ACADEMICS' BEHAVIORAL INTENTION AND USAGE OF IOT IN E-LEARNING: MODERATION OF GENDER AND EXPERIENCE

Abstract

As the world shifts to e-learning, IoT is becoming increasingly important in the learning and teaching environment. The key concerns are the elements that influence academics' behavioral intention to adopt IoT, and how the whole operation affects their performance. However, there is a research gap in past studies that have not addressed this issue sufficiently. As a result, this study employed the Unified Theory of Acceptance and Use of Technology (UTAUT) as a guideline to examine the factors that influence academics' behavioral intentions to use IoT. Furthermore, the moderating effects of both gender and level of experience on this relationship were inspected. The structural models were validated, and the predefined hypotheses were presented (n = 321). The results from the Structural Equation Modeling approach using Amos 26 indicate that performance expectancy, social influence, and effort expectancy directly influenced behavioral intentions to utilize IoT. The findings also showed that facilitating conditions were the most important determinant of academics' actual usage of IoT. The structural model was further investigated according to the experiences of the male and female academic groups. The findings revealed a different pattern of strength and significant relationships between groups with the overall model, implying that gender and experience act as moderators. This study provides a wealth of antecedents from which to construct a thorough theory of IoT adoption. The theory explores the elements that influence academics' willingness to utilize IoT from the standpoints of the technology itself, social context, and individual user characteristics. By employing the proposed approach, Universities can modify their EL strategies to make the most of their resources and in turn improve efficiency.

Full text

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1376
ACADEMICS’ BEHAVIORAL INTENTION AND USAGE OF
IOT IN E-LEARNING: MODERATION OF GENDER AND
EXPERIENCE
FAIQ AZIZ 1,*, AZIZI SAFIAI 2, NOR WAHIZA ABDUL WAHAT 3, SITI RABA'AH HAMZAH 4,
SEYEDALI AHRARI 5, NOMAHAZA MAHADI 6
1,2,3,4,5 Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, 43400, Malaysia
6Azman Hashim International Business School, Universiti Teknologi Malaysia, 54100, Kuala Lumpur,
Malaysia
E-mail: 1 mohdfaiq@upm.edu.my, 2 azizis1128@gmail.com, 3 wahiza@upm.edu.my, 4 srh@upm.edu.my,
5 seyedaliahrari@upm.edu.my , 6 nomahaza.kl@utm.my
ABSTRACT
As the world shifts to e-learning, IoT is becoming increasingly important in the learning and teaching
environment. The key concerns are the elements that influence academics’ behavioral intention to adopt
IoT, and how the whole operation affects their performance. However, there is a research gap in past studies
that have not addressed this issue sufficiently. As a result, this study employed the Unified Theory of
Acceptance and Use of Technology (UTAUT) as a guideline to examine the factors that influence
academics’ behavioral intentions to use IoT. Furthermore, the moderating effects of both gender and level of
experience on this relationship were inspected. The structural models were validated, and the predefined
hypotheses were presented (n = 321). The results from the Structural Equation Modeling approach using
Amos 26 indicate that performance expectancy, social influence, and effort expectancy directly influenced
behavioral intentions to utilize IoT. The findings also showed that facilitating conditions were the most
important determinant of academics’ actual usage of IoT. The structural model was further investigated
according to the experiences of the male and female academic groups. The findings revealed a different
pattern of strength and significant relationships between groups with the overall model, implying that gender
and experience act as moderators. This study provides a wealth of antecedents from which to construct a
thorough theory of IoT adoption. The theory explores the elements that influence academics’ willingness to
utilize IoT from the standpoints of the technology itself, social context, and individual user characteristics.
By employing the proposed approach, Universities can modify their EL strategies to make the most of their
resources and in turn improve efficiency.
Keywords: IoT adoption; UTAUT; Academics; E-Learning; Gender; Experience
1. INTRODUCTION
The traditional face-to-face learning approach
is no longer enough in today’s quickly evolving
information society of web-based e-learning
(EL) environments [1]. With EL, learners may
develop their knowledge, strategize their learning
routes and tactics, and access a wide range of
information and self-directed learning
experiences [2]. The COVID-19 pandemic,
which forced the closure of all academic
institutions, further highlighted EL’s
technological importance and its many
advantages [3]. However, EL relies heavily on
the internet as its information gateway and uses
various cutting-edge technologies to access
online educational programs [4].
The internet has recently become a vital,
omnipresent communication network that
connects billions of people across several
platforms. EL can not only allow learners to
access educational information on the internet
but can also extend its services through EL help
and community knowledge-sharing networks [5].
EL materials may be transferred through the
internet swiftly and efficiently. Significant
advancement has further improved the internet’s
ubiquity in ICT, namely by the effortless linking
of common devices to a ubiquitous information
network known as the Internet of Things (IoT)
[6]. The phrase “IoT” was invented by Ashton
[7]. IoT extends the benefits of the ordinary
digital internet to physical items, with the
primary objective of linking everything [8].

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1377
Currently, IoT connects over 25 billion
devices worldwide. Despite its novelty, the
possibility of applying IoT in the education
sector piqued the curiosity of many academics
[9]. The current era is characterized by smart
learning, which combines EL and IoT [10]. The
arrival of IoT has impacted earlier models of EL,
allowing EL to adopt a more participatory
approach [11]. According to Mathivanan et al.
