Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education

Abstract

Mobile-learning (M-learning) apps have grown in popularity and demand in recent years and have become a typical occurrence in modern educational systems, particularly with the deployment of M-learning initiatives. The key objective of this study was to reveal the key factors that impact university students’ behavioural intention and actual use of mobile learning in their education. The technology acceptance model (TAM) is used in this study to investigate the impacts of several factors found in the literature on students' adoption of M-learning systems in higher education. The data was gathered from 176 university students who completed a paper questionnaire. The data was analyzed using the SEM technique. The findings revealed that perceived mobile value (PMV), academic relevance (AR), and self-management of M-learning (SML) are the primary drivers of students' acceptance of M-learning and, as a result, the success of M-learning projects’ implementation. The findings of this study give crucial information on how higher education institutions may improve students' acceptance of M-learning in order to promote students' attitudes toward M-learning (ATT) it and their behavioural intentions (BIM) to use it in the teaching and learning process. These findings have significant implications for the acceptance and use of M-learning.

Full text

Vol.:(0123456789)
Education and Information Technologies
https://doi.org/10.1007/s10639-022-10934-8
1 3
Acceptance ofmobile technologies andM‑learning
byuniversity students: Anempirical investigation inhigher
education
AliMugahedAl‑Rahmi1· WaleedMugahedAl‑Rahmi2 · UthmanAlturki3·
AhmedAldraiweesh3· SultanAlmutairy3· AhmadSamedAl‑Adwan4
Received: 14 September 2021 / Accepted: 31 January 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
2022
Abstract
Mobile-learning (M-learning) apps have grown in popularity and demand in recent
years and have become a typical occurrence in modern educational systems, particu-
larly with the deployment of M-learning initiatives. The key objective of this study
was to reveal the key factors that impact university students’ behavioural intention
and actual use of mobile learning in their education. The technology acceptance
model (TAM) is used in this study to investigate the impacts of several factors found
in the literature on students’ adoption of M-learning systems in higher education.
The data was gathered from 176 university students who completed a paper ques-
tionnaire. The data was analyzed using the SEM technique. The findings revealed
that perceived mobile value (PMV), academic relevance (AR), and self-management
of M-learning (SML) are the primary drivers of students’ acceptance of M-learn-
ing and, as a result, the success of M-learning projects’ implementation. The find-
ings of this study give crucial information on how higher education institutions may
improve students’ acceptance of M-learning in order to promote students’ attitudes
toward M-learning (ATT) it and their behavioural intentions (BIM) to use it in the
teaching and learning process. These findings have significant implications for the
acceptance and use of M-learning.
Keywords Technology acceptance model· Perceived mobile value· M-learning·
Academic relevance· Perceived usefulness· Facilitating conditions
* Waleed Mugahed Al-Rahmi
waleed.alrahmi1@gmail.com
Extended author information available on the last page of the article

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1 Introduction
Higher education graduates are increasingly encouraged to think creatively,
methodically solve problems, and engage in logical critique (Al-Adwan etal.,
2021; Tkáčová et al., 2021). However, classroom training alone may not be
enough to improve these critical thinking abilities. Instructors must construct
learning environments that allow students to develop practical skills and emulate
real-world experiences as a result of rapid technological innovation (Pavlíková
etal., 2021; Sophonhiranrak, 2021). Input, sensing, output, and networking are
four crucial features of mobile devices, according to an educational tech-nology
expert (Hamidi & Chavoshi, 2018). When creating appropriate learning exercises,
each feature of a gadget should be considered. Touch, speech, keyboard input,
and other ways of input are available (Fagan, 2019). Sensing through numerous
channels, such as a touch screen, enables the device to capture numerous forms of
data (Barreh & Abas, 2015).The speakers, headphones, and screen are all output
components that offer video, picture, audio, and text output. Such output is trig-
gered by voice recognition input, a touch screen, or a keyboard (Demir & Akpi-
nar, 2018). The device’s connectivity refers to how it is connected to a network
or other tools in order to run applications (Suartama et al., 2019). Continuous
network connection provides cloud-based storage, which encourages students to
learn outside of the class of students and link their learning with internet knowl-
edge (Wong et al., 2015). The devices of Mobile are categorized as "resource
access tools" (Bano et al., 2018; Hwang et al., 2015). Learners can use search
engines or other programs, for instance, those that offer news feeds or language
learning capabilities, to obtain more material, and they may use social media to
share their ideas with other students (Almaiah et al., 2019a, 2019b; Maksaev
etal., 2021). Mobile devices, on the other hand, aren’t just for accessing infor-
mation; they’re also for linking users who are participating in activities and hav-
ing simultaneous experiences (Almaiah et al., 2019a, 2019b). Mobile devices
facilitate a variety of learning activities, including accessing and searching for
documents, conducting surveys and summarizing information, reading books,
making video recordings and taking pictures, sharing information, and taking
notes (Camilleri & Camilleri, 2017; Sönmez etal., 2018). By 2025, it is expected
that 5 billion people will be using mobile devices to access the internet, thanks
to rising mobile device usage and the progression of mobile connection to 5G
technologies (Sophonhiranrak, 2021). Although M-learning is popular among
those who are technologically savvy, its utilization necessitates suitable infra-
structure and instructors with fundamental instructional abilities (Irby & Strong,
2015; Onojah etal., 2021).Many nations, like Singapore, Malaysia, and Taiwan,
have been constructing infrastructure in their countries and promoting the use of
mobile devices in many educational sectors (Khan etal., 2015). Language learn-
ing has been a major application of mobile devices in underdeveloped nations
(Alalwan etal., 2019). Governments and educational institutions must consider
the ability of teachers to use mobile devices in the classroom when investing in
infrastructure (Alenazy et al., 2019). Personalized learning is easier to manage

