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Introduction
Nowadays, the necessity for online learning is increasing quickly, and was given a llip in the
pandemic of Covid-19, when regular educational service delivery was prevented in most contexts. This
compelled educational institutions to hastily adopt e-learning methods and platforms, with varying degrees
of success and challenges. However, the development of online learning capabilities has been underway
for decades, accompanied by identication of numerous prerequisites for effective deployment in practice
(Colleges, 2017). Advanced digital technologies are increasingly essential in all dimensions of life, but
their application in education remains relatively limited (Qashou, A., 2021). However, advanced learning
techniques have been developed, including methods of learning through mobile devices, palmtops,
laptops, and private media players) as a result of the fast growth of information networks and the Internet
(Moya and Camacho, 2021; Tan, G. et al., 2012; Pedro, Barbosa and Santos, 2018). The rapid consumer-
driven development of mobile technologies has allowed people to access information on the move, and
enabled the potential facilitation of online learning methods (Al Masarweh, 2019; Yu-Lin Jeng. et al.,
2010). The appearance of new educational technology helps society to gain experience and knowledge
broadly by using mobile technologies, which has mainly been driven by the commercial potential of such
technologies, but which offers promise for innovative solutions in education (Vallejo-Correa, Monsalve-
Pulido and Tabares-Betancur, 2021).
M-learning is a modern learning model formed by employing technological mobile mechanisms and
wireless technology to assist in collaborative and approachable education at all stages, from primary to
postgraduate education, which will be the next generation in distance learning and e-learning approaches,
since it revolutionizes the capabilities of ubiquitous learning (anytime, anywhere) (Al-Nawayseh, M. et al.,
2019; Al Masarweh, 2018; Motiwalla, L.F., 2007; Jouicha, Burgos and Berrada, 2022).
Mobile-based applications for learning as being one of the fastest developing mobile technologies
Investigating Factors M-Learning Acceptance and Use for Distance
Learning Students in Higher Education
Mohammed Al Masarweh
1*
, Waleed Afandi
1
1
Management Information System Department, College of Business in Rabigh, King Abdulaziz University, Saudi Arabia,
e-mail: malmasarweh@kau.edu.sa; wafndy@kau.edu.sa
Abstract: Many research has been conducted to examine the acceptance factors to use mobile learning (m-learning) for
regular students. During the COVID-19 most of the higher education institutions around the world were converted to m-learning
especially for regular students, in order to continue supporting the educational stage for these students. This situation, allow
researches to tested the use of m-learning for regular students while they are studying in distance learning environment.
However, limited researches, especially in developing countries, have been tested the acceptance factors to use m-leaning for
distance learning students. In this study the behavioral intention to use mobile learning (m-learning) were examined as well as
the m-learning factors that affecting its acceptance amongst the distance learning students were outlined. The study framework
was depended on the model of Unied Theory of Acceptance and Use of Technology (UTAUT). A quantitative approach was
used to analyze the data that collected from a random sample of 154 male and female participants from Saudi universities. The
results indicated that signicant factors inuencing distance learning students’ behavioral intention include quality of service,
effort expectancy, facilitating conditions, gender, educational level, and type of device. The regulations governing distance
learning programs and the implementation of mobile learning by Saudi universities under the direction of the Ministry of Higher
Education are having a good impact and encouraging widespread use of m-learning.
Keywords: distance learning, UTAUT, higher education, m-learning, user acceptance, Saudi Arabia.
Original scientic paper
Received: October, 09.2022.
Revised: November, 28.2022.
Accepted: December, 05.2022.
UDK:
37.018.43:004(532)
10.23947/2334-8496-2022-10-3-117-128
© 2022 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*
Corresponding author: malmasarweh@kau.edu.sa
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in education, with particular advantages in eliminating many barriers to traditional educational service
delivery formats (e.g., geographical or nancial barriers) (Johnson, L. et al., 2016). Modern revolutions in
mobile devices have simplied the exchange of information in mobile applications. This permits mobile
students to access a broad diversity of highly expanded learning resources (Tan, G. et al., 2012).