[12], most universities will adopt IoT to boost
EL. However, studies have shown that IoT
integration continues to be challenging for most
users, particularly academics [13]. Scholars have
discovered the factors that affect academics’
adoption of technology-based learning [14].
However, there has been a lack of thorough
studies to validate their usage of IoT. Most
previous studies have concentrated on
technology concerns around IoT, with little focus
on its actual adoption.
There haven't been many attempts up to now
to look at factors connected to the real adoption
of IoT. Additionally, there haven't been any
studies done on academics' behavioral intents to
use IoT. In order to give higher education (HE)
administrators more information regarding the
acceptance of and effects of this technology on
academics, researchers should do further
empirical study. To provide them with guidelines
on how to use IoT in EL for HE, this study
sought to answer that question. Guidelines were
determined based on the empirical evidence on
psychological factors that may impact academic
intention and use, as shown by the UTAUT [15].
1.1 Study Context
It is thought that EL would provide an
answer to the growing need for HE in many
developing countries. HEIs from these nations
are thus urged to participate, promote, and
coordinate high-quality learning, teaching, and
research [16]. Nonetheless, studies demonstrate
that IoT usage for EL at HEIs is still in its
infancy [17]. The Malaysian government has
made an effort to advance IoT in many areas.
The Malaysian Ministry of Science, Technology,
and Innovation (MOSTI) predict IoT will
significantly influence Malaysia’s economy in
the coming years [18].
The ICT sector in Malaysia has to be
improved to increase the nation’s output.
MOSTI, a pioneer in Malaysia’s IT industry,
unveiled the National IoT Strategic Plan in 2015
[18]. By 2025, it is anticipated that Malaysia’s
IoT industry will reach $42.5 billion [19]. The
current problems and challenges encountered in
the supply chain concerning implementing
Industry 4.0 (IR4) have also been highlighted by
Malaysia's Ministry of International Trade and
Industry. This was done to further promote the
necessity for an IoT curriculum. Except for a few
private colleges with specialized engineering
disciplines, none of the Malaysian research
universities (MRUs) have used IoT for EL
systematically. The IoT-heavy programs that
they do have are isolated, unconnected, and
disconnected.
2. LITERATURE REVIEW
EL is determined by three factors: first, it is
networked, enabling it to share training or
information; second, conventional internet
technologies are used to deliver it to the
computer user; and third, it focuses on the most
comprehensive understanding of learning that
goes beyond the typical training paradigms [20].
The demands for flexible modalities of
educational curriculum delivery in HE from the
standpoint of EL have steadily risen [21]. Several
elements are at play now that encourage an
increase in academics participating in EL [22].
HEIs recognize the value of a student’s
ongoing education that is flexible to their own
time, creating the convenience needed for
learning without taking away from working time
[23]. Moreover, IoT can be useful in universities
because it increases learners’ interaction with
digital resources, combines autonomous control
and higher infrastructure reliability, and enables
open access to anything without requiring a
particular path or service. Today, the adoption of
technology tools by academics is crucial to EL’s
success [24]. IoT is a pervasive technological
phenomenon that fosters invention in various
disciplines, EL being one of them. For instance,
Vharkute and Wagh (2015) discovered that
combining several EL apps with the aid of the
IoT is associated with students’ pleasure. It
ensures that student and instructor interaction is
enhanced while offering a cost-effective
education. IoT is regarded as the primary
supplier of the smart agent for EL contexts [25].
The IoT application’s scope permits switching
from an EL model of knowledge transmission to
a collaborative kind that advances knowledge
transmission [26]. Animations, online lessons,
virtual classroom study materials, video tutorials,

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1378
and many more resources can be misused as part
of an EL method. IoT allows academics to use
various educational approaches, including
personal, active, and participatory models [27].
IoT is evolving to become a key component of
modern EL. By building the right infrastructure
and using it to its full potential, institutions may
reap the rewards of IoT-based EL models [28].
2.1 Conceptual Model and Hypothesis
Development
Based on ideas of human behavior,
researchers have tried to explain why users adopt
new technologies. However, there are certain
limitations to these technology adoption models
in determining how open individuals are to
embracing new technologies [29]. The UTAUT
uses the fundamental components based on eight
standard technology acceptance models to solve
these constraints [15]. This study used this model
to explore the potential factors of academics’
behavioral intention (BI) and IoT adoption in EL.
2.1.1 Performance Expectancy and
Academics’ Intention to Use IoT
Performance expectancy (PE) refers to the
notion that using technology would improve a
person’s ability to execute his job [15]. The PE
idea, which foresees academics’ BI to use
emerging technologies, is frequently included in
the UTAUT [30]. For instance, Sung et al. [31]
used the UTAUT to examine mobile learning in
the South Korean setting and concluded that PE
is strongly related to BI. IoT can speed up
academic work, shorten wait times, and improve
customer impressions of service quality.
Numerous researchers have employed the
UTAUT, and there is evidence of a relationship
between PE and the BI use technology [32].
Studies have also shown that PE significantly
affects a person’s long-term motivation to use
EL [33]. As a result, the present study formulated
the following hypothesis:
H1: PE is positively associated with academics’
BI to use IoT in EL.
2.1.2 Social Influence and Academics’
Intention to Use IoT
The social influence (SI) component of the
UTAUT measures how important adopting a new
technological instrument is to a person [15].
