IJLTER.ORG Vol 21 No 1 January 2022

International Journal
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Vol.21 No.1

International Journal of Learning, Teaching and Educational Research
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VOLUME 21 NUMBER 1 January 2022
Table of Contents
The Influence of Career Commitment and Workload on Academics’ Job Satisfaction: The Moderating Role of a
Supportive Environment .......................................................................................................................................................1
Jamali Janib, Roziah Mohd Rasdi, Zeinab Zaremohzzabieh
Teachers' Acceptance of Technologies for 4IR Adoption: Implementation of the UTAUT Model ............................ 18
Habibah Ab Jalil, Manjula Rajakumar, Zeinab Zaremohzzabieh
Barriers of Online Education in the New Normal: Teachers’ Perspectives...................................................................33
Gino G. Sumalinog
Organizing Students’ Independent Work: An Approach for Graduate and Undergraduate Students .................... 51
Aleksandra Zakharova, Elena Soboleva, Galia Biserova
A Socio-Cognitive Perspective on the Factors Affecting Malaysian Business Students’ Learning when Spoken in
English in a Second-Language Classroom......................................................................................................................... 67
Siti Amirah Ahmad Tarmizi, Najihah Mahmud, Amaal Fadhlini Mohamed, Ariezal Afzan Hassan, Nazatul Syima Mohd
Nasir, Nor Hazwani Munirah Lateh
Delving into Personalisation Behaviours in a Language MOOC ................................................................................... 92
Napat Jitpaisarnwattana, Pornapit Darasawang, Hayo Reinders
The Impact of Stephen Covey’s 7 Habits on Students’ Academic Performance during the COVID-19 Pandemic
............................................................................................................................................................................................... 109
Chee Kooi Lian, Tan Kim Hua, Nur-Ehsan Mohd Said
The Impact of Coworker and Supervisor Support on Stress among Malaysian School Teachers during the
COVID-19 Pandemic .......................................................................................................................................................... 127
Lin Dar Ong, Faizul Adib bin Sulaiman Khan
Effectiveness of Contextualization in Science Instruction to Enhance Science Literacy in the Philippines: A Meta-
Analysis................................................................................................................................................................................ 140
Marchee T. Picardal, Joje Mar P. Sanchez
The Dynamics of Design- Knowledge Construction: The Case of a Freshman Architectural-Design Studio in
Egypt .................................................................................................................................................................................... 157
Nouran Mohammed Haridy, Marwa Hassan Khalil, Ramy Bakir
Mathematics Learners’ Perceptions of Emergency Remote Teaching and Learning during the COVID-19
Lockdown in a Disadvantaged Context........................................................................................................................... 179
Brantina Chirinda, Mdutshekelwa Ndlovu, Erica Spangeberg
Factors Impacting Heads of Department’s Management of Teaching and Learning in Primary Schools: A South
African Perspective............................................................................................................................................................. 195
Pule David Kalane, Awelani Melvin Rambuda

The Influence of Teacher Efficacy on 21st Century Pedagogy...................................................................................... 217
Nur Syarima Shafiee, Mariny Abdul Ghani
Developing a Multimodal Interactive Learning Environment to Enhance the Reading Comprehension of Grade 4
Students in the UAE Public Schools.................................................................................................................................231
Wedad Alhabshi, Hamdy A. Abdelaziz
The Gap between Perceived and Achieved English Communication Needs of Saudi Management and Business
Administration Students: An ESP Paradigm .................................................................................................................. 256
Abdullah Ahmad M. Alfaifi, Mohammad Bahudhailah, Mohammad Saleem
Evaluation of University Review Program for Teachers’ Licensure Examination: A Transformative Mixed
Methods Study Using Bourdieu-Scheerens Framework................................................................................................ 277
Fernigil L. Colicol, Charmine Z. Puig, Shielamar J. Judan
Online Teaching Barriers, Motivations, and Stress of In-Service Teachers: Renewed Challenges and Opportunities
with Future Perspectives ................................................................................................................................................... 301
Hyun Seon Ahn, Pauline Anne Therese M. Mangulabnan, Jeesoo Lee
Al-Qur’an Literacy: A Strategy and Learning Steps in Improving Al-Qur’an Reading Skills through Action
Research ............................................................................................................................................................................... 323
Udin Supriadi, Tedi Supriyadi, Aam Abdussalam
Using Genially Games for Enhancing EFL Reading and Writing Skills in Online Education.................................. 340
Luz Castillo-Cuesta

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©Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License (CC BY-NC-ND 4.0).
Vol. 21, No. 1, pp. 1-17, January 2022
https://doi.org/10.26803/ijlter.21.1.1
Received Oct 13, 2021; Revised Dec 13, 2021; Accepted Dec 20, 2021
The Influence of Career Commitment and
Workload on Academics’ Job Satisfaction: The
Moderating Role of a Supportive Environment
Jamali Janib , Roziah Mohd Rasdi*
, Zeinab Zaremohzzabieh
Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, Malaysia
Abstract. This paper serves to study the influences of career commitment
and workload on job satisfaction among academics in higher education.
We investigated whether a supportive environment is a significant
moderator between workload and job satisfaction. For this cross-sectional
study, the stratified random sampling method yielded 191 academics
from five research universities in Malaysia. Partial least squares-
structural equation modeling (PLS-SEM) showed that high levels of
career commitment correspond with high levels of satisfaction at work of
academics. Also, a greater workload diminishes job satisfaction among
academics. The analysis of the interaction-moderation dynamics showed
that a supportive environment reduces workload effects on academics’
job satisfaction. This study contributes to confirming the important roles
of career commitment and workload in predicting job satisfaction. It also
expands literature on the buffering role of a supportive environment in
the interaction between workload and job satisfaction among academics.
Keywords: academic staff; career commitment; job satisfaction;
supportive environment; workload
1. Introduction
Higher education is the driving force in closing socio-economic inequalities
within broad national development goals. The quality of higher education is
integral to the human capital value that determines a country’s prosperity.
Countries are paying closer attention to higher education delivery, as indicated
by institutional and technological reforms spearheaded by intellectually esteemed
and dedicated faculties. In addition to meeting institutional requirements, job
satisfaction should be promoted among academics to ensure that each university
is the beneficiary of the positive impact on employees’ experience at work. Indeed,
academics, as success determiners, walk and talk the university’s vision and
mission. Escardíbul and Afcha (2017) concurred that high-quality faculty
* Corresponding author: Roziah Mohd Rasdi; roziahmohdrasdi@gmail.com

