The impact of the COVID-19 pandemic on the adoption of e-learning among academics in Saudi Arabia

Abstract

Saudi universities have suspended the traditional face-to-face classes and moved to online learning to ensure the continuation of the educational process during the COVID-19 pandemic. The speed at which the move to online learning has happened was unprecedented and staggering. Academics, had to improvise and try different e-learning solutions in less-than-ideal circumstances. No matter how powerful and easy-to-use e-learning solutions might be, many academics understandably found the transition to e-learning stressful. This research project looks at the transition experience to online learning from the viewpoint of academics in Saudi universities. It investigates how the COVID-19 pandemic has affected the adoption of e-learning solutions and how this forced experience would affect the long-term adoption of e-learning solutions. The study employed an online survey administered to academics from all the 24 public universities. The results were quantitatively analysed by using structural equation modelling (SEM). It was found that attitudes toward e-learning, self-efficacy and perceived reliability have significant positive effects on behavioural intention to adopt e-learning. It was also found that COVID-19 has positively affected the long-term adoption of e-learning. Therefore, it is recommended that universities take immediate actions and capitalise on the e-learning experience that academics have gained during COVID-19 pandemic.

Full text

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The impact of COVID-19 pandemic on the adoption of
e-learning among academics in Saudi Arabia
Ali Alammary 1, Moneer Alshaikh2 and Areej Alhogail 3
1 College of Computing and Informatics, Saudi Electronic University, Saudi Arabia;
a.alammary@seu.edu.sa
2 Department of Cybersecurity, College of Computer Science and Engineering, University of
Jeddah, Saudi Arabia, malshaikh@uj.edu.sa
3 College of Computer and Information Sciences, King Saud University, Saudi Arabia;
aalhogail@ksu.edu.sa
Abstract
Saudi universities have suspended the traditional face-to-face classes and moved to online
learning to ensure the continuation of the educational process during COVID-19 pandemic.
The speed in which the move to online learning has happened, was unprecedented and
staggering. It was not clear if information technology infrastructure in universities were
ready to handle this huge and sudden transition to online learning. It was also not clear
whether technical support teams were able to provide sufficient amount of support to all
academics in such a short time. Academics, who might have limited technical skills, had
to improvise, and try different e-learning solutions in less-than-ideal circumstances. No
matter how powerful and easy to use e-learning solutions might be, many academics
understandably found the transition to e-learning stressful. This research project looks at
the transition experience to online learning from the viewpoint of faculty members in Saudi
universities. It investigates how COVID-19 pandemic has affected the adoption of e-
learning solutions among academics and how this forced experience would affect the long-
term adoption of e-learning solutions. The study employed an online survey administered
to academics from all the 24 public universities. The results were quantitatively analyzed
by using structural equation modeling (SEM). It was found that attitudes toward e-learning,
self-efficacy and perceived reliability have significant positive effects on behavioral
intention to adopt e-learning. It was also found that COVID-19 has positively affected the
long-term adoption of e-learning. Therefore, it is recommended that universities take
immediate actions and capitalize on the e-learning experience that academics have gained
during COVID-19 pandemic, leverage the opportunities, mitigate the challenges, and
accelerate the adoption of e-learning.
Key word
E-learning in Saudi, COVID-19 pandemic, Higher Education; E-learning Adoption, Behaviour
Intention

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1. Introduction
In an attempt to improve the quality of learning and teaching in Saudi universities, the Ministry of
Education decided to suspend all distance learning programs in 2017 (Albjali, 2018). This had a
substantial impact on the adoption of e-learning in many Saudi universities. Traditional teaching
became the dominant model of teaching in higher education. Although all public universities have
a variety of e-learning solutions (Aldiab et al., 2019), only a small number of instructors were
using these solutions. In a study conducted in one of the biggest universities in Saudi, i.e., King
Saud University, Al Meajel and Sharadgah (2018) found that more than 82% of instructors are not
using e-learning solutions at all. A similar result was reported by Tawalbeh (2018) from Taif
University who found that most instructors rarely or never used e-learning solutions, and that those
who are using these solutions were merely using them to upload course syllabus, share grades and
send emails to students. Naveed et al. (2017) surveyed 247 instructors from different Saudi
universities and found that lack of technology skills, lack of e-learning knowledge, lack of time
and lack of motivation were the main barriers to e-learning adoption in Saudi universities.
Due to the threat of COVID-19, Saudi universities were required by the Ministry of Education
(MOE) in March 2020 to cancel all face-to-face classes, including lectures, laboratory-based
classes, seminars, and tutorials. They then moved all courses online by using the e-learning
solutions that they already had. MOE has formed several committees to monitor and support the
transition to online learning. Universities were requested to send weekly reports outlining their
progress, challenges that they face and support that they need (Ministry of Education, 2020b). The
ministry provided different types of support including e-learning solutions’ subscriptions and
training workshops. Telecommunication companies were also requested to provide free internet
access to e-learning platforms (Saudi Press Agency, 2020). Saudi universities have also put
considerable effort to support their instructors in their transition to online learning. This includes
training workshops, technical support and developing online teaching and assessments guideline
(King Abdulaziz University, 2020; Umm Al-Qura University, 2020).
The sudden and obligatory transition to online learning has raised several concerns that require
investigation. These concerns are related to (i) the suitability, reliability, and ease of use of the
utilized e-learning solutions; (ii) Technical skills of academics; (iii) sufficiency and
appropriateness of technical support that has been provided to academics; and (iv) academics
awareness of cybersecurity risks.
It is also of great interest to understand how academics’ perception of e-learning has changed
because of this transition and whether they became more eager to incorporate e-learning in their
future teaching plans. Most important, it is essential to dig deep into how e-learning solutions are
being utilized by academics and whether they are exploiting the maximum potential of these
solutions or merely using it to deliver their virtual classes.
Taking into consideration the Minister of Education recent remark that e-learning will be a
strategic option (Ministry of Education, 2020a), findings of this study can help decision-makers in
the Ministry of Education as well as in Saudi universities to understand the state of the art in e-

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learning adoption, factors that appear to influence the level of adoption and challenges that could
impede the adoption from the point of view of faculty members and teaching staff. It can also help
them identify areas of improvement and plan their e-learning strategies accordingly.
2. Research background
2.1 E-learning during the COVID19 pandemic
In a very short period, millions of academics worldwide started to teach online as their students
must stay home because of the worldwide spread of COVID‐19. More than 146 countries during
March 2020 have declared school and university closures or localized closures (UNESCO, 2020).
This massive shift has required critical changes to teaching styles as well as the requirement of
technology support teams. The situation and the sudden full distance online learning have
obviously created a new mindset that everyone is willing with a strong commitment to teach and
evaluate students online (Ebner et al., 2020). Fortunately, the availability of e-learning platforms
played a significant factor in boosting the distance learning shift throughout the COVID-19
pandemic (Alqahtani & Rajkhan, 2020). Online distance learning proved to be adequate and
required in times of social distancing due to COVID-19 pandemic (Ali, 2020).
Ebner et al. (2020) study showed that the crises have increased the use of video conferencing
platforms whether live streaming, or recording as an alternative to face-to-face learning, which is
not the ideal setting of high-quality e-learning settings. Higher education institutes were adopting
different available platforms like Microsoft Teams, Zoom, Cisco WebEx, Blackboard and Google
Classroom to cope with the shift to distance online learning (Shahzad et al., 2020).
Nevertheless, teaching staff were challenged with the lack of online teaching practices, appropriate
content preparation, or technical support. In the same vein, students were faced with the lack of a
decent e-learning mindset and skills such as lack of self‐discipline, appropriate studying content,
or proper learning environments when they are studying behind the screens (Bao, 2020).
Bao (2020) have listed in his study the most influencing factors for successful e-learning during
COVID-19 pandemic that are; (1) strong relevance between student learning and course design. In
other words, the quantity, difficulty, and length of teaching content must be compatible with the
students learning behaviour attributes and readiness. (2) effective online delivery of instructional
content whether speed of delivery or type of activities and content (3) appropriate support provided
by tutors to students through appropriate feedback, online guidance and tutoring and so on; (4)
adequate in-class participation to enhance student's learning, and (5) contingency plan to handle
surprising online incidents and problems such as the platform’s traffic overload issue.
Ebner et al. (2020) have identified the major enablers for the transfer such as, available and
functional learning management systems prior to the crises; technical support teams were available
and responsible; available electronic transaction and communication systems; clear decisions and
flexible communication channels between Higher Education authorities, university’s management
and the IT services; sharing experiences, difficulties and advices and publishing performance

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outcomes; teams were equipped to work remotely; and task forces were formed to quickly
collaborate and support e-learning experience. Moreover, during the transfer decisions were taken
promptly and required resources were supplied as demanded.
The major barriers as Ebner et al. (2020) listed were mainly: size of the technical team; the
infections within the team; lack of hardware equipment or poor internet connections; time pressure
that affect system updates; communication practices between teams were challenging; dependence
on external services or providers; providing technical support was doubled or tripled also there is
a need for many written instructions for action; also the universities network line’s bandwidth
might also be a bottleneck.
In addition, a study by Almaiah et al. (2020) have noted the critical aspects that influence the use
of e-learning solutions throughout the time of COVID-19 pandemic. They advise universities to
take them into the future plans for successful e-learning which are: (i) technological factors
whether hardware, software, technical infrastructure or technical support; (ii) e-learning system
quality factors such as ease of use, usefulness, and reliability (iii) cultural aspects such as e ICT
literacy and skills of e-learning users ;(iv) self-efficacy and skills for the e-learning platform usage
(v) trust factors such as system protection, information privacy, and system reliability; and
(vi)financial support issues.
Alqahtani and Rajkhan (2020) have identified the e-learning critical success factors to enhance the
educational process during COVID-19 pandemic applying multi-criteria Analytic Hierarchy
Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).
They identified the following factors: management support, enhanced e-learning platforms usage
awareness for students and faculties, as well as requiring a high-level of information technology.
Even if resources are available, findings by Ali (2020) reveals that faculty members and support
team’s readiness, motivation, trust, and resources accessibility are crucial success factors in
emergency e-learning adoption. Therefore, decision makers shall take these factors into their
account when designing e-learning strategies.
2.2 E-learning in Saudi Arabia
Saudi Arabia has established the National E-learning Center (NELC) in 2005 to take the role of
governing and regulating e-learning in Saudi Arabia. Its ultimate aim is to enhance the e-learning
experience in educational institutions, and to support and implement the most effective practices
of e-learning (Alqahtani & Rajkhan, 2020). The center sets out the policies concerning the offering
and management of e-learning programs. The policies state the type of technologies that
educational institutions have to possess, support that they should provide to their academics and
students, practices for instructional design and, fairness and accessibility standards in online
learning environments (National E-learning Center 2020).
NELC has developed guidelines and standards to universities regarding of having an adequate e-
learning infrastructure that includes appropriate hardware (i.e., servers, storage and networking),
e-learning portals, establishing deanships or offices dedicated to handling e-learning matters,
providing training and awareness programs and other e-learning initiatives (Malik et al., 2018).
Thorpe and Alsuwayed (2019) found that Saudi universities had adequate e-learning infrastructure.

