Learning from anywhere, anytime: Utilitarian motivations and facilitating
conditions to use mobile learning applications
By Mark Anthony Camilleri
and Adriana Caterina Camilleri
1. Department of Corporate Communication, Faculty of Media and Knowledge Sciences, University of Malta,
Malta. Email: firstname.lastname@example.org; https://orcid.org/0000-0003-1288-4256
2. The Business School, University of Edinburgh, Scotland.
3. Curriculum Department, Malta College of Arts, Science and Technology, Paola, Malta.
This is a prepublication version.
Suggested Citation: Camilleri, M.A. & Camilleri, A.C. (2022). Learning from anywhere, anytime:
Utilitarian motivations and facilitating conditions to use mobile learning applications. Technology,
Knowledge and Learning, https://doi.org/10.1007/s10758-022-09608-8
This contribution investigates higher education students’ perceptions about mobile learning (m-
learning) applications, as well as the effects of social influences and of appropriate facilitating
conditions, on their intentions to continue using them. A structured survey questionnaire
integrated valid measures from the Technology Acceptance Model (TAM) and from the Unified
Theory of Acceptance and Use of Technology (UTAUT) to better explain their acceptance and
use of m-learning software. The findings reported that facilitating conditions including the
provision of resources, ongoing training opportunities and technical support, were affecting the
respondents’ engagement with m-learning programs. The respondents indicated that they were
not influenced by others, to use mobile technologies for educational purposes. The results also
suggest that they were well acquainted (and habituated) with the use of mobile devices and their
applications. Evidently, they helped them improve their learning journeys.
Keywords: technology acceptance; mobile applications; mobile learning; social
influences; facilitating conditions; higher education.
Previous studies relied on reliable measures that were drawn from key theoretical
underpinnings from information systems, marketing or psychology literature to investigate the
students’ perceptions on the utilization of educational technologies (Almaiah & Alismaiel,
2019; Camilleri & Camilleri, 2021; Casey, Pennington & Mireles, 2020; Dlab, Boticki, Hoic-
Bozic & Looi, 2020; Dreimane & Daniela, 2020; Yang, Feng & MacLeod, 2019). A number of
contributions examined the students' engagement with mobile applications (apps) in higher
education (Crompton & Burke, 2018; Nguyen, Barton & Nguyen, 2015; Park, Nam &
Cha, 2012; Sevillano-Garcia & Vázquez-Cano, 2015). Very often, academic researchers
discussed about their benefits and costs (Camilleri & Camilleri 2017a; Chang, Lai & Hwang,
Dreimane & Daniela, 2020;
Gialamas, Lavidas & Komis, 2021; Jahnke, Lee, Pham, He
& Austin, 2020; Lameu, 2020; Nikolopoulou, Swanson, 2020).
The majority of higher education students have their own mobile devices. They can use
them to access educational content, whenever and wherever they are, as long as they are
connected to the Internet (Al-Emran, Elsherif & Shaalan, 2016; Chang et al., 2018; Sevillano-
Garcia & Vázquez-Cano, 2015). Therefore, they can use mobile technologies, at their
convenience, to access learning management systems, course notes, recorded videos,
assessments, quizzes, games, et cetera. These devices also allow their users to connect to
conferencing software like Zoom or Microsoft Teams to interact with others, including with
their course instructor, in real time (Camilleri & Camilleri, 2022).
There are different factors that can influence the mobile learners’ (m-learners’)
readiness to utilize m-learning apps in higher education contexts (Crompton & Burke, 2018;
Furió, Juan, Seguí & Vivó, 2015; Hamidi & Chavoshi, 2018; Sung, Chang & Liu, 2016). This
study builds on the extant literature in academia as it explores the university students’
perceptions and attitudes on their acceptance and use of mobile technologies (Bokolo,
Kamaludin, Romli, Raffei, Phon, Abdullah & Ming, 2020; Casey et al., 2020; Lameu, 2020;
Nikolopoulou et al., 2021; Swanson, 2020; Zogheib & Daniela, 2021). Furthermore, it
investigates the effects of the respondents’ social influences as well as of their institution’s
facilitating conditions on their intentions to continue utilizing m-learning technologies.