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http://ijlter.org/index.php/ijlter
members contribute to the success of a supportive edcational system. Therefore,
academics’ job satisfaction should be given utmost attention.
Job satisfaction is significant when examining the performance levels of
employees and organizations. As succinctly put forth by de Lourdes Machado-
Taylor et al. (2016), faculty members who are satisfied and well-motivated tend to
enhance their reputation as academics, representing national and institutional
standards, as well as impacting student learning in the classroom. Without a
doubt, the academic workforce is the backbone behind every successful
university. Previous studies have equally shown that job satisfaction directly
influences the retention of academics in higher education (De Sousa Sabbagha et
al., 2018). The norm is that satisfied employees pose reduced absenteeism and
turnover issues to organizations and chart higher productivity (Singh & Jain,
2013). In contrast, unsatisfied employees are less productive, leading to constant
thoughts of moving on to a better job. Liu et al. (2019) called on organizations to
take note of human resource goals, which include the job satisfaction of
academics.
Given the importance of job satisfaction among academics, extant literature on job
satisfaction predictors was reviewed, focusing on academic context. One of the
most important aspects that we need to consider for academics could be
increasing their job satisfaction through positive career commitment (Gendron et
al., 2016). Career commitment to a given task allows employees to explore the
meaning of work, and in the process, offers some recovery from emotional
exhaustion. Positively, career commitment could establish the job crafting process
in which academics could create the meaning of their job at emotional, social, and
institutional level (Amin et al., 2017). Cerci and Dumludag (2019) highlighted that
this intrinsic motivation of academics contributes to a high level of commitment,
not so much attributed to the extrinsic factors of remuneration and workplace
conditions. Another employee job satisfaction determiner is workload assigned.
Literature has shown that in recent decades, the research on workload issues has
gained momentum and undergone scrutiny. Higher education institutions
around the world have noted it as a serious concern (Tight, 2010).
In the distant past, the working conditions and requirements of university
teaching were relatively less demanding, such that employees enjoyed less
academic constraints and pressure (Mudrak et al., 2018). Following global
changes, universities have undergone a shift in quality and standards, resulting
in managing higher expectations on performance (Wolf et al., 2021). Without a
doubt, academics’ performance is now more complex, characterized by the
function of universities in the duality of generating and transmitting knowledge
through various teaching and research endeavors (Houston et al., 2006).
According to Akob (2016), there is a significant connection in the mix of workload,
work ethics of educators and job satisfaction, and work execution performance.
Besides, work overload impacts educators’ job satisfaction negatively (Imondi,
2011), where teachers have commonly professed that an excess of workload
strongly relates to low performance. In the same vein, lecturers subjected to
excessive workload in the form of academic activities have been reported to

3
experience lower job satisfaction (Sabagh et al., 2018). Song et al.’s (2013) study
further validated that excessive workload is the main cause of defusing job
satisfaction. Ahmad et al. (2015) researched pharmacy academics’ level of
workload and job satisfaction in India’s public and private universities. More than
half (57.9%) of the cohort professed their satisfaction over the amount of workload
they have. Generally, private-sector faculty members have reported that a greater
burden of teaching load resulted in diminishing satisfaction levels.
Past researchers have advocated the use of specific resources to manage the
negative effects of heavy workload (Ahmad et al., 2015). One of the resources that
has been robustly examined is how a supportive environment moderates the
interaction between workload and job satisfaction (Marsaditha, 2017). It makes
sense for organizations to maintain a learning and working ecosystem that fosters
a valued workforce (Caldana et al., 2021). Organizations are obligated to provide
a sustainable supportive environment that nurtures employees’ positive growth
and performance outcomes (Newman et al., 2018). Accordingly, “organizational
support” is the vital element for employee performance, characterized as an
individual’s effort, support, and ability (Laihonen & Mäntylä, 2017). Researchers
have found that a workplace ecosystem that provides a supportive camaraderie
results in positive employee outcomes, in particular job satisfaction (Berberoglu,
2018).
It is interesting to note that although the value of a supportive environment is
widely recognized and researched, there is a resounding gap in the investigation
of its effect on the interactions between workload and job satisfaction, more so in
faculty settings. Responding to this, this study aimed to provide empirical
evidence of the benefits of a supportive environment among academics. We
examined if a supportive environment could moderate the relationship between
workload and job satisfaction. Therefore, this study aims to study the
performance of academics by investigating: (i) the link between career
commitment and workload with job satisfaction, and (ii) the moderating role of a
supportive environment on the relationship between workload and job
satisfaction.
2. Theoretical Background and Hypothesis Testing
In this study, Herzberg’s (1959) two-factor theory of motivation is utilized to
determine which motivational factors are linked to job satisfaction among
academics in Malaysian research universities. Herzberg’s two-factor theory has
been widely used in employee satisfaction research (Alrawahi et al., 2020).
According to Herzberg’s theory of motivation, there are two categories of
motivating factors applied to the workplace – satisfiers and dissatisfiers
(Herzberg, 1966). According to this theory, in order to increase productivity,
satisfiers and dissatisfiers must be identified and addressed. A previous study
has revealed that a heavy workload is a major source of dissatisfaction in
organizations (Halder, 2018). Employees, according to Herzberg et al. (1959), are
dissatisfied with the fulfilling of lower order requirements at work, such as those
related to minimum pleasant working conditions. On the other hand, other
studies have shown that recognition for high commitment to do something

4
meaningful and a supportive environment in organizations are the main sources
of satisfaction (Agbozo et al., 2017; Indarti et al., 2017). The motivators provide
positive satisfaction, arising from intrinsic conditions of the job itself.
Acknowledging this theory, workload was identified as a negative factor of job
satisfaction in this study, while career commitment and a supportive
environment were identified as positive ones.
2.1 Career Commitment and Job Satisfaction
Dorenkamp and Ruhle (2019) defined career commitment as the level of desire to
work in a certain field. Initially, career commitment was thought to be a
professional obligation for professionals. As defined by Hall et al. (2018), a career
is a set of events and activities associated with a person’s life-long employment.
Thus, the notion of career dedication has shifted from professionals to anybody
who establishes a career. It was further characterized as the emotional notion of
linking career commitment with a connection to one’s career objectives, the
emotional concept of equating oneself with the work required in a specific area,
and the capacity to persevere in pursuing career goals in following research (Kim
et al., 2020). In sum, career commitment refers to a strong psychological
attachment to one’s present field of work and a firm psychological mindset of
continuing to do a series of duties relevant to that profession.
Job satisfaction can be defined as the employee’s subjective feelings towards how
satisfied they are at the workplace based on their state of physical and
psychological well-being (Hsiao & Lin, 2018). Indeed, job satisfaction is
determined as a basic and principal factor that can be the main cause of
performance, behavior, and staff reactions at the workplace (Hee et al., 2019).
Accordingly, Choi and Chiu (2017) suggested the possibility of a link between an
employee’s work satisfaction and commitment to their career. Xie et al. (2017)
believed that an individual’s positive attitude towards career identification would
improve their job satisfaction and thus reduce their turnover intention. On this
basis, Duffy et al. (2017) showed that the level of professional commitment is
directly related to the employee’s satisfaction at work. If an employee has a high
degree of identification with their occupation, their feelings about work would
not be affected by external conditions such as salary, promotion, and so on.
Therefore, we formulated the following hypothesis:
H1: Career commitment has a positive effect on job satisfaction among academics.
2.2 Workload and Job Satisfaction
Inegbedion et al. (2020) defined employee workload as the perceived
relationship between the volume of mental processing or resources required and
the completion of a task. Researchers have provided empirical evidence that
workload affects job performance and satisfaction (Liu & Lo, 2018). Osifila and
Aladetan (2020) studied the workload of lecturers at Adekunle Ajasin University.
They found that excessive workload assigned to lecturers reduced their job
satisfaction causing an adverse effect on performance. Increased workload
intensity thus hampers academics’ work performance. Liu and Lo (2018) also
determined an important relationship between workload, news autonomy, and

