SlideShare a Scribd company logo
International Journal
of
Learning, Teaching
And
Educational Research
p-ISSN:
1694-2493
e-ISSN:
1694-2116
IJLTER.ORG
Vol.21 No.1
International Journal of Learning, Teaching and Educational Research
(IJLTER)
Vol. 21, No. 1 (January 2022)
Print version: 1694-2493
Online version: 1694-2116
IJLTER
International Journal of Learning, Teaching and Educational Research (IJLTER)
Vol. 21, No. 1
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part
of the material is concerned, specifically those of translation, reprinting, re-use of illustrations,
broadcasting, reproduction by photocopying machines or similar means, and storage in data banks.
Society for Research and Knowledge Management
International Journal of Learning, Teaching and Educational Research
The International Journal of Learning, Teaching and Educational
Research is a peer-reviewed open-access journal which has been
established for the dissemination of state-of-the-art knowledge in the
fields of learning, teaching and educational research.
Aims and Objectives
The main objective of this journal is to provide a platform for educators,
teachers, trainers, academicians, scientists and researchers from over the
world to present the results of their research activities in the following
fields: innovative methodologies in learning, teaching and assessment;
multimedia in digital learning; e-learning; m-learning; e-education;
knowledge management; infrastructure support for online learning;
virtual learning environments; open education; ICT and education;
digital classrooms; blended learning; social networks and education; e-
tutoring: learning management systems; educational portals, classroom
management issues, educational case studies, etc.
Indexing and Abstracting
The International Journal of Learning, Teaching and Educational
Research is indexed in Scopus since 2018. The Journal is also indexed in
Google Scholar and CNKI. All articles published in IJLTER are assigned
a unique DOI number.
Foreword
We are very happy to publish this issue of the International Journal of
Learning, Teaching and Educational Research.
The International Journal of Learning, Teaching and Educational
Research is a peer-reviewed open-access journal committed to
publishing high-quality articles in the field of education. Submissions
may include full-length articles, case studies and innovative solutions to
problems faced by students, educators and directors of educational
organisations. To learn more about this journal, please visit the website
http://www.ijlter.org.
We are grateful to the editor-in-chief, members of the Editorial Board
and the reviewers for accepting only high quality articles in this issue.
We seize this opportunity to thank them for their great collaboration.
The Editorial Board is composed of renowned people from across the
world. Each paper is reviewed by at least two blind reviewers.
We will endeavour to ensure the reputation and quality of this journal
with this issue.
Editors of the January 2022 Issue
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
1
©Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License (CC BY-NC-ND 4.0).
International Journal of Learning, Teaching and Educational Research
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
2
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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,
7
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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.
9. References
Agbozo, G. K., Owusu, I. S., Hoedoafia, M. A., & Atakorah, Y. B. (2017). The effect of work
environment on job satisfaction: Evidence from the banking sector in Ghana.
Journal of Human Resource Management, 5(1), 12–18.
https://doi.org/10.11648/j.jhrm.20170501.12
Ahmad, A., Khan, M. U., Srikanth, A. B., Patel, I., Nagappa, A. N., & Jamshed, S. Q. (2015).
Evaluation of workload and its impact on satisfaction among pharmacy
academicians in Southern India. Journal of Clinical and
Diagnostic Research: JCDR, 9(6), 1–6.
https://doi.org/10.7860/JCDR/2015/12921.6023
Akob, M. (2016). Influence workload, work ethic and job satisfaction toward teacher’s
performance (Study of Islamic-based school in Makasar- Indonesia). Advanced
Research Journal of Management and Business Studies, 5(7), 172–177.
Ali, N. H. M., Hassan, S. A., Jailani, O., Zaremohzzabieh, Z., & Lee, Z. J. (2020). The impact
of supervisory styles on satisfaction of undergraduate counselling interns in
Malaysia. Asian Journal of University Education, 16(3), 138–147.
https://doi.org/10.24191/ajue.v16i3.11079
13
http://ijlter.org/index.php/ijlter
Alrawahi, S., Sellgren, S. F., Altouby, S., Alwahaibi, N., & Brommels, M. (2020). The
application of Herzberg’s two-factor theory of motivation to job satisfaction in
clinical laboratories in Omani hospitals. Heliyon, 6(9), 1–9.
https://doi.org/10.1016/j.heliyon.2020.e04829
Al-Sada, M., Al-Esmael, B., & Faisal, M. N. (2017). Influence of organizational culture and