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Besides, a study by Alqahtani and Rajkhan (2020) confirmed that the IT infrastructure in Saudi
universities was a significant success factor in boosting the transfer during the COVID-19
pandemic. However, not all universities had the ability to switch smoothly as this was depending
on the maturity of utilizing e-learning solutions before the pandemic.
Regarding broadband penetration, Saudi Arabia is ranked in the top five percent of countries
worldwide. According to Statista (2021), Saudi Arabia is ranked 8th worldwide with a broadband
penetration rate of 95.7% and around 34 million of its citizens having subscriptions to fixed or
mobile broadband services. Saudi Arabia also has an excellent internet speed. According to
Speedtest website, which specialized in measuring internet speeds worldwide, Saudi Arabia has
jumped six places universally in terms of mobile internet speed, ranking 4th worldwide with a
download speed of 160.40 MB/s and upload speed of 23.57 (Speedtest, 2021).
Regarding the acceptance of e-learning among academics and students in Saudi, a study by Hoq
(2020) has confirmed that the academics hold positive opinion towards e-learning. On the other
hand, Ebaid (2020) mentioned that students felt increasing flexibility in their studying, enhancing
communication with teachers and other students. Nonetheless, his survey showed that students felt
that e-learning is less beneficial than traditional face-to-face teaching. Prior experience and
readiness for e-learning has affected Saudi students' overall e-learning satisfaction (Alqahtani et
al., 2021). The perception of the students varies depending on gender, level of the course, and
quality of e-learning approaches (Elumalai et al., 2021).
2.3 Saudi Universities and COVID-19 pandemic
COVID-19 pandemic has tested the Saudi society adaptability and flexibility in response to
emerging crisis. The challenges for education decision makers were enormous. To facilitate the
transition to e-learning during COVID-19 and to address the enormous challenges, Saudi
government and universities have taken several initiatives. According to a report by the World
Bank, the Saudi’s Ministry of Education has utilized different channels for the delivery of virtual
classes such as TV programs and online platforms (World Bank, 2020). Moreover, the Ministry of
Education has supported the transfer to e-learning through facilitating the e-learning solutions’
paid subscriptions and by following up with universities to check their progress and requirements
(Ministry of Education, 2020b). NELC played an essential role in supervising and monitoring the
transfer to e-learning during the pandemic and have provided innovative solutions to handle the
situation. Telecommunication providers have provided free internet access to e-learning platforms
and have increased the bandwidth to accommodate the concurrent high demand on the internet
connection. In addition, different initiatives have been announced to support needy students with
required equipment and training (Alqahtani & Rajkhan, 2020).
UNESCO report has praised the Saudi Arabian efforts for the successful continuation of the
education process for more than six million students in schools and universities (Elumalai et al.,
2021). Table 1 shows an example of the efforts of seven Saudi universities during the period of
the transfer to online learning according to the reports that they have published on their websites.
The Ministry of Education has announced in the May 2020 report that 1,626,749 students have
conducted more than 4,54,727 tests online in 43 private and public universities. 58179 faculty

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members were part of this transfer though conducting online lectures, electronic exams and
discussions with a weekly average of over 1.5 million virtual classes weekly (Ministry of
Education, 2020b).
Table. 1 The use of e-learning solutions during the COVID-19 pandemic in Saudi universities
University Name
Average
Number of
online
classes
(sections)
Number of
online
assessments
number
of
faculties
who have
used
online
courses
number
of
students
used
online
courses
Average
number of
active
users of
university
platform
Average of support
tickets to e-learning
platform
Deliver
awareness
and training
Saudi Electronic
University
4553
46799
809
89575
87071
15515
yes
King Saud University
22995
27978
4912
51698
56756
4163
yes
Imam Mohammed
Bin Saud Islamic
University
18098
16409
5000
79028
459211
3724
yes
Jazan University
3577
12187
2722
30077
30077
15573
yes
King Abdulaziz
University
18377
10885
5155
56710
56710
2260
yes
Jeddah University
13253
36302
1755
21745
23690
3138
yes
Taiba University
13500
46897
3000
62600
62600
3146
yes
2.4 Technology Acceptance Model
Technology adoption is normally studied by utilizing one or more of the existing models in the
literature. The most widely used models are Technology Acceptance Model (TAM) and the
Unified Theory of Acceptance and Use of Technology (UTAUT) (Marchewka & Kostiwa, 2007;
Tan, 2013).
TAM was first introduced in 1985 by Fred Davis (Davis, 1985). It was developed from the Theory
of Reasoned Action and was the first model to identify psychological factors as predictors of
technology acceptance and use (Chuttur, 2009). As it can be seen in Figure 1, TAM posits that
user motivation to use a technology is determined by three factors: attitude toward using, perceived
usefulness and perceived ease of use. It hypothesized that perceived usefulness and perceived ease
of use are influenced by the design characteristics of the technology. Besides, perceived ease of
use and perceived usefulness jointly determine the attitude of the user. Then, a user's decision to
accept or reject a technology is determined by the user’s attitude toward that technology. Davis
has later revised his model to include several other factors. He also modified some of the
relationships that he initially suggested (Samaradiwakara & Gunawardena, 2014). Similarly, many
other researchers have proposed several modifications to the original TAM model (Dishaw &
Strong, 1999; Venkatesh & Davis, 2000).
UTAUT was developed in 2003 by (Venkatesh et al., 2003). Their aim was to consolidate the
fragmented research and theories on technology acceptance and use into a single theoretical model.

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User Motivation
As it can be seen in Figure 2, UTAUT posits four factors i.e., facilitation conditions, social
influence, effort expectancy and performance expectancy as predictors of the behavioral intention
to accept and use a new technology. Furthermore, the model tries to explain how differences
between individuals impact technology acceptance and use. It hypothesizes that ease of use,
perceived usefulness and intention to use are moderated by age, gender, voluntariness, and
experience. Similar to TAM, many researchers have proposed different modifications to the
original UTAUT model (Jaradat & Al Rababaa, 2013; Mandal & McQueen, 2012).
Design
Features
Cognitive
Response
Affective
Response
Behavioral
Response
Figure 1. Original TAM Model proposed by Davis (1985)
Perceived
Usefulness
Actual
System
Use
X1
X2
X3
Perceived
Ease of Use
Attitudes
Toward
Using

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Figure 2. Original UTAUT Model proposed by Venkatesh et al. (2003)
2.5 Technology acceptance in COVID-19 pandemic
The sudden and obligatory transition to online learning as result of COVID-19 pandemic has
focused more attention on technology acceptance in higher education. Numerous studies have
examined technology acceptance from the perspective of students and academics. The vast
majority of these studies have focused on students. Abbad (2021), for example, investigated
students’ intentions to use and their actual usage of e-learning systems. He used UTAUT model to
design his research framework. He then surveyed 370 students from Hashemite University in
Jorden. He found that effort expectancy and performance expectancy have significant effect on
behavioural intentions to use e-learning systems. He also identified a direct impact of behavioural
intentions and facilitating conditions on students’ use of e-learning systems. Similarly, Jameel et
al. (2020) used UTAUT model to study students’ behavioral intention to use e-learning during
COVID-19 pandemic. They surveyed 213 students studying at Cihan University Erbil in Iraq. They
found significant and positive impact of performance expectancy, effort expectancy, facilitating
conditions, and habit on students’ behavioral intention to use e-learning. Alyoussef (2021) also
studied e-learning acceptance and its role in the sustainability of learning from the perspective of
students. He utilized the TAM model. He collected his data from 432 students studying at several
public universities in Saudi. His finding indicate that perceived ease of use has a significant impact
on usefulness and perceived enjoyment, which in turn has a significant impact on the use of e-
learning solutions. Our study differs from these studies in that it investigates technology
acceptance from the perspective of academics.
Less studies have examined technology acceptance from the perspective of academics. Some have
focused on certain technologies, while other studied the acceptance of e-learning solution in
general. For example, Bhatt and Shiva (2020) studied the adoption of Zoom software by academics
during Covid-19 pandemic. They utilized a modified TAM model. They collected their data from

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125 academics who teach in several Indian universities. They found that perceived ease of use,
perceived usefulness and environment concern have significant positive effect on academics’
attitude towards using Zoom. Vusumuzi et al. (2020) studied the acceptance of Moodle learning
management system by academic in Zimbabwe. They adapted the UTAUT model and collected
data from 114 faculty members from Lupane State University. They found that facilitating
conditions have the greatest impact on the acceptance of Moodle. Alshammari (2021) also
investigated the factors that may influence the use of virtual classrooms during COVID-19
pandemic. He used a modified UTAUT model. He surveyed 235 academics from the University
of Hail, Saudi Arabia. His finding indicate that performance expectancy and effort expectancy are
significant predictors of behaviour intention to use virtual classrooms. As these studies focus on
certain technologies that are used in certain universities, they can only provide partial
understanding of the acceptance of e-learning solutions in higher education institutions.
Jere (2020) investigated factors that influence academics’ intention to adopt e-learning solutions
in general. He adopted the Teo’s model that combines elements from the TAM and the UTAUT
models. He collected his data from 132 academics teaching at the University of KwaZuluNatal in
South Africa. He found that academics’ attitude towards e-learning is the most influential factor
on academics’ behavioural intention to use e-learning solutions. Zalat et al. (2021) utilized the
TAM model to investigate factors that influence the acceptance and use of e-learning solutions for
teaching during COVID-19 pandemic. They surveyed 346 academics from the Faculty of
Medicine, Zagazig University, Egypt. They found that perceived usefulness, perceived ease of use,
and acceptance of e-learning have significant impact on the adoption of e-learning solutions. In
Saudi context, very few studies have examined academics acceptance and use of the different e-
learning solutions that are available during COVID-19 pandemic. Alyoussef (2021) studied the
acceptance and adoption of e-learning solutions by academics during COVID-19 pandemic in one
of the small universities in Saudi. He utilized the TAM model and the theory of a reasoned behavior
(TRA). He collected his data from 100 academics teaching in Hail University. His findings indicate
that perceived usefulness, perceives ease of use, normative pressure and management support have
significant impact on attitudes and intention to use e-learning solutions.
Our study employs a revised theoretical framework that was developed based on TAM and
UTAUT as well as the authors’ experiences and knowledge of the situation in Saudi. It examines
e-learning from the viewpoints of academics. It differs from previous studies in that is was
conducted with participants from all the public universities in Saudi. These participants are from
different academic departments. Therefore, the results can better inform decision-makers in the
Ministry of Education as well as in Saudi universities about the state of the art in e-learning
adoption. It is also, to the best of our knowledge, the first study to dig deep into the actual usage
of e-learning solutions to find out whether academics are exploiting the maximum potential of
these solutions or merely using it to deliver their virtual classes.
3. Theoretical framework
To develop the research model, we reviewed several theories to assess which theoretical model is
suitable to explain the use of e-learning solutions during COVID-19 pandemic. Although Saudi
universities have provided e-learning solutions such as blackboard to academics, the percentage