Studies have examined the impact of SI, such as
the influences of close acquaintances, on
people’s adoption behavior [34]. SI includes the
users’ assessment of whether those who are
significant to them believe they should engage in
the action [15]. For instance, Jain and Jain [35]
claimed that teachers who interact with students
have a stronger BI to adopt new technology in
the classroom. Additionally, SI has impacted the
adoption of IoT [36]. As a result, Hypothesis 2
suggests:
H2: SI is positively linked to academics’ BI to
use IoT for EL.
2.1.3 Effort Expectancy and Academics’
Intention to Use IoT
The formal definition of effort expectancy
(EE) is the degree of comfort associated with
using technology instruments. The main use of
EE, a crucial component of the UTAUT, is to
gauge users’ intent to use technological tools
[37]. Jang and Koh [38] have highlighted the
impact of EE in determining the acceptability of
learning technologies. Researchers like Kaliisa et
al. [39] have highlighted an association between
EE and BI in contemporary technology. Other
studies that have used the UTAUT have also
found EE and BI to be related [40].
Consequently, Hypothesis 3 was proposed as
follows:
H3: EE is positively related to academics’ BI to
use IoT for EL.
2.1.4 Facilitating Conditions and Academics’
IoT Usage Behavior
Facilitating circumstances (FC) are the idea
that there are sufficient administrative and
technical infrastructures in place to make the
system simpler to utilize [15]. It is believed that
having access to training and support will make
it easier for businesses to embrace new
technologies. In this study, FC was evaluated
based on academics’ perceptions about their
capacity to obtain the tools and help they need to
utilize IoT. FCs have a favorable impact on
individuals’ inclinations to utilize technology
[41]. As a result, the present study suggested
that:
H4: FCs are positively related to academics’ IoT
UB in EL.

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1379
2.1.5 Behavioral Intention and Academics’
IoT Usage Behavior
A person's level of dedication to a certain
action may be a sign of BI [14]. As a result, the
degree of academics’ dedication to accepting and
implementing IoT to achieve their educational
objectives may indicate their BI toward IoT
usage in EL [27]. Therefore, Hypothesis 5
implies:
H5: Behavioral intention is positively related to
academics’ IoT usage behavior.
2.1.6 Moderating of Gender and Experience
Learner characteristics are a crucial factor for
effective EL settings [42]. Understanding the
intended audience is essential while building EL.
Kwiek and Roszka [43] estimate that almost half
of all academics worldwide are women.
However, there hasn’t been abundant research on
how gender variations impact academics’
attitudes towards the usage of IoT in EL. The
trend of technology adoption and usage is
skewed toward one gender over the other [44].
Furthermore, it has been discovered that
people who use technology but are inexperienced
prefer minimum effort. They are more likely to
be swayed by the attitudes of people in their
social circle and are also less concerned with FC
[15]. Given that the academic participants of this
research used IoT voluntarily, the moderating
effects of gender and IoT experience were also
added to the research framework to improve the
model’s predictive validity. It is hypothesized
that experience with IoT leads to altered
perspectives of PE, EE, SI, and FC, which thus
impact differently upon the BI to use EL.
Henceforth, the subsequent hypotheses were
formulated:
H6. Gender moderates the relationships between
(a) PE, (b) EE, and (c) SI and academic’ BI to
use IoT in EL.
H7. Academics’ level of experience with IoT
moderates the relationships between (a) PE, (b)
EE, and (c) SI and their BI to use IoT in EL.
3. METHODOLOGY
3.1 Participants and Procedure
Academics from five Malaysian research
institutions (MRUs), namely UPM, UKM, UM,
USM, and UTM, were included in the study's
sample. The scale for data collection was created
using elements from the accessible literature.
The factors were assessed using 44 questions
collected from earlier investigations [15,45]. A
sample size of 50 is deemed inadequate for factor
analysis, 300 is tolerable, and 500 is very good,
while 1000 is excellent [46]. The present study
got 321 replies from 350 dispersed
questionnaires (91.71% response rate). The
questionnaire had two primary elements to assess
the theoretical model: (1) demographics of
respondents and (2) development measures of the
model. For this research, all components of the
original UTAUT were integrated and changed. A
5-point Likert scale was defined, ranging from 1
(strongly disagree) to 5 (strongly agree).
3.2 Measures
EE stands for the degree of comfort
associated with using technology. In line with
this, the initial seven items covered perceptions
of difficulty and usefulness in using IOT during
EL [15]. A sample item is as follows: ‘I find this
technology to be simple to use, and IoT in EL
would be easy for me to understand’ (α = 0.808).
Furthermore, a six-item scale that evaluated PE,
work fit, extrinsic motivation, relative benefit,
and technology-predicted output was used to
measure PE [47]. A sample item is ‘Using IoT
for EL helps me to do tasks quickly’ (α = 0.808).
Moreover, SI was measured using Venkatesh et
al.’s [15] ten-item scale. An example of an item
is as follows: ‘IoT is something that individuals
significant to me would advise me to use’ (α =
0.867). Next, eight items measured FC [15]. A
sample item is ‘I have the necessary resources to
implement IoT in education’ (α = 0.702). The BI
component was further measured using five
items [48]. A sample item is ‘I aim to adopt IoT
technologies during EL during the next few
months’ (α = 0.857). Finally, UB, defined as the
actual frequency of the usage of a particular
technology, was measured using eight items [15].