leadership style on employee satisfaction, commitment and motivation in the
educational sector in Qatar. EuroMed Journal of Business, 12(2), 163–188.
https://doi.org/10.1108/EMJB-02-2016-0003
Amin, S., Arshad, R., & Ghani, R. A. (2017). Spousal support and subjective career success:
The role of work-family balance and career commitment as mediator. Jurnal
Pengurusan (UKM Journal of Management), 50, 133–142.
https://doi.oeg/10.17576/pengurusan-2017-50-12
Ather, S. M., & Nimlathasan, B. (2006). Association between quality of work life (QWL)
and job satisfaction (JS): A study of academic professionals of private universities
in Bangladesh. The Chittagong University Journal of Business Administration, 21, 9–
23.
https://www.researchgate.net/publication/205019610_Quality_of_Work_life_
QoWL_and_Job_Satisfaction_JS_A_Study_of_Academic_Professionals_of_Privat
e_Universities_in_Bangladesh
Berberoglu, A. (2018). Impact of organizational climate on organizational commitment
and perceived organizational performance: Empirical evidence from public
hospitals. BMC Health Services Research, 18(1), 399.
https://doi.org/10.1186/s12913-018-3149-z
Bibi, P., Ahmad, A., & Majid, A. H. A. (2018). The impact of training and development and
supervisor support on employees retention in academic institutions: The
moderating role of work environment. Gadjah Mada International Journal of
Business, 20(1), 113–131. https://doi.org/10.22146/gamaijb.24020
Blau, G. J. (1985). The measurement and prediction of career commitment. Journal of
Occupational Psychology, 58(4), 277–288. https://doi.org/10.1111/j.2044-
8325.1985.tb00201.x
Caldana, A. C. F., Eustachio, J. H. P. P., Sampaio, B. L., Gianotto, M. L., Talarico, A. C., &
da Silva Batalhão, A. C. (2021). A hybrid approach to sustainable development
competencies: The role of formal, informal and non-formal learning experiences.
International Journal of Sustainability in Higher Education (ahead of print), 1–24.
https://doi.org/10.1108/ijshe-10-2020-0420
Cerci, P. A., & Dumludag, D. (2019). Life satisfaction and job satisfaction among university
faculty: The impact of working conditions, academic performance and relative
income. Social Indicators Research, 144(2), 785−806.
https://doi.org/10.1007/s11205-018-02059-8
Choi, H., & Chiu, W. (2017). Influence of the perceived organizational support, job
satisfaction, and career commitment on football referees’ turnover intention.
Journal of Physical Education and Sport, 17, 955–959.
https://doi.org/10.7752/jpes.2017.s3146
Chong, Y., & Thi, L.-S. (2020). University freshman mentoring effectiveness and scale
enhancement. Asian Journal of University Education, 16(4), 181–189.
https://doi.org/10.24191/ajue.v16i4.11950
Clarke, M. (2015). Creating a supportive working environment in European higher education
[technical report]. Education International Research Institute.
de Lourdes Machado-Taylor, M., Meira Soares, V., Brites, R., Brites Ferreira, J.,
Farhangmehr, M., Gouveia, O. M. R., & Peterson, M. (2016). Academic job
satisfaction and motivation: Findings from a nationwide study in Portuguese
14
http://ijlter.org/index.php/ijlter
higher education. Studies in Higher Education, 41(3), 541–559.
https://doi.org/10.1080/03075079.2014.942265
De Sousa Sabbagha, M., Ledimo, O., & Martins, N. (2018). Predicting staff retention from
employee motivation and job satisfaction. Journal of
Psychology in Africa, 28(2), 136–140.
https://doi.org/10.1080/14330237.2018.1454578
Dorenkamp, I., & Ruhle, S. (2019). Work-life conflict, professional commitment, and job
satisfaction among academics. The Journal of Higher Education, 90(1), 56–84.
https://doi.org/10.1080/00221546.2018.1484644
Duffy, R. D., England, J. W., Douglass, R. P., Autin, K. L., & Allan, B. A. (2017). Perceiving
a calling and well-being: Motivation and access to opportunity as moderators.
Journal of Vocational Behavior, 98, 127–137.
https://doi.org/10.1016/j.jvb.2016.11.003
Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived
organizational support. Journal of Applied Psychology, 71(3), 500–507.
https://doi.org/10.1037/0021-9010.71.3.500
Escardíbul, J.-O., & Afcha, S. (2017). Determinants of the job satisfaction of PhD holders:
An analysis by gender, employment sector, and type of satisfaction in Spain.
Higher Education, 74(5), 855–875. https://doi.org/10.1007/s10734-016-0081-1
Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity
testing: A comparison of four procedures. Internet Research, 29(3), 430–447.
https://doi.org/10.1108/IntR-12-2017-0515
Gendron, T., Welleford, E. A., Pelco, L., & Myers, B. J. (2016). Who is likely to commit to a
career with older adults? Gerontology & Geriatrics Education, 37(2), 208–228.
https://doi.org/10.1080/02701960.2014.954042
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial
least squares. European Journal of Marketing, 53(4), 566–584.
https://doi.org/10.1108/EJM-10-2018-0665
Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in
PLS-SEM using confirmatory composite analysis. Journal of
Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares