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of utilising these services was very low. However, the decision to close schools and universities
campuses that the many countries around the world have taken to slow the spread of the virus has
led to unpresented transformation from traditional face-to-face teaching to online. Academics had
no choice but to use e-learning solutions to deliver their courses online including teaching,
discussion, and assessment.
Whilst TAM and UTAUT are widely accepted for examining Information System (IS) adoption
behavior, we argue that existing theoretical models might not fully explain the emergency adoption
of e-learning solutions during COVID-19 crisis. Therefore, we propose the following model (see
Figure 3) to explore the factors that affect the adoption of e-learning solutions by Saudi academics
during COVID-19 crisis. The model is informed by existing theoretical models, mainly TAM and
UTAUT and experiences and knowledge of the situation. All three authors were involved in
teaching courses and supporting their fellow academics at three universities during COVID-19
pandemic. Therefore, authors were fully aware of challenges and problems both universities and
academics were facing to continue delivering their mission and responsibilities.
Considerable research has been done using different variations of technology acceptance models
with one or two additional constructs, or with some moderating variables in predicting user
intention to adopt technologies. As many have identified, different contexts may involve different
factors that affect the technology adoption (e.g., government, health, and education). However,
Dwivedi et al. (2017) argued that the constructs from the original technology adoption models
might not be appropriate in certain contexts. As Martins et al. (2014) pointed out that the factors
affecting IS acceptance can vary by time, in addition to the nature of the technology and the target
users. Riffai et al. (2012) argue that in the case of IS development and implementation, TAM
overlooks some important social aspects and context as it focuses more on the perceived usefulness
from individual perspective.
After comprehensively examining and reflecting on various technology adoption models, we
concluded that the UTAUT model was aimed to achieve a unified view of technology acceptance
(Venkatesh et al., 2003). UTAUT has been broadly adopted in different contexts including e-
learning (Salloum & Shaalan, 2018; Tan, 2013). Many researchers have adopted the UTAUT
model to explore factors influencing behavior intention and technology adoption, including in IS
context (Hong et al., 2011). This model has been rigorously tested in numerous domains and has
been proven to be useful in analysing technology acceptance from users’ point of view (Park et
al., 2007; Venkatesh et al., 2012). However, UTAUT, too, still focuses on a single subject (a
community, culture, organisation, person, or age group). Therefore, many studies have used the
UTAUT theory with some additional constructs from other theories and based on the specific
context and circumstances (Gagnon et al., 2012). Thus, this research has included context specific
determinants to improve the predictability of the UTAUT of e-learning solutions on Saudi
universities during COVID-19 pandemic.
The authors’ direct involvement on the transition from face-to-face traditional learning to e-
learning during COVID-19 has informed them of issues concerning academics behaviour on
accepting and adopting e-learning solutions. Two of the main issues which have received
considerable attention amongst academics was perceived reliability and cybersecurity risk

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awareness, both of which are major concerns that influence many individuals’ decision to adopt e-
learning solutions in Saudi universities. Reliability was important due to the limited access to
internet services especially in remote and regional areas as well as high demand and sudden
transformation have overloaded the IT services to provide support and solve technical issues.
Similarly, the wide adoption of e-learning solutions without adequate security training and
awareness have exposed individuals and their organisations to the high volume of cybersecurity
attacks (e.g., social engineering and phishing attacks).
In this study, we use the UTAUT model (Venkatesh et al., 2003) as our framework in identifying
the factors that affect the adoption of e-learning solutions in Saudi universities. To put some
relevant context, we added two additional variables i.e., perceived reliability and cybersecurity
risk awareness (Ganguli & Roy Sanjit, 2011); (Gunawardana et al., 2015); (Alam et al., 2020). We
also include major as a new moderator variable in addition to gender from UTAUT. Table 2 defines
all seven constructs used in the proposed model.
Table 2. Construct Definition
No
Construct
Definition
Source
1
Attitudes toward
Using
An individual’s positive or negative feelings about
performing the target behaviours
Venkatesh et al. (2003)
2
Effort Expectancy
The degree of which a person believes that using e-learning
would be free of effort.
Venkatesh et al. (2003)
3
Self-Efficacy
Judgment of one’s ability to use e-learning solutions.
Venkatesh et al. (2003)
4
Perceived
Reliability
the perception of confidence and trust of users while
interacting with technology, in its proper and accurate
functioning as promised by its service providers.
(Lee et al., 2003; Shareef et
al., 2012)
5
Facilitating
Conditions
The degree to which an individual believes that an
organization and technical infrastructure exist to support the
use of the system
Venkatesh et al. (2003)
6
Cybersecurity Risk
Awareness
The degree of awareness of cyber risk.
Self-developed
7
Behavioural
intention
the degree to which a person perceives his/her willingness to
use e-learning solutions
Venkatesh et al. (2003)
3.1 Attitudes toward Using
Attitude towards e-learning solutions is defined as an individual’s positive or negative feelings
about performing the target behaviours (Venkatesh et al., 2003). According to Bruess (2003),
attitudes play a significant role in influencing student’s adoption of instructional technology in the
classroom. Likewise, Wangpipatwong (2008) posits that students’ attitude towards computers
influence their intention and perception of using e-learning. While those studies were focused on
the students’ part, we argue that it would not be a different case on the university professors’ part.
Therefore, we postulate the following hypothesis:
H1. Attitudes positively influence the behavioral intention to use e-learning solutions during
COVID-19 pandemic.

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3.2 Effort Expectancy
Effort Expectancy is defined as “the degree of ease associated with the use of the system”
(Venkatesh et al., 2003). Introduced in the UTAUT as the antecedent of perceived ease of use in
TAM, this construct has been consistently shown as a crucial predictor to technology acceptance
in many different domains and places, including e-learning in Saudi Arabia (Bellaaj et al., 2015).
Technologies that are convenient and simple to use can help make users feel connected (Alalwan
et al., 2017; Shareef et al., 2016). Therefore, the following hypothesis is postulated:
H2. Effort expectancy positively influences the behavioral intention to use e-learning solutions
during COVID-19 pandemic.
3.3 Self-Efficacy
Self-efficacy refers to an individual's belief in their capacity to execute behaviors necessary to
produce specific performance attainments (Bandura, 1977). In this context, self-efficacy is an
individual’s personal belief that they possess the aptitude and skills to succeed when engaging in
e-learning activities. Chao (2019) showed that self-efficacy is influential in the context of learning
technology acceptance. As self-efficacy reflects confidence in their own ability, it is an
understandably important predictor of e-learning acceptance. As such, we postulate the following
hypothesis:
H3. Self-efficacy positively influences the behavioral intention to use e-learning solutions during
COVID-19 pandemic.
3.4 Perceived Reliability
Technical reliability plays an important role of adoption of technology. Several studies found that
reliability has a strong effect on users' satisfaction with technology. In this research we added
perceived reliability as one of the constructs needed to be measured to understand the use of e-
learning solutions during COVID-19 pandemic. Perceived reliability is critical to user satisfaction
and intention to use a new technology as it measures user belief that the said technology will
perform consistently, safely, and accurately as promised by the service providers (Lee et al., 2003;
Parasuraman et al., 1988; Shareef et al., 2012). Thus, the following hypothesis is postulated:
H4. Perceived reliability positively influences the behavioral intention to use e-learning solutions
during COVID-19 pandemic.
3.5 Facilitating Conditions
Facilitating Conditions is defined as “the degree to which an individual believes that an
organization and technical infrastructure exist to support the use of the system” (Venkatesh et al.,
2003). In the case of e-learning solutions, the rapid transformation from face-to-face teaching to
e-learning has overwhelmed universities' support services to provide necessary training and
facilitate such transition during the pandemic. Moreover, the transition to full distance learning
solution due to COVID-19 has caught many educators off guard. Some of them may not have the
readiness to make such a sudden transition without the necessary support. Therefore, the following
hypothesis is postulated:

13
H5. Facilitating Conditions positively influence the behavioral intention to use e-learning solutions
during COVID-19 pandemic.
3.6 Cybersecurity Risk Awareness
Cybersecurity Risk Awareness is defined as the user's awareness of cyber risks and
countermeasures to prevent these risks. During lockdown because of COVID-19, many individuals
have started working from home and accessing services remotely to perform their job. Working
from a less security environment (home) has made systems and information susceptible to
cyberthreat as recent security reports show an increase in cyberattacks which in many cases
resulted in loss of data and interruption of the services (NTT Security, 2019). During the transition
several cybersecurity departments at universities provided awareness emails and newsletters
giving the significance risks that cyberthreats pose to universities and academics working from
home. Therefore, we decided to explore academic awareness of risks and security practices and its
effect on using e-learning solutions during COVID-19 pandemic by postulating the following
hypothesis:
H6. Cybersecurity Risk Awareness positively influences the behavioral intention to use e-learning
solutions during COVID-19 pandemic.
3.7 Moderating Role of Gender and Academic Major
Past studies utilizing TAM and UTAUT have shown how some demographic factors, especially
gender, can have a moderating role between the exogenous and endogenous variables. For
example, compared to women, men were more strongly influenced by their attitude towards using
a new technology (Khatun et al., 2016) and paid more attention to the effectiveness and reliability
of technology but were less concerned by security issues (Alam et al., 2020). Gender differences
were also found to moderate the effect of some other variables, such as effort expectancy (Afonso
et al., 2012), self-efficacy (Tarhini et al., 2014), and facilitating conditions (Ghalandari, 2012).
Meanwhile regarding academic major, past studies show that non-STEM academics have a more
positive perception of blended learning strategies than their STEM counterparts while the opposite
is true in terms of self-efficacy and effort expectancy (Owston et al., 2020). In terms of cyber
hygiene, which is a manifestation of cybersecurity awareness, Neigel et al. (2020) found that it
differs across academic majors. Specifically, people with a STEM background generally do better
than the non-STEM counterparts. Another study shows that STEM and Health Science majors are
generally on a different spectrum compared to the Non-STEM (Social and Humanities) majors
when it comes to their use of technology for learning (Pratama & Scarlatos, 2020). Based on the
previous literature, the following hypotheses are postulated:
H7. Gender has a significant moderating role in the relationship between attitudes and the
behavioral intention to use e-learning solutions during COVID-19 pandemic.
H8. Gender has a significant moderating role in the relationship between effort expectancy and the
behavioral intention to use e-learning solutions during COVID-19 pandemic.
H9. Gender has a significant moderating role in the relationship between self-efficacy and the
behavioral intention to use e-learning solutions during COVID-19 pandemic.