A sample item is ‘I intend to use IoT service in
the future’ (α = 0.814).
3.3 Questionnaire Development
With the assistance of three language
specialists, the questionnaire was translated into
Malay to evaluate the face and content validity
and to guarantee their adaptation to the regional
cultural environment. A pilot study was then
carried out on 30 academics to assess the

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1380
reliability of the instruments (SI, α= .76; EE, α =
.75; PE, α =.74, FC, α =.79, BI, α = .76, UB, α =
.78). Based on participant feedback, the
questionnaire was then further improved to
increase its face validity. The primary sample of
the study did not include any academics who
took part in the pilot study.
3.4 Demographics
The respondents who participated in the
study had an average age of 41 years (SD=2.87),
with the majority being males (N = 179, 56%). A
fraction of the respondents (71.02%) reported
having less than five years of experience in the
online teaching field. Moreover, most of the
respondents (N = 299, 93.2%) have Ph.D.
degrees. A fair amount of them (47.6%) were
also senior lecturers (see Table 1).
Table 1: Responders' backgrounds.
Sample Chrematistics Frequency (%) Mean SD
Age 41 2.87
Gender
Male 179 56
Female 142 44
Online teaching experience
<5 years 228 71.02
>5 years 93 28.9
Background in education
PhD 299 93.2
Master 22 6.8
Position in education
Professors 42 13.1
Associate professors 107 33.5
Senior lecturers 152 47.6
lecturers 18 5.8
In addition to acceptable levels of reliability,
the reliability test further revealed considerable
composite reliability for all construct items. The
loading for the PE build was less than 0.5. The
lowest loading can be eliminated if the extracted
average variance (AVE) is lower than the
threshold level [49]. Therefore, PE Item PE4 was
removed. Each scale has a Cronbach’s α score
between 0.652 and 0.902, which indicates
satisfactory reliability for each construct. The
lowest loading can be deleted if the AVE is less
than the normal level [50].
4. DATA ANALYSIS
The kurtosis and skew values, which are
between ±2, which reflect the bounds of
normality, validated the data’s normal
distribution in SPSS v.26. SEM analysis was
used in the investigation. SEM includes the
exploration of complex models such as
moderation and mediation. Moreover, using the
AVE and construct reliability, reiterates the high
validity and construct reliability (CR).
4.1 Descriptive statistics of constructs
Table 2 displays the means and standard
deviations for the study constructs. The results
reveal that FC is significantly above average for
the respondents, while EE appears to be a little
higher. On average, it is perceived that SI to use
of IoT among academics is above average
compared to PE.
Table 2: Descriptive statistics of latent constructs.
Constructs Mean SD
PE 3.654 0.567
SI 3.783 0.585
EE 4.06 0.569
FC 3.816 0.67
BI 3.75 0.671
UB 3.873 0.528
Note. PE= Performance Expectancy, SI = Social influence, FC =
Facilitating condition, EE= Effort Expectancy, BI = Behavioral
intention, UB= Usage behavior.

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1381
4.2 Data preparation
The measurement model was validated by a
reliable approach to demonstrate that certain
items replicate the unobserved constructs [51].
The outcomes of the CFA showed that the
measurement model was acceptable with strong
factor loadings for all of the items on the
predictable factors and commonality of each item
over 0.50. The results show that all the constructs
achieved convergent concept validity. The
number of items for each construct is listed in
Table 3 as follows: PE (5 items), SI (7 items),
FC (4 items), EE (4 items), BI (5 items), and UB
(3 items); altogether there are a total of 28 items.
The present study used AMOS 26 software to
examine the data. Convergent validity is
concluded for all constructs when the AVE is
more than 0.50 and the CRs for all scales are
better than 0.80.
Table 3. AVE and construct reliability of study instruments.
Construct Initial item Final No. of items AVE CR
PE 6 5 0.657 0.905
SI 10 7 0.538 0.890
EE 7 4 0.597 0.855
FC 8 4 0.607 0.860
BI 5 5 0.642 0.899
UB 8 3 0.576 0.800
Note. PE= Performance Expectancy, SI = Social influence, FC = Facilitating condition, EE= Effort
Expectancy, BI = Behavioral intention, UE= Usage behavior.
Kline (2016) suggested using model fit
indices such as the χ2/degree of freedom ratio
(CMIN/DF), the comparative fit index (CFI), the
Tucker–Lewis index (TLI), the goodness-of-fit
index (GFI), Incremental Fit Index (IFI), and
Normed Fit Index (NFI). The fit indices’ values
equal to or greater than 0.90 are considered
satisfactory [52]. Moreover, the model is
considered adequate if the root means squared
error of approximation, (RMSEA) is between
0.03 and 0.08. The model of the current study
had high fit indices: CMIN/DF = 2.028, p < 0.01,
CFI = 0.956, GFI = 0.903, IFI = 0.957, TLI =
0.950, NFI = 0.918, RMSEA = 0.049.
The correlations between constructs varied from
0.293 to 0.657, as shown in Table 4. When R2 is
smaller than AVE, discriminant validity is
declared. (Henseler et al., 2015). Every AVE
value exceeded the values of R2, demonstrating
the excellent discriminant validity of the
constructs.