There are several studies that validated technology adoption models. Many authors have
adapted Davis, Warshaw and Bagozzi’s (1989) technology acceptance model (TAM) (Dumpit
& Fernandez, 2017; Granić & Marangunić, 2019; Scherer, Siddiq & Tondeur, 2019) or
Venkatesh, Morris, Davis and Davis’ (2003) unified theory of acceptance and use of technology
(UTAUT/UTAUT2) (Gunasinghe, Abd Hamid, Khatibi & Azam, 2019; Nistor, Lerche,
Weinberger, Ceobanu & Heymann, 2014; Tosuntaş, Karadağ & Orhan, 2015; Yang et al.,
2019), among others, in different settings. They used TAM to explore the users’ perceptions on
the usefulness and the ease of use of technologies. Very often they found that these factors have
a significant effect on their intentions to use them (Almaiah & Alismaiel, 2019; Camilleri &
Chavoshi & Hamidi, 2019). Similarly, others relied on the UTAUT/UTAUT2
theoretical frameworks to investigate the effects of performance and effort expectancies, social
influences and facilitating conditions on technology adoption (Almaiah, Alamri & Al-Rahmi,
2019; Camilleri & Camilleri, 2019b).
For the time being, there are no other contributions in academia, that integrated
perceived ease of use, perceived usefulness and attitudes (from TAM) with social influences
and facilitating conditions constructs (from UTAUT), to shed light on higher education
students’ utilitarian motivations to use m-learning apps. Therefore, this research clarifies
whether the research participants were pressurized by course instructors or by their peers to
make use of these ubiquitous technologies. At the same time, it investigates their perceptions
about the provision of ongoing support, resources and infrastructures (e.g. appropriate WiFi
facilities, at home and at university) that are intended to facilitate their engagement with m-
2. Literature review
Mobile devices are portable technologies that can be used in different locations when
students and educators, are out and about (Camilleri & Camilleri, 2019a; Callaghan, 2018).
Students can utilize mobile devices as instruments to improve the quality of their education
(Fokides, Atsikpasi & Karageorgou, 2020; Moya & Camacho, 2021; Nikolopoulou, Gialamas,
Lavidas & Komis, 2021). They may avail themselves of ubiquitous technologies to enhance
their knowledge across multiple contexts, through social and content interactions (Crompton,
2013). There are a number of potential benefits that are derived from m-learning technologies
(Chang et al., 2018). Their educational apps can play a significant, supplemental role in the in
improving the learning outcomes of students (Butler, Camilleri, Creed & Zutshi, 2021).
M-learning apps enable students to access and revise their course material from virtually
everywhere. They provide access to rich sources of information via the Internet including to
asynchronous learning management systems like Blackboard and Moodle, among others.
(Camilleri & Camilleri, 2017a; Bergdahl & Nouri, 2020
Dlab et al., 2020; Kuznetcova, Lin &
They also offer synchronous learning opportunities if users install video conferencing
programs including Skype, Google Meet, Zoom and Microsoft Teams (Camilleri & Camilleri,
2021; 2022). Course participants may use mobile apps to interact with other online users in
collaborative learning settings, including with their course instructor (Maqtary, Mohsen &
Bechkoum, 2019). This allows them to apply their theoretical knowledge in an authentic context
(i.e. in situated and informal learning contexts), as they are expected to engage in online
communications (Kwong, Wong & Yue, 2017). Therefore, conferencing programs can be used
to organize virtual meetings with students in real time (Nikolopoulou et al., 2021).
Nevertheless, previous literature reported that not all students are willing to utilize their
mobile phones or tablets for educational purposes (Casey et al., 2020; Zogheib & Daniela,
2021). A few commentators argued that smart phones have small screens with low resolutions,
slow connection speeds, and lacked standardization options (Al-Furaih & Al-Awidi, 2020;
Lowenthal, 2010). In fact, Android, Apple and Microsoft Windows have their own operating
systems. As a result, m-learning applications have to be programmed or customized to be
compatible with these systems (Camilleri & Camilleri, 2021).
Some commentators contended that individuals may possess different attitudes on the
usage of the mobile technologies (Al-Emran et al., 2016). There are individuals who may hold
different opinions and perspectives on the use mobile technologies (Ciampa, 2014). They may
be willing to utilize these devices to keep in contact with their friends on social media, to listen
to their preferred music, or to watch video clips and live streaming (Park et al., 2012). They
may use their mobile devices for hedonic and entertainment purposes, rather than to participate
in learning activities.
Many academic researchers laid out their recommendations on how to plan, organize
and implement the use of mobile apps across different levels of education (Butler et al., 2021;
Camilleri & Camilleri, 2017b; Crompton & Burke, 2018; Hwang & Chang, 2011). M-learning
is highly relevant in tertiary levels of education, as higher education students will probably have
their own mobile devices (Sevillano-Garcia & Vázquez-Cano, 2015).