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when mobile devices are used in the classroom. Learners should be allowed to
establish their own learning outcomes and milestones, evaluate their own perfor-
mance, and select their own learning platforms. (Zhu etal., 2019). Learners can
perform these assignments at their own speed and based on their own preferences
using mobile devices (Kuhnel etal., 2018). As a result, M-learning experiences
are dependent on individual goals or needs, as well as different learning styles of
learners (Ekanayake & Wishart, 2015). Situated learning, or learning in real-life
circumstances while looking for relevant information to confirm information or
enrich the in-site experience, is also achievable with mobile devices (Al-Rahmi
etal., 2020; Cheon etal., 2012; Domingo & Garganté, 2016). The growing Inter-
net of Things (Dachyar etal., 2019) offers even more possibilities for improv-
ing learning experiences. As a result, learning environments have expanded out-
side the classroom (Sampson etal., 2012), with M-learning available at any time
and in any location (Althunibat, 2015). Despite the fact that M-learning systems
allow university students to analyze learning content using their mobile devices,
there is little research on students’ intentions to use them. Allowing pupils to
use AUML does not guarantee that students will actually use it in their every-
day life.The system may be seen differently by students. For example, before
they started using the system, several pupils were unaware of its potential benefit
(Heflin etal., 2017). There is no empirical study on university students’ use of
M-learning technologies in Malaysia that we are aware of. As a result, this study
aims to fulfil this gap. Malaysia has a strong internet infrastructure and a high
mobile phone penetration rate, making it one of the most developed countries
in the world (Rassameethes, 2012). With a solid wireless network infrastructure,
university students in Malaysia may readily access a variety of mobile services.
The goal of this study is to look at how M-learning technologies are used in
Malaysian higher education by looking at student attitude towards using M-learn-
ing (ATT) and behavioural intention to use M-learning (BIM). To address this, a
research model is built based on current technological acceptance theories, and
nine research hypotheses are offered as a result. This study approach was empiri-
cally tested using survey data acquired from 176 university students in Malaysia.
2 Research model anddevelopment ofhypotheses
In this research, we developed a model based on the technology acceptance model to
examine the main factors the actual use of M-learning by the students of Universiti
Tun Hussein Onn Malaysia (UTHM). Figure1 illustrates the influence of academic
relevance (AR) on perceived usefulness (PU)and perceived ease of use (PEOU),
the influence of perceived mobile value (PMV) on PUand PEOU, theinfluence of
facilitating conditions (FC) on PUand PEOU, the influence of self-management of
M-learning (SMM) on PUand PEOU, the influence of PU on PEOU, the influence
of PU on ATT, the influence of PU on BIM, the influence of PEOU on ATT, the
influence of PEOU and BIM, and integration of Attitude towards using M-learning
(ATT) for BIM to Actual use M-learning (AUML) among learners. Based on prior
research regarding constructivist theory and the TAM model (Alamri etal., 2020a,

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2020b; Davis, 1989), this research developed hypotheses about how ATM and BIM
can affect AUML in Malaysian colleges and universities. Furthermore, frameworks
that demonstrate the adoption of M-learning are based on a transient aspect, and
their influences on sustainability concerns are not accessible for higher education.
As a result, this study aims to bring together key aspects of the TAM model method
and constructivist learning for education. Figure1 shows the situation.
2.1 Academic relevance (AR)
AR refers to the importance of using M-learning technologies in university edu-
cation in general in this study. According to Venter et al., (2012), the variables of
online learning management system usage in South Africa were explored, and it
was discovered that Major Relevance had a positive influence on BIM (Venter etal.,
2012). In addition, Venkatesh and Davis (2000), discovered that Job Relevance has
a direct beneficial influence on BIM. As a result, this study claims that AR has an
impact on people’s attitude and intention when it comes to M-learning systems. As a
result, the following hypothesis is proposed in this study.
Hypothesis (H1). AR is positively related to PU.
Hypothesis (H2). AR is positively related to PEOU.
2.2 Perceived mobile value (PMV)
The term "PMV" (PMV) refers to a user’s knowledge of M-mobility learning’s ben-
efits. Convenience, expediency, and immediacy are three different aspects of mobil-
ity (Seppälä & Alamäki, 2003). Mobile devices allow consumers to access services
and information from anywhere at any time. To put it another way, mobility allows
users to be guided and supported in novel learning situations when and when they
are needed (Eames & Aguayo, 2020). Previous research has revealed that mobile
users consider efficiency and availability as the most important benefits of M-learn-
ing, and that these benefits are a result of a mobile device’s "mobility" (Almaiah
etal., 2019a, 2019b; Jeno etal., 2017; Saroia & Gao, 2019). As a result, M-learning
Fig. 1 Research model