Smartphone use is increasingly universal among university learners, and their use as a supportive-
learning technology in the education process can supply and deliver learning between students globally.
This wide spread of smart devices on educational institutions offers a new scope to merge traditional
learning with m-learning (Anshari, M. et al., 2017). Empirical research attests that smart devices can
expedite university learners’ access to teaching resources through the Internet, ability to handle group
tasks and assignments, and even to interact with instructors (Syafar and Husain, 2017).
The reason behind the particular popularity of mobile/smart devices among other potential
e-learning tools is that they are relatively inexpensive in comparison with PCs, and being “mobile” they
are easy to handle, as well as being simple to use (Tan, G. et al., 2012; Syafar and Husain, 2017; Syafar,
F. et al., 2017). However, mobile devices in themselves, along with any learning technology, are useless
without the support of high-quality mobile learning applications and learning resources per se, which can
meet user needs with regard to learning objectives (e.g., curriculum content and examination relevance)
(Almaiah, Jalil and Man, 2016; Almaiah and Man, 2016; Arain, A. et al., 2019).
In order to bridge the gap between inherently advanced mobile technologies and the practical
achievement of learning goals, researchers have studied e-learning phenomena of information technology
assumptions by using theoretical models, such as UTAUT model, which is used to categorize mobile
learning students’ approval on the use and acceptance of technologies in relation to their principles
and behavioral purposes of use. Much of this research has considered the elements of mobile learning
approval, such as cultural, social, facilitating conditions, and cost (Abu-Al-Aish and Love, 2013; Alahmari,
2017; Althunibat, 2015; Mohammadi, 2015).
UTAUT-based research indicates that the following components affect the interactive purpose and
use of conduct to implement online and mobile learning: effort anticipation, performance anticipation,
quality of service, the inspiration of lecturers, and personal creativity (Abu-Al-Aish, A. et al., 2013; Al
Masarweh, 2018). Building on this consensus, the current study seeks to analyse student acceptance
of m-learning for Saudi students in higher educational institutions, a context where such research has
hitherto been lacking.
Distance Learning
Distance learning is any shape of teaching and learning assisted by the use of computer networks
based on information technology (Daniel, 2020). It can also be known as a method of delivering knowledge
electronically, with using suitable computer applications and the Internet for data communication. Recently,
distance learning has expanded along two main avenues: the Individual Flexible Teaching Model (IFTM)
and the Extended Classroom Model (ECM) (Gabriska and Pribilova, 2021). IFTM permits learners to
begin their lessons at any time, choose customized special environments, and interact with their lecturers
and colleagues through specic tools. ECM arranges learners into groups, expects them to gather at a
local study place, and lets them exploit some interactive technologies like video conferencing to facilitate
their mutual interactions (Mergany, Dafalla and Awooda, 2021).
Because of the fast growth of technology, classes can now use different types of media to deliver
educational services and content to students in different locations, to meet the educational requirements
of larger or more geographically diffuse student populations. Interactive video, print materials, satellite
telecommunication, broadcast television, electronic mail, multimedia computer technology, broadcast
radio, and computer conferencing have all been used to help teacher-student interactions, albeit mainly
in the narrow context of providing feedback to distant learners. Although the methods by which distance
learning is applied vary among countries and particular context, distance education programs in general
depend on technologies that are currently available, or are considering investment in such technologies,
because of their increasing cost-effectiveness (Al-Fahad, 2009). The goals of distance learning as a
complementary way of delivering classes include granting degrees to students, tackling illiteracy in
developing countries, providing training opportunities for economic growth, and enriching the curriculum
in non-traditional schools (Sarrab, Al-Shihi and Rehman, 2013).