14
H10. Gender has a significant moderating role in the relationship between perceived reliability and
the behavioral intention to use e-learning solutions during COVID-19 pandemic.
H11. Gender has a significant moderating role in the relationship between facilitating conditions
and the behavioral intention to use e-learning solutions during COVID-19 pandemic.
H12. Gender has a significant moderating role in the relationship between cybersecurity risk
awareness and the behavioral intention to use e-learning solutions during COVID-19 pandemic.
H13. Academic major has a significant moderating role in the relationship between attitudes and
the behavioral intention to use e-learning solutions during COVID-19 pandemic.
H14. Academic major has a significant moderating role in the relationship between effort
expectancy and the behavioral intention to use e-learning solutions during COVID-19 pandemic.
H15. Academic major has a significant moderating role in the relationship between self-efficacy
and the behavioral intention to use e-learning solutions during COVID-19 pandemic.
H16. Academic major has a significant moderating role in the relationship between perceived
reliability and the behavioral intention to use e-learning solutions during COVID-19 pandemic.
H17. Academic major has a significant moderating role in the relationship between facilitating
conditions and the behavioral intention to use e-learning solutions during COVID-19 pandemic.
H18. Academic major has a significant moderating role in the relationship between cybersecurity
risk awareness and the behavioral intention to use e-learning solutions during COVID-19
pandemic.

15
Figure 3. Proposed Model
4. Research Design and Method
4.1 The target population
According to Kotrlik et al. (2001), target population is defined as the group of individuals with the
specific attributes of interest and relevance for the study. The accessible population, on the other
hand, is reached after excluding all individuals of the target population who cannot participate or
cannot be accessed when conducting the study.
In the middle of Spring 2020 semester, Saudi authorities had suspended the face-to-face teaching
in all Saudi Universities during the outspread of COVID-19 pandemic which led to the transfer to
distance e-learning style. This teaching style has continued in the Summer and Fall semesters of
year 2020. This study targets active Saudi universities academics, that consist of professors,
associate professors, assistant professors, lecturers, and teaching assistants, who were previously
delivering face-to-face classes during that semester. According to the latest statistics from the
Attitudes toward Using
Effort Expectancy
Self-Efficacy
Perceived Reliability
Facilitating Conditions
Cybersecurity Risk
Awareness
Behavioral Intention
Gender
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
Academic
Major
H13
H14
H15
H17
H18
H16

16
Ministry of Education in Saudi Arabia, there are around 71,000 academics working in Saudi
universities (Ministry of Education, 2020b). The goal was to collect academics’ opinions and
behavior during the enforced transfer to distance e-learning and the influence on their attitude
towards the adoption of e-learning solutions in the future. Considering that academics in Saudi
universities are typically provided with computer devices, assigned university email addresses,
and are required by their universities to check their emails on a regular basis, therefore the entire
target population of this study is theoretically accessible.
4.2 Measures
To achieve scales’ content validity, items must be selected to ensure the coverage of each concept.
Each construct was translated into set of statements that mostly adapted from previous research
and then modified to match the context of this research. The main constructs were: Attitude,
Perceived Ease of Use, Self-Efficacy (Venkatesh et al., 2003), Perceived Reliability (Gunawardana
et al., 2015), Facilitating Conditions, Security practices, Behavioral intention (Gunawardana et al.,
2015).
4.3 Pretesting
Pilot study has been performed to confirm the content validity of the scale items before the
proceeding with distributing the survey. Fifteen respondents were participating in testing the
questionnaire. Their feedback has been used to improve the survey questions in regards of
wordings and clearance of survey questions. Based on their feedback the questionnaire has been
edited to reflect the suggestions from reviewers. Some questions have been reworded to increase
clarity or relativity, some have been removed to eliminate repetitions or relativity, and some
questions were added to measure missing factors. Based on the results, 21 items were maintained.
4.4 Questionnaire design and data collection
A questionnaire has been designed to validate the model by forming the consensus of attitudes
from a broad range of teaching staff who have transferred to e-learning solutions during the corona
COVID-19 epidemic in all Saudi universities. Qualtrics, an online survey program, was used to
develop and distribute the questionnaire online.
The questionnaire consists of three parts; firstly, the demographic information about participants
that is used for segmentation and comparison of the data and respondents. It collects information
regarding age group, gender, academic level, academic rank, university name, college
specialization, and computer and technology skills’ level.
The second part collects information about e-learning experience of the participants before the
sudden transfer to distant e-learning. It includes questions that measure the level of usage of e-
learning solutions before the crisis of COVID-19. It aims to compare the usage before and during
the crisis to compare the effect of transfer on usage.
The last part aims to assess the attitude, beliefs, and behavior towards the e-learning solutions. It
consists of seven sections in which each construct has been translated into three or more statements
that aim to measure the agreement with each statement on a 5-point base Likert scale.

17
 Section 1 asked participants to rate statements related to the suitability of the e-learning
solutions that have been used during COVID-19 pandemic.
 Section 2 asked participants to rate statements related to the utilized e-learning solutions
ease of use.
 Section 3 asked participants to rate statements related to the skills and ability to use e-
learning solutions.
 Section 4 asked participants to rate statements related to the reliability of the e-learning
solutions that are provided by their universities during COVID-19 pandemic.
 Section 5 asked participants to rate statements related to the level of support provided to
them while using e-learning solutions during COVID-19 pandemic.
 Section 6 asked participants to rate statements related to the level of awareness of cyber
security risks while using e-learning solutions.
 Section 7 asked participants to rate statements related to the intention to use e-learning
solutions after COVID-19 pandemic.
The questionnaire was administered to academics from 24 public and private universities in Saudi
Arabia. To achieve a satisfactory sample, the questionnaire has been distributed through emails
and several social media platforms such as WhatsApp, Facebook, and Twitter to all possible
academic communities. The survey was collected during the months of May and June 2020.
Approximately 500 responses were received, however only 399 valid responses were collected
after eliminating the incomplete cases and outliers.
5. Data analysis and results
The data collected through the survey were quantitatively analyzed through two steps. Firstly, we
have tested the model reliability and validity by using Confirmatory Factor Analysis (CFA)
Cronbach's Alpha, Composite Reliability, and Average Variance Extraction (AVE). Then the
conceptual model was examined through structural equation modeling (SEM) to test the research
hypotheses and model fitness.
The respondent’s demographic profiles are presented in Table 3. More than 60% of respondents
were female which is common in similar studies that have been conducted in Saudi (Britiller &
Abbas, 2020; Tayyib et al., 2020) and seems to be related to female’s willingness to participate in
research. Most respondents aged between 25 to 40 years old and around 53% of them were PhD
holders. More than 56% of respondents worked in STEM or Health sciences colleges whereas
around 44% of them are working in Social or Art colleges. The study was successful in recruiting
participants from all academic ranks with most of them being assistant professors (40%) which is
compatible with academic ranks distribution in Saudi universities (King Saud University, 2021;
Saudi Electronic University, 2021). Regarding technology use and computer skills, 70% of
respondents believed that they have excellent skills and less than 5% believed that they have
average or limited skills.
Table 3. Participants’ description (n = 391)

18
Variables
Freq
Percentages
Variables
Freq
Percentages
Gender
- Male
- Female
Age - 25-40
- 41 and
older
Specialization
- Social
Sciences
- STEM &
Health
Education Level
- Bachelor &
Master
- PhD
143
248
261
130
171
220
184
207
36.57%
63.43%
66.75%
33.25%
43.73%
56.27%
47.06%
52.94%
Academic Rank
- Teaching Assistant
- Lecturer
- Assistant Professor
- Associate Professor
- Full Professor
Computer Skills
- Excellent
- Good
- Average
- Limited
e-learning Experience Prior to
COVID-19
- Never
- Occasionally
- Weekly Basis
- Always
51
139
160
27
14
274
99
15
3
85
78
99
129
13.04%
35.55%
40.92%
6.91%
3.58%
70.08%
25.32%
3.84%
0.77%
21.74%
19.95%
25.32%
32.99%
5.1 Model Reliability and Validity
To establish the reliability and validity of the constructs to assess the structural relationship of the
proposed model, Anderson and Gerbing (1988) recommend using a two-step approach of
measurement model and a structural model. Confirmatory factor analysis (CFA) using R 3.6.3
with the lavaan 6.6 library was conducted. Upon evaluation of the initial results, we decided to
omit one item from the Attitude construct (i.e., ATT3) due to its low factor loading score that
lowered the scores of composite reliabilities and Average Variance Extracted (AVE). AVE
measure of the amount of variance of a construct in relation to the variance of a measurement error.
The final results of the assessment of the measurement model are presented in Table 4 where it
shows that all the measurement items have factor loadings of 0.601 at minimum and all the
constructs used in the research model have Cronbach’s alpha and composite reliability values of
0.704 or higher, which satisfy the threshold value of 0.7 recommended by Fornell and Larcker
(1981). Cronbach’s alpha test and composite reliability measure internal consistency of the scale
items. Therefore, it is inferred that all factors in the measurement model have adequate reliability.
The measurement model in Table 4 also shows that the AVE ranged from 0.546 to 0.722, whereas
the estimated constructs’ loadings ranged from 0.639 to 0.866. Therefore, the conditions for the
convergent validity requirement were supported.
Table 4. Convergent Validity and Internal Reliability
Constructs
Items
Standard Loadings
Cronbach’s Alpha
Composite
Reliability
Average Variance
Extraction (AVE)