structural equation modeling (PLS-SEM): An emerging tool in business research.
European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-
0128
Halder, N. (2018). Investing in human capital: Exploring causes, consequences and
solutions to nurses’ dissatisfaction. Journal of Research in Nursing, 23(8), 659–675.
https://doi.org/10.1177/1744987118807251
Hall, D. T., Yip, J., & Doiron, K. (2018). Protean careers at work: Self-direction and values
orientation in psychological success. Annual Review of Organizational Psychology
and Organizational Behavior, 5, 129–156. https://doi.org/10.1146/annurev-
orgpsych-032117-104631
Hee, O. C., Ong, S. H., Ping, L. L., Kowang, T. O., & Fei, G. C. (2019). Factors influencing
job satisfaction in the higher learning institutions in Malaysia. International Journal
of Academic Research in Business and Social Sciences, 9(2), 10–20.
https://doi.org/10.6007/IJARBSS/v9-i2/5510
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology
research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20.
https://doi.org/10.1108/IMDS-09-2015-0382
15
http://ijlter.org/index.php/ijlter
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant
validity in variance-based structural equation modeling. Journal of the Academy of
Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Herzberg, F.I., Mausner, B., & Snyderman, B. (1959). The motivation to work (2nd ed.). John
Wiley.
Herzberg, F. I. (1966). Work and the Nature of Man. Wold.
Houston, D., Meyer, L. H., & Paewai, S. (2006). Academic staff workloads and job
satisfaction: Expectations and values in academe. Journal of Higher Education
Policy and Management, 28(1), 17–30.
https://doi.org/10.1080/13600800500283734
Hsiao, J.-M., & Lin, D.-S. (2018). The impacts of working conditions and employee
competences of fresh graduates on job expertise, salary and job satisfaction.
Journal of Reviews on Global Economics, 7, 246–259.
https://ideas.repec.org/a/lif/jrgelg/v7y2018p246-259.html
Imondi, P. J. N. (2011). The influence of workload on performance of teachers in public primary
schools in Kombewa Division, Kisumu West District, Kenya (Master’s dissertation).
University of Nairobi.
Indarti, S., Fernandes, A. A. R., & Hakim, W. (2017). The effect of OCB in relationship
between personality, organizational commitment and job satisfaction on
performance. Journal of Management Development, 36(10), 1283–1293.
https://doi.org/10.1108/JMD-11-2016-0250
Inegbedion, H., Inegbedion, E., Peter, A., & Harry, L. (2020). Perception of workload
balance and employee job satisfaction in work organisations. Heliyon, 6(1), 1–9.
https://doi.org/10.1016/j.heliyon.2020.e03160
Janib, J., Rasdi, R. M., Omar, Z., Alias, S. N., Zaremohzzabieh, Z., & Ahrari, S. (2021). The
relationship between workload and performance of research university
academics in Malaysia: The mediating effects of career commitment and job
satisfaction. Asian Journal of University Education, 17(2), 85–99.
https://doi.org/10.24191/ajue.v17i2.13394
Kenny, J. (2018). Re-empowering academics in a corporate culture: An exploration of
workload and performativity in a university. Higher Education, 75(2), 365–380.
https://doi.org/10.1007/s10734-017-0143-z
Kim, S. J., Song, M., Hwang, E., Roh, T., & Song, J. H. (2020). The mediating effect of
individual regulatory focus in the relationship between career commitment and
job satisfaction. European Journal of Training and Development, 45(2/3), 166−180.
https://doi.org/10.1108/EJTD-02-2020-0030
Laihonen, H., & Mäntylä, S. (2017). Principles of performance dialogue in public
administration. International Journal of Public Sector Management, 30(5), 414–428.
https://doi.org/10.1108/IJPSM-09-2016-0149
Liu, H.-L., & Lo, V. (2018). An integrated model of workload, autonomy, burnout, job
satisfaction, and turnover intention among Taiwanese reporters. Asian
Journal of Communication, 28(2), 153–169.
https://doi.org/10.1080/01292986.2017.1382544
Liu, J., Yu, W., Ding, T., Li, M., & Zhang, L. (2019). Cross-sectional survey on job
satisfaction and its associated factors among doctors in tertiary public hospitals in
Shanghai, China. BMJ Open, 9(3), 1–10. http://dx.doi.org/10.1136/bmjopen-
2018-023823
Marsaditha, P. H. (2017). The influence of work load, job satisfaction, and working environment
towards woman work life balance (Case study in Pt Hasta Ayu Nusantara Jakarta) (PhD
thesis). President University, West Java, Indonesia.
16
http://ijlter.org/index.php/ijlter
Mudrak, J., Zabrodska, K., Kveton, P., Jelinek, M., Blatny, M., Solcova, I., & Machovcova,
K. (2018). Occupational well-being among university faculty: A job demands-
resources model. Research in Higher Education, 59(3), 325–348.
https://doi.org/10.1007/s11162-017-9467-x
Naseer, S., Raja, U., Syed, F., & Bouckenooghe, D. (2018). Combined effects of workplace
bullying and perceived organizational support on employee behaviors: Does
resource availability help? Anxiety, Stress, & Coping, 31(6), 654–668.
https://doi.org/10.1080/10615806.2018.1521516