Table 4: AVE and R2 for study instruments
No. Construct 1 2 3 4 5 6
1 PE 0.657
2 SI 0.398 0.538
3 EE 0.293 0.405 0.607
4 FC 0.454 0.452 0.407 0.597
5 BI 0.521 0.533 0.436 0.544 0.642
6 UB 0.341 0.462 0.344 0.381 0.459 0.576
Note. PE= Performance Expectancy, SI = Social influence, FC = Facilitating
condition, EE = Effort Expectancy, BI = Behavioral intention, UB= Usage behavior.
5. RESULT AND DISCUSSION
5.1 Structural Model
PE, SI, FC, EE, and BI are exogenous
variables in this model, whereas BI and UB are
endogenous variables. As seen in Figure 1, PE
had a significant association with BI (β = 0.324,
p-value = 0.000). Thus, H1 was supported. This
outcome is consistent with earlier research [53],
in which PE was discovered to significantly
impact the intention to use new technologies by
academics. Therefore, this finding suggests that
academics who anticipate adopting IoT as a
helpful EL tool are more likely to have the
intention to utilize IoT.
This study also found that SI had a major
impact on BI among Malaysian academics (β =
0.497, p = 0.000). Hence, H2 was supported. In

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1382
particular, the results of this research are in line
with the idea that academics from the Southeast
Asian context are more influenced by the SI
factor in their intention to use IoT, unlike other
counterparts [54]. Their decision to use IoT may
be readily influenced by peer or media pressure.
Hence, IoT service providers need to consider
the SI factor to encourage the adoption of IoT.
The findings further support the UTAUT [15], in
which SI is positioned as a key factor. This
finding can be attributed to the comparatively
significant effect of close co-workers and
acquaintances in educational environments.
Furthermore, Zhao et al. [55] found that people’s
opinions mattered when selecting whether to
embrace new technologies in the collectivist
cultures of Asian countries.
Furthermore, the findings showed that EE is a
significant predictor of BI in adopting EL (β =
0.192, p = 0.000), supporting the conclusions of
prior research, such as that of Alammary et al.
[56] on academics from Saudi Arabia. Therefore,
H3 was accepted. Additionally, the results of the
path coefficient show that the FC (β = 0.233,
t=2.939, p = 0.000) is significantly related to UE,
thus, supporting H4. The findings align with
other studies, such as that of Paul et al. [57] on
Ugandan academics. Paul et al. [57]
demonstrated that FC improves the UB to adopt
modern technologies for EL. Additionally, the
results of this study also confirmed the
association between BI and UB among
academics adopting IoT for EL (β = 0.451, p =
0.000), which supports Venkatesh et al.’s [15]
UTAUT. Finally, the exogenous constructs
explained 76.3% of the variance in BI, and BI
was responsible for 51% of the variance in UB
(Table 5 and Figure 1).
Table 5: Unstandardized and standardized regression weights in the hypothesized path model.
Hypothesis Relationship β S. E Beta C.R p Decision
H1 BI ← PE
0.324
0.050
0.316 6.537
*** SU
H2 BI ←SI 0.497 0.057 0.500 8.675 *** SU
H3 BI ←EE 0.192 0.050 0.184 3.885 *** SU
H4 UE ← FC 0.233 0.057 0.288 4.113 *** SU
H5 UE ←BI 0.451 0.068 0.486 6.671 *** SY
Note. PE= Performance Expectancy, SI = Social influence, FC = Facilitating condition, EE= Effort Expectancy, BI = Behavioral
intention, UE= Usage of e-learning, SU= Supported.
Figure 1. Study’s structural model
Further investigation on the moderating role of
gender on the intention to use IoT and its
relations with the four factors of the model (as
indicated in H6) was performed. Findings
revealed that PE, as proposed in H6a was
significantly different between males (β = 0.440,
p< 0.05) and females (β = 0.250, p< 0.05). This
finding is consistent with that of Venkatesh et al.
[15] and contrary to that of Gupta et al. [58].
However, the results also revealed that gender
did not moderate the relation between the other
two factors (SI and EE) and BI. Thus, H6b and
H6c were rejected.
Moreover, the results showed that experience
moderated the relation between the factors (PE,
SI, and EE) and BI. Thus, H7a, H7b, and H7c were
supported. Thus, in line with other studies [59],
academics’ previous experience using IoT
technologies will help them. This is because IoT
provides greater performance, is easy to use, is

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1383
welcomed by their colleagues, and previously
enabled their creative abilities to be unleashed.
IoT is relatively new in Malaysia, so users’
experience with IoT would play a critical role.
Table 6: Moderating effects of gender and experience level.
Hypothesized paths Gender t p Experience t p
Male
(n =
179)
Female
(n = 142)
5>
(n = 228)
5<
(n = 93)
BI ← PE 0.440
*
0.250
*
2.093 0.037 0.237
*
0.616
*
2.157 0.032
BI ←SI 0.243 0.175 0.619 0.536 0.692
*
0.168
*
2.837 0.005
BI ←EE 0.354 0.555 1.666 0.096 0.469
*
0.055
*
2.200 0.028
Note. PE= Performance Expectancy, SI = Social influence, FC = Facilitating condition, EE= Effort Expectancy, BI = Behavioral
intention. *p < 0.05.