19
BI
BI1
BI2
BI3
0.750
0.792
0.846
0.836
0.840
0.637
ATT
ATT1
ATT2
0.692
0.786
0.704
0.706
0.546
EE
EE1
EE2
EE3
0.601
0.841
0.865
0.799
0.796
0.566
SE
SE1
SE2
SE3
0.820
0.866
0.856
0.883
0.886
0.722
PR
PR1
PR2
PR3
0.747
0.836
0.786
0.830
0.834
0.627
FC
FC1
FC2
FC3
0.770
0.868
0.639
0.786
0.797
0.569
CA
CA1
CA2
CA3
0.831
0.922
0.619
0.823
0.832
0.626
Recommended Threshold:
Cronbach’s Alpha > 0.7 (Fornell & Larcker, 1981); Composite Reliability > 0.7 (Fornell & Larcker, 1981);
AVE > 0.5 (Fornell & Larcker, 1981)
The calculated square root of the AVE, which is shown in Table 5, was greater than the
corresponding correlation, confirming the discriminant validity of the data. To avoid
multicollinearity, the correlations among all constructs should be below the 0.85 threshold (Kline,
2015). Table 5 indicates all diagonal elements were higher than the off-diagonal elements in the
corresponding rows and columns and that all inter-correlation estimates were 0.704 or below;
therefore, the discriminant validity was satisfied.
Table 5. Correlation Matrix and Square Root of the Average Variance Extracted (AVE)
Constructs
BI
ATT
EE
SE
PR
FC
CA
BI
ATT
EE
SE
PR
FC
CA
0.798
0.367
0.481
0.562
0.560
0.509
0.440
0.739
0.247
0.245
0.368
0.294
0.238
0.753
0.583
0.704
0.621
0.510
0.850
0.563
0.699
0.693
0.792
0.633
0.544
0.754
0.635
0.791
Recommended Threshold:
Square root of the AVE > corresponding correlation; Correlation < 0.85 (Kline, 2015)
Table 6 shows nine common model-fit measures that were used to assess the model’s overall
goodness of fit: the ratio of χ2 to degrees of freedom (df), goodness-of-fit index (GFI), adjusted
goodness-of-fit index (AGFI), normalised fit index (NFI), Tucker-Lewis Index (TLI), comparative
fit index (CFI), root mean square error of approximation (RMSEA), root mean square residual
(RMR), and standardized root mean square residual (RMR). As shown in Table 6, all the model-
fit indices exceeded their respective common acceptance levels suggested by previous research,
thus demonstrating that the measurement model exhibited a good fit with the data collected.
Table 6. Goodness-of-fit Measures
Measure
Model
Recommended
Source

20
Value
Value
χ2/df
GFI
AGFI
NFI
TLI
CFI
RMSEA
RMR
SRMR
2.602
0.913
0.877
0.912
0.928
0.944
0.064
0.052
0.053
< 3.00
> 0.90
> 0.80
> 0.90
> 0.90
> 0.90
< 0.08
< 0.10
< 0.08
Kline (2005)
Scott (1994)
Scott (1994)
Wang et.al. (2009)
Bentler and Bonnet (1980)
Bagozzi and Yi (Rajman & Besançon,
1998)
MacCallum et.al. (Biggs, 1996)
Wang et.al. (2009)
Hu and Bentler (Dishaw & Strong,
1999)
5.2 Model Structure Analysis
Standardized path coefficients are used to examine the possible causal link between constructs in
the hypothesised model. Analysis is presented in Table 7 and Figure 4. Out of six hypotheses, only
Hypotheses 1, 3, and 4 were supported; attitudes toward using, self-efficacy, and perceived
reliability all had a significant positive effect on behavioral intention to adopt e-learning solution
after COVID-19 pandemic (β = 0.170, β = 0.330 and β = 0.263 respectively). Altogether, the model
accounted for 43% of the variance in behavioral intention, with self-efficacy contributing more to
intention than any other constructs, followed by Perceived Reliability then Attitude. The least
effective construct was Security Risk Awareness.
Table 7. Results of the Hypotheses Testing
Hypothesis
Relationship
Std.Beta
Std.Error
z-value
p-value
H1
H2
H3
H4
H5
H6
ATT → BI
EE → BI
SE → BI
PR → BI
FC → BI
CR → BI
0.170
0.043
0.330
0.263
0.052
-0.027
0.049
0.082
0.100
0.084
0.089
0.061
2.769
0.506
3.721
2.882
0.580
-0.357
0.006
0.613
<0.001
0.004
0.562
0.721
*p < 0.05; **p < 0.01; ***p < 0.001

21
*p < 0.05; **p < 0.01; ***p < 0.001
Figure 4. Path Coefficient with standardized coefficient for all respondent
5.3. Moderation effect of gender
To explore gender differences, data were divided into two groups according to gender and tested
separately in the modified model. For the female group, the research model accounted for 35.2%
of the variance with the path coefficients for the ATT-BI, SE-BI, and PR-BI links in the model
were significant (see Figure 5), which is like the first model for all respondents. Meanwhile for the
male group, the research model accounted for 60.9% of the variance and one additional path
coefficient for the EE-BI link was found to be significant apart from the other two links (i.e., ATT-
BI and SE-BI) found to be significant in all-respondents model and the female group model. To
examine the role of gender as a moderator, we conducted two multisample tests where we
compared the standardized path coefficients between the two groups of gender (female vs male).
Then, we calculated the p-values to determine the significance of the difference. As shown in
Table 8, the difference in EE-BI link is significant at 0.05 level. As it turned out, effort expectancy
is moderated by gender as in it is a significant predictor of behavioral intention for the male groups
but not for the female group. On the other hand, the difference in FC-BI link is only significant at
0.1 level and the link itself is not significant in all respondent models. However, the effect is
positive for the female group but is negative for the male group. Meanwhile, no moderation effect
was found on the other four links (i.e., ATT-BI, SE-BI, PR-BI, and CA-BI). Taking all these
findings into consideration, cybersecurity risk awareness is the only one of all six exogenous
variables in this model that has no significant effect towards behavioral intention to adopt e-
learning solutions, even after factoring in gender differences.
Attitudes toward
Using
Effort Expectancy
Self Efficacy
Perceived Reliability
Facilitating
Conditions
Cybersecurity Risk
Awareness
Behavioral Intention
0.170*
*
0.043
0.330*
0.263*
*
0.052
-0.022

22
*p < 0.05; **p < 0.01; ***p < 0.001
Figure 5. Path Coefficient moderating effect of gender with standardized coefficients for female
(Zhu et al., 2019) and male (bottom)
Table 8. Moderating Effects of Gender
Hypothesis
Relationship
Female
Std.Beta
Male
Std.Beta
Std.Beta
Difference
p-value
H7
H8
H9
H10
H11
H12
ATT → BI
EE → BI
SE → BI
PR → BI
FC → BI
CR → BI
0.174
-0.096
0.272
0.237
0.196
-0.004
0.282
0.314
0.437
0.116
-0.136
-0.051
0.108
0.410
0.165
0.121
0.331
0.047
0.720
0.020
0.336
0.470
0.077
0.757
5.4. Moderation effect of academic major
To explore the effect of academic major, data were divided into two groups with social sciences
in the first group and health sciences & STEM in the second group to be tested separately in the
modified model. For the first group, the research model accounted for 39.8% of the variance with
the path coefficients for the ATT-BI, SE-BI, PR-BI, and CR-BI links in the model were significant
(see Figure 6). Meanwhile for the second group, the research model accounted for 59.1% of the
variance with only the path coefficients for the ATT-BI and SE-BI found to be significant. Just
like in the previous analysis, to examine the role of major as a moderator, we conducted two
Attitudes toward Using
Effort Expectancy
Self Efficacy
Perceived Reliability
Facilitating Conditions
Cybersecurity Risk
Awareness
Behavioral Intention
Gender
0.174*
0.282**
-0.096
0.314*
0.272*
0.437**
0.237*
0.116
0.196
-0.136
-0.004
-0.051

23
multisample tests where we compared the standardized path coefficients between the two groups
of majors. Then, we calculated the p-values to determine the significance of the difference. As
shown in Table 9, the difference in PR-BI and CR-BI links are significant at 0.1 level. As it turned
out, perceived reliability and cybersecurity risk awareness are moderated by academic majors as
they both are significant predictors of behavioral intention for the social sciences academia but not
health sciences and STEM academia. It is worth noting that cybersecurity risk awareness has a
negative effect on behavioral intention for the social sciences academia. Meanwhile, no
moderation effect was found on the other four links (i.e., ATT-BI, EE-BI, SE-BI, and FC-BI).
Taking all these findings into consideration, cybersecurity risk awareness is the only one of all six
exogenous variables in this model that has no significant effect towards behavioral intention to
adopt e-learning solutions, even after factoring in gender differences.
^p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001
Figure 6. Path Coefficient moderating effect of gender with standardized coefficients for social
sciences (Zhu et al., 2019) and health/STEM (bottom)
Attitudes toward Using
Effort Expectancy
Self-Efficacy
Perceived Reliability
Facilitating Conditions
Cybersecurity Risk
Awareness
Behavioral Intention
Academic Major
0.277**
0.154^
-0.020
0.007
0.237^
0.349**
0.442**
0.151
0.183
0.070
-0.188^
0.085

24
Table 9. Moderating Effects of Academic Major
Hypothesis
Relationship
Social
Sciences
Std.Beta
STEM & Health
Std.Beta
Std.Beta
Difference
p-value
H13
H14
H15
H16
H17
H18
ATT → BI
EE → BI
SE → BI
PR → BI
FC → BI
CR → BI
0.277
-0.020
0.237
0.442
0.183
-0.188
0.154
0.007
0.349
0.151
0.070
0.085
0.123
0.027
0.112
0.291
0.113
0.273
0.137
0.877
0.645
0.057
0.509
0.064
5.5 The use of e-learning solutions before, during and after COVID-19
The data showed 22% have never used any e-learning solutions before COVID-19 epidemic, 20%
have used once every few weeks, 26% were using the platform once a week, and the remaining
32% were using it more than once a week. Thus, we can conclude that more than 78% were
exposed to e-learning platforms before, although some are not active users. The major categories
of usage were: Delivering lectures; Communicating with students; Uploading subject syllabus and
materials; Exam and assignment; Self-paced activities (e.g, reading articles or watching video);
Collaborative activities (e.g., online discussion) and others. The usage of e-learning solutions
before, during and the intention to use after the pandemic was analyzed to figure out in which way
academics was and will use e-learning solutions to deliver effective face to face learning. Before
the forced use due to the pandemic mainly for communicating with students and uploading and
delivering materials for students. During the suspension of face-to-face learning, all academics had
to use online tools. 87% have delivered lectures through e-learning solutions and 83% have used
it to conduct online assignments and exams. In all the investigated categories, respondents indicate
that they are willing to effectively utilize e-learning platforms in the future. From the data, it is
noticed that 90% of those respondents who said that they do not know what their university
platform is, claimed that their computer skills are limited. Results are displayed in Figure 7.
Figure 7. The usage of e-learning platforms Before, During and potentially after COVID-19
Pandemic
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Delivering lectures
Communicating with students
Uploading subject syllabus and materials
Exam and assignment
Self-paced activities (e.g, reading articles or watching video)
Collaborative activities (e.g., online discussion)
After During Before