Newman, A., Nielsen, I., Smyth, R., Hirst, G., & Kennedy, S. (2018). The effects of diversity
climate on the work attitudes of refugee employees: The mediating role of
psychological capital and moderating role of ethnic identity. Journal of Vocational
Behavior, 105, 147–158. https://doi.org/10.1016/j.jvb.2017.09.005
Osifila, G. I., & Aladetan, T. A. (2020). Workload and lecturers’ job satisfaction in Adekunle
Ajasin University, Akungba-Akoko, Ondo State, Nigeria. Journal of Education and
Learning (EduLearn), 14(3), 416–423. https://eric.ed.gov/?id=EJ1266299
Prieto, I. M., & Pérez-Santana, M. P. (2014). Managing innovative work behavior: The role
of human resource practices. Personnel Review, 43(2), 184–208.
https://doi.org/10.1108/PR-11-2012-0199
Purba, S. D. (2017). Career management dan subjective career success: Dapatkah
meningkatkan kepuasan kerja wanita karir? [Career management and subjective
career success: Can women’s job satisfaction improve their career?]. MIX: Jurnal
Ilmiah Manajemen, 7(1), 113–131.
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares
structural equation modeling in HRM research. The International Journal of Human
Resource Management, 31(12), 1617–1643.
https://doi.org/10.1080/09585192.2017.1416655
Sabagh, Z., Hall, N. C., & Saroyan, A. (2018). Antecedents, correlates and consequences of
faculty burnout. Educational Research, 60(2), 131–156.
https://doi.org/10.1080/00131881.2018.1461573
Singh, J. K., & Jain, M. (2013). A study of employees’ job satisfaction and its impact on
their performance. Journal of Indian Research, 1(4), 105–111.
Song, S. L., Barker, K., & Kerbyson, D. (2013). Unified performance and power modeling
of scientific workloads. Proceedings of the 1st International Workshop on Energy
Efficient Supercomputing, 4, 1–8. https://doi.org/10.1145/2536430.2536435
Suifan, T. S., Abdallah, A. B., & Al Janini, M. (2018). The impact of transformational
leadership on employees’ creativity: The mediating role of perceived
organizational support. Management Research Review, 41(1), 113–132.
https://doi.org/10.1108/MRR-02-2017-0032
Tight, M. (2010). Are academic workloads increasing? The post-war survey evidence in
the UK. Higher Education Quarterly, 64(2), 200–215.
https://doi.org/10.1111/j.1468-2273.2009.00433.x
Toropova, A., Myrberg, E., & Johansson, S. (2021). Teacher job satisfaction: The importance
of school working conditions and teacher characteristics.
Educational Review, 73(1), 71–97. https://doi.org/10.1080/00131911.2019.1705247
Wolf, P., Harboe, J., Sudbrack Rothbarth, C., Gaudenz, U., Arsan, L., Obrist, C., & Van
Leeuwen, M. (2021). Non-governmental organisations and universities as
transition intermediaries in sustainability transformations building on grassroots
initiatives. Creativity and Innovation Management, 30(3), 596–618.
https://doi.org/10.1111/caim.12425
Xie, B., Zhou, W., Huang, J. L., & Xia, M. (2017). Using goal facilitation theory to explain
the relationships between calling and organization-directed citizenship behavior
17
http://ijlter.org/index.php/ijlter
and job satisfaction. Journal of Vocational Behavior, 100, 78–87.
https://doi.org/10.1016/j.jvb.2017.03.001
Zhang, J., Wu, Q., Miao, D., Yan, X., & Peng, J. (2014). The impact of core self-evaluations
on job satisfaction: The mediator role of career commitment.
Social Indicators Research, 116(3), 809–822. https://doi.org/10.1007/s11205-013-
0328-5
18
©Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License (CC BY-NC-ND 4.0).
International Journal of Learning, Teaching and Educational Research
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
21
http://ijlter.org/index.php/ijlter
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.
22
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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
http://ijlter.org/index.php/ijlter
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.
8. References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Processes, 50(2), 179–211.
Al-Emran, M., & Granić, A. (2021). Is it still valid or outdated? A bibliometric analysis of
the technology acceptance model and its applications from 2010 to 2020. In M. Al-
Emran & K. Shaalan (Eds.), Recent advances in technology acceptance models and
theories (Vol. 335, pp. 1–12). Springer. https://doi.org/10.1007/978-3-030-64987-
6_1
Almaiah, M. A., & Al Mulhem, A. (2019). 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,
24(2), 1433–1468.
Al-Mamary, Y., Al-nashmi, M., Shamsuddin, A., & Hassan, Y. A. G. (2018). Development
of an Integrated Model for Successful Adoption of Management Information
Systems in Organizations. Progress in Machines and Systems, 7(1), 1–27.
Amadin, F. I., Obienu, A. C., & Osaseri, R. O. (2018). Main barriers and possible enablers
of Google apps for education adoption among university staff members. Nigerian
Journal of Technology, 37(2), 432–439.
Andrade, C. (2019). Describing research design. Indian Journal of Psychological Medicine,
41(2), 201–202.
