain, A. A. A. (2019). Technological innovativeness and information quality as neoteric
predictors of users’ acceptance of learning management system: An expansion of
UTAUT2. Interactive Technology and Smart Education,16(3), 239–254. https://doi.
org/10.1108/ITSE-09-2018-0065
Author Biographies
Dr. Syed Ali Raza is a Full Professor of Finance at the Iqra University Karachi,
Pakistan. He received his Ph.D. degree in Finance from the Northern University
of Malaysia. He is Director of Academics, Chairperson of Department of
Accounting & Finance, member Academic Council, member Board of
Advance Studies and Research (BASAR), and member Doctoral Committee
for Business Administration at Iqra University. Prof. Dr. Raza is also a
Committee Member of the Ministry of Planning, Development, and Reform
to develop an Innovative Financing Mechanism under the "Ehsaas Program"
initiated by the Prime Minister of Pakistan.
Wasim Qazi is the Vice-Chancellor of Iqra University. He is currently
involved in diverse assignments in the Department of Education and Learning
Sciences, specifically, in the area of continuous professional development. As
an expert in education policy, management, and research, he has numerous
international publications to his credit. He has presented in a range of global
forums and transnational conferences; peer-reviewed academic papers in pres-
tigious journals; mentored and successfully supervised several MPhil and PhD
students. His academic interests comprise gender-specific educational
Raza et al. 25
interventions, school management committees, instructional, learning, and cog-
nitive sciences.
Komal Akram Khan received her BBA degree from IQRA University and cur-
rently working as Research Assistant at the same organization. She published
some research articles. Moreover, currently, she is pursuing her Master’s
Degree. Her area of interest includes Higher Education, Quantitative
Research Methods, Finance, and Economics.
Javeria Salam is a business student at IQRA University. Her area of interest
includes Higher Education and Quantitative Research Methods.
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