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is advantageous due to its portability. As a result, individual variances in perceived
mobility value have a significant impact on user behavior. As a result, the following
hypothesis is proposed in this study:
Hypothesis (H3). PMV is positively related to PU.
Hypothesis (H4). PMV is positively related to PEOU.
2.3 Facilitating conditions (FC)
The extent to which a person thinks that the available infrastructure in their company
supports their usage of technology is referred to as facilitating conditions. Unlike
the other constructs, this one influences actual use of mobile learning directly rather
than BIM. Furthermore, age and experience modify the facilitating conditions con-
struct, implying that, according to the UTAUT hypothesis, the facilitating conditions
construct has a considerable impact on actual use of mobile learning among older
employees, particularly those with advanced levels of expertise (Venkatesh & Davis,
2000). As a result of the paradigm shift, enabling conditions (infrastructure) are pre-
served as technical solutions implemented and maintained by reliable companies,
ensuring their long-term viability and the quality of services provided to consumers
(Al-Rahmi etal., 2018).In some studies, the FC construct was shown to be non-sig-
nificant in predicting BIM. However, it is expected that facilitating conditions will
affect Saudi high school students’ behavior in using M-learning technology by add-
ing privacy and security issues (Ameri etal., 2020).
Hypothesis (H5). FC is positively related to PU.
Hypothesis (H6). FC is positively related to PEOU.
2.4 Self‑management ofM‑learning (SMM)
The degree to which a student is self-disciplined with the capability of engaging
in learning autonomously is referred to as SML (Smith etal., 2003). To thrive in
M-learning settings, students must have a high degree of self-management, which
includes activities such as critical thinking, defining learning objectives, assess-
ing learning materials, and self-evaluation (Al-Adwan, Al-Adwan, et al., 2018;
Al-Adwan, Al-Madadha, et al., 2018; Kang etal., 2015; Wang etal. 2009; Liew
etal. 2013). Educators and administrators use the system of learning management to
generate and assign course materials, track student progress, and assess and report
student results, regardless of the form online education takes (Fenton, 2018). The
degree to which a person recognizes self-discipline and can engage in autonomous
learning is referred to as self-management of learning. "Successful self-manage-
ment of M-learning is the consequence of gaining competence and skills in learning
how to learn. In M-learning, instructors, peers, and institutional support are absent,
and hence students are expected to take full responsibility of their learning (Pra-
japati & Jayesh, 2014). Self-management of M-learning is important in predicting
M-learning uptake and students’ intentions to utilize it in higher education. Wang

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and Li (2012) also suggest that a person’s level of M-learning self-management has
a favorable impact on their behavioral intention to embrace mobile learning.
Hypothesis (H7). SMM is positively related to PU.
Hypothesis (H8). SMM is positively related to PEOU.
2.5 Perceived usefulness (PU)
According to Davis, (1989), PU is defined as the level to which someone believes
that adopting a technology would help them improve their performance. Similarly,
PU is related to work performance, reliability, and quality, and is described as "the
level to which someone believes using a certain system will improve his or her job
performance" (Davis, 1989). It assesses how much someone thinks technology will
help them achieve their goals. A usefulness aspect is expected to have a considerable
impact on BIM when this construct is included in TAM (Hamidi & Jahanshaheefard,
2019; Al-Rahmi etal., 2019a, b, c; Alamri etal., 2020a, b). As a result, the follow-
ing hypothesis develops:
Hypothesis (H9). PU is positively related to PEOU.
Hypothesis (H10). PU is positively related to ATT.
Hypothesis (H11). PU is positively related to BIM
2.6 Perceived ease ofuse (PEOU)
PEOU is defined as "the level at which someone believes that using a specific sys-
tem will require no physical or mental effort" (Davis, 1989). Mutambara and Bayaga
(2020) PEU is defined in the context of mobile learning as the extent to which stu-
dents believe that adopting mobile learning would be painless. If the platform for
mobile isn’t user-friendly, this rise will be exacerbated. According to Davis (1998),
in the early phases of any information system’s adoption, the perception that the dif-
ficulty of using the system might be a barrier that affects users’ attitudes, usefulness,
and BIM (AAlamri etal., 2020a, b; Al-Rahmi etal., 2019a, b, c). As a result, this
component is critical in the adoption of a new technical system; it plays a critical
role and is readily apparent in individual usage (Al-Rahmi etal., 2019a, b, c). As a
result, the following hypothesis emerges:
Hypothesis (H12). PEOU is positively related to ATT.
Hypothesis (H13). PEOU is positively related to BIM.
2.7 Attitude towardsusing M‑learning (ATT)
Attitude toward M-learning is an emotional and psychological construct that char-
acterizes a person’s ideas and state of mind accumulated as a result of their expe-
riences (Al-Rahmi et al., 2019a, b, c; Hsu & Lin, 2008). An individual’s attitude
toward a specific behavior is simply their positive or negative feelings regarding the