25
Different tools have been used during the pandemic. Most universities are using Blackboard
services as an official medium for e-learning solutions as that have been supported by the Ministry
of Education in Saudi Arabia. Nevertheless, due to technical problems at video streaming at the
beginning of the crisis, most of the sample have used different tools in addition to their official
tools to deliver online lectures. Tools that are mainly used are: Zoom (11%), Webex (80%),
Microsoft Teams (Abual-Hamael, 2008). Other tools such as Google Meet, Shams Platform, Noon
Academy, Pearson, and others have been used as well. Some respondents said that they have used
social media groups such as WhatsApp, Telegram, Instagram for delivering the course content or
communication with students.
6. Discussion
During COVID-19 pandemic Saudi universities adopted e-learning solutions as an alternative to
traditional face to face classes. This research paper investigates factors that play a role in using e-
learning solutions by academics. The study shows that attitudes towards e-learning solutions, self-
efficacy and perceived reliability have a positive effect on academics’ intention to use the e-
learning solutions.
6.1 Factors affecting the adoption of e-learning solutions
Our findings show that attitudes toward using e-learning solutions play an important role in the
successful adoption of academics to e-learning solutions during the pandemic. This finding is
consistent with the literature. For instance, Wu and Zhang (2014) found that attitude affects
technology acceptance and use duration. Similar to our findings, (e.g. Jere (2020), Zalat et al.
(2021) and Alyoussef (2021)) studies, who used TAM to study technology acceptance during
Covid-19 pandemic, have also found academics’ attitude to have significant effect on technology
acceptance. Therefore, improving academics' attitude towards e-learning solutions is important to
facilitate full utilization of e-learning solutions during the pandemic and the continued use of such
solutions after the pandemic. Al Gamdi and Samarji (2016) argued that to improve academics'
attitude towards e-learning, universities need to have strategic e-learning policies and provide
adequate professional development to their academics.
Another important factor that was found to have a significant effect is self-efficacy. The ability to
use e-learning solutions by academics plays an important part on their intention to adopt the
solutions. This was particularly important during the sudden and forced transition to e-learning
solutions where there was not enough time for universities to provide sufficient training and
support for their academics to use the e-learning solutions (Almaiah et al., 2020; Hodges et al.,
2020). This, however, has slowly changed as universities have had enough time since the
beginning of the pandemic to prepare enough guidelines and conduct training sessions to improve
their staff skills to use e-learning solutions (King Abdulaziz University, 2020; Umm Al-Qura
University, 2020). In line with this finding, Jere (2020) and Alyoussef (2021) found that perceive
ease of use play an important role in the acceptance of e-learning technologies during Covid-19
pandemic. Although our model did not include perceive ease of use, it can be linked to self-efficacy
as the ability and skills to use e-learning solutions increase users’ confidence to use these solutions
and therefore their intentions to adopt e-learning.

26
Further, perceived reliability also had a significant positive effect on behavioral intention to adopt
e-learning. This finding is not surprising as the quality of the e-learning solutions and its reliability
were put to test during the increased demand from academics and students trying to assess the
services. Based on our experience and discussions with academics from different universities
across the country, there were problems with access to e-learning solutions and internet
connections. Such problems may create a negative attitude towards e-learning solutions and affect
the adoption of the services both during and after the pandemic (Alshammari, 2021; Alyoussef,
2021). Therefore, universities should pay attention and put more investments into increasing the
reliability of e-learning solutions.
Contrary to our predictions, security risk awareness did not have any effect on academics’
intentions to adopt e-learning solutions. Perhaps, this is due to the forcing nature of COVID-19
pandemic in its early stage. People were left with no other choice. Should they not adopt e-learning,
there was no other way to deliver education during the pandemic amidst all the restrictions (Hodges
et al., 2020). This may also indicate low level of awareness of security risks when using e-learning
solutions. Security scholars such as (Alshaikh, 2020; Guo et al., 2011) stated that employees are
goal oriented and that they may choose to ignore security practices if it means doing their job and
accomplishing their goals. Academics in this case are no exceptions, they were more concern in
accessing the services to deliver their lectures. This could explain why security risk awareness had
the least effect on their intentions to use e-learning solutions.
6.2 Moderating effect of gender and academic major
Gender was found to significantly moderate the effect of effort expectancy on the behavioral
intention to adopt e-learning solutions for the male groups only. In addition, facilitating conditions
impacting the behavioral intention to adopt e-learning solutions was positive for the female group
but negative for the male group. It could be the case that the female group needs more support
from their educational institutions for them to be more willing to adopt e-learning solutions after
this pandemic, which is not the case for the male group who are more inclined to adopt e-learning
solutions if they deem easier and require less effort to use. Meanwhile, no moderation effect of
gender was found on the relationships between ATT-BI, SE-BI, PR-BI, and CA-BI. This agrees
with the finding of Alshahrani and Qahmash (Alshahrani & Qahmash, 2020) who did not find
gender to significantly moderate the effect of ATT on the behavioral intention to adopt e-learning
solutions.
Academic major, on the other hand, was found to significantly moderate the effect of PR and CR
on the behavioral intention to adopt e-learning solutions. Surprisingly, however, there were no
moderating effects of academic major on the relationship between ATT and BI, EE and BI, SE,
and BI, and between FC and BI. It contradicts the findings of Abuhashesh (Abuhashesh, 2020)
who found that academic major has a significant impact on academics' attitude toward using e-
learning solutions.
6.3 Using e-learning solutions before-during-after COVID-19
The study shows that the usage of e-learning solutions has significantly impacted by the COVID-
19 pandemic. Before the pandemic, most academics were only using e-learning solutions to

27
communicate with their students and to upload course materials and contents. This indicates that
the traditional face-to-face teaching was the dominant mode of delivery in Saudi. This result was
surprising, considering the generous support that the Saudi government has provided for e-learning
infrastructure and the fact that all Saudi universities use popular learning management systems
that could facilitate the adoption of e-learning (Aldiab et al., 2019; Alnahdi, 2019). It seems that
Saudi universities have not yet realised the potentials of e-learning. This raises fundamental
questions about the role of the Deanships of E-Learning and Distance Learning that most Saudi
universities have (Aldiab et al., 2017).
During COVID-19 pandemic, and due to the enforced transfer to distance e-learning, there has
been a significant surge in the use of e-learning solutions. However, it seems that academics have
approached this transition with their traditional mindset. Most of them used e-learning solutions
to deliver lectures, share assignments and conduct exams. They were trying to find alternatives for
instruction and assessments that would otherwise be conducted face-to-face. Other online activities
such as self-paced and collaborative activities, that have shown to enrich the learning experience
of students (Alammary et al., 2016), were only used by less than half of the participants. This
agrees with Hodges et al. (Hodges et al., 2020) remark that the main goal of academics during
COVID-19 was not to produce a robust educational ecosystem but rather to provide students with
immediate access to instruction and learning resources in a manner that is easy to set up and is
reliably accessible during the pandemic. Nevertheless, it might be possible to increase the
participation in self-paced and collaborative learning activities by facilitating and encouraging the
use of e-learning through mobile devices (Pratama, 2021). As suggested by a previous study,
students who prefer conducting e-learning activities on mobile devices over personal computers
tend to be more active and collaborative learners (Pratama & Scarlatos, 2020).
The good news is that academics' exposure to e-learning solutions during the pandemic seems to
positively change their perception of e-learning. When compared to before COVID-19, much more
academics have indicated their willingness to try more e-learning solutions after the pandemic.
This willingness does not seem to be limited to the solutions that they have used during the
pandemic, but also to other solutions that they did not try. This is consistent with the study by
Allen and Seaman (Allen & Seaman, 2012) who found that academic exposure to e-learning would
lead to a more positive attitude toward it and better understanding of its potential. This finding
presents a prime opportunity for Saudi universities to accelerate the integration of e-learning,
benefiting from the increased interest and the current exposure to e-learning.
6.4 Policy, infrastructure, and professional development
To synthesise this section, we will discuss the study findings in relation to three main aspects:
policy, infrastructure, and professional development.
First, the findings of this study could play a key role in assisting policy makers at both national
level and university level to identify the factors that influence academics intentions to adopt e-
learning solutions. For instance, the study findings show that attitude plays a key role in academics
acceptance of e-learning solutions. This is highly important in relation to the recent announcement
by ministry of education which announce that education will be returning to traditional face-to-

28
face as vaccine is mandatory to entre universities. There is a risk that adoption rate of e-learning
solutions might fall back to pre-COVID-19 rate and all investment and effort on tools and training
will be wasted. Therefore, policy makers at both ministry and university level should be aware of
such risk and implement strategies to positively influence academics attitude towards e-learning
solutions. Examples of suggested strategies may include the use of e-learning in the evaluation of
academics, requesting academics to implement blended learning approaches and incentivise
academics to use e-learning solutions. These strategies are important to ensure adoption rate
remains high after COVID-19 pandemic.
Second, in recent years, Saudi Arabia has invested in infrastructure as part of digital
transformational plan to facilitate the country 2030 vision. Saudi has been ranked as 4th in mobile
internet speed and made an excellent progress in ICT infrastructure. The effort made in digital
transformation has pay off during COVID-19 as it enabled academic to access e-learning solutions.
Therefore, we did not find facilitating conditions statistically significate unlike studies conducted
in some developing countries such as Iraq and Zimbabwe. For instance, both Jameel et al. (2020)
and Vusumuzi et al. (2020) found facilitating conditions to have significant effect on academics’
intentions to adopt e-learning solutions. However, universities should continue to invest on new
technologies and improve its infrastructure to support better adoption and meet their staff and
student’s expectations.
Lastly, regarding professional development, e-learning Deanships at the universities should
provide more support and training to academic’s post COVID-19. Training academics (more
sessions and technical support) and improving user experience are crucial to improve efficacy and
ease of use and therefore the continuous adoption of e-learning after COVID-19. Academics
feedback should also be taken into consideration when embarking on implementing new tools and
technologies. This will help in facilitating and enabling better adoption of appropriate
technologies.
6.5 Recommendations for POST-COVID-19 e-learning
Based on the findings from this study, some recommendations can be made for the post COVID-
19 adoption of e-learning as follows:
1. Change attitude towards the use of e-learning solutions and raise staff awareness about the role
e-learning solutions have in improving learning outcomes and its importance to overcome.
2. Provide support staff to effectively use e-learning solutions.
3. Decision makers should not judge the quality of e-learning based on the emergency e-learning
instruction that has taken place during COVID-19 pandemic.
4. A reconsideration of the role of the Deanships of e-learning and Distance Learning in Saudi
universities must occur to ensure a rapid and successful adoption of e-learning.
5. E-learning training must be provided to enhance academics' knowledge of e-learning and allow
them to understand the capabilities of the different e-learning solutions.
6. Saudi universities need to take immediate actions and capitalize on the e-learning experience
that academics have gained during COVID-19 pandemic and accelerate the adoption of e-
learning.