Such contexts exist around the world, but became immediate and pressing issues during the
Covid-19 pandemic, when latent resources were suddenly shut off for most educational services due
to social distancing public health requirements. Montenegro’s education system moved through various
phases from the beginning of the virus. During the rst stage, distance learning started to be used in all
schools and universities. At this point, Viber groups were created by lecturers, teachers, and tutors to
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send students sufcient literature and guidance. After that, the education system was switched to Google
Classroom applications. Class teachers were required to organize their classrooms by subject and by
class, facilitating distance learning. Additionally, state TV channels offered services to enable students
to learn at home, providing video tutorials with material delivered by educators from various subjects
(Gabriska and Pribilova, 2021).
Simultaneously, seminars were arranged for all Montenegrin tutors and teachers to train them
on how to use Microsoft Teams (Gabriska and Pribilova, 2021), which provides modern, high-quality
workspaces, particularly for team environments in virtual work organizations, and this platform outmatched
Skype and Viber for such uses in the Covid-19 e-learning context, being available in 181 countries and
18 languages (Alahmari, 2017).
Logically, M-learning is the current method for distance and E-learning technology. The most
important features of distance learning are the time and distance shifting between tutor and learners.
E-learning proposes new approaches for distance learning which depend on computer and net technologies
(Abu-Al-Aish, A. et al., 2013).
Mobile Learning
Several previously deployed M-learning frameworks and models are analysed and compared in this
section. The following characteristics are listed in Table 1 as the distinctions between prior frameworks:
the method used to develop the model, the presence of deployment stages, the key components used,
sustainability reection, validation and assessment, and link with e-learning (Daniel, 2020; Mostakhdemin-
Hosseini, A., 2009).
Table 1
Frameworks Evaluation for M-Learning
The earlier frameworks or models for m-learning are not examined specic stages deployment for
m-learning. Moreover, a limited discussion on sustainability issues has been conducted to ensure that
m-learning systems would be continuously improved and assessed after deployment. Building a schema
that detects the earlier deployment success factors for m-learning and provides assistance for after-
deployment sustainability is therefore necessary (Venkatesh, V. et al., 2003).
Unied Theory of Acceptance and Use of Technology
The UTAUT paradigm denes the acceptance of technology depending on eight technology
acceptance models, the most widely used of which are use behavior (UB), facilitating conditions (FCs),
social factors (SFs), effort expectancy (EE), behavioral intentions (BI), and performance expectancy (PE)
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117-128.
(Venkatesh, 2000). Behavioral intention is affected directly by effort expectancy, Performance expectancy,
and social factors, whereas, use behavior is ancillary impacted by facilitating conditions. All of these
aspects are fundamentally determined by behavioral intention, which is the main underlying concern of
UTAUT (Venkatesh, 2000). Furthermore, other aspects might affect the structure for example age of the
user, user experience, voluntariness of use, and gender. The UTAUT paradigm thus interprets technology
use behavior based on behavioral intention. The eight factors of technology are established, which related
interpreters of behavioral intention, illustrate in Figure 1.
Figure 1. The model of UTAUT (Venkatesh, V. et al., 2003).
Performance Expectancy (PE)
Performance expectancy can be understood as the people think level that they can achieve
their tasks with the aid of ICT (Venkatesh, 2000). In terms of PE, e-learning can be a huge support
for e-learners, by enabling them to complete learning events more expediently, and novel technological
solutions in themselves can inspire learning, educational skills, and production. PE thereby inuences
behavioral intention to control the E-learning system favourably.
Effort Expectancy (EE)
The level of smoothness that related to the information systems and their administration is referred
to as effort expectancy (Alshurideh, 2010). Based on previous research, concepts about EE relate to
users’ individual objectives and prociency in relation to the associated tools (Salloum and Shaalan,
2018). Particular e-learning applications (if not the concept in general) are usually relatively new for most
learners and educators, because it is believed that EE is behavioral intention key component to use
e-learning systems. Individual acceptance of e-learning is inuenced by the usability and simplicity of
technology, which also has an impact on behavioral intention more broadly. Consequently, EE has a
convenient effect on behavioral intent to use an e-learning system.