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out-comes of that behavior (Raza etal., 2018). In this study, attitude refers to how
learners’ eagerness to utilize de-vices of mobile for studying in higher education
(Alhussain etal., 2020; Al-Rahmi etal., 2019a, b). According to several recent stud-
ies, a learner’s attitude toward M-Learning influences their behavior when using the
system (Gan etal., 2017; Sharma etal., 2017). A person’s attitude toward a given
activity is comparable to their entire perspective on their activities (Abramson etal.,
2015). Attitude has been postulated and proven to have a direct influence on BIM
(Alalwan etal., 2018; Park etal., 2012), as well as mediating the influence of PU,
ease of use, AR, and mobile value on BIM (Alalwan etal., 2018; Al-Maatouk etal.,
2020; Saroia, & Gao, 2019).
Hypothesis (H14). ATT is positively related to BIM.
Hypothesis (H15). ATT is positively related to USML.
2.8 Behavioural intention touse M‑learning (BIM)
According to Cheng (2019), BIM is the cognitive representation of a person’s pre-
paredness to perform a specific behavior. BIM is predicted by system adoption and
consequently by actual usage (Cheng, 2019). Teachers’ or learners’ BIM has been
illustrated to be highly correlated with adoption of the system and, consequently,
employment in studies (Cheng, 2019). This study aimed to forecast the acceptabil-
ity of mobile learning systems in remote areas where they were not yet in use and
hence had no ATT (Alamri etal., 2019; Kanwal & Rehman, 2017). As a result, the
concept ATT was not included in this study’s model. Understanding the characteris-
tics that determine remote college technology students’, their teachers’, and parents’
BIM m-learning, according to Davis (1989) and Venkatesh (2000), leads to a bet-
ter knowledge of the factors that influence acceptance and ATT (Al-Rahmi etal.,
2019a, b).
Hypothesis (H16). BIM is positively related to AUML.
2.9 Actual use M‑learning (AUML)
Individual wishes or individual behavioural intent to utilize a mobile learning sys-
tem are the factors pertaining to its use. It also indicates a person’s effectiveness in
terms of a given method. Values of personal character, usefulness, ease of use, and
self-belief are all factors that influence how the system is used (Hamidi & Chavoshi,
2018). According to the literature study, those concerns are critical to comprehend-
ing the TAM research trend in relation to M-learning studies. Furthermore, research
has shown that the causes of mobile learning acceptability are still unclear, and this
is one of the continuing and crucial topics for IS academics (Almaiah etal., 2016;
Althunibat, 2015; Huang, 2014; Mohammadi, 2015).The gathered papers were eval-
uated in order to shed light on the elements that influence the adoption of AUML.
Understanding the variables that impact mobile learning (M-learning) acceptability
in the studies gathered will help mobile learning researchers in their future planning

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to examine the effects of additional elements that have been overlooked in the exist-
ing body of literature (Naveed etal., 2020).
3 Research methodology
To test the proposed hypotheses presented in the research model, 500 question-
naires were sent out conveniently on students of UTHM. A total of185 question-
naires were returned, giving a response rate of 37%. After a manual screening, nine
questionnaires were identified as incomplete, and therefore these questionnaires
were excluded. Hair etal., (2012) advocated for such exclusions, stating that outliers
might lead to erroneous statistical conclusions and must be excluded. Consequently,
a total of 176 questionnaires were valid and usable. The questionnaire utilized in this
study includes both open and closed-ended questions. Specifically, the questionnaire
consisted of two main sections. The respondents in the first section were asked to
provide their demographic information. The second section was devoted to measure
the constructs of the research model and included 46 items. Corresponding to Hair
etal. (2012), the survey was evaluated two experts who were chosen based on their
research interests and expertise in the area of educational technology adoption. They
were asked to assess the survey in terms of readability clarity and content relevancy
and validity. The evaluation outcomes suggested minor amendments which have
been fully considered. The respondents were invited to complete the questionnaire
anonymously and assured that their responses would remain confidential. The struc-
tural equation modeling (SEM) was used to evaluate the data collected with Amos
version 23 and SPSS Statistics version 23.
As Table 1 shows, 150 (85.2%) were males and 26 (14.8%) were females.
Additionally, 14 (7.9%) of the respondents were between the ages of 18 and 22,
48 (27.2%) were between the ages of 23 and 29, 73 (41.5%) were between the
Table 1 Demographic profile Items Description N % Cumulative %
Gander Male
Female
150
26
85.2
14.8
85.2
100.0
Age 18 – 22
23 – 29
30 – 35
36–40
41– Above
14
48
73
37
4
8.0
27.3
41.5
21.0
2.3
8.0
35.2
76.7
97.7
100.0
Specialization Social Science
Engineering
Science &Technology
Management
Others
29
15
89
40
3
16.5
8.5
50.6
22.7
1.7
75.6
8.5
59.1
98.3
100.0
Use _M several times a day
Once a day
several times a month
Once in a month
137
19
15
5
77.8
10.8
8.5
2.8
77.8
88.6
97.2
100.0