Bardakcı, S., & Alkan, M. F. (2019). Investigation of Turkish preservice teachers’ intentions
to use IWB in terms of technological and pedagogical aspects. Education and
Information Technologies, 24(5), 2887–2907.
Botero, G. G., Questier, F., Cincinnato, S., He, T., & Zhu, C. (2018). Acceptance and usage
of mobile assisted language learning by higher education students. Journal of
Computing in Higher Education, 30(3), 426–451.
Butler-Adam, J. (2018). The fourth industrial revolution and education. South African
Journal of Science, 114(5–6), 1–1. https://doi.org/10.17159/sajs.2018/a0271
Butt, J. (2020). A strategic roadmap for the manufacturing industry to implement industry
4.0. Designs, 4(2), 1–31.
Cha, K., & Kwon, S. (2018). Understanding the adoption of e-learning in South Korea:
Using the extended Technology Acceptance Model approach. KEDI Journal of
Educational Policy, 15(2).
Chao, C.-M. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning:
An Application and Extension of the UTAUT Model. Frontiers in Psychology, 10,
1–14.
Chick, R. C., Clifton, G. T., Peace, K. M., Propper, B. W., Hale, D. F., Alseidi, A. A., &
Vreeland, T. J. (2020). Using technology to maintain the education of residents
during the COVID-19 pandemic. Journal of Surgical Education, 77(4), 729–732.
https://doi.org/10.1016/j.jsurg.2020.03.018
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user
information systems: Theory and results [PhD Thesis]. Massachusetts Institute of
Technology.
30
http://ijlter.org/index.php/ijlter
Elshafey, A., Saar, C. C., Aminudin, E. B., Gheisari, M., & Usmani, A. (2020). Technology
acceptance model for Augmented Reality and Building Information Modeling
integration in the construction industry. Journal of Information Technology in
Construction, 25, 161–172. https://doi.org/10.36680/j.itcon.2020.010
Farella, M., Arrigo, M., Taibi, D., Todaro, G., Chiazzese, G., & Fulantelli, G. (2020).
ARLectio: An Augmented Reality Platform to Support Teachers in Producing
Educational Resources. Proceedings of the 12th International Conference on Computer
Supported Education (CSEDU 2020), 2, 469–475.
https://doi.org/10.5220/0009579104690475
Farjon, D., Smits, A., & Voogt, J. (2019). Technology integration of pre-service teachers
explained by attitudes and beliefs, competency, access, and experience. Computers
& Education, 130, 81–93. https://doi.org/10.1016/j.compedu.2018.11.010
Ferguson, R., Coughlan, T., Egelandsdal, K., Gaved, M., Herodotou, C., Hillaire, G., Jones,
D., Jowers, I., Kukulska-Hulme, A., Mcandrew, P., Misiejuk, K., Ness, J., Rienties,
B., Scanlon, E., Sharples, M., Wasson, B., Welle, M., & Whitelock, D. (2019).
Innovating Pedagogy 2019 (No. 7). The Open University.
Francisco, K., & Swanson, D. (2018). The supply chain has no clothes: Technology
adoption of blockchain for supply chain transparency. Logistics, 2(1), 1–13.
https://doi.org/10.3390/logistics2010002
Ghauri, P., Grønhaug, K., & Strange, R. (2020). Research methods in business studies.
Cambridge University Press.
Gómez-Trigueros, I. (2020). Digital teaching competence and space competence with
TPACK in social sciences. International Journal of Emerging Technologies in Learning
(IJET), 15(19), 37–52.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares
structural equation modeling (PLS-SEM) (2nd ed.). Sage.
Harmandaoğlu Baz, E., Cephe, P. T., & Balcıkanlı, C. (2019). Understanding EFL pre-
service teachers’ behavioral intentions to use cloud applications. E-Learning and
Digital Media, 16(3), 221–238.
Henderson, M., Selwyn, N., & Aston, R. (2017). What works and why? Student perceptions
of ‘useful’digital technology in university teaching and learning. Studies in Higher
Education, 42(8), 1567–1579. https://doi.org/10.1080/03075079.2015.1007946
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant
validity in variance-based structural equation modeling. Journal of the Academy of
Marketing Science, 43(1), 115–135.
Henson, R., Stewart, G., & Bedford, L. (2020). Key challenges and some guidance on using
strong quantitative methodology in education research. Journal of Urban
Mathematics Education, 13(2), 42–59. https://doi.org/10.21423/jume-v13i2a382
Ibili, E., Resnyansky, D., & Billinghurst, M. (2019). Applying the technology acceptance
model to understand maths teachers’ perceptions towards an augmented reality
tutoring system. Education and Information Technologies, 24, 2653–2675.
https://doi.org/10.1007/s10639-019-09925-z
Ismail, A. A., & Hassan, R. (2019). Technical competencies in digital technology towards
industrial revolution 4.0. Journal of Technical Education and Training, 11(3), 55–62.
Jang, H., & Koh, J. (2019). 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, 11(1), 87–103.
Kaliisa, R., Palmer, E., & Miller, J. (2019). Mobile learning in higher education: A
comparative analysis of developed and developing country contexts. British
Journal of Educational Technology, 50(2), 546–561.
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022
IJLTER.ORG Vol 21 No 1 January 2022