29
7. All lessons learned should be collected and analysed to capitalize the opportunities and
minimize challenges.
8. Well-set long-term strategies should be set to guarantee effective use of e-learning solutions in
the future, that should include but not limited to increasing awareness, setting KPI measures,
providing funds, and so forth.
9. There should be a good investment in establishing educational instructional design units to
support academic faculties in designing and delivering their courses.
6.6 Contributions of the study
This study has several contributions to the field of e-learning adoption in Saudi Arabia higher
education. The study has suggested a list of influencing factors affect the acceptance and use of e-
learning solutions during crisis time. That are: Attitudes, Effort Expectancy, Self-Efficacy,
Perceived Reliability, Facilitating Conditions, Cybersecurity Risk Awareness and Behavioural
intention. These factors could be addressed by policymakers, instructors, researchers as a guide
for effective e-learning adoption especially during sudden transfer.
The study identified that attitudes toward e-learning, self-efficacy and perceived reliability have a
significant positive effect on behavioural intention to adopt e-learning, thus should be carefully
addressed during any e-learning adoption. The factors can be used as a reference guide for decision
makers to draw policies and strategies to adopt e-learning solutions, and prepare faculty members
and students with required tools, knowledge and skills.
The findings of this study could assist researchers and decision makers to understand the Saudi
universities’ faculty members’ attitudes towards e-learning during COVID-19 pandemic. The
study is the first in Saudi to dig deep into the actual usage of e-learning solutions to find out
whether academics are exploiting the maximum potential of these solutions or merely using it to
deliver their virtual classes. The study confirms that the experience of transition to e-learning
during COVID-19 pandemic has left a very positive attitude towards the future use of e-learning
solutions among faculty members in Saudi universities.
7 Conclusion
The aim of this research was to identify factors that affect the acceptance and use of e-learning
solutions by academics at Saudi universities after the enforced use of distance learning due to the
COVID-19 pandemic and the decision of universities’ closure. A model of influencing factors has
been developed based on the Technology Acceptance Model (TAM) and the Unified Theory of
Acceptance and Use of Technology (UTAUT). The model has been tested through surveying the
opinion of 391 faculty members from different Saudi universities. The results show that attitudes
toward e-learning, self-efficacy and perceived reliability have a significant positive effect on
behavioral intention to adopt e-learning. The results also confirm that the pandemic has left a very
positive attitude towards the future use of e-learning solutions among faculty members. Those who
never used e-learning solutions before the pandemic have been forced to get out of their comfort
zone to explore different possibilities of using e-learning solutions and found that use is beneficial
and an added value.

30
This study however was conducted on a sample population which may not provide a true reflection
of the attitudes towards the intention to adopt. Also, the sample from each university was not big
enough to specify the influencing factors based on actual provided resources and opportunities.
Future work may consider further investigation into applying online during the following
semesters and compare the attitude and behaviour of faculty members to identify factors that
influence the acceptance and effective use of the e-learning solutions. Further interviews shall be
conducted with different faculty members to understand the challenges and opportunities and to
dig deep at each factor that influences the effective adoption of e-learning solutions. Focused and
deep investigation could be conducted using specific universities case studies to study the
influence of the COVID-19 distance learning experience on faculty members towards e-learning
based on the resources, facilities and awareness delivered at each specific university. Moreover,
the model could be enhanced with performance indicators for measuring effective use of e-learning
solutions to support traditional learning.
REFERENCES
Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning
systems in developing countries. Education and Information Technologies.
https://doi.org/10.1007/s10639-021-10573-5
Abual-Hamael, A. A. (2008).  . Retrieved 6/3/2021, from
https://www.kau.edu.sa/search.aspx?Site_ID=0&lng=EN&q=%d8%a7%d9%84%d8%a7
%d8%b3%d8%a6%d9%84%d8%a9%20%d8%a7%d9%84%d8%b5%d9%81%d9%8a%d
8%a9
Abuhashesh, M. (2020). E-Learning Adoption among Academic Staff during COVID-19
Pandemic Outbreak: The KAP Model. E-Learning, 29(03), 12260-12272.
Afonso, C. M., Roldán Salgueiro, J. L., Sánchez Franco, M. J., & González, M. d. l. O. (2012).
The moderator role of Gender in the Unified Theory of Acceptance and Use of Technology
(UTAUT): A study on users of Electronic Document Management Systems.
Al Gamdi, M., & Samarji, A. (2016). Perceived barriers towards e-Learning by faculty members
at a recently established university in Saudi Arabia. International Journal of Information
Education Technology, 6(1), 23.
Al Meajel, T. M., & Sharadgah, T. A. (2018). Barriers to using the blackboard system in teaching
and learning: Faculty perceptions. Technology, Knowledge and Learning, 23(2), 351-366.
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile
banking by Jordanian bank customers: Extending UTAUT2 with trust. International
Journal of Information Management, 37(3), 99-110.
https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2017.01.002
Alam, M. Z., Hoque, M. R., Hu, W., & Barua, Z. (2020). Factors influencing the adoption of
mHealth services in a developing country: A patient-centric study. International Journal
of Information Management, 50, 128-143.
Alammary, A., Carbone, A., & Sheard, J. (2016). Blended learning in higher education: Delivery
methods selection. Twenty-Fourth European Conference on Information Systems (ECIS
2016), İstanbul,Turkey.
Albjali, A. (2018). Suspending distance learning porograms for bachelor degrees in Saudi
universities Retrieved 25/4/2020 from https://sabq.org/Br7bDk

31
Aldiab, A., Chowdhury, H., Kootsookos, A., & Alam, F. (2017). Prospect of eLearning in higher
education sectors of Saudi Arabia: A review. Energy Procedia, 110, 574-580.
Aldiab, A., Chowdhury, H., Kootsookos, A., Alam, F., & Allhibi, H. (2019). Utilization of
Learning Management Systems (LMSs) in higher education system: A case review for
Saudi Arabia. Energy Procedia, 160, 731-737.
Ali, W. (2020). Online and remote learning in higher education institutes: A necessity in light of
COVID-19 pandemic. Higher Education Studies, 10(3), 16-25.
Allen, I. E., & Seaman, J. (2012). Conflicted: Faculty and Online Education, 2012. Babson Survey
Research Group.
Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges
and factors influencing the E-learning system usage during COVID-19 pandemic.
Education and Information Technologies, 25, 5261-5280.
Alnahdi, A. (2019). Blended learning in Saudi Arabia-A review. Global Journal of Education and
Training, 2(6), 1-7.
Alqahtani, A. Y., & Rajkhan, A. A. (2020). E-learning critical success factors during the covid-19
pandemic: A comprehensive analysis of e-learning managerial perspectives. Education
Sciences, 10(9), 216.
Alqahtani, N., Innab, A., & Bahari, G. (2021). Virtual Education During COVID-19: Exploring
Factors Associated With E-Learning Satisfaction Among Saudi Nursing Students. Nurse
educator, 46(2), E18-E22.
Alshahrani, A., & Qahmash, A. (2020). Factors Influencing Intentions to Teach Online Among
Physical Therapy Faculty in Saudi Arabia from Faculty Perspectives of View. Journal of
Education, 80.
Alshaikh, M. (2020). Developing cybersecurity culture to influence employee behavior: A practice
perspective. Computers & Security, 98, 102003.
Alshammari, S. H. (2021). Determining the Factors That Affect the Use of Virtual Classrooms: A
Modification of UTAUT Model. Journal of Information Technology Education: Research,
20, 117-135.
Alyoussef, I. Y. (2021). E-Learning Acceptance: The Role of Task–Technology Fit as
Sustainability in Higher Education. Sustainability, 13(11), 6450.
https://www.mdpi.com/2071-1050/13/11/6450
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and
recommended two-step approach. Psychological bulletin, 103(3), 411.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological
review, 84(2), 191.
Bao, W. (2020). COVID‐19 and online teaching in higher education: A case study of Peking
University. Human Behavior and Emerging Technologies, 2(2), 113-115.
Bellaaj, M., Zekri, I., & Albugami, M. (2015). The continued use of e-learning system: An
empirical investigation using UTAUT model at the University of Tabuk. Journal of
Theoretical & Applied Information Technology, 72(3).
Bhatt, S., & Shiva, A. (2020). EMPIRICAL EXAMINATION OF THE ADOPTION OF ZOOM
SOFTWARE DURING COVID-19 PANDEMIC: ZOOM TAM. Journal of Content,
Community & Communication, 12(6).
Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Educ., 32(3), 347–
364. https://doi.org/10.1007/BF00138871

32
Britiller, M. C., & Abbas, S. H. Z. (2020). Readiness and Attitudes of Primary Users of E-Learning
at a Medical College in the Kingdom of Saudi Arabia.
Bruess, L. (2003). University ESL instructors' perceptions and use of computer technology in
teaching.
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.
Chuttur, M. Y. (2009). Overview of the technology acceptance model: Origins, developments and
future directions. Working Papers on Information Systems, 9(37), 9-37.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user
information systems: Theory and results Massachusetts Institute of Technology].
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–
technology fit constructs. Information & management, 36(1), 9-21.
Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M. (2017). An
empirical validation of a unified model of electronic government adoption (UMEGA).
Government Information Quarterly, 34(2), 211-230.
https://doi.org/https://doi.org/10.1016/j.giq.2017.03.001
Ebaid, I. E. S. (2020). Accounting students' perceptions on e-learning during the covid-19
pandemic: Preliminary evidence from saudi arabia. Journal of Management Business
Education, 3(3), 236-249.
Ebner, M., Schön, S., Braun, C., Ebner, M., Grigoriadis, Y., Haas, M., Leitner, P., & Taraghi, B.
(2020). COVID-19 epidemic as E-learning boost? Chronological development and effects
at an Austrian university against the background of the concept of “E-Learning Readiness”.
Future Internet, 12(6), 94.
Elumalai, K. V., Sankar, J. P., Kalaichelvi, R., John, J. A., Menon, N., Alqahtani, M. S. M., &
Abumelha, M. A. (2021). Factors Affecting the Quality of E-Learning During the COVID-
19 Pandemic from the Perspective of Higher Education Students. COVID-19 Education:
Learning Teaching in a Pandemic-Constrained Environment, 189.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and
measurement error: Algebra and statistics. In: Sage Publications Sage CA: Los Angeles,
CA.
Gagnon, M. P., Orruno, E., Asua, J., Abdeljelil, A. B., & Emparanza, J. (2012). Using a modified
technology acceptance model to evaluate healthcare professionals' adoption of a new
telemonitoring system. Telemedicine and e-Health, 18(1), 54-59.
Ganguli, S., & Roy Sanjit, K. (2011). Generic technology‐based service quality dimensions in
banking: Impact on customer satisfaction and loyalty. International Journal of Bank
Marketing, 29(2), 168-189. https://doi.org/10.1108/02652321111107648
Ghalandari, K. (2012). The effect of performance expectancy, effort expectancy, social influence
and facilitating conditions on acceptance of e-banking services in Iran: The moderating
role of age and gender. Middle-East Journal of Scientific Research, 12(6), 801-807.
Gunawardana, H., Kulathunga, D., & Perera, W. (2015). Impact of Self Service Technology
Quality on Customer Satisfaction; A Case of Retail Banks in Western Province in Sri
Lanka.
Guo, K. H., Yuan, Y., Archer, N. P., & Connelly, C. E. (2011). Understanding Nonmalicious
Security Violations in the Workplace: A Composite Behavior Model. Journal of
management information systems, 28(2), 203-236. https://doi.org/10.2753/MIS0742-
1222280208