5
burnout. The researchers reported a significant negative relationship between the
interaction of burnout and job satisfaction that affect turnover intention
significantly. Kenny (2018) observed that when workload and pressure are
increased, academics’ job satisfaction diminishes. A mounting workload has been
observed as the main contributor to stress, against the backdrop of an absence of
recognizing effort being put in. Unsurprisingly, demotivation and poor work
performance ensue. It is well recognized that academics are motivated to deliver
their core skills of teaching and research. However, being subjected to obstacles,
the pursuit of academic interests is hindered, thus significantly affecting overall
job satisfaction (Kenny, 2018). Given these empirical findings, we formulated the
following hypothesis:
H2: Workload has a negative effect on job satisfaction among academics.
2.3 Moderating Effects of Supportive Environment
A supportive environment is characterized as a workplace ecosystem that hosts
supervisory or peer support. It also has elements of constraints and opportunities
for individuals to perform learned skills as they work (Bibi et al., 2018). Within a
supportive environment, employees enjoy support and encouragement from
peers and the management. Researchers have identified support from
supervisors, the organization, and peers as factor affecting the work environment
(Chong & Thi, 2020). The legacy theories of organizational and social support have
promoted how “organizational support” establishes affective commitment
among employees, strengthening the employees’ emotional bond towards their
organization (Suifan et al., 2018). Given this, it makes sense for higher education
institutions to nurture a supportive environment to meet diverse sectoral
challenges in the present and future.
It has been observed that employees in a supportive environment enjoy a boost of
interest towards their job, which translates into improved productivity (Prieto &
Pérez-Santana, 2014). In addition, it provides valuable inputs for employees
regarding desired workplace behavior, which also promotes innovative work
behavior. A notable study reported that excessive workload coupled with vague
or opposing role demands inevitably creates undesirable work experiences. In
terms of supervisory support, employees respond positively to some degree of
work practice that calls for their self-directedness and autonomy (Clarke, 2015).
Employees who are highly satisfied with their jobs enjoy various aspects of their
jobs and meaningful friendships with co-workers. At the workplace, an
employee’s capacity to build supportive relationships is one of the requisites of a
productive environment (Clarke, 2015).
On the contrary, the act of organizations extending support may be subjected to
negative reactions from employees. According to social exchange theorists,
employees establish relationships if they deem the benefits offered to be
worthwhile and administered fairly (Ali et al., 2020). However, in a highly
demanding environment, it is more likely that valuable benefits and fair
conditions will be violated. Employees competing in highly demanding jobs can
account for their stress as a cost of investment incurred from staying in their work

6
organization. When job pressure is high, these employees tend to view their
organization’s supportive actions negatively because they perceive them as being
in the self-interest of the organization or management. The employees feel that
these supportive actions do not particularly benefit or suit their work situations
(Naseer et al., 2018). Therefore, highly demanding environments may hinder an
organization’s efforts to establish social exchange relationships with potential
benefits. As a result, this negative effect may reveal itself in the form of lower job
productivity. Also, with the lack of social exchange relationships, the workplace
may see increasing turnover, reduced commitment, and diminishing job
satisfaction. This logical structure is referenced against the model of an “energy
reservoir”, where the coping energy of employees is used for positive behavior or
adopt potentially harmful consequences within their organization (Naseer et al.,
2018). From this follows our next hypothesis:
H3: A supportiveenvironment moderates the relationship between workload and
job satisfaction among academics.
3. Methodology
3.1 Study Design and Participants
This study was a cross-sectional study. The model in this study is quite similar to
the model employed in a previous study that used the same data set (Janib et al.,
2021). The population of this study consisted of faculty members serving five
Malaysian research universities, including USM, UM, UPM, UKM, and UTM. A
total of 191 respondents were selected through stratified random sampling. The
sample consisted of 102 males and 89 females, with an average age of 45 years.
From the sample, 87% were married, 11% single, and 2% widowed. About 93.2%
were PhD holders and only 6.8% of the respondents had a master’s degree as their
highest academic qualification. Regarding academic position, 13.1% were
professors and 33.5% associate professors, followed by senior lecturers (47.6%)
and lecturers (5.8%). In terms of employment, 49.1% of the respondents stated that
they were involved in various administrative positions at the faculty level, such
as the dean of faculty (3.5%), the deputy dean (18.4%), and head of the department
(27.2%). The remaining 50.9% were appointed in other positions. Table 1 shows
the academic background of the respondents.
Table 1: Respondents’ area of study (N = 191)
Background n %
Engineering and architecture 46 24.08%
Social sciences 32 16.7%
Physical sciences 29 15.18%
Medical sciences and health sciences 26 13.6%
Business and administrations 21 10.99%
Humanities and arts 20 10.47%
Education 17 8.98%
3.2 Procedure
Permission to complete the questionnaire was then sought from the respective
universities and faculty deans. After respondents had signed a consent form, the
questionnaires were distributed. Data were collected over a period of two months,

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wherein the respondents spent an average of 30 minutes on questionnaire
completion. A total of 250 questionnaires were distributed with a return rate of
78% (195 questionnaires). We excluded four incomplete questionnaires, leaving
only 191 questionnaires for this study.
3.3 Measures
3.3.1 Career commitment
The scale of Blau (1985) was utilized to measure career commitment. Some sample
items are: “I don’t want to give up my advocacy work since I enjoy it” and “I am
dissatisfied with my career as a lawyer” (reverse-scored). A five-point scale
assessed this measure, ranging from 1 (strongly disagree) to 5 (strongly agree).
This scale has a Cronbach alpha of 0.90.
3.3.2 Job satisfaction
This measure was assessed using a scale developed by Ather and Nimlathasan
(2006). The sample items of this six-item scale included: “What level of satisfaction
do you have with the nature of the work you do?” and “How pleased are you
with your present career position, given everything?” A five-point scale assessed
this measure, ranging from 1 (very dissatisfied) to 5 (very satisfied). This scale has
a Cronbach alpha of 0.79.
3.3.3 Workload
This component of the construct consisted of nine items, including academic
workloads in management over the past 12 months; education and research-
related activities, both in terms of quality and quantity; sufficient time; and a
sufficient number of consultations (Houston et al., 2006). A sample item is: “I often
need to work after hours to meet my work requirements.” The responses were
obtained using a five-point Likert scale. This scale ranged from the lowest score
of 1 (strongly disagree) to the highest score of 5 (strongly agree). This scale has a
Cronbach alpha of 0.872.
3.3.4 Supportive environment
This construct was measured using an adaptation of four multiple-item scales,
which are perceived climate, supervisory relationship, peer group interaction,
and perceived organizational support (Eisenberger et al., 1986). Before
deployment, modifications were made on two scales, supervisory relationship
and perceived organizational support. All the responses were obtained using a
five-point Likert scale. This scale ranged from the lowest score of 1 (strongly
disagree) to the highest score of 5 (strongly agree). This scale has a Cronbach alpha
of 0.801.
4. Statistical Methods
Data analysis was conducted using components-based structural equation
modeling (SEM) with the support of the SmartPLS v. 3.3.3. The partial least square
(PLS) method yielded numerous advantages to this study. First, it is suitable to
analyze a proposed model that studies a small sample size. Next, it is insensitive
to data normality and is proficient in the analysis of complex path models. Finally,
the PLS method allows the analysis of moderation (Ringle et al., 2020). After
making comparisons against various regression models, we decided on the PLS