More Related Content

Similar to IJLTER.ORG Vol 21 No 1 January 2022

IJLTER.ORG Vol 21 No 5 May 2022
IJLTER.ORG Vol 21 No 5 May 2022IJLTER.ORG Vol 21 No 5 May 2022
IJLTER.ORG Vol 21 No 5 May 2022
ijlterorg
 
ILJTER.ORG Volume 22 Number 09 September 2023
ILJTER.ORG Volume 22 Number 09 September 2023ILJTER.ORG Volume 22 Number 09 September 2023
ILJTER.ORG Volume 22 Number 09 September 2023
ijlterorg
 
IJLTER.ORG Vol 20 No 4 April 2021
IJLTER.ORG Vol 20 No 4 April 2021IJLTER.ORG Vol 20 No 4 April 2021
IJLTER.ORG Vol 20 No 4 April 2021
ijlterorg
 
IJLTER.ORG Vol 19 No 11 November 2020
IJLTER.ORG Vol 19 No 11 November 2020IJLTER.ORG Vol 19 No 11 November 2020
IJLTER.ORG Vol 19 No 11 November 2020
ijlterorg
 
IJLTER.ORG Vol 22 No 3 March 2023
IJLTER.ORG Vol 22 No 3 March 2023IJLTER.ORG Vol 22 No 3 March 2023
IJLTER.ORG Vol 22 No 3 March 2023
ijlterorg
 
IJLTER.ORG Vol 21 No 11 November 2022
IJLTER.ORG Vol 21 No 11 November 2022IJLTER.ORG Vol 21 No 11 November 2022
IJLTER.ORG Vol 21 No 11 November 2022
ijlterorg
 
ILJTER.ORG Volume 23 Number 4 April 2024
ILJTER.ORG Volume 23 Number 4 April 2024ILJTER.ORG Volume 23 Number 4 April 2024
ILJTER.ORG Volume 23 Number 4 April 2024
ijlterorg
 
IJLTER.ORG Vol 20 No 6 June 2021
IJLTER.ORG Vol 20 No 6 June 2021IJLTER.ORG Vol 20 No 6 June 2021
IJLTER.ORG Vol 20 No 6 June 2021
ijlterorg
 
ILJTER.ORG Volume 22 Number 11 November 2023
ILJTER.ORG Volume 22 Number 11 November 2023ILJTER.ORG Volume 22 Number 11 November 2023
ILJTER.ORG Volume 22 Number 11 November 2023
ijlterorg
 
IJLTER.ORG Vol 21 No 6 June 2022
IJLTER.ORG Vol 21 No 6 June 2022IJLTER.ORG Vol 21 No 6 June 2022
IJLTER.ORG Vol 21 No 6 June 2022
ijlterorg
 
ILJTER.ORG Volume 22 Number 12 December 2023
ILJTER.ORG Volume 22 Number 12 December 2023ILJTER.ORG Volume 22 Number 12 December 2023
ILJTER.ORG Volume 22 Number 12 December 2023
ijlterorg
 
IJLTER.ORG Vol 19 No 7 July 2020
IJLTER.ORG Vol 19 No 7 July 2020IJLTER.ORG Vol 19 No 7 July 2020
IJLTER.ORG Vol 19 No 7 July 2020
ijlterorg
 
IJLTER.ORG Vol 22 No 2 February 2023
IJLTER.ORG Vol 22 No 2 February 2023IJLTER.ORG Vol 22 No 2 February 2023
IJLTER.ORG Vol 22 No 2 February 2023
ijlterorg
 
IJLTER.ORG Vol 21 No 2 February 2022
IJLTER.ORG Vol 21 No 2 February 2022IJLTER.ORG Vol 21 No 2 February 2022
IJLTER.ORG Vol 21 No 2 February 2022
ijlterorg
 