6. CONCLUSIONS
This study, which was directed by the
UTAUT, provides a thorough knowledge of the
variables influencing IoT adoption in EL among
academics. Practitioners and academics will find
it useful to use the verified adoption model to
investigate how widely used technology is
becoming to permeate daily life. In this study,
the importance of IoT was discussed, along with
a particular emphasis on EL. SI was found to be
the most significant predictor among the
antecedents of the BI toward IoT technologies.
Furthermore, to fill a clear gap in the literature,
this study evaluated the effects of gender and
experience on the factors influencing academics’
BI toward IoT. Findings showed that the PE’s
influence on BI varied significantly depending
on participants’ gender. Moreover, experience
had a moderating effect on all other factors
influencing BI.
We expect that this study can help HEIs get
more scientific understanding of EL utilizing
IoT. The fact of IoT adoption for digital learners
should be acknowledged by educators and
curriculum writers in the twenty-first century.
For the purpose of enhancing the academic staff's
knowledge and skills, HEIs are required to
provide lectures and courses. Thus, users’
perceptions of EE to use IoT can be improved,
which may evoke their intention to use IoT.
Although FC has no direct effect on the usage of
IoT, its intention should not be ignored.
Academics anticipate an IoT application that is
helpful and simple to use. Thus IoT service
providers are recommended to supply consumers
with this kind of application and other related
services. By creating a user-friendly and reliable
IoT interface and platform, IoT can deliver more
efficient and effective services. Additionally,
HEIs should consider the SI factor’s impact on
academics’ embrace of IoT. Even if HEIs cannot
alter it, reference groups may be inferred as
crucial to the spread of IoT. Therefore, HEIs
must find early adopters and encourage their use
of IoT services so that they may act as a model
for future efforts to promote broad
dissemination. Additionally, universities in
Malaysia must integrate IoT into one or more
current courses, such as programming,
networking, ubiquitous computing, data mining
and acquisition, computer security, embedded
systems, databases, and others to meet industrial
demands and close the gap.
7. LIMITATIONS AND DIRECTIONS FOR
FUTURE RESEARCH
There are some limitations with this study.
The recommended approach, at start, did not
account for real user behavior. However, a large
body of empirical evidence backs up the causal
link between BI and UB. A longitudinal study
design provides more information than a cross-
sectional research strategy, researchers should
mention in their second point. Thirdly, although
the present study intended to investigate the
academic acceptability of IoT in EL, Malaysia
was the only country of interest. Future research
should widen its focus to analyze IoT
acceptability in other developing nations. This
will ensure further validation of the model
proposed in this study. This is because diverse
societal attitudes, governmental restrictions, and
conventions may influence the model differently.
Furthermore, future research should replicate the
present study using various IoT product
categories to increase the research model’s
generalizability. Qualitative research is also
suggested for future studies to better understand
customers’ attitudes towards IoT.

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1384
REFERENCES
[1] Gerasimova VG, Melamud MR, Tutaeva
DR, Romanova YD, Zhenova NA. The
adoption of e-learning technology at the
faculty of distance learning of plekhanov
russian university of economics. Journal of
Social Studies Education Research
2018;9:172–188.
[2] Theng Y-L, Ei Tun E, Zaw MMH, Cho
SYY, Miao C, Kan M-Y, et al. An empirical
study of students’ perceptions on e-learning
systems. Proceedings of the 2nd
International Convention on Rehabilitation
Engineering & Assistive Technology, 2008,
p. 245–249.
[3] Torrisi G, Davis G. Online learning as a
catalyst for reshaping practice–the
experiences of some academics developing
online learning materials. International
Journal for Academic Development
2000;5:166–176.
[4] Meskhi B, Ponomareva S, Ugnich E. E-
learning in higher inclusive education:
needs, opportunities and limitations.
International Journal of Educational
Management 2019;33:424–37.
[5] Yilmaz R. Knowledge sharing behaviors in
e-learning community: Exploring the role of
academic self-efficacy and sense of
community. Computers in Human Behavior
2016;63:373–382.
[6] Kim KJ. Interacting socially with the
Internet of Things (IoT): effects of source
attribution and specialization in human–IoT
interaction. Journal of Computer-Mediated
Communication 2016;21:420–435.
[7] Ashton K. That ‘internet of things’ thing.
RFID Journal 2009;22:97–114.
[8] Khanna A, Kaur S. Internet of things (IoT),
applications and challenges: a
comprehensive review. Wireless Personal
Communications 2020;114:1687–1762.
[9] Rafique W, Qi L, Yaqoob I, Imran M,
Rasool RU, Dou W. Complementing IoT
services through software defined
networking and edge computing: A
comprehensive survey. IEEE
Communications Surveys & Tutorials
2020;22:1761–1804.
[10] Madni SHH, Ali J, Husnain HA, Masum
MH, Mustafa S, Shuja J, et al. Factors
Influencing the Adoption of IoT for E-
Learning in Higher Educational Institutes in
Developing Countries. Frontiers in
Psychology 2022;13:3415.
[11] Rahmani AM, Ali Naqvi R, Hussain Malik
M, Malik TS, Sadrishojaei M, Hosseinzadeh
M, et al. E-Learning Development Based on
Internet of Things and Blockchain
Technology during COVID-19 Pandemic.
Mathematics 2021;9:3151.
[12] Mathivanan SK, Jayagopal P, Ahmed S,
Manivannan SS, Kumar PJ, Raja KT, et al.