Social Inuence (SI)
Social inuence can be described as the impact that the opinions or experiences of others on
the way in which an individual understands and conceptualizes how technologies should be handled
(Alshurideh, 2010). Empirical studies based on the UTAUT have reported that people’s intention to use
new e-learning technological solutions is heavily affected by SI, which can be understood as word-of-
mouth or peer pressure (Jogezai, N. et al., 2021; Abbad, 2021). Accordingly, SI affects behavioral intention
to utilize an e-learning system favourably.
Facilitating Conditions (FC)
Facilitating conditions pertain to the ambience and infrastructure in which technologies are
deployed, relating to environmental and behavioral inuences that shape user deployment of tools. The
designer of the UTAUT paradigm found that FC is a very valuable factor inuencing the use of information
systems (Yu, 2012). The level of which people think technical and organizational infrastructures are
latently accessible to adopt and ongoing usage of novel technologies is what FC refers to; any social,
behavioral, and personal factors conducive to e-learning system use do not guarantee successful use
without commensurate FC, including materials, individual support, and training for improving knowledge
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and familiarity, as well as access to the system of e-learning itself. Accordingly, FC will have a major and
favourable impact on students’ utilization of the e-learning system.
Use Behavior (UB)
Use behavior refers to the pattern or routine of people handling ICT, which is affected by behavioral
intention and assisting prerequisites (Yu, 2012). In other words, the behavior of learners to use information
technology has been inuenced by their intention and interest of it’s used, and the accessibility of
equipment and facilities to provide this intention.
Behavioral Intention (BI)
Behavioral intention was originally developed as an expansion of the Theory of Reasoned Action
(TRA) (Moya, M. et al., 2017). BI is described as a theory to clarify the motivational inuences that shape
behavior. This theory pertains to the attempts and efforts expected from users seeking to execute specic
tasks. It is shaped by personal factors regarding the individual’s intention to perform something.
Research Framework and Hypotheses
This research adopts the UTAUT framework in order that explore the main elements of behavioral
intention of using m-learning and its challenges for distance learning students in Saudi universities.
It investigates the main factors affecting behavioral intention among 154 male and female distance
learning students. Many research studies have used a similar approach to study regular students in
higher education, but limited research has been conducted on distance learning students, particularly in
developing countries. for the reason of customize the main scope of the research intention, participants’
demographic information was included in this research to nd out if the participants’’ demographic have
any signicant impact between the participants.
Figure 2. Research framework.
H1: Behavioral intention to use m-learning (BI) is signicantly affected by performance expectancy
(PE).
H2: Behavioral intention to use m-learning (BI) is signicantly affected by effort expectancy (EE).
H3: Behavioral intention to use m-learning (BI) is signicantly affected by quality of services (QoS).
H4: Behavioral intention to use m-learning (BI) is signicantly affected by social factors (SFS).
H5: Behavioral intention to use m-learning (BI) is signicantly affected by facilitating conditions
(FCS).
H6: Gender has signicantly affected on m-learning acceptance for distance learning students.
H7: Educational level has signicantly affected on m-learning acceptance for distance learning
students.
H8: Type of devices in using has signicantly affected on m-learning acceptance for distance
learning students.