Currently, there is still limited research that investigates the university students’
willingness to use educational apps through their mobile devices (Camilleri & Camilleri, 2022;
Jahnke et al., 2020), although there are a number of contributions that have explored the
students’ or educators’ perceptions toward other media, including digital learning resources,
WebCT or Moodle systems, in different contexts (Granić & Marangunić, 2019; Scherer et al.,
2019). Such academic studies have often relied on theoretical frameworks that are focused on
the utilitarian motivations to use technology in the realms of education (Al-Furaih & Al-Awidi,
2020; Bokolo. Kamaludin, & Romli, 2021; Briz-Ponce, Pereira, Carvalho, Juanes-Méndez &
García-Peñalvo, 2017; Tosuntas et al., 2015).
3. Key theoretical underpinnings and the formulation of hypotheses
Various researchers have often relied on technology adoption models including
TAM/TAM2/TAM3 or UTAUT/UTAUT2, among others, to investigate the acceptance and use
of technologies (Camilleri & Camilleri, 2022; Nikou & Economides, 2017; Schoonenboom,
2014). Table 1 clarifies the meanings of the constructs that are used for this empirical research.
Table 1. A definition of the key constructs of this research
Construct Source Definition
Perceived ease of use
Model (Davis, 1989; Davis, Bagozzi & Warshaw, 1989).
Perceived ease of use refers to the degree to which individuals expect
technologies to be simple to use, as they are straightforward and free of
Perceived usefulness Technology Acceptance
odel (Davis, 1989; Davis, Bagozzi & Warshaw, 1989).
Perceived usefulness refers to the individuals’ beliefs about the utilitarian
Model (Davis, 1989; Davis, Bagozzi & Warshaw, 1989).
Theory of Reasoned Action (Ajzen & Fishbein, 1975).
Theory of Planned Behavior (Ajzen,
Attitudes refer to the individuals’ positive or negative feelings about
performing target behaviors (like using technologies).
Social influences Unified Theory of Acceptance and Use of Technology
(Venkatesh, Morris, Davis & Davis, 2003)
Social influences refer to the degree to which individuals believe that they
can be influenced by the presence or actions of other persons.
Unified Theory of Acceptance and Use of Technology
Venkatesh, Morris, Davis & Davis, 2003).
Facilitating conditions is defined as the degree to which individuals
believe that they can avail themselves of technical resources, knowledge
‘Behavioral intention’ construct – from Technology
Model (Davis, 1989; Davis, Bagozzi & Warshaw, 1989).
Theory of Reasoned Action (Ajzen & Fishbein, 1975).
Theory of Planned Behavior (Ajzen, 1991).
Intentions refer to the individuals’ willingness to perform specified
behaviors (like using technologies).
‘Actual system use’ construct – from Technology
Model (Davis, 1989; Davis, Bagozzi & Warshaw, 1989).
‘Actual behavior’ construct – from Theory of Reasoned
Action (Ajzen & Fishbein, 1975). Theory of Planned
Behavior (Ajzen, 1991).
Behaviors refer to actions (like using/engaging with technologies).
3.1 The technology acceptance model
Davis (1989) suggested that the individuals’ intentions would usually anticipate their
actual behaviors. He argued that their intentions towards using technologies would usually
precede their actual utilization. The theory of reasoned action also presumed that the persons’
behavioral intentions would anticipate their actions (Ajzen, 1991). Many academic
commentators reiterated that intentions are assumed to capture the motivational factors that
influence specific behaviors (Camilleri & Camilleri, 2017c). Ajzen (1991) contended that the
stronger the individuals’ intentions to engage in volitional behaviors, the more likely they will
perform their actions. Arguably, the persons’ intentions represent their actual control over
certain behaviors; to the extent that when an opportunity arises, if they have access to certain
resources, they will be in a better position to engage in their desired activities (Camilleri &
Camilleri, 2019b). Therefore, the individuals’ intentions to use m-learning technologies is a
potential antecedent for their active engagement with them. This argumentation leads to the
H1: The students’ intentions to use m-learning apps positively affects their active engagement
Individuals may think that the technologies would be easy to use, user friendly or free
of effort (Davis, 1989). Individuals will probably engage with technologies if they perceive
them as uncomplicated and easy to understand (Joo, Park & Lim, 2018; Camilleri & Camilleri,
2017a). TAM researchers noted that the perceived ease of use of technology is a precursor of
their perceived usefulness (Faqih & Jaradat 2015; Scherer et al., 2019). Very often, they argued
that individuals are more likely to use those technologies that are simple and straightforward to
use. In addition, many researchers including Teo (2009), Huang, Huang, Huang and Lin (2012)
as well as Siyam (2019) reported that the respondents’ perceived ease of use of technology was
significantly affecting their positive attitudes towards them. Hence, this study hypothesizes the
H2: The students’ perceived ease of use of m-learning apps positively affects their
H3: The students’ perceived ease of use of m-learning apps positively affects their
attitudes towards them.