Adoption of e-learning during lockdown in
India. International Journal of System
Assurance Engineering and Management
2021:1–10.
[13] Leong Y-M, Letchumanan C. Effective
learning in higher education in Malaysia by
implementing internet of things related tools
in teaching and introducing IoT courses in
curriculum. 2019 1st International
Conference on Artificial Intelligence and
Data Sciences (AiDAS), Ipoh, Malaysia:
IEEE; 2019, p. 152–157.
[14] Aziz F, Rasdi RM, Rami AAM, Razali F,
Ahrari S. Factors Determining Academics’
Behavioral Intention and Usage Behavior
towards Online Teaching Technologies
During Covid-19: An Extension of the
UTAUT. International Journal of Emerging
Technologies in Learning 2022;17.
[15] Venkatesh V, Morris MG, Davis GB, Davis
FD. User acceptance of information
technology: Toward a unified view. MIS
Quarterly 2003;27:425–478.
[16] Kenno S, Lau M, Sainty B, Boles B.
Budgeting, strategic planning and
institutional diversity in higher education.
Studies in Higher Education 2021;46:1919–
1933.
[17] Sani N, Mostafavi Z, Keykhah A, Ebeadi R.
Explanation of retention rates based on the
types of student interaction in the e-learning
context. Information and Communication
Technology in Educational Sciences
2019;10:27–48.
[18] Gerdeman D. Internet of Things in
healthcare keeps patients healthy, safe 2016.
http://internetofthingsagenda.techtarget.com/
feature/Internet-of-Things-in-
healthcarekeeps-patients-healthy-safe
(accessed August 27, 2021).
[19] Sivakumar D, Jusman MFB, Mastan A. A
case study review: Future of Internet of
Things (IoT) in Malaysia. ASCENT
International Conference Proceedings–

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1385
Information Systems and Engineering, Tam
Ky City, Vietnam: 2017, p. 23–24.
[20] Rosenberg MJ. E-learning: Strategies for
Delivering Knowledge in the Digital. New
York, NY: McGraw-Hill; 2001.
[21] Maatuk AM, Elberkawi EK, Aljawarneh S,
Rashaideh H, Alharbi H. The COVID-19
pandemic and E-learning: challenges and
opportunities from the perspective of
students and instructors. Journal of
Computing in Higher Education
2022;34:21–38.
[22] Long KC. E-learning, information
technology, and student success in higher
education. Oxford Research Encyclopedia of
Business and Management 2017.
[23] Mohan A. Application and usefulness of
internet of things in information technology.
International Journal of Engineering and
Management Research (IJEMR) 2018;8:57–
61.
[24] Ab Jalil H, Rajakumar M, Zaremohzzabieh
Z. Teachers’ Acceptance of Technologies
for 4IR Adoption: Implementation of the
UTAUT Model. International Journal of
Learning, Teaching and Educational
Research 2022;21:18–32.
[25] Şoavă G, Sitnikov C, Dănciulescu D.
Optimizing quality of a system based on
intelligent agents for e-learning. Procedia
Economics and Finance 2014;16:47–55.
[26] Zanella A, Bui N, Castellani A, Vangelista
L, Zorzi M. Internet of things for smart
cities. IEEE Internet of Things Journal
2014;1:22–32.
[27] Bayani M, Leiton K, Loaiza M. Internet of
things (IoT) advantages on e-learning in the
smart cities. International Journal of
Development Research 2017;7:17747–
17753.
[28] Zahedi MH, Dehghan Z. Effective E-
learning utilizing Internet of Things. 2019
13th Iranian and 7th National Conference on
e-Learning and e-Teaching (ICeLeT), IEEE;
2019, p. 1–6.
[29] Elshafey A, Saar CC, Aminudin EB,
Gheisari M, Usmani A. Technology
acceptance model for Augmented Reality
and Building Information Modeling
integration in the construction industry.
Journal of Information Technology in
Construction 2020;25:161–172.
https://doi.org/10.36680/j.itcon.2020.010.
[30] Francisco K, Swanson D. The supply chain
has no clothes: Technology adoption of
blockchain for supply chain transparency.
Logistics 2018;2:1–13.
https://doi.org/10.3390/logistics2010002.
[31] Sung H-N, Jeong D-Y, Jeong Y-S, Shin J-I.
The relationship among self-efficacy, social
influence, performance expectancy, effort
expectancy, and behavioral intention in
mobile learning service. International
Journal of U-and e-Service, Science and
Technology 2015;8:197–206.
[32] Almaiah MA, Al Mulhem A. Analysis of the
essential factors affecting of intention to use
of mobile learning applications: A
comparison between universities adopters
and non-adopters. Education and
Information Technologies 2019;24:1433–
1468.
[33] Al-Emran M, Arpaci I, Salloum SA. An
empirical examination of continuous
intention to use m-learning: An integrated
model. Education and Information
Technologies 2020;25:2899–2918.
[34] Lu L, Yuan Y, Chen X, Li Z. A Hybrid
Recommendation Method Integrating the
Social Trust Network and Local Social
Influence of Users. Electronics 2020;9:1–27.
[35] Jain V, Jain P. From Industry 4.0 to
Education 4.0: acceptance and use of
videoconferencing applications in higher
education of Oman. Journal of Applied
Research in Higher Education 2021;ahead-
of-print:1–20.
https://doi.org/10.1108/JARHE-10-2020-
0378.