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ages of 30 and 35, 37 (21.0%) were between the ages of 36 and 40, and 4 (2.2%)
were above 41. Gender, age, specialization, and mobile application usage are all
presented in Table1.
4 Data analysis
When it came to M-learning, the connected elements affected attitudes and BIM.
As a consequence, all of the variables satisfy the Cronbach Alpha coefficient cri-
teria, which range from 0.70 to 0.90. The reliability analysis looks at Cronbach’s
reliability coefficient, which is 0.977. The discriminant validity was also assessed
using three criteria: the index value of the variable must be less than 0.80 (Hair
et al., 2012), the AVE rate must be greater than or equal to 0.5, and the AVE
square must be greater than the inter-construct correlations linked to variables
(IC) (Fornell & Larcker, 1981) Furthermore, confirmatory factor loadings of 0.7
and higher were found. Composite reliability and a Cronbach’s Alpha grade of
0.70 or above were considered acceptable (Hair etal., 2012).
4.1 Measurement model analysis
A total of 176 sample questionnaires were distributed to university students. They
have all been shown to be useful. The construction elements verified the validity
of the material of the measurement scales was confirmed by previous studies. The
survey questionnaire that was determined as follows: AR, PMV, FC, SMM, PU,
PEOU, ATT, BIM, and AUML, and all outer loading acceptable, See Table2.
In this study, SEM was utilized as a major statistical technique in AMOS 23
to analyze the results based on (CFA). This model was used to investigate over-
convergence (Fornell & Larcker, 1981).Furthermore, according to Hair et al.
(1998) and Byrne (2013), should be used to assess the score model’s "goodness-
of-fit strategies, such as standard chi-square, chi-square, (RFI), (TLI), and the IFI,
When the (CFI) should be equal to or more than 0.90, it indicates that the model
fits well. Furthermore, the root indicates that the (RMSEA) meets the recom-
mended requirement of less than or equal to.08 to support the required suit (Hair
et al., 2012; Kline, 2018), as shown in Table 3 " (RMR)" are acceptable. The
model’s suitability indices specifically are AVE, CA, to meet all requirements and
CR are acceptable, and CA values ranged from 0.835 to 0.938, all above 0.70
are acceptable. In addition, the AVE varied between 0.508 and 0.752, exceeding
the calculated value of 0.50. This indicates that all of the loading factors are not
insignificant and pass the value of 0.50, and hence satisfy the presented correla-
tions (Hair etal., 2012), (Fornell & Larcker, 1981) as shown in Table4, the meas-
urement of the mediator and dependent variables mentioned in Fig.3. Figure2
displays the TAM theory of measurement (Fig.3).

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Table 2 Constructs, items, and outer loading
Construct Items Outer loading References
Academic Relevance
(AR)
AR1
AR 2
AR 3
AR 4
AR 5
0.65
0.82
0.88
0.87
0.77
(Venter etal., 2012)
Perceived mobile value
(PMV)
PMV 1
PMV 2
PMV 3
PMV 4
PMV 5
0.82
0.80
0.84
0.83
0.76
(Huang etal., 2007)
Facilitating Conditions
(FC)
FC 1
FC 2
FC 3
FC 4
FC 5
0.76
0.81
0.83
0.87
0.78
(Chavoshi, & Hamidi, 2019; Hu etal., 2020)
Self-Management of M-learning
(SMM)
SMM 1
SMM 2
SMM 3
SMM 4
SMM 5
0.68
0.78
0.65
0.73
0.79
(Huang & Yu, 2019; Naveed etal., 2020)
perceived usefulness
(PU)
PU1
PU2
PU3
PU4
PU5
0.67
0.76
0.81
0.77
0.79
(Briz-Ponce etal., 2017; Lin etal., 2016; Sabah, 2016)
perceived ease of use
(PEOU)
PEOU1
PEOU2
PEOU3
PEOU4
PEOU5
0.62
0.70
0.79
0.72
0.72
(Briz-Ponce etal., 2017; Naveed etal., 2020; Sabah, 2016)