Alternatively, individuals may believe that certain technologies are difficult to
understand and use (Scherer et al., 2019). They may feel uncomfortable with the use of
technological innovations if they consider them as time consuming and/or complicated. In these
cases, they will probably hold negative perceptions towards such technologies. Thong Hong
and Tam (2002) reported that there may be a negative relationship between the use of complex
technologies and their perceived usefulness.
Individuals will probably use technologies to enhance the quality of their work or job
performance (Camilleri, 2020a; Camilleri, 2021a; Cheon, Lee, Crooks & Song, 2012; Davis,
1989; Garcia & Silva, 2017). They may perceive that some technologies are useful for them,
particularly if they help them increase their productivity. In this case, this research sought to
explore whether the use of m-learning technologies would support students to achieve their
learning outcomes. It investigates the respondents’ attitudes towards educational apps. Previous
literature indicated that there a significant relationship between the students’ perceptions about
the usefulness of technologies and their attitudes towards them (Camilleri & Camilleri, 2017a;
Siyam, 2019; Teo, Zhou & Noyes, 2016). The researchers hypothesize that if the students
perceive their usefulness, they will probably hold positive attitudes towards them. Therefore,
this research hypothesizes:
H4: The students’ perceived usefulness of m-learning apps positively affects their
attitudes toward them.
TAM theorists postulated that the individuals’ positive attitudes toward technology can
have a significant effect on their acceptance (Huang et al., 2012; Ngai et al., 2007; Thong Hong
& Tam, 2002). A number of researchers relied on Ajzen’s (1991) theory of planned behavior’s
(TPB’s) key constructs, reported that the individuals’ positive attitudes toward the usage of
technology would precede their intentions to continue using them in the future (Cheon et al.,
2012; Teo et al., 2016;). This argumentation leads to the following hypothesis:
H5: The students’ attitudes toward m-learning apps positively affects their intentions to use
Of course, there may be instances where individuals will not always hold positive attitudes
toward technologies. Many studies, including Davis (1989) indicated that the individuals’
attitudes towards the use of technology did not emerge as a very significant antecedent of the
individuals’ technology acceptance. In a similar vein, other researchers also reported that the
individuals’ attitudes towards technology did not always correlate with their intentions to use
them (Nikou & Economides, 2017). One of the most plausible reasons for this is that many
individuals may be expected to use certain technologies as a requirement for their work
performance, whether they like them or not (Camilleri & Camilleri, 2022).
3.2 The unified theory of acceptance and use of technology
TAM has been adapted by various scholars. Venkatesh, Morris, Davis and Davis (2003)
unified theory of acceptance and use of technology (UTAUT) as well Venkatesh, Thong &
Xu’s (2012) (UTAUT2) have included various elements from Davis’ (1989) TAM. Venkatesh
et al. (2003; 2012) also incorporated a ‘social influences’ factor in their theoretical models. The
social influences construct is synonymous with Ajzen and Fishbein’s (1975) and Ajzen’s (1991)
‘subjective norm’. In sum, these authors argued that individuals may be influenced by others,
including by their family members, friends or even by acquaintances, to use certain
technologies. The normative pressures from society can have a significant effect on the
individuals’ intentions to perform certain behaviors (Ajzen, 1991; Bokolo et al., 2021;
Camilleri, 2020; Park et al., 2012).
In this case, the researchers presume that course instructors or other persons including
the students’ peers can influence their intentions to use mobile learning apps. Hence, the
H6: The social influences positively affect the students’ intentions to use m-learning apps.
Venkatesh et al. (2003) also included a ‘facilitating conditions’ construct in UTAUT.
They justified the inclusion of this factor as they wanted to measure their research participants’
perceptions on physical environmental features (e.g. infrastructures and equipment) and
intangible aspects (like training and development, or the provision of ongoing support and
assistance to technology users, among others). Venkatesh et al. (2003) contended that
facilitating conditions significantly affect the individuals’ intentions as well as their actual
engagement with technologies.
Similarly, many researchers reported that facilitating conditions were influencing the
students’ intentions to use education technologies (García Botero, Questier, Cincinnato, He &
Zhu, 2018; Peñarroja, Sánchez, Gamero, Orengo & Zornoza, 2019; Thomas, Singh & Gaffar,
2013). Others noticed that they had an impact on their actual behaviors (Gunasinghe et al.,
2019). Thus, the researchers hypothesize that:
H7: The facilitating conditions positively affect the students’ intentions to use m-learning apps.