8
method as it better serves complex study models, such as the one in this study. In
addition, this method is suitable as an analysis technique for this study as it has a
small sample size (N = 191) (Hair et al., 2019).
We employed the interaction-moderation method to test if the supporting
environment moderates the association between workload and job satisfaction.
Then, a bootstrapping procedure was conducted and the standard error for t-
value computation was obtained. Mean effects are significant at 0.05 when
confidence intervals do not contain zero. The evaluation of model fit was
conducted by both the standardized root mean square residual (SRMR) and
Bentler-Bonett normed fit index (NFI). The discrepancies between observed and
anticipated correlations were assessed by SRMR. Meanwhile, NFI displays the
goodness-of-fit incremental measure.
5. Results
5.1 Measurement Model
We maintained all items, as the results indicated factor loading scores above 0.60.
Table 2 shows that each research variable item achieved convergent validity. As
mentioned by Hair Jr et al. (2014), convergent validity is achieved with the
following values: average variance extracted (AVE) = 0.50, composite reliability
(CR) = 0.70, and Cronbach alpha = 0.70, respectively (see Table 2).
Table 2: Partial least squared- confirmatory factor analysis results
Construct No. of items α rho_A CR AVE VIF
CC 7 0.879 0.888 0.907 0.582 1.77
WL 7 0.778 0.801 0.847 0.527 1.175
JS 7 0.898 0.903 0.919 0.620 1.54
SE 17 0.933 0.988 0.965 0.618 1.014
Note. CC = career commitment, WL = workload, JS = job satisfaction, SE =
supportive environment, VIF = Variance inflation factor.
Discriminant validity was tested. We found that the square root of each
construct’s AVE was larger than the correlation values of the other constructs,
according to the Fornell-Larcker criteria (see Table 3). The Heterotrait-Monotrait
(HTMT) values were smaller than 0.85 (range 0.122 to 0.513) (Franke & Sarstedt,
2019).
Table 3: Measurement model: discriminant validity
Fornell-Larcker criterion HTMT
Construct 1 2 3 4 1 2 3
1 JS 0.788
2 CC 0.467 0.763 0.513
3 SE 0.141 0.074 0.786 0.122 0.094
4 WL -0.305 -0.376 0.056 0.726 0.341 0.460 0.137
Note. JS = job satisfaction, CC = career commitment, SE = supportive environment, WL =
Workload

9
5.2 Structural Model
H1 and H2 were evaluated by path analysis. The path coefficients, coefficient of
determination (R2), and predictive relevance (Q2) of the structural model were all
evaluated. To obtain the β and associated t-values, the model was evaluated using
a nonparametric bootstrapping technique with a resample of 5,000 (Table 4).
Table 4: Structural model (bootstrapping)
Path β SE P t Bias corrected
bootstrap (95%)
Decision
LL UL
CC → JS 0.41 0.099 0.000 4.148 0.005 0.194 Supported
WL → JS -0.178 0.088 0.042 2.036 -0.021 -0.315 Supported
JS R2 Q²
0.254 0.335
Note. CC = career commitment, JS = job satisfaction, WL = workload
The R2 statistic was used to quantify the variation in job satisfaction based on
career commitment and workload. Job satisfaction had an R2 of 0.254, indicating
a weak association (Henseler et al., 2015). Collinearity was determined by
computing VIF values, which were less than 5 for all constructs in the
investigation, suggesting that collinearity did not pose a concern (Henseler et al.,
2015). Job satisfaction had a medium predictive significance in Q2, with a score of
0.335. Thus, the model fit well due to SRMR values less than 0.08 and NFI values
greater than 0.8 (Henseler et al., 2016). According to Henseler et al. (2015), when
the SRMR is less than 0.10, the overall fit of the PLS structural model can be
validated.
The results from the structural model showed a significant positive relationship
between career commitment and job satisfaction (β = 0.41, t = 4.148, p < 0.000), and
a significant negative association between workload and job satisfaction (β = -
0.178, t = 2.036, p < 0.042). As shown in Figure 1, these results support H1 and H2
(see Table 4).
Figure 1: Structural model for job satisfaction in academics

10
5.3 Moderating Effect of Supportive Environment
The moderating impact of a supportive environment on the connection between
workload and job satisfaction was investigated using the interaction-moderation
approach in Smart-PLS. According to Hair Jr et al. (2020), moderation, according
to this approach, distinguishes between the roles of the two factors involved in
the interaction. The outcomes revealed significant relationships between
supportive environment and job satisfaction (β = 0.178, t = 1.987, p < 0.038), and
between workload and job satisfaction (β = -0.512, t = 2.036, p < 0.042). The
interaction between workload and supportive environment had a negative and
significant relationship with job satisfaction (β = –0.165, t = 3.61, p < 0.001),
indicating that supportive environment played a moderating role in the link
between workload and academics’ job satisfaction. Thus, H3 is supported.
6. Discussion and Implications
The PLS-SEM results are consistent with those of prior studies (Al-Sada et al.,
2017) which reported that greater career commitment was closely linked to greater
levels of job satisfaction among Indian and Qatari university faculty members.
Most studies have argued that career commitment has a significant and positive
influence on job satisfaction (Zhang et al., 2014). It thus follows that highly
committed academics would not compromise on high standards of
professionalism, would chart a prolific career, and would thus become highly
satisfied with their jobs. Even if the high career commitment levels increase in
congruence with job satisfaction levels, the momentum may not be sustained at a
high level without the intervention of training and development for career
growth. Therefore, training and development programs employing psychological
assessment could be expanded to play a major role in providing opportunities
where academics perceive the type of regulatory focus that they have and adjust
it according to the job situation. Psychological assessments can be conducted to
confirm which regulatory focus they have. Academics may be encouraged to have
a promotion focus for academic positions through training and development
sessions.
Another finding that was consistent with past studies is that a heavier workload
is linked to low levels of job satisfaction among staff (Hee et al., 2019). This finding
is also in line with Toropova et al.’s (2021) study that found workload influences
job satisfaction. Correspondingly to improve job satisfaction, organizations can
reconsider the amount of work loaded onto their employees, as it has been
observed that an excessive workload causes great dissatisfaction (Liu & Lo, 2018).
A descriptive clarification of this finding is that work-induced stress, such as
pressures and extended working hours, can lead to multiple health risks that
impact the quality of work among staff, ultimately diminishing job satisfaction
(Purba, 2017). In the absence of good self-regulation, employees subjected to high
work pressure can experience interpersonal conflict, which results in inferior
performance.
Unsurprisingly, high job satisfaction will influence the staff’s productivity.
Therefore, seeking a balanced workload should be a priority, because failure to
do so will result in health and psychological consequences on academics. Human
resource (HR) managers should be concerned about managing staff perceptions