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Table 2 (continued)
Construct Items Outer loading References
Attitude towards using
(ATT)
ATT 1
ATT 2
ATT 3
ATT 4
ATT 5
0.85
0.89
0.87
0.86
0.87
Naveed etal., 2020; Briz-Ponce etal., 2017)
Behavioral intention to use
(BIM)
BIM1
BIM2
BIM3
BIM4
BIM5
0.84
0.88
0.86
0.85
0.82
(Briz-Ponce etal., 2017; Sabah, 2016)
Actual Use of M-learning
(AUML)
AUML 1
AUML 2
AUML 3
AUML 4
AUML 5
AUML 6
0.72
0.79
0.84
0.71
0.78
0.75
(Naveed etal., 2020; Sabah, 2016)

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Table 3 Goodness fit indices for
the measurement model Type of measure Acceptable level of fit Values
“Root-Mean Residual” (RMR) Near to 0 (perfect fit) 0.046
“Normed Fit Index” (NFI) > 0.90 0.902
“Relative Fit Index” (RFI) > 0.90 0.900
“Incremental Fit Index” (IFI) > 0.90 0.919
“Tucker Lewis Index” (TLI) > 0.90 0.913
“Comparative Fit Index” (CFI) > 0.90 0.920
“Root-Mean Square Error of
Approximation” (RMSEA)
< 0.05 indicates a good fit 0.048
Table 4 Validity and reliability evaluation
AR PMV FC SMM PU PEOU ATT BIM AUML AV E CR CA
AR 0.858 0.644 0.899 0.894
PMV 0. 674 0. 824 0.658 0.906 0.903
FC 0. 512 0. 510 0. 877 0.659 0.906 0.904
SMM 0. 494 0. 456 0. 498 0. 706 0.532 0.850 0.849
PU 0. 627 0. 616 0. 549 0. 566 0. 770 0.574 0.871 0.868
PEOU 0. 597 0. 629 0. 503 0. 478 0. 587 0. 649 0.508 0.837 0.835
ATT 0. 467 0. 448 0. 557 0. 507 0. 537 0. 496 0. 919 0.752 0.938 0.938
BIM 0. 541 0. 465 0. 556 0. 484 0. 567 0. 540 0. 523 0. 889 0.717 0.927 0.926
AUML 0. 531 0. 476 0.507 0. 576 0. 632 0. 512 0. 520 0. 524 0. 685 0.590 0.896 0.892
Fig. 2 Measurement model of independent factors

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4.2 Structural equation model analysis
Using route modeling analysis, the impact of AR and PMV factors on mobile sys-
tem usage, as well as the model of TAM factors on mobile learning utilization for
BIM through ATT mobile learning, were investigated. The results are presented and
discussed in conformity with the testing outcomes of hypothesis. In the following
phase of the process, the authors utilized CFA to analyze the structural equation
model. Thus, as a result, Fig. 4 depicts the structural model, demonstrating that
all assumptions made between the sixteen main components, or hypotheses, were
accepted. Table5 depicts the structural model; the table shows that the model’s main
statistics are excellent, recommending suitability and a viable model for testing the
hypotheses. According to the findings of this study, mobile learning has a favorable
Fig. 3 Measurement model of mediator and dependent factors
Fig. 4 Results of the proposed model for all students group