H8: The facilitating conditions positively affect the students’ usage of m-learning apps.
Figure 1 illustrates the research model and the formulation of hypotheses. It explores
the effects of the students’ utilitarian motivations, social influences and facilitating conditions
on their acceptance and usage of m-learning programs.
In sum, this research hypothesizes that there are positive and significant relationships
between the students’ perceived ease of use and perceived usefulness of m-learning
technologies and between their attitudes and intentions to continue using them. It presumes
that facilitating conditions are significantly correlated with the students’ willingness to use
them, as well as with their active engagement with them. In addition, this study theorizes that
the respondents were, in some way, influenced (by their course instructor and/or by their peers)
to utilize these m-learning programs to continue their learning journeys.
Figure 1. Factors affecting the students’ engagement with m-learning programs
4.1 The questionnaire’s measures
The survey’s measures were adapted from key theoretical underpinnings. ‘Perceived
usefulness’, ‘perceived ease of use’ and ‘attitudes toward technology’ were drawn from TAM’s
basic model (Camilleri & Camilleri, 2017b; Cheung & Vogel, 2013; Davis, 1989). The users’
‘attitudes toward technology’ construct was also featured in TPB (Park et al., 2012; Shonfeld
& Magen-Nagar, 2020). Moreover, ‘social influences’ and ‘facilitating conditions’ constructs
were used in UTAUT/UTAUT2 models
Camilleri, 2020; Camilleri & Camilleri, 2022; Bokolo
et al., 2020; Bokolo et al., 2021; Venkatesh et al., 2003; 2012).
Davis (1989) reported that his TAM constructs were reliable. He indicated that the
Cronbach Alpha values were above 0.90. His analysis also confirmed that these constructs had
appropriate convergent and discriminant validities. Other studies yielded similar validity and
reliability values as many researchers explored the use and acceptance of different technologies
in various contexts (Park et al., 2012; Teo & Zhou, 2014). Venkatesh et al. (2012) found that
their UTAUT constructs had internal consistency values that exceeded 0.75 (these figures were
higher than the recommended threshold of 0.7). They held that their convergent and
discriminant validity results were consistent with previous research. The constructs that were
adopted in the survey instrument are featured in Table 2.
Table 2. The survey questionnaire’s constructs (and their items) that were used in this
ease of use
PEoU1 Learning how to use mobile learning technologies is easy for
PEoU2 My interaction with mobile learning technologies is clear and
I find mobile learning technologies easy to use.
PEoU4 It is easy for me to become skillful at using mobile learning
The mobile learning
technologies are useful in my daily life.
PU2 The mobile learning technologies increase my chances of
achieving things that are important to me.
The mobile technologies help me learn things more quickly.
The mobile learning
technologies increase my productivity.
ATT1 Using the mobile learning technologies is frustrating for me.
ATT2 I get bored quickly when I use the mobile learning technologies.
SI1 People who are important to me think that I should use mobile
SI2 People who influence my behavior think that I should use
mobile learning technologies.
SI3 People whose opinions that I value prefer that I use mobile
FC1 I have the resources necessary to use mobile learning
FC2 I have the knowledge necessary to use mobile learning
FC3 I can get help from others when I have difficulties using mobile
FC4 The mobile learning technologies are compatible with other
technologies I use.
INT1 It is very likely that I shall continue using mobile learning
technologies in the future.
INT2 Probably, I will use mobile learning technologies in my daily
INT3 I will use mobile learning technologies as frequently as
ENG1 I use mobile learning technologies to browse the web.
ENG2 I search for information with mobile learning technologies.
ENG3 I use educational apps on my mobile device (smartphone or
The questionnaire consisted of 26 multiple choice questions including three
demographic ones, that were placed in the latter part of the survey. The participants disclosed
information about their ‘age’ and ‘gender’. They also indicated their ‘experience with m-
learning technologies’. The respondents could complete the questionnaire in a few minutes.
The responses were coded on a five-point Likert scale, ranging from 1 (strongly disagree) to 5
(strongly agree), with 3 signaling a neutral position.
4.2 Data capture and analysis
The research participants were registered students at a Southern European university.