11
of workload balance as these influence how satisfied they are with their job, which
translates into staff turnover and performance. Thus, university HR managers
should first measure employees’ displayed talents and capabilities within their
work conditions before tasks are defined and assigned. For assignments that are
challenging, direction and supervision should be provided, including reasonable
and negotiable deadlines, so that academics can achieve optimal quality in task
completion. New assignments should be accompanied by clear instructions and
ready assistance. Accordingly, managers should adjust assignment loads against
employees’ physical and cognitive abilities. The desired outcomes of these efforts
are proper task execution, employees feeling satisfied with the results of their
work, and a maintained motivation in task completion.
This research has made a significant contribution to the field of human resource
management (HRM). Although numerous studies on employee workload have
been conducted, none have confirmed that a supportive environment could
reduce the effects of workload and increase the likelihood of job satisfaction
among academics.
Our interaction-moderation analysis showed that a supportive environment
mitigates the impact that workload has on job satisfaction among Malaysian
university academics. This moderating role of a supportive environment can be
potentially clarified. Academics will adjust their perception about workload and
work-related problems upon receiving support from their co-workers and
supervision from superiors. In addition, they will practice autonomy/authority
for work completion. Our results also demonstrated that a healthy workplace
ecosystem incorporating elements of managerial support, a supportive work
environment, and open communication with superiors would boost the
satisfaction of academics. Understandably, the features of a supportive
environment act as a protective cushion against workload which provides
potential satisfaction among academics in Malaysian higher education. We
deduce that by improving the features of a supportive environment in higher
education, the mental workload of academics would decrease and job satisfaction
would increase. As an extension, organizational best practices should incorporate
aspects of employee communication, reward, recognition, and employee
development as a means to foster robust engagement within the organization. In
summary, the dynamism of supportive faculty environments should be
encouraged and nurtured in universities to realize motivation and retention goals.
7. Limitations and Recommendations
The current study had some limitations. The sample size was small, data
collection was conducted on a self-reporting basis, and a cross-sectional method
was used. We therefore recommend that future studies examine a larger sample
size using the longitudinal method. Another recommendation is the use of other
data collection methods, specifically interviews and observations. Faced with an
unequal gender sample size between the male group (102) and the female group
(89), we found it impossible to conduct a variance analysis for the proposed
model. Therefore, future studies should benefit from a variance analysis for
gender on the proposed model, accounting for approximate and equal sample

12
sizes of male and female respondents. As far as geographical and cultural contexts
are concerned, this study was limited to a sub-context within the Malaysian
context of public universities. As such, cross-regional, cross-national, and cross-
institutional generalizations and comparisons of the findings and conclusions
should be done with caution. Finally, we suggest that future research include
other Asian countries and other types of universities, such as private universities,
and to place performance at the core of such research. As our study was only
restricted to workload as the sole job demand, future studies should consider
other job demands and resources, because these may provide comprehensive
information into how the faculty workplace may affect its academics’ ability to
function. Notwithstanding, we cautioned workload as a hindrance stressor, while
other studies either reported it as a challenge stressor or a stressor with curvilinear
effects, such that an individual’s functioning may not chart adverse effects before
a threshold. Further study could scrutinize if workload presents counterintuitive
effects on academics’ function quality in higher education.
8. Conclusion
This study intended to expand the literature by developing an integrated model
that articulates the theoretical linkages among career commitment, workload, and
job satisfaction of academics in Malaysia. The results provided support for the
hypothesized model linking career commitment, workload, and job satisfaction.
The study found that career commitment is one of the intrinsic aspects that
increases job satisfaction among Malaysian academics in universities. On the
other hand, the results of the study suggest that workload has a negative influence
on job satisfaction. These findings shed some light on how career commitment
and workload influence the job satisfaction of academics in universities and
colleges. Furthermore, this study provided a deeper understanding of the role of
a supportive environment as a moderator between workload and job satisfaction
among academics. This has implications for human resource development in
higher education, through which highly skilled personnel, such as academics, are
trained and developed.
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©Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License (CC BY-NC-ND 4.0).
Vol. 21, No. 1, pp. 18-32, January 2022
https://doi.org/10.26803/ijlter.21.1.2
Received Oct 15, 2021; Revised Dec 12, 2021; Accepted Dec 28, 2021
Teachers' Acceptance of Technologies for 4IR
Adoption: Implementation of the UTAUT Model
Habibah Ab Jalil*
Innovative Learning Sciences Research Centre of Excellence (INNOVATE),
Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, Malaysia
Manjula Rajakumar and Zeinab Zaremohzzabieh
Universiti Putra Malaysia, Serdang, Malaysia
Abstract. Education departments all around the globe are working to
increase the extent to which teachers adopt innovative technology, in
order to scale up pedagogical innovation that uses new technologies.
However, only a few studies have been done on the adoption and use of
these tools for teaching by instructors in non-Western contexts. Therefore,
the objective of this study was to examine teachers’ behavior intention to
adopt and use Industry 4.0 (IR4.0) technologies in Malaysia, in accordance
with the unified theory of acceptance and the use of technology (UTAUT)
model. A questionnaire was employed to acquire data from a randomly
selected sample of 62 primary school teachers in Malaysia. The findings
reveal that only two variables (namely, the facilitating conditions, and
social influence variables) have a direct impact on the behavior intention
of Malaysian primary school teachers to use IR4.0 technologies. Neither
effort expectancy nor performance expectancy have an impact on the
intention to use these technologies. The study concludes with a set of
recommendations for improving policy and research on teachers’ use of
IR4 for education. This work demonstrates how the findings may assist
primary school teachers to improve their understanding of 4IR adoption,
and provides valuable suggestions for 4IR scholars, producers, and users.
Keywords: behavior intention, primary school teachers, fourth industrial
revolution, Malaysia
1. Introduction
Technology plays a key role in education, as it can help students understand and
retain concepts better. Technology stimulates curiosity in students’ minds and
transforms passive students into reactive, reactive-to-interactive, and aggressive
agents (Owoseni et al., 2020; Raja & Nagasubramani, 2018). Recent technological
*
Corresponding author: Habibah Ab Jalil, habibahjalil@upm.edu.my

19
developments have made modern technology particularly appealing for the
school setting, and can readily be incorporated into classroom activities (Farella
et al., 2020; Gómez-Trigueros, 2020). The capacity of these technologies to
stimulate ‘learning-by-making’ experiences is a common aspect of these
technologies (Ferguson et al., 2019). Furthermore, studies have found that using
technologies has the potential to enhance education results through more
innovative teaching and learning approaches (Chick et al., 2020). The fourth
industrial revolution (4IR) will transform the future of education further (Ismail
& Hassan, 2019). 4IR refers to the current trend in industrial technology of
computerization and exchanging data, such as cyber and physical systems, cloud
computing, augmented and virtual reality (AR/VR), robotics, three-dimensional
printing (3DP), and quantum computing (Butt, 2020). According to Kayembe and
Nel (2019), these technologies make the process of teaching and learning simpler,
in relative terms. Thus, it is critical to include 4IR technologies in teaching and
learning, particularly for primary school students who must start preparing for
the future workplace, which is always changing, and unexpected (see Henderson
et al., 2017).
Education in the 21st century is challenging, especially for primary school
students, who are proficient users of technology even when they enrol at school
for the first time (Zaremohzzabieh et al., 2016). Moreover, because conventional
teaching methods are losing their functionality in 21st century learning conditions,
interactive learning is becoming more important, especially in the new norm
caused by the COVID-19 pandemic, characterised by distance learning. Teachers
must acquire skills in integrating 4IR, and they should be experts who are
adaptable to new technologies and global issues (Lase, 2019; Tomczyk, 2020).
However, as explained by Rumengan et al. (2018), it is the human component of
the implementation cycle, not the technology, that will hinder progress in
ensuring that the delivered technologies are used successfully. As a result,
teachers, as key players in the education process, must accept new technologies
and must gain confidence in incorporating them into lessons (Farjon et al., 2019).
Furthermore, the effective application of the 4IR in education necessitates that
teachers develop suitable skills to deploy, manage, and interact with new
technologies (Butler-Adam, 2018).
Research studies have demonstrated that technology integration still poses a
challenge to the majority of teachers. Researchers have investigated the
determinants of teachers’ attitudes towards the acceptance of technology-based
education (Cha & Kwon, 2018), but less extensive research has been done to verify
instructors’ use of IR4.0 technologies. Although the use of IR4.0 technologies is
fast expanding, and their use in education has been broadly verified, not all school
teachers are willing to employ these technologies (Farella et al., 2020). As stated
by Razak et al. (2018), one of the main problems encountered by schools is
teachers’ unwillingness to accept modern teaching technologies. Different models
of technology adoption exist, and some of them, such as the technology
acceptance model (TAM), have been used to assess people’s willingness to accept
novel technologies (Elshafey et al., 2020). This research applied the unified theory
of acceptance and use of technology (UTAUT) framework (Venkatesh et al., 2003),
to examine teachers’ intention of using 4IR technology tools for teaching and