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Materials and Methods
A quantitative approach was adopted, which provided statistical results related to the research
scope, by systematic and empirical investigation of the gathered numerical information, which was
statistically analysed. The data was gathered from a survey based on previous studies, designed to
target distance learning students in Saudi higher education institutions. Five public universities which
provide distance learning programs were selected: King Abdulaziz University, Taiba University, Umm
Al Qura University, University of Tabuk, and Imam Mohammad Ibn Saud Islamic University. An online
questionnaire was prepared using Google Forms in both English and Arabic languages, which helped the
participants to understand the theme of this research. Furthermore, the participants had to be experienced
in using technological aspects of m-learning services as provided by their universities. Inclusion criteria
for the randomly selected students included that all of them had been enrolled in distance learning
programs, and that they were sufciently familiar with the use of technology and mobile devices due to
the nature of these programs, which depend on the use of technology and mobile devices. Moreover, the
researcher analysed the m-learning orientation delivered by these universities, to ensure that students
were provided with adequate knowledge, courses, training videos, and guidelines for using technology
and mobile devices. A pilot study was conducted among 28 students at King Abdulaziz University to
obtain feedback and test the readiness of the instrument, and based on the feedback received some
minor modications were made to the instrument, after which it was implemented with the study sample.
Data Collection
University administrators were contacted by email in order to share the survey with their students,
with an explanation of the study nature and the link of online survey. Moreover, the data was collected form
154 participants, who voluntarily completed the survey by clicking on the Google Forms link via the invitation
email. The data for 154 participants was analysed using SPSS. The sample size was sufcient in order to
represent the opinions of distance learning students towards the intention of using m-learning (Lai, 2017).
The demographics of this study were based on three factors: gender, education level, and type of device.
In terms of gender, there were 93 and 62 male and female participants (respectively). The vast majority of
respondents (n = 151) were in the third to fth years of their programs. Concerning the type of device used
for online learning, all respondents selected mobile devices. The survey section concerning demographic
features was analysed using percentages and frequencies; the section directly relating to students’ level of
acceptance and behavioral intention factors asked participants to rate items using a ve-point Likert scale.
Questions Examining Factors in Level of Acceptance and Behavioral Intention
The survey, second part, included questions that related to examine the investigating of acceptance
level, based mainly on a previous instrument (Yu, 2012; Abbad, 2021), with some additional modications
to meet the objectives of this study. Table 2 illustrates the statements that participants rated using the
Likert scale.
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Table 2
Questions to Explore the Level of Acceptance
Results
Statistical Analysis of the Reliability and Suitability of Study Model
The Cronbach’s alpha coefcients for all variables were between (0.711-0.861), which it is more
than the required threshold (0.6) (Table 3), indicating the stability of the tool used in this study (Benitez,
J. et al., 2020).
Table 3
Results of Cronbach’s Αlpha Coefcients
In order to ensure that there was no signicant multiple linear connection between the dimensions
of the independent variable, the correlation coefcients between them were examined. The results shown
in Table 4 reveal that the greatest correlation was (0.738), showing that there was no signicant multiple
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linear correlation between the independent variables (values below 80% indicate that the sample was free
from this issue) (Hair, Howard and Nitzl, 2020).
Table 4
Pearson Correlation Between Independent Variables
Participances Demographic and the Usage of Mobile Device
The characteristics of participants demographic and usage of mobile device is illustrated Table
5, including cumulative percentages, percentages, and frequencies for each category. The majority
of participants were male (60.4%), and most were in their fourth and third years (42.2% and 35.1%,
respectively). Mobile phones were the most commonly used devices to access m-learning resources
during their distance learning (57.1%), followed by laptops (31.8%).
Table 5
Participants’ Demographic Characteristics
Results for Independent and Dependent Variables
The means, standard deviations, and degrees of acceptance of m-learning are illustrated in Table
6. It can be seen that the studied Saudi distance learning students held positive attitudes towards using
m-learning (3.75). The highest scores for m-learning factors were for facilitating conditions (3.99), followed
by performance expectancy (3.96), and social factors (3.90). Medium acceptance was reported for effort
expectancy (3.66) and quality of services (3.52). The behavioral intention for students to use m-learning
also achieved a high score (3.68), indicating positive attitudes and a high degree of willingness.
Table 6
Level of M-Learning Acceptance
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Regression Analysis for UTAUT Construct
Regression analysis has been used in order to examine the association between the ve model
elements and the BI towards using m-learning. Figure 3 illustrates the β-value for the used elements.