There were more than 10,500 students who were pursuing full time, part time and distance
learning courses. The university’s registrar disseminated this study’s survey questionnaire and
a cover letter that informed the research participants about the aims and objectives of this
empirical investigation. It also provided them with guidelines on how to complete the
questionnaire. After two weeks, there were 141 responses to the survey. The returned
questionnaires were scrutinized and checked for incomplete responses. There were three
questionnaires that were not included in the analysis as they had several missing values. Hence,
the research sample of this study comprised 138 valid responses.
The data were uploaded onto IBM SPSS statistical software. The researchers evaluated
the socio-demographic profile of their respondents and explored the descriptive statistics. They
indicated the reliability of their constructs. Moreover, they carried out a principal component
analysis (PCA) to reduce the dimensionality of the dataset, and to detect the underlying
structure among the measures. PCA also confirmed the validity of the chosen measures. Only
factor loadings that were above the 0.5 benchmark were considered in the analyses.
Subsequently, this study’s hypotheses were investigated through stepwise regression analyses
(that shed light on the coefficients of determination and on the significance of the relationships).
5.1 The research sample
The frequency table reported that there were seventy-five females and sixty-three males
(n = 138) who participated in this study. The respondents were classified into five age groups
(18-23; 24-29; 30-35; 36-41 and over 42 years of age). Most of the research participants were
between 18 and 23 years of age (n = 93), this group was followed by those between 24 and 29
years of age (n = 27). The majority of respondents (n = 48) revealed that they have been using
m-learning technologies between 2 to 3 years. Table 3 describes the profile of the research
Table 3. The research participants
Less than a year
Between 1 and 2 years
Between 2 and 3 years
Total: 138 100
Between 3 and 4 years
More than 4 years
Total: 138 100
Total: 138 100
5.2 The descriptive statistics
Generally, the respondents indicated that they agreed with the questionnaire’s
statements as there were high mean (M) scores that were above the midpoint (3). There was
only one value (that represented a behavioral intention item – INT3) that was slightly below 3
(M = 2.93). Moreover, the standard deviation values (SD) indicated that there were small
variances in the participants’ responses. These values varied from 0.743 to 1.31. Overall, there
was a normal distribution in the dataset except for PEoU1, PEoU2, PEoU3 and FC2.
5.3 The principal component analysis
The Kaiser Meyer Olkin test reported a KMO of 0.654. Therefore, the sampling
adequacy was acceptable as it was well above 0.5 (Field, 2005). Bartlett’s test of sphericity
revealed that there was sufficient correlation in the dataset to run a principal component analysis
(PCA) since p < 0.001. Therefore, a PCA assessed the validity of the constructs and provided a
factor solution of salient components that shared relevant similarities (and differences) (Ngai
et al., 2007).
A varimax rotation was used to reconstruct this study’s seven composite factors. The
items with the highest loadings were used to identify the factor components. The values of the
factor loadings were more than 0.5. Hence, they indicated that there were highly significant
correlations among the factors in our research model. Table 4 illustrates the findings from PCA.
It features the extracted components, their respective eigenvalues, percentages of variance,
cumulative percentages of variances as well as the values that represented Cronbach’s alpha for
Table 4. The findings from the principal component analysis
Component Initial Eigenvalues Rotation Sum of
Eig. % of Var. Cum.
Eig. % of Var. Cum.
ease of use
4.800 25.468 25.468
3.226 17.117 17.117
2.526 13.399 38.868
2.190 11.620 28.737
2.202 11.681 50.549
1.852 9.823 38.560
1.597 8.471 59.020
2.000 10.611 49.171
1.305 6.921 65.941
2.386 12.658 61.828
0.994 5.276 71.216
1.639 8.697 70.526
0.922 4.892 76.109
1.052 5.583 76.109
The factors components accounted for 76% of the variance. Cronbach’s alpha values
were higher than 0.7 for all constructs (this finding is consistent with the recommended
threshold). The alpha coefficient ranged from 0.76 (for ENG) to 0.92 (for PEoU).
5.4 The results from the regression analysis
A stepwise procedure was used to investigate whether there were significant
correlations. The p-value had to be less than 0.05 benchmark. Therefore, the insignificant
variables were excluded from this empirical investigation.
The first five hypotheses were related to the TAM (‘attitudes’ construct is also used in
TPB), and the latter three hypotheses were associated with UTAUT. The following results
represent the strength and the significance of the hypothesized relationships.
H1: The results from the linear regression analysis revealed that the students’ intentions
to use m-learning technologies anticipated their usage, where the adj. r
= 0.418 and the t value
= 2.235. This relationship was significant, as p = 0.026. H2: There was also a positive and
significant relationship between the students’ perceived ease of use of m-learning technologies
and their perceived usefulness, where the adj. r
= 0.303 and the t value = 1.904. This
relationship was significant, as p = 0.043.