20
learning. As stated by Al-Mamary et al. (2018), it is one of the most broadly used
models for predicting technology use in a variety of circumstances. The model
suggests that teachers’ intention to use technologies is determined by their
performance expectancy, as well as facilitating conditions and social influence.
Thus, the objective of this study was to determine how UTAUT model constructs
affect teachers’ adoption of IR4.0 technology.
2. Theoretical Background and Hypothesis Testing
To ensure the successful implementation of 4IR technologies by primary school
teachers, acceptance and adoption are among the aspects that must be considered.
4IR technologies can be beneficial in schools, because they improve students’
engagement with digital resources while they learn in real-world settings
(Karakoyun & Lindberg, 2020; Owoseni et al., 2020). Nowadays, the success of
various methods of teaching and learning is highly dependent on teachers’
acceptance of new tools and techniques (Scherer & Teo, 2019).
Researchers have attempted to explain technology user acceptance based on
theories of human behavior. Among these theories, the TAM, introduced by Davis
(1985), has been a popular paradigm for studying aspects that influence users'
adoption of technology. Using a complicated link between system characteristics
(external factors) and potential system use, the TAM assumes that two variables
– termed perceived ease of use and perceived usefulness – play a mediating
function.
Academics (e.g., Sánchez-Prieto et al., 2016) mention that the TAM has some
limitations. To address these limitations, Venkatesh et al. (2003) used the core
items from a total of eight common technology acceptance models, including the
TAM, and created a unified model, and called it the UTAUT model. This model
adds significantly to research on technology acceptance and use, because of its
capacity to combine multiple TAMs (Venkatesh et al., 2003). As a result, the
UTAUT framework was used as the theoretical foundation in this study to
evaluate the effects of technology-related variables on 4IR technology adoption.
The model was used to investigate how different factors can promote teachers’
behavioral (or behavior) intentions towards 4IR technologies in their teaching (see
Figure 1).
Figure 1. The research framework

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2.1 Effort Expectancy and Teachers’ Intention to Use 4IR Technology
Effort expectancy is formally defined as the level of ease related to the use of
technological tools (Venkatesh et al., 2012). As an important component of the
UTAUT model, effort expectancy is mostly employed to assess users’ intention to
use technological tools (Venkatesh et al., 2003). Jang and Koh (2019) identified the
role played by effort expectancy in identifying the acceptance of learning
technologies. Information system researchers, such as Kaliisa et al. (2019),
emphasize a relationship between effort expectancy and behavior intention
in new, modern technologies. Other researchers who have employed the UTAUT
model found that effort expectancy and behavior intention are linked (Oke &
Fernandes, 2020). As stated previously, effort expectancy has a positive influence
on behavior intentions while using virtual reality (Shen et al., 2019).
Consequently, Hypothesis 1 was proposed as follows:
Hypothesis 1: Effort expectancy is positively related to teachers’ behavior
intention to use 4IR technology.
2.2 Performance Expectancy and Teachers’ Intention to Use 4IR Technology
Performance expectancy can be defined as a person’s belief that adopting
technology will enhance job performance (Venkatesh et al., 2003). The UTAUT
model commonly incorporates the performance expectancy concept that predicts
behavior intention to use new technologies (Francisco & Swanson, 2018). Sung et
al. (2015), for instance, applied the UTAUT framework to investigate mobile
learning in the South Korean context and conclude that it is significantly linked to
behavior intention. The UTAUT model has been employed by several researchers,
and evidence supports the notion that performance expectancy and the behavior
intention to use technologies are linked (Almaiah & Al Mulhem, 2019; Botero et
al., 2018; Nikolopoulou, 2018). Studies have also shown the significant influence
of performance expectancy on continuous intention to use mobile learning (Al-
Emran & Granić, 2021). As a result, the following hypothesis was formulated:
Hypothesis 2: Performance expectancy is positively associated with teachers’
behavior intentions to use 4IR technology.
2.3 Social Influence and Teachers’ Intention to Use 4IR Technology
The UTAUT model factor of social influence is characterized as an individual’s
assessment of the importance of accepting a new technological tool, according to
others (Venkatesh et al., 2003). Studies have investigated the role of social
influence, which includes that of friends, family, co-workers, and peer influences,
on individual behavior adoption (Shen et al., 2019), and conclude that it is a
significant influencing factor for behavior intention (Lu et al., 2020). A study by
Jain and Jain (2021) implies that, when teachers engage with others, they are more
likely to have a strong behavior intention to use IR4.0 technologies for teaching.
As a result, Hypothesis 3 suggests
Hypothesis 3: Social influence is positively linked to teachers’ behavior intention
to use 4IR technologies.

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2.4 Facilitating Conditions and Teachers’ Intentions to Use 4IR Technology
A facilitating condition is an individual’s confidence that an organizational and
technological structure is in place to make system use easier (Venkatesh et al.,
2003). In other words, facilitating conditions supply the external resources
required to make a specific activity easier to complete (Ajzen, 1991). The
availability of training and assistance are considered to be helpful circumstances
in the context of workplace technology adoption. In the context of this study,
facilitating conditions were assessed by teachers' perceptions of their ability to
acquire the necessary resources and assistance to use IR4.0. Amadin et al. (2018)
found that facilitating conditions have a positive influence on intentions to use
technology. As a result, it was suggested that
Hypothesis 4: Facilitating conditions are positively related to teachers’ behavior
intention to use 4IR technologies.
3. Methodology
3.1 Research Design
A research design is an essential component of a study, and choosing the right
design can help researchers obtain accurate results and, subsequently, achieve the
aim of the study (Henson et al., 2020). As part of its hypothesis-generating
research, the present study used a survey, and quantitative methodologies based
on the positivist paradigm (Andrade, 2019).
3.2 Participants and Data Collection Instruments
A questionnaire was generated for the survey, which was developed and
administered in both English and Malay. To evaluate the theoretical model, the
questionnaire comprised two main sections: (1) respondent demographics, and
(2) the model’s construct measures.
All of the measuring items of the original UTAUT model were included and
modified for use by this research. A five-point Likert scale was defined, from 1 (or
strongly disagree) to 5 (or strongly agree).
Effort Expectancy: Effort expectancy means the level of easiness related while using
technologies. Some of the original four items included perceived usability and
difficulty (Venkatesh et al., 2003). The sample items are: I have found this technology
easy to adopt, and My interaction with 3DP/AR/VR/Robotics technologies in teaching as
well as learning would be simple to comprehend. A Cronbach’s alpha value of 0.808 is
reported for this scale.
Performance Expectancy (perceived usefulness): This measure was calculated using
a four-item scale for perceived usefulness, job fit, extrinsic motivation, relative
benefit, and technology predicted output, including 3DP, AR/VR, and robotics,
in teaching and learning (Zhang et al., 2020). The sample item is Using
3DP/AR/VR/Robotics technologies for teaching and learning allow us to do
responsibilities rapidly. The Cronbach’s alpha value for this scale is 0.808.
Social Influence: This measure was estimated using Venkatesh et al.’s (2003) four-
item scale. The following are examples of items: If my colleagues adopt it, I will