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impact on the adoption model in higher education, and that all assumptions were
shown to be true. Furthermore, the findings validate the structural model as well as
theories about the directional relation between the model’s variables. Table5 shows
the parameters of the structural model’s un-standardized coefficients and standard
errors.
4.3 Results ofhypothesis testing
AR is positively and significantly related to PU for adopting M-learning in higher
education (β = 0.214, t = 3.487, p < 0.001), according to the findings of this study
(Table5and Figs.4). Therefore, Hypothesis1 is acceptable, suggesting the influence
of mobile learning on AR and perceived academic usefulness. AR was also shown to
be positively and significantly related to PEU for adopting M-learning in higher edu-
cation (β = 0.116, t = 2.320, p < 0.001). Consequently, the second hypothesis is that
the results show that mobile learning has an impact on AR and PEU. The findings
then revealed that PMV was positively and significantly related to PU for adopting
M-learning in higher education (β = 0.289, t = 4.720, p < 0.001).Hence, hypothesis3
is supported, showing that mobile learning has an impact on PMV and academic
usefulness. Furthermore, the findings revealed that PMV was positively and sig-
nificantly related to PEU for adopting M-learning in higher education (β = 0.404,
t = 7.853, p < 0.001). Hence, hypothesis4 is acceptable; elucidating that mobile
learning has an impact perceived value of a mobile device and it’s PEU. The results
demonstrate that enabling conditions are positively and significantly associated to
Table 5 Structural model for Hypothesis testing results
0p < 0.05; 00p < 0.01; 000p < 0.001
H Independent Relationship Dependent Estimate S.E C.R P Result
H1 AR → PU 0.214 0.061 3.487 0.000 Supported
H2 AR → PEOU 0.116 0.050 2.320 0.020 Supported
H3 PMV → PU 0.289 0.061 4.720 0.000 Supported
H4 PMV → PEOU 0.404 0.051 7.853 0.000 supported
H5 FC → PU 0.114 0.047 2.415 0.016 Supported
H6 FC → PEOU 0.075 0.038 1.972 0.049 Supported
H7 SMM → PU 0.385 0.054 7.093 0.000 Supported
H8 SMM → PEOU 0.118 0.049 2.430 0.015 Supported
H9 PU → PEOU 0.203 0.060 3.399 0.000 Supported
H10 PU → ATT 0.368 0.110 3.343 0.000 Supported
H11 PU → BIM 0.270 0.101 2.676 0.007 Supported
H12 PEOU → ATT 0.431 0.120 3.595 0.000 Supported
H13 PEOU → BIM 0.465 0.110 4.212 0.000 Supported
H14 ATT → BIM 0.161 0.067 2.404 0.016 Supported
H15 ATT → AUML 0.346 0.053 6.504 0.000 Supported
H16 BIM → AUML 0.386 0.054 7.140 0.000 Supported

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PU (β = 0.114, t = 2.415, p < 0.001). Therefore, hypothesis 5 is supported, indicating
that there are facilitating factors for students’ perceptions of M-learning’s educa-
tional use. Similarly, the results demonstrate that facilitating circumstances are con-
nected to PEU in a favorable and significant way (B = 0.075, t = 1.972, p < 0.001).
Hence, hypothesis 6 is supported. SML was shown to be positively and significantly
associated to PU (β = 0.385, t = 7.093, p < 0.001); thus, hypothesis7 is acceptable,
suggesting the simplicity with which mobile learning may be used in higher educa-
tion for educational purposes. Eighth hypothesis the results show that SML posi-
tively and significantly associated to PEU (β = 0.118, t = 2.430, p < 0.001). Moreo-
ver, hypothesis 8 is supported, indicating that PEU is beneficial to the adoption of
M-learning for education. Moreover, hypothesis 9 holds true, indicating that PU has
an influence on PEU among students (β = 0.203, t = 3.399, p < 0.001). Moreover,
Hypothesis 10 indicated that PU was positively and substantially associated to ATT
(β = 0.368, t = 3.343, p < 0.001). Therefore, hypothesis 10 is supported, indicating
that a positive attitude toward M-learning is beneficial to its PU in education. The
findings show that PU has a positive significant relationship with BIM for adopting
M-learning in higher education (β = 0.270, t = 2.676, p < 0.001). Thus, hypothesis11
is acceptable, suggesting the PUof mobile learning has a positive impact on stu-
dents’ BIM for educational purposes among students. The results then confirmed
that PEOU was positively and substantially associated to ATT (β = 0.431, t = 3.595,
p < 0.001). Therefore, the hypothesis12 is acceptable, showing that mobile learning
has an impact on PEU and ATT for adopting mobile learning in higher education.
Hypothesis 13 was showed that PEOU was positively and significantly a relationship
with BIM for adopting mobile learning in higher education (β = 0.465, t = 4.212,
p < 0.001). Therefore, hypothesis 13 is supported, showing that students’ BIM
M-learning for education is influenced by PEOU. Furthermore, BIM was positively
and significantly associated to ATT for adopting mobile learning in higher educa-
tion (β = 0.161, t = 2.404, p < 0.001). Hence, hypothesis 14 is acceptable, indicating
that students’ BIM play a positive role in determining their ATT. Similarly, Hypoth-
esis 15 verified that Attitude towards mobile learning was positively and strongly
related to actual use of mobile learning for academic purpose (β = 0.346, t = 6.504,
p < 0.001). Therefore, hypothesis 15 is supported, indicating that a positive attitude
toward M-learning is beneficial to boost AUML for education. Finally, hypothesis
16 hypothesized that BIM is connected to AUML (β = 0.386, t = 7.14, p < 0.001).
Therefore, Hypothesis 16 is supported, demonstrating that the influence of BIM pur-
poses has a favorable influence on AUML for educational purposes adoption.
5 Discussion andimplications
The purpose of this study was to examine the factors influence the actual use of
M-learning (AUML). A modified version on TAM was proposed by incorporat-
ing a set of external factors. The study’s findings showed that the TAM as a theo-
retical base is an effective and reliable theoretical model for predicting students’
AUML. The results of this study show that AR, PMV, facilitating conditions,
SMM, and PU all play a role in the adoption of the intention to use a mobile