33
Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between
emergency remote teaching and online learning. Educause Review, 27.
Hong, W., Thong, J. Y., Chasalow, L. C., & Dhillon, G. (2011). User acceptance of agile
information systems: A model and empirical test. Journal of management information
systems, 28(1), 235-272.
Hoq, M. Z. (2020). E-Learning during the period of pandemic (COVID-19) in the kingdom of
Saudi Arabia: an empirical study. American Journal of Educational Research, 8(7), 457-
464.
Jameel, A. S., Abdalla, S. N., & Karem, M. A. (2020). Behavioural Intention to Use E-Learning
from student's perspective during COVID-19 Pandemic. 2020 2nd Annual International
Conference on Information and Sciences (AiCIS),
Jaradat, M.-I. R. M., & Al Rababaa, M. S. (2013). Assessing key factor that influence on the
acceptance of mobile commerce based on modified UTAUT. International Journal of
Business and Management, 8(23), 102.
Jere, J. N. (2020). Investigating university academics behavioural intention in the adoption of e-
learning in a time of COVID-19. Journal of Information Management, 22(1), 9.
Khatun, F., Heywood, A. E., Ray, P. K., Bhuiya, A., & Liaw, S.-T. (2016). Community readiness
for adopting mHealth in rural Bangladesh: a qualitative exploration. International journal
of medical informatics, 93, 49-56.
King Abdulaziz University. (2020). KAU prevention COVID-19 report. Retrieved 27/2/2021 from
https://vp-academic-
affairs.kau.edu.sa/Files/838/Files/161566_kau_prevention_covid_19_report.pdf
King Saud University. (2021). College of Engineering - Faculty Members. Retrieved 5/3/2021
from https://engineering.ksu.edu.sa/en/CE_faculty
Kotrlik, J., Higgins, C. J. I. t., learning,, & journal, p. (2001). Organizational research: Determining
appropriate sample size in survey research appropriate sample size in survey research.
19(1), 43.
Lee, E.-J., Lee, J., & Eastwood, D. (2003). A Two-Step Estimation of Consumer Adoption of
Technology-Based Service Innovations. Journal of Consumer Affairs, 37(2), 256-282.
https://doi.org/10.1111/j.1745-6606.2003.tb00453.x
Malik, H. A. M., Abid, F., Kalaicelvi, R., & Bhatti, Z. (2018). Challenges of computer science and
IT in teaching-learning in Saudi Arabia. Sukkur IBA Journal of Computing Mathematical
Sciences, 2(1), 29-35.
Mandal, D., & McQueen, R. J. (2012). Extending UTAUT to explain social media adoption by
microbusinesses. International Journal of Managing Information Technology, 4(4), 1.
Marchewka, J. T., & Kostiwa, K. (2007). An application of the UTAUT model for understanding
student perceptions using course management software. Communications of the IIMA,
7(2), 10.
Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the Internet banking adoption: A
unified theory of acceptance and use of technology and perceived risk application.
International Journal of Information Management, 34(1), 1-13.
https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2013.06.002
Ministry of Education. (2020a). After Coronavirus, e-learning will be a strategic option in Saudi.
Retrieved 22/4/2020 from https://www.moe.gov.sa/ar/news/Pages/el-2020-coe.aspx
Ministry of Education. (2020b). Distance Learning Statistics https://www.moe.gov.sa/ar/e-
education/Pages/statistics.aspx

34
E-Learning policies for Higher Education, (2020). https://nelc.gov.sa/ar/node/230
Naveed, Q. N., Muhammed, A., Sanober, S., Qureshi, M. R. N., & Shah, A. (2017). Barriers
Effecting Successful Implementation of E-Learning in Saudi Arabian Universities.
International Journal of Emerging Technologies in Learning, 12(6).
Neigel, A. R., Claypoole, V. L., Waldfogle, G. E., Acharya, S., & Hancock, G. M. (2020). Holistic
cyber hygiene education: Accounting for the human factors. Computers & Security, 92,
101731.
NTT Security. (2019). Global Threat Intelligence Report.
https://www.nttsecurity.com/docs/librariesprovider3/resources/2019-
gtir/2019_gtir_report_2019_uea_v2.pdf
Owston, R., York, D. N., Malhotra, T., & Sitthiworachart, J. (2020). Blended Learning in STEM
and Non-STEM Courses: How do Student Performance and Perceptions Compare? Online
Learning, 24(3).
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for
measuring consumer perc. Journal of retailing, 64(1), 12.
Park, J., Yang, S., & Lehto, X. (2007). Adoption of mobile technologies for Chinese consumers.
Journal of electronic commerce research, 8(3), 196.
Pratama, A. R. (2021). Fun first, useful later: Mobile learning acceptance among secondary school
students in Indonesia. Education Information Technologies, 26(2), 1737-1753.
Pratama, A. R., & Scarlatos, L. L. (2020). The roles of device ownership and infrastructure in
promoting E-learning and M-learning in Indonesia. International Journal of Mobile and
Blended Learning (IJMBL), 12(4), 1-16.
Rajman, M., & Besançon, R. (1998). Text mining: natural language techniques and text mining
applications. In Data mining and reverse engineering (pp. 50-64). Springer.
Riffai, M. M. M. A., Grant, K., & Edgar, D. (2012). Big TAM in Oman: Exploring the promise of
on-line banking, its adoption by customers and the challenges of banking in Oman.
International Journal of Information Management, 32(3), 239-250.
https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2011.11.007
Salloum, S. A., & Shaalan, K. (2018). Factors affecting students’ acceptance of e-learning system
in higher education using UTAUT and structural equation modeling approaches.
International Conference on Advanced Intelligent Systems and Informatics,
Samaradiwakara, G., & Gunawardena, C. (2014). Comparison of existing technology acceptance
theories and models to suggest a well improved theory/model. International technical
sciences journal, 1(1), 21-36.
Saudi Electronic University. (2021). Saudi Electronic University - Faculty Members Directory.
https://seu.edu.sa/caic/en/staff/
Saudi Press Agency. (2020). Minister of Education during his presidency of G20 education
ministers' Meeting 2021. Retrieved 22/2/2021 from https://www.spa.gov.sa/2102848
Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2020). Effects of COVID-
19 in E-learning on higher education institution students: the group comparison between
male and female. Quality & quantity, 1-22.
Shareef, M. A., Archer, N., & Dwivedi, Y. K. (2012). Examining Adoption Behavior of Mobile
Government. Journal of Computer Information Systems, 53(2), 39-49.
https://doi.org/10.1080/08874417.2012.11645613

35
Shareef, M. A., Dwivedi, Y. K., Laumer, S., & Archer, N. (2016). Citizens’ Adoption Behavior of
Mobile Government (mGov): A Cross-Cultural Study. Information Systems Management,
33(3), 268-283. https://doi.org/10.1080/10580530.2016.1188573
Speedtest. (2021). Saudi Arabia's Mobile and Fixed Broadband Internet Speeds. Retrieved
25/6/2021 from https://www.speedtest.net/global-index/saudi-arabia
Statista. (2021). Countries with the highest internet penetration rate. Retrieved 29/6/2021 from
https://www.statista.com/statistics/227082/countries-with-the-highest-internet-
penetration-rate/
Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-
learning websites in Taiwan. Sage Open, 3(4), 2158244013503837.
Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’
behaviour in developing countries: A structural equation model. Computers in Human
Behavior, 41, 153-163. https://doi.org/https://doi.org/10.1016/j.chb.2014.09.020
Tawalbeh, T. I. (2018). EFL Instructors' Perceptions of Blackboard Learning Management System
(LMS) at University Level. English Language Teaching, 11(1), 1-9.
Tayyib, N., Ramaiah, P., Alshmemri, M., Alsolami, F., Lind-say, G., Alsulami, S., & Asfour, H.
(2020). Faculty members' readiness implementing e-learning in higher education Saudi
Universities: A cross-sectional study. Indian Journal of Science and Technology, 13(25),
2558-2564.
Thorpe, S. J., & Alsuwayed, H. M. (2019). Saudi academic perceptions of e-learning systems.
International Journal of Learning Technology, 14(3), 251-268.
https://doi.org/10.1504/IJLT.2019.105710
Umm Al-Qura University. (2020). The emergency plan to manage the Coronavirus (COVID19)
crisis at Umm Al-Qura University. Retrieved 15/2/2021 from
https://uqu.edu.sa/elearn/86535
UNESCO. (2020). COVID‐19 educational disruption and response. Retrieved 13/3/2021 from
https://en.unesco.org/themes/education-emergencies/coronavirus-school-closures
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model:
Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information
technology: Toward a unified view. MIS quarterly, 425-478.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information
Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS
quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
Vusumuzi, M., Sisasenkosi, H., & Nodumo, D. (2020, 3 - 4 December 2020). An Evaluation of
the Acceptance of Moodle by the Faculty at a Rural University in Zimbabwe During the
COVID-19 Lockdown Period. digiTAL 2020,
Wangpipatwong, S. (2008). Factors Influencing the Intention to Use E-Learning: A Case Study of
Bangkok University. EdMedia+ Innovate Learning,
World Bank. (2020). How countries are using edtech (including online learning, radio, television,
texting) to support access to remote learning during the COVID-19 pandemic.
Wu, B., & Zhang, C. (2014). Empirical study on continuance intentions towards E-Learning 2.0
systems. Behaviour & Information Technology, 33(10), 1027-1038.
Zalat, M. M., Hamed, M. S., & Bolbol, S. A. (2021). The experiences, challenges, and acceptance
of e-learning as a tool for teaching during the COVID-19 pandemic among university
medical staff. PLOS ONE, 16(3), e0248758. https://doi.org/10.1371/journal.pone.0248758

36
Zhu, Z., Liang, J., Li, D., Yu, H., & Liu, G. (2019). Hot Topic Detection Based on a Refined TF-
IDF Algorithm. IEEE Access, 7, 26996-27007.
https://doi.org/10.1109/ACCESS.2019.2893980