23
include it into my teaching and learning, and The adoption of the technology was
supported by the school. The Cronbach’s alpha value for this scale is 0.867.
Facilitating Conditions: Four items were measured under facilitating conditions
(OECD, 2020; Venkatesh et al., 2003). The initial item is, I have the required resources
to adopt 3DP/AR/VR/Robotics technologies in teaching and/or learning. The Cronbach’s
alpha value for this scale is 0.692.
Behavior Intention: This component was measured with three items adopted from
the studies of Venkatesh et al. (2003) and Rahi et al. (2018). The first item is, I aim
to adopt 3DP/AR/VR/Robotics technologies during my teaching and/or learning during
the next few months. The value of Cronbach’s alpha for this scale is 0.857.
The respondents in this research were teachers of science, design and technology,
mathematics, and ICT who had been randomly selected from 74 primary schools
from the Alor Gajah district of Malacca, Malaysia. Before collecting the data, four
experts validated the face and content validity of the questionnaire. All the
respondents provided informed consent before completing the questionnaire.
The researcher used the sample size criteria suggested by Ghauri et al. (2020),
namely that the intention to do factor analysis means that answers numbering five
to ten times more than the total number of items, must be gathered. As a result,
the current study required a minimum of 95 (19×5) and a maximum of 190 (19×10)
responses. The final sample consisted of 62 respondents, and had a response rate
of 62.26%.
Of the respondents who participated in the survey, 17 were men (27.41%) and 45
were women (72.58%) (see Table 1). The average age of the participants was 27.02
years (SD=8.34), and 22.6% reported having 11–15 years of experience in the
teaching field. A total of 27 (43.5.4%) of the schools at which respondents taught
were located in urban areas, while 35 (56.5%) were situated in rural areas.
Table 1. Demographic characteristics of respondents (N=62)
No. Item Type Frequency Percent Mean SD
1 Age (in years) 27.02 8.34
25-35 15 24.19
36-45 28 45.16
46-55 18 29.03
>56 6 9.67
2 Gender
Male 17 27.41
Female 45 72.58
3 Work experience
(years)
≤ 5 5 8.1
6-10 15 24.2
11-15 14 22.6
16-20 9 14.5
21-25 13 21
26-30 4 6.5
31-35 2 3.2

24
4 School location
Urban 27 43.5
Rural 35 56.5
All the construct items exhibited significant composite reliability, as well as
acceptable levels of reliability (α), according to the reliability test. This means that
teachers had made significant changes from performance expectancy to
performance expectancy, which had a loading of less than 0.5. If the average
variance extracted (AVE) is below standard level, the lowest loading can be
removed (Henseler et al. (2015). Thus, performance expectancy Item PE4 was
discarded. The Cronbach’s alpha value for each scale ranged from 0.652 to 0.902,
which represents acceptable reliability for each construct (see Table 2). The
average variance extracted scores, which were between 0.589 and 0.836, imply that
all five constructs have good convergency (Hair et al., 2020).
4. Data Analysis and Results
PLS-SEM approach and SmartPLS 3.3.3 software were used to analyze the data,
based on Hair et al.’s (2017) recommendation for studies with small to medium
sample sizes. Kock et al.’s (2019) two-step method was used to assess the data that
had been gathered. First, the study investigated the measurement model’s
reliability, as well as the convergent and the discriminant validity. The structural
model was, then, assessed to determine the direction and power of the
connections between the theoretical components.
4.1 Measurement Model
The reliability and validity of the constructs were validated, and the measurement
model was examined for reflecting indicators. The various latent constructs were
subjected to factor analysis (Hair et al., 2017). The reliability of the composite
variables varied from 0.692 to 0.902, which is deemed satisfactory (Hair et al.,
2017).
Convergent validity was used to establish the validity of the model. First, the data
revealed that factor loading values were above 0.70. This means that the items of
each construct have adequate convergent validity. The AVE was above 0.50,
composite reliability (CR) was 0.70, as shown in Table 2.
Table 2. Construct reliability and validity measures
Constructs Item No. Loading α rho_A CR AVE
Effort Expectancy 1 0.73 0.808 0.831 0.874 0.635
2 0.79
3 0.87
4 0.77
Performance
Expectancy
1 0.88 0.902 0.916 0.939 0.836
2 0.92
3 0.93
Social Influence 1 0.81 0.867 0.872 0.91 0.734
2 0.92

25
3 0.86
4 0.78
Facilitating
Conditions
1 0.74 0.692 0.654 0.811 0.589
2 0.78
3 0.77
Behavior
Intention
1 0.82 0.857 0.859 0.913 0.779
2 0.90
3 0.91
Discriminant validity measures the degree to which one construct differs from
another, using empirical standards. This study combined Fornell and Larcker’s
criteria with the heterotrait-monotrait (or HTMT) ratios of relations, to integrate
multiple approaches (Henseler et al., 2015). We found that discriminant validity
was attained, due to the square root of the AVE of every construct being greater
than the correlation values of any construct pairs, according to the Fornell–
Larcker criteria. In addition, as indicated in Table 3, the standards of HTMT were
all below the 0.85 cutoff value. As a result, this study reveals that effort
expectancy, facilitating conditions, social influence, performance expectancy, and
behavior intention could all be differentiated.
Table 3. The Measurement model and discriminant validity
Constructs
Fornell-Larcker Heterotrait-Monotrait
1 2 3 4 5 1 2 3 4 5
1. Behavior
Intention
0.883
2. Effort
Expectancy
0.66 0.797 0.783
3. Facilitating
Conditions
0.604 0.604 0.768 0.807 0.813
4. Performance
Expectancy
0.448 0.554 0.306 0.914 0.505 0.645 0.399
5. Social Influence 0.669 0.814 0.517 0.568 0.846 0.766 0.575 0.671 0.64
4.2 Structural Model
Once the measurement model evaluation had been performed, and reliability and
validity had been determined, the structural relationships were created.
Exogenous variables explained 52.6% of the variance in behavior intention, which
indicates moderate predictive ability (See Figure 2). The bootstrapping approach
was then used to assess the significance of the connections among the variables
(see Table 3). The bootstrap process involved a resampling of the subsample of
5,000 occurrences, which are equivalent to the validated results, to determine the
significance of path estimations. It was computed using a 5% two-tail significance.
The findings indicate that there is no association between teachers’ effort
expectancy and behavior intentions (β=0.154, t=1.371, p<0.001). Thus, Hypothesis
1 is rejected. The findings confirm the results of other studies, such as that of
Bardakcı and Alkan (2019), that effort expectancy is not a good predictor of
teachers' behavior intention. H2 is rejected too, as the result demonstrates that
there is no significant relationship between performance expectancy and behavior
intention (β=0.073, t=0.77, p<0.001). This finding contrasts with that of other
studies, such as that of Harmandaoğlu Baz et al. (2019), which found that