H3: The students’ perceptions on the ease of use of m-learning technologies had a
positive and very significant effect (p < 0.001) on their attitudes towards their utilization, where
the adj. r
= 0.157 and the t value = 4.877. H4: Similarly, the students’ perceptions on the
usefulness of m-learning technologies had a positive and highly significant effect (p < 0.001)
on their attitudes, where adj. r
= 0.163 and t = 3.984. H5: There was also a positive and
significant relationship between the students’ attitudes toward m-learning technologies and
their behavioral intentions to use them, as adj. r
= 0.111 and t value = 5.136. The measurement
of significance indicated a confidence level of 97% (where p = 0.03).
H6: The research participants’ social influences (from their course instructor or from
their peers) did not have a significant effect on their intentions to use m-learning technologies.
In this case the results were inconclusive as p > 0.05. H7: Similarly, there was no correlation
between facilitating conditions and the students’ intentions to use m-learning technologies, as
p > 0.05. H8: Nevertheless, the university’s facilitating conditions had a significant effect (p =
0.02) on the students’ engagement with m-learning technologies, where adj. r
= 0.435 and t
value = 13.608.
6.1 Theoretical implications
This contribution has presented a critical review of the relevant literature that was
focused on the use of m-learning. It reported that the university students were using mobile
technologies to improve their learning outcomes. In the past years, a number of academic
authors contended that educational apps were supporting many students in different contexts
(Butler et al., 2021; Crompton & Burke, 2018; Hamidi & Chavoshi, 2018; Sung et al., 2016;
Tosuntas et al., 2015). In the main, they maintained that ubiquitous technologies enable them
to access learning management systems and to engage in synchronous conversations with other
individuals (Camilleri & Camilleri, 2021).
One may argue that the m-learning paradigm is associated with the constructivist
approaches (Chang et al., 2018), including those related with discovery-based learning
(Camilleri & Camilleri, 2019c). Relevant theoretical underpinnings suggest that the use of
mobile apps can improve the delivery of quality, student-centered education (Camilleri &
Camilleri, 2021; Camilleri, 2021b; Chang et al., 2018; Crompton & Burke, 2018; Furió et al.,
2015; Lameu, 2020; Nikolopoulou et al., 2021; Sung et al., 2016; Swanson, 2020). This
research raises awareness on m-learning technologies that enable students to search for
solutions for themselves through the Internet and via learning management systems. It also
indicated that mobile apps like Microsoft Teams or Zoom, among others, allow them to engage
in synchronous conversations with course instructors and with their peers, in real time.
This study explored the users’ perceptions about m-learning technologies. It validated
key constructs from TAM (Briz-Ponce et al., 2017; Cheung & Vogel, 2013; Granić &
Marangunić, 2019; Ngai et al., 2007; Scherer, Siddiq & Tondeur, 2019; Thong Hong & Tam,
2002) and UTAUT (Gunasinghe et al., 2019; Yang et al., 2019).
The descriptive statistics clearly indicated that the research participants felt that m-
learning technologies were useful for them to continue their course programs. The principal
component analysis confirmed that the students’ engagement with their educational apps was
primarily determined by their ease of use. This is one of the main factors that influenced their
intentions to engage with m-learning apps.
The findings revealed that higher education students were using m-learning apps as they
considered them as useful tools to enhance their knowledge. Evidently, their perceptions about
the ease of use of m-learning technologies were significantly correlated with their perceived
usefulness. In addition, it transpired that both constructs were also affecting their attitudes
towards usage, that in turn preceded their intentions to use m-learning apps.
The results also revealed that the respondents were satisfied by the technical support
they received during COVID-19. Apparently, their university provided appropriate facilitating
conditions that allowed them to engage with to m-learning programs during the unexpected
pandemic situation and even when the preventative restrictions were eased.
The stepwise regression analyses shed light on the positive and significant relationships
of this study’s research model. Again, these results have proved that the respondents were
utilizing m-learning apps because their university (and course instructors) supported them with
adequate and sufficient resources (i.e. facilitating conditions). The findings indicated that they
were assisted (by their institution’s helpdesk) during their transition to emergency remote
learning. In fact, the study confirmed that there was a positive and significant relationship
between facilitating conditions and the students’ engagement with m-learning technologies.
On the other hand, this empirical research did not yield a statistically significant
relationship between the students’ social influences and their intentions to use the mobile
technologies. This is in stark contrast with the findings from past contributions, where other
researchers noted that students were pressurized by course instructors to use education
technologies (Camilleri & Camilleri, 2020; Teo & Zheng, 2014). The researchers presume that
in this case, the majority of university students indicated that they were not coerced by educators
or by their peers, to use m-learning apps. This finding implies that students became accustomed
or habituated with the use of mobile technologies to continue their course programs.