ILJTER.ORG Volume 22 Number 07 July 2023
ILJTER.ORG Volume 22 Number 07 July 2023ILJTER.ORG Volume 22 Number 07 July 2023
ILJTER.ORG Volume 22 Number 07 July 2023
ijlterorg
 
IJLTER.ORG Vol 21 No 3 March 2022
IJLTER.ORG Vol 21 No 3 March 2022IJLTER.ORG Vol 21 No 3 March 2022
IJLTER.ORG Vol 21 No 3 March 2022
ijlterorg
 
IJLTER.ORG Vol 20 No 7 July 2021
IJLTER.ORG Vol 20 No 7 July 2021IJLTER.ORG Vol 20 No 7 July 2021
IJLTER.ORG Vol 20 No 7 July 2021
ijlterorg
 
IJLTER.ORG Vol 20 No 2 February 2021
IJLTER.ORG Vol 20 No 2 February 2021IJLTER.ORG Vol 20 No 2 February 2021
IJLTER.ORG Vol 20 No 2 February 2021
ijlterorg
 
IJLTER.ORG Vol 20 No 5 May 2021
IJLTER.ORG Vol 20 No 5 May 2021IJLTER.ORG Vol 20 No 5 May 2021
IJLTER.ORG Vol 20 No 5 May 2021
ijlterorg
 
IJLTER.ORG Vol 19 No 9 September 2020
IJLTER.ORG Vol 19 No 9 September 2020IJLTER.ORG Vol 19 No 9 September 2020
IJLTER.ORG Vol 19 No 9 September 2020
ijlterorg
 

Similar to IJLTER.ORG Vol 21 No 1 January 2022 (20)

IJLTER.ORG Vol 21 No 5 May 2022
IJLTER.ORG Vol 21 No 5 May 2022IJLTER.ORG Vol 21 No 5 May 2022
IJLTER.ORG Vol 21 No 5 May 2022
 
ILJTER.ORG Volume 22 Number 09 September 2023
ILJTER.ORG Volume 22 Number 09 September 2023ILJTER.ORG Volume 22 Number 09 September 2023
ILJTER.ORG Volume 22 Number 09 September 2023
 
IJLTER.ORG Vol 20 No 4 April 2021
IJLTER.ORG Vol 20 No 4 April 2021IJLTER.ORG Vol 20 No 4 April 2021
IJLTER.ORG Vol 20 No 4 April 2021
 
IJLTER.ORG Vol 19 No 11 November 2020
IJLTER.ORG Vol 19 No 11 November 2020IJLTER.ORG Vol 19 No 11 November 2020
IJLTER.ORG Vol 19 No 11 November 2020
 
IJLTER.ORG Vol 22 No 3 March 2023
IJLTER.ORG Vol 22 No 3 March 2023IJLTER.ORG Vol 22 No 3 March 2023
IJLTER.ORG Vol 22 No 3 March 2023
 
IJLTER.ORG Vol 21 No 11 November 2022
IJLTER.ORG Vol 21 No 11 November 2022IJLTER.ORG Vol 21 No 11 November 2022
IJLTER.ORG Vol 21 No 11 November 2022
 
ILJTER.ORG Volume 23 Number 4 April 2024
ILJTER.ORG Volume 23 Number 4 April 2024ILJTER.ORG Volume 23 Number 4 April 2024
ILJTER.ORG Volume 23 Number 4 April 2024
 
IJLTER.ORG Vol 20 No 6 June 2021
IJLTER.ORG Vol 20 No 6 June 2021IJLTER.ORG Vol 20 No 6 June 2021
IJLTER.ORG Vol 20 No 6 June 2021
 
ILJTER.ORG Volume 22 Number 11 November 2023
ILJTER.ORG Volume 22 Number 11 November 2023ILJTER.ORG Volume 22 Number 11 November 2023
ILJTER.ORG Volume 22 Number 11 November 2023
 
IJLTER.ORG Vol 21 No 6 June 2022
IJLTER.ORG Vol 21 No 6 June 2022IJLTER.ORG Vol 21 No 6 June 2022
IJLTER.ORG Vol 21 No 6 June 2022
 
ILJTER.ORG Volume 22 Number 12 December 2023
ILJTER.ORG Volume 22 Number 12 December 2023ILJTER.ORG Volume 22 Number 12 December 2023
ILJTER.ORG Volume 22 Number 12 December 2023
 
IJLTER.ORG Vol 19 No 7 July 2020
IJLTER.ORG Vol 19 No 7 July 2020IJLTER.ORG Vol 19 No 7 July 2020
IJLTER.ORG Vol 19 No 7 July 2020
 
IJLTER.ORG Vol 22 No 2 February 2023
IJLTER.ORG Vol 22 No 2 February 2023IJLTER.ORG Vol 22 No 2 February 2023
IJLTER.ORG Vol 22 No 2 February 2023
 