[36] Gao L, Bai X. A unified perspective on the
factors influencing consumer acceptance of
internet of things technology. Asia Pacific
Journal of Marketing and Logistics
2014;26:211–31.
[37] Venkatesh V, Thong JY, Xu X. Consumer
acceptance and use of information
technology: extending the unified theory of
acceptance and use of technology. MIS
Quarterly 2012;36:157–178.
[38] Jang H, Koh J. The Influence of the
Perceived Value of the Elderly on the
Intention of Smart Device Internet Usage: A
Lifelong Learning Perspective for the
Elderly. Journal of Practical Engineering
Education 2019;11:87–103.
[39] Kaliisa R, Palmer E, Miller J. Mobile
learning in higher education: A comparative
analysis of developed and developing
country contexts. British Journal of
Educational Technology 2019;50:546–561.

Journal of Theoretical and Applied Information Technology
28th February 2023. Vol.101. No 4
© 2023 Little Lion Scientific
ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195
1386
[40] Oke A, Fernandes FAP. Innovations in
teaching and learning: Exploring the
perceptions of the education sector on the
4th industrial revolution (4IR). Journal of
Open Innovation: Technology, Market, and
Complexity 2020;6:1–22.
[41] Amadin FI, Obienu AC, Osaseri RO. Main
barriers and possible enablers of Google
apps for education adoption among
university staff members. Nigerian Journal
of Technology 2018;37:432–439.
[42] Liaw SY, Tan KK, Wu LT, Tan SC, Choo
H, Yap J, et al. Finding the right blend of
technologically enhanced learning
environments: randomized controlled study
of the effect of instructional sequences on
interprofessional learning. Journal of
Medical Internet Research 2019;21:1–10.
https://doi.org/10.2196/12537.
[43] Kwiek M, Roszka W. Gender disparities in
international research collaboration: A study
of 25,000 university professors. Journal of
Economic Surveys 2021;35:1344–1380.
[44] Ameen N, Tarhini A, Shah MH, Madichie
NO. Employees’ behavioural intention to
smartphone security: A gender-based, cross-
national study. Computers in Human
Behavior 2020;104:106184.
[45] Garone A, Pynoo B, Tondeur J, Cocquyt C,
Vanslambrouck S, Bruggeman B, et al.
Clustering university teaching staff through
UTAUT: Implications for the acceptance of
a new learning management system. British
Journal of Educational Technology
2019;50:2466–2483.
[46] Goretzko D, Pham TTH, Bühner M.
Exploratory factor analysis: Current use,
methodological developments and
recommendations for good practice. Current
Psychology 2021;40:3510–3521.
[47] Zhang Z, Cao T, Shu J, Liu H. Identifying
key factors affecting college students’
adoption of the e-learning system in
mandatory blended learning environments.
Interactive Learning Environments 2020:1–
14.
[48] Rahi S, Ghani M, Ngah A. A structural
equation model for evaluating user’s
intention to adopt internet banking and
intention to recommend technology.
Accounting 2018;4:139–152.
[49] Henseler J, Ringle CM, Sarstedt M. A new
criterion for assessing discriminant validity
in variance-based structural equation
modeling. Journal of the Academy of
Marketing Science 2015;43:115–135.
[50] Hair JF, Gabriel M, Patel V. AMOS
covariance-based structural equation
modeling (CB-SEM): Guidelines on its
application as a marketing research tool.
Brazilian Journal of Marketing 2014;13:44–
55.
[51] Hair J, Black W, Babin B, Anderson R.
Multivariate data analysis. Englewood
Cliffs, NJ: Prentice Hall; 2010.
[52] Kline RB. Principles and practice of
structural equation modeling. 4rd ed. New
York, NY: Guilford publications; 2016.
[53] Pham T, Dang L, Le T. Factors affecting
teachers’ behavioral intention of using
information technology in lecturing-
economic universities. Management Science
Letters 2020;10:2665–2672.
[54] Lin C-Y, Huang C-K, Ko C-J. The impact of
perceived enjoyment on team effectiveness
and individual learning in a blended learning
business course: The mediating effect of
knowledge sharing. Australasian Journal of
Educational Technology 2020;36:126–141.
[55] Zhao J, Ye B, Yu L. Peer Phubbing and
Chinese College Students’ Smartphone
Addiction During COVID-19 Pandemic:
The Mediating Role of Boredom Proneness
and the Moderating Role of Refusal Self-
Efficacy. Psychology Research and
Behavior Management 2021;14:1725.
[56] Alammary A, Alshaikh M, Alhogail A. The
impact of the COVID-19 pandemic on the
adoption of e-learning among academics in
Saudi Arabia. Behaviour & Information
Technology 2021:1–23.
[57] Paul KJ, Musa M, Nansubuga AK.
Facilitating condition for E-learning
adoption—Case of Ugandan universities.
Journal of Communication and Computer
2015;12:244–249.
[58] Gupta B, Dasgupta S, Gupta A. Adoption of
ICT in a government organization in a
developing country: An empirical study. The
Journal of Strategic Information Systems
2008;17:140–154.
[59] Suki NM, Suki NM. Determining students’
behavioural intention to use animation and
storytelling applying the UTAUT model:
The moderating roles of gender and
experience level. The International Journal
of Management Education 2017;15:528–
538.