This research builds on previous technology adoption models (Davis et al., 1989;
Venkatesh et al., 2003; 2012) to better understand the students’ dispositions to engage with m-
learning apps. It integrated constructs from TAM with others that were drawn from
UTAUT/UTAUT2. To the best of the researchers’ knowledge, currently, there are no studies
that integrated facilitating conditions and social influences (from UTAUT/UTAUT2) with
TAM’s perceived ease of use, perceived usefulness and attitudes. This contribution addresses
this knowledge gap in academia. In sum, it raises awareness on the importance of providing
appropriate facilitating conditions to students (and educators). This way, they will be in a better
position to use educational technologies to improve their learning outcomes.
6.2 Practical implications
This research indicated that students held positive attitudes and perceptions on the use
of m-learning technologies in higher educational settings. Their applications allow them to
access course material (through Moodle or other virtual learning environments) and to avail
themselves from video conferencing facilities from everywhere, and at any time. The
respondents themselves considered the mobile technologies as useful tools that helped them
improve their learning journeys, even during times when COVID-19’s preventative measures
were eased. Hence, there is scope for university educators and policy makers to create and adopt
m-learning approaches in addition to traditional teaching methodologies, to deliver quality
education (Camilleri, 2021).
Arguably, m-learning would require high‐quality wireless networks with reliable
connections. Course instructors have to consider that their students are accessing their
asynchronous resources as well as their synchronous apps (like Zoom or Microsoft Teams) on
campus or in other contexts. Students using m-learning technologies should have appropriate
facilitating conditions in place, including adequate Wi-Fi speeds (that enable access to high-res
images, and/or interactive media, including videos, live streaming, etc.). Furthermore, higher
education institutions ought to provide ongoing technical support to students and to their
members of staff (Camilleri & Camilleri, 2021).
This study has clearly shown that the provision of technical support, as well as the
utilization of user-friendly, m-learning apps, among other factors, would probably improve the
students’ willingness to engage with these remote technologies. Thus, course instructors are
encouraged to create attractive and functional online environments in formats that are suitable
for the screens of mobile devices (like tablets and smartphones). There can be instances where
university instructors may require technical training and professional development to learn how
to prepare and share customized m-learning resources for their students.
Educators should design appealing content that includes a good selection of images and
videos to entice their students’ curiosity and to stimulate their critical thinking. Their
educational resources should be as clear and focused as possible, with links to reliable academic
sources. Moreover, these apps could be developed in such a way to increase the users’
engagement with each other and with their instructors, in real time.
Finally, educational institutions ought to regularly evaluate their students’ attitudes and
perceptions toward their m-learning experiences, via quantitative and qualitative research, in
order to identify any areas of improvement.
6.3 Research limitations and future research directions
To date, there have been limited studies that explored the institutions’ facilitating
conditions and utilitarian motivations to use m-learning technologies in higher education, albeit
a few exceptions. A through review of the relevant research revealed that researchers on
education technology have often relied on different research designs and methodologies to
capture and analyze their primary data. In this case, this study integrated measures that were
drawn from TAM and UTAUT. The hypotheses were tested through stepwise regression
analyses. The number of respondents that participated in this study was adequate and sufficient
for the statistical purposes of this research.
Future research could investigate other factors that are affecting the students’
engagement with m-learning technologies. For example, researchers can explore the students’
intrinsic and extrinsic motivations to use educational apps. These factors can also have a
significant effect on their intentions to continue their learning journeys. Qualitative research
could shed more light on the students’ in-depth opinions, beliefs and personal experiences on
the usefulness and the ease of use of learning via mobile apps, including serious games and
simulations. Inductive studies may evaluate the effectiveness as well as the motivational appeal
of gameplay. They can possibly clarify how, where and when mobile apps can be utilized as
teaching resources in different disciplines. They can also identify the strengths and weaknesses
of integrating them in the curricula of specific subjects.
Prospective researchers can focus on the design, structure and content of m-learning
apps that are intended to facilitate the students’ learning experiences. Furthermore, longitudinal
studies may provide a better understanding of the students’ motivations to engage with such
educational technologies. They can measure their progress and development, in the long term.
The students’ perceptions, attitudes and intentions to use m-learning technologies can change
over time, particularly as they become experienced users.
The authors thank the editor and his reviewers for their constructive remarks and suggestions.
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