* Signicance at p ≤0.05, ** Signicance at p ≤0.01
Figure 3. β-value Graphical representations.
Discussions
Hypotheses Testing Results (H1-H5)
Multiple regression has been used to test hypotheses (H1-H5). Table 7 illustrates the results of
the statistical testing for the hypothesis model, represented by a set of independent variables (social
factors, effort expectancy, facilitating conditions, performance expectancy, and quality of services) and the
dependent variable (behavioral intention). The outcomes indicate that FC, QoS, and EE had a signicant
impact on behavioral intention (with beta values of 0.326, 0.312, and 0.216, respectively), with a statistically
signicant p-value of less than (0.05). This means that there are differences in facilitating conditions
between the distance learning students, although all participants were capable to use m-learning as a
main application to communicate during their distance learning experience. This conrms ndings in other
countries worldwide concerning m-learning during the Covid-19 crisis (Afandi, 2022). However, the beta
values for the dimensions PE and SF were statistically insignicant (<0.05).
The current study’s ndings on social factors disagree with the results of previous studies, which
may be attributable to the distance learning students in this study having only one way (i.e., distance
learning) to undertake their studies in the Covid-19 context. Regular students (i.e., under normative
situations) are more affected by social factors pertaining to the use of m-learning that seems to be linked
to the greater variety of choices and options open to them (Afandi, 2022; Nikolopoulou, Gialamas and
Lavidas, 2020).
Based on the above, the results conrm the hypotheses of: (H2) effort expectancy (EE) signicantly
affects behavioral intention to use m-learning (BI); (H3) quality of services (QoS) signicantly affects
behavioral intention to use m-learning (BI); (H5) facilitating conditions (FCS) signicantly affect behavioral
intention to use m-learning (BI). There is no statistically signicant evidence to support H1 or H4.
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Al Masarweh, M., & Afandi, W. (2022). Investigating Factors m-learning acceptance and use for distance learning students
in higher education, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 10(3),
117-128.
Table 7
Testing Hypotheses H1-H5
Hypotheses Testing Results (H6-H8)
This study one-sample T-test was used to test H6, and one-way analysis of variance (ANOVA)
in order to test H7 and H8. Table 8 shows the results, which indicate that the T- and F-values are not
signicant (p<0.05). Therefore, the students’ gender, educational level, and type of devices using in
m-learning have no substantive impacts on m-learning acceptance. This means that the distance learning
students are homogenous with regard to their m-learning user behavior for distance learning, evidencing
that the nature of distance learning programs can assume commensurate levels of technical skills and
resources to use m-learning resources.
Table 8
Testing Hypotheses H6-H8
Conclusion
This study examined a variety of m-learning adoption and acceptance issues in relation to the
UTAUT paradigm. According to the ndings, Saudi public university distance learning students have
good latent readiness and positive attitudes toward using m-learning to further their academic objectives.
This is in light of the key elements identied by the UTAUT model. Examining UTAUT model-based
components on behavioral intention to employ m-learning indicated positive effects. When evaluating
the questionnaire ndings, it was discovered that performance expectancy, social factors, and facilitating
conditions all received high scores. The ndings of this study also provided support for three of the ve
hypotheses. The ndings of the T-test and ANOVA tests provided a distinct viewpoint on the impact of
various factors on the use of mobile learning, showing that gender, educational level, and the types of
used devices have no appreciable effects on students’ attitudes toward m-learning.
Overall, the ndings indicate that the regulations governing distance learning programs and the
implementation of mobile learning by Saudi universities under the direction of the Ministry of Higher
Education are having a good impact and encouraging widespread use of m-learning.
Acknowledgements
This project was funded by the Deanship of Scientic Research (DSR) at King Abdulaziz University,
Jeddah, under grant no. G:729-849-1441. The author, therefore, acknowledge with thanks DSR for
technical and nancial support.
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Conict of interests
The authors declare no conict of interest.
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