26
performance expectancy is generally a predictor of teachers’ behavior intention to
use novel technologies. The present findings suggest a significant influence by
social influence on teachers’ behavior intention (β=0.340, t=2.412, p=0.05), H3 is,
therefore, supported. This finding is in line with that of studies that report a
meaningful association between social influence and behavior intentions of
teachers to use new technologies (e.g., Yilmaz & Baydas, 2016). Moreover, the
findings acquired from the path coefficient indicate that the facilitating conditions
factor (β=0.313, t=2.939, p<0.001) is significantly related to behavior intention,
thus, H4 is supported. Our findings are in line with that of Nikou and Economides
(2019), which demonstrates that facilitating conditions improve the intentions of
STEM teachers to adopt modern devices. The independent variables explain
61.7% of the variance in behavioral intention.
Figure 2. Structural model
Table 4. The output of structural model
Hypothesis Path M SD t P
BCB (95% CI)
Decision
LB UB
Hypothesis 1 EE→BI 0.155 0.112 1.371 0.171 -0.05 0.38 Rejected
Hypothesis 2 FC→BI 0.328 0.107 2.939 0.003
***
0.091 0.512 Accepted
Hypothesis 3 PE→BI 0.075 0.095 0.77 0.442 -0.148 0.243 Rejected
Hypothesis 4 SI→BI 0.332 0.141 2.412 0.016
**
0.074 0.604 Accepted
Note EE–Effort Expectancy; BI–Behavior Intention; FC–Facilitating Condition; PE=4; Performance Expectancy; SI–
Social Influence; ** R2 (BI)=0.526; *** P<0.001; **P<0.05
Finally, the risks of collinearity were ruled out by the variance inflation factor
(VIF) values being below 5 (see Table 5).
Table 5. Structural model collinearity (inner VIFs)
Construct 1 2 3 4 5
1. Behavior Intention 1.386 1.203 1.284 1.001
2. Effort Expectancy
3. Facilitating Condition 1.000
4. Performance Expectancy 1.006
5. Social Influence 1.000

27
The structural model’s predictive significance was also assessed using Q2 value,
in addition to R2 and f2. According to the rule, the structural model has a
predictive value if the Q2 value for a particular reflective endogenous latent
variable is higher than 0 – otherwise, the model has no predictive value (Hair et
al., 2017). The blindfolding findings show that behavior intention (0.39), effort
expectancy (0.068), facilitating conditions (0.09), performance expectancy (0.048),
and social influence (0.081) are all predictively significant (Henseler et al., 2015).
With the standardized root-mean-square residual score at 0.06 – significantly
below the 0.10 criterion – the study, thus, validates the overall fit of the structural
model (Henseler et al., 2015) (Table 6).
Table 6. Predictive relevance of the structural model
No. Construct SSO SSE Q² (=1-SSE/SSO)
1 Behavior Intention 225 137.282 0.39
2 Effort Expectancy 300 300 0.068
3 Facilitating Condition 225 225 0.09
4 Performance Expectancy 225 225 0.048
5 Social Influence 300 300 0.081
5. Discussion
This work aimed to determine the factors that impact the behavior intentions of
primary school teachers to use 4IR technologies in education. The findings
concerning the UTAUT model variables reveal that effort expectation has no
significant beneficial influence on behavior intention related to using 4IR
technologies. The results contradict the initial hypothesis of the UTAUT model
(Venkatesh et al., 2003). Teachers’ willingness to adopt new technology tools for
teaching increases when they believe the technologies are user-friendly,
straightforward, and easy to use. The study also discovered that facilitating
conditions have a significant and positive impact on the behavior intentions to use
4IR technologies. This finding confirms the initial hypothesis of the UTAUT
model, and also supports the findings of Kung-Teck et al. (2019), which state that
facilitating conditions predict teacher intentions to use cutting-edge technologies.
It can be said that factors such as time and fiscal and technological resources can
increase teachers’ intention to use technologies such as 3DP, AR/VR and robotics
for teaching purposes. Concerning performance expectancy, the present findings
contradict the original UTAUT model (Chao, 2019). This finding indicates that
performance expectancy does not have a significant and positive impact on the
behavioral intentions of teachers. In addition, it is inconsistent with other studies,
which report that teachers believe that using new technologies will help them to
improve students’ performance (Ibili et al., 2019). In this study, the impact of
social influence on behavior intention to adopt IR4.0 technologies was significant.
This result corresponds with the original theoretical foundation of the UTAUT
model (Venkatesh et al., 2003), in which social influence is a key factor in the
model. This finding can be explained by the relatively strong influence close
colleagues and acquaintances have in education settings. In addition, Zhao et al.
(2021) found that the collectivist cultures of Asian countries mean others’ ideas
are salient for the decision to adopt new technologies. According to Zhang et al.
(2018), variations in technology adoption are associated with cultural factors.

28
Individualistic cultures focus on straight and formal sources for knowledge, while
individuals from collectivist cultures, such as those in Southeast Asia, rely more
on subjective innovation evaluations that are conveyed by like-minded
individuals who have already accepted the innovation (Zhao et al., 2021).
6. Conclusion and Recommendations
The theoretical foundation of the UTUAT model was used by this study to
examine teachers’ intentions to adopt 4IR technologies for teaching and learning.
As theorized, facilitating conditions and social influence were found to affect
teachers’ intentions to use IR4.0. However, no statistically significant pathways
connect the other two variables (i.e. effort expectancy and performance
expectancy) with behavior intention. The results provide a significant
contribution to the current work on IR4.0 acceptance. This is one of the first studies
to consider the context of schools, namely that, owing to limited resources, they
face particular problems in maximizing teachers' ability to apply IR4 technologies.
Furthermore, the study of IR4 acceptance requires a well-established model that
includes the characteristics that can predict IR4 acceptance by school teachers.
This research is significant because it was the first application of the UTAUT
model to investigate teachers’ intentions to use IR4.0 for teaching. These findings
can assist IR4.0 researchers and developers to create better educational
experiences.
Different factors should be incorporated in future versions of the model, to
improve understanding of teachers’ intentions to accept and use IR4.0
technologies for education, and these constructs should be fully explored by
future studies. Furthermore, studies on how teachers can use IR4.0 technologies
in teaching, how to distribute educational content simply and instantly on all
devices by school teachers, and how to encourage students to engage in
collaborative learning, would also be useful. Additionally, providing teachers
with analytical data that allow them to monitor their students’ progress will
improve the likelihood of IR4.0 teaching tools being used in the future. Finally,
the findings of this study may be useful to future research on the use of IR4.0-
based teaching aids in education. Future academics, educational IR4.0 technology
developers, instructors, and curriculum designers could benefit from these
findings.
7. Study Limitations
The results of this investigation were limited by several issues. The study focused
on some of the elements that influence teachers' acceptance of 4IR technology. An
inability to generalize the study conclusions is one disadvantage of the current
analysis. Only small groups of teachers took part in this study, and teachers were
asked to complete questionnaires. The chosen respondents may not be
representative of, and their inputs may not be generalizable to the overall sample
population. Furthermore, the study's findings cannot be applied to other
individuals or school personnel. While this study, through validity and reliability
testing, established a fair testing instrument and measuring scales, the study's
internal validity may require further attention, as a consequence of how the
respondents completed the questionnaires. The study, like any other, used a self-

29
administered questionnaire, which implies that respondents may have given
superficial responses. Moreover, this situation may have been exacerbated by
some respondents providing information that they believed would impress the
researchers. To remedy this limitation, future research should employ a new
approach to investigations, such as a longitudinal study. A different quantitative
or qualitative technique may provide additional insight into the analysis.
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IJLTER.ORG Vol 21 No 1 January 2022

IJLTER.ORG Vol 21 No 1 January 2022

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