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learning system. Similarly, the findings demonstrate that the association between
AR, PMV, Facilitating Conditions, SML PEOU and M-learning adoption is sig-
nificant. This conclusion is in line with earlier research undertaken in developed
nations such as Malaysia and Taiwan (Aburub & Alnawas, 2019; Alasmari &
Zhang, 2019; Al-Emran etal., 2018; Al-Rahmi etal., 2019a, b, c; Bakon & Has-
san, 2013; Soleimani etal., 2014). H1 and H2 were accepted. Furthermore, the
findings show that the association between PMV on PU and PEOU is crucial for
the intention to use a mobile learning system in the actual world. This discovery
is in line with the findings of previous research (Saroia etal.,2019; Huang etal.,
2007). Hypothesis (H5) and hypothesis (H6) were also accepted. Specifically,
FC positively influence both PU and PEOU of mobile learning. Furthermore, H7
and H8 were accepted indicating that SMM is a direct enabler of PU and PEOU,
and the findings are consistent with prior research (Alasmari & Zhang, 2019;
Badwelan etal., 2016; Huang et al., 2012). The hypotheses (H6 and H7) that
SML is a direct predictor of PU and PEOU were also found to be significant,
and the results are consistent with previous research (Alasmari & Zhang, 2019;
Badwelan etal., 2016; Huang etal., 2012). As a result, higher education institu-
tions should require students to attend certain training sessions before beginning
their degree programs. The aim of such training courses is to increase students’
self-learning competencies. The influence of ATT on BIM and AUML is inves-
tigated in this study. The findings show that ATT are important in the adoption
of mobile learning systems for BIM and AUML. The findings of this study are
in line with earlier research (Lin etal., 2020; Rafique et al., 2020; Raza etal.,
2018), and show that functionality, interaction, and reaction all work together
to improve learners’ motivation and ATT using M-learning systems. Theoreti-
cal and managerial contributions are made in this work. The current findings
revealed that BIM and ATT are substantial predictors of attitude and use of
M-learning systems. Practitioners and academics can better recommend new
techniques for improving the AUML. The findings not only validated the TAM
concept, but also revealed the impact of external factors on M-learning usage.
5.1 Limitatins oftheresearch
Despite the fact that this study followed a thorough research approach, there
are some potential limitations that might be recognized and explored in future
investigations. The response rate of UTHM students in Johor Bahru province
was quite low, which is a restriction. The sample of this study was limited as
it included a sample from one public university in Malaysia (Universiti Tun
Hussein Onn Malaysia). Such a limited sample poses questions about the gen-
eralization of the findings, particularly to other private and public universities.
Therefore, additional studies are recommended to cover larger and more repre-
sentative samples from public and private universities with various psychologi-
cal and demographic attributes. In doing so, the generalizability of the findings
will be enhanced.

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Education and Information Technologies
6 Conclusion
The TAM model was used in this study to describe the important elements that
influence students’ adoption of M-learning systems for academic purpose. As a con-
sequence, the most important aspects influencing students’ acceptance of the system
were discovered, and a new model was created as a strong tool to aid in the adoption
of mobile learning systems. Through the following findings, this study contributes
significantly to previous research on M-learning adoption. First, external criteria
such as AR and PMV, Facilitating Conditions, and SML were found to be the most
important determinants of M-learning system acceptability. Second, this study dem-
onstrates that PU and PEOU are important determinants of students’ use of mobile
learning systems.Second, this study demonstrates that students’ ATT and BIM to
use it are important variables in their adoption of the system. Third, in terms of
the TAM model factors, the findings show that PU and PEU all play a role in stu-
dent adoption of M-learning systems. As a result, M-learning systems are becoming
more popular. This suggests that pupils are more inclined to adopt and utilize the
mobile learning system because of its perceived value.
Acknowledgements The authors extend their appreciation to the Deanship of Scientific Research at King
Saud University for funding this work through research group No (RGP-1435-033).
Declarations
Conflict of interest statement None.
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Authors and Aliations
AliMugahedAl‑Rahmi1· WaleedMugahedAl‑Rahmi2 · UthmanAlturki3·
AhmedAldraiweesh3· SultanAlmutairy3· AhmadSamedAl‑Adwan4
1 Faculty ofTechnology Management andBusiness, Universiti Tun Hussein Onn Malaysia,
86400BatuPahat, Johor, Malaysia
2 Faculty ofSocial Sciences & Humanities, School ofEducation, Universiti Teknologi Malaysia,
UTM, 81310Skudai, Johor, Malaysia
3 Educational Technology Department, College ofEducation, King Saud University,
Riyadh11451, SaudiArabia
4 Electronic Business andCommerce Department, Business School, Al-Ahliyya Amman
University, Amman19328, Jordan