IJLTER.ORG Vol 21 No 2 February 2022
IJLTER.ORG Vol 21 No 2 February 2022IJLTER.ORG Vol 21 No 2 February 2022
IJLTER.ORG Vol 21 No 2 February 2022
 
ILJTER.ORG Volume 22 Number 07 July 2023
ILJTER.ORG Volume 22 Number 07 July 2023ILJTER.ORG Volume 22 Number 07 July 2023
ILJTER.ORG Volume 22 Number 07 July 2023
 
IJLTER.ORG Vol 21 No 3 March 2022
IJLTER.ORG Vol 21 No 3 March 2022IJLTER.ORG Vol 21 No 3 March 2022
IJLTER.ORG Vol 21 No 3 March 2022
 
IJLTER.ORG Vol 20 No 7 July 2021
IJLTER.ORG Vol 20 No 7 July 2021IJLTER.ORG Vol 20 No 7 July 2021
IJLTER.ORG Vol 20 No 7 July 2021
 
IJLTER.ORG Vol 20 No 2 February 2021
IJLTER.ORG Vol 20 No 2 February 2021IJLTER.ORG Vol 20 No 2 February 2021
IJLTER.ORG Vol 20 No 2 February 2021
 
IJLTER.ORG Vol 20 No 5 May 2021
IJLTER.ORG Vol 20 No 5 May 2021IJLTER.ORG Vol 20 No 5 May 2021
IJLTER.ORG Vol 20 No 5 May 2021
 
IJLTER.ORG Vol 19 No 9 September 2020
IJLTER.ORG Vol 19 No 9 September 2020IJLTER.ORG Vol 19 No 9 September 2020
IJLTER.ORG Vol 19 No 9 September 2020
 

More from ijlterorg

ILJTER.ORG Volume 23 Number 3 March 2024.pdf
ILJTER.ORG Volume 23 Number 3 March 2024.pdfILJTER.ORG Volume 23 Number 3 March 2024.pdf
ILJTER.ORG Volume 23 Number 3 March 2024.pdf
ijlterorg
 
ILJTER.ORG Volume 23 Number 2 February 2024
ILJTER.ORG Volume 23 Number 2 February 2024ILJTER.ORG Volume 23 Number 2 February 2024
ILJTER.ORG Volume 23 Number 2 February 2024
ijlterorg
 
ILJTER.ORG Volume 22 Number 10 October 2023
ILJTER.ORG Volume 22 Number 10 October 2023ILJTER.ORG Volume 22 Number 10 October 2023
ILJTER.ORG Volume 22 Number 10 October 2023
ijlterorg
 
ILJTER.ORG Volume 22 Number 06 June 2023
ILJTER.ORG Volume 22 Number 06 June 2023ILJTER.ORG Volume 22 Number 06 June 2023
ILJTER.ORG Volume 22 Number 06 June 2023
ijlterorg
 
IJLTER.ORG Vol 22 No 5 May 2023
IJLTER.ORG Vol 22 No 5 May 2023IJLTER.ORG Vol 22 No 5 May 2023
IJLTER.ORG Vol 22 No 5 May 2023
ijlterorg
 
IJLTER.ORG Vol 21 No 12 December 2022
IJLTER.ORG Vol 21 No 12 December 2022IJLTER.ORG Vol 21 No 12 December 2022
IJLTER.ORG Vol 21 No 12 December 2022
ijlterorg
 
IJLTER.ORG Vol 21 No 10 October 2022
IJLTER.ORG Vol 21 No 10 October 2022IJLTER.ORG Vol 21 No 10 October 2022
IJLTER.ORG Vol 21 No 10 October 2022
ijlterorg
 
IJLTER.ORG Vol 21 No 9 September 2022
IJLTER.ORG Vol 21 No 9 September 2022IJLTER.ORG Vol 21 No 9 September 2022
IJLTER.ORG Vol 21 No 9 September 2022
ijlterorg
 
IJLTER.ORG Vol 19 No 12 December 2020
IJLTER.ORG Vol 19 No 12 December 2020IJLTER.ORG Vol 19 No 12 December 2020
IJLTER.ORG Vol 19 No 12 December 2020
ijlterorg
 
IJLTER.ORG Vol 19 No 10 October 2020
IJLTER.ORG Vol 19 No 10 October 2020IJLTER.ORG Vol 19 No 10 October 2020
IJLTER.ORG Vol 19 No 10 October 2020
ijlterorg
 
IJLTER.ORG Vol 19 No 8 August 2020
IJLTER.ORG Vol 19 No 8 August 2020IJLTER.ORG Vol 19 No 8 August 2020
IJLTER.ORG Vol 19 No 8 August 2020
ijlterorg
 

More from ijlterorg (11)

ILJTER.ORG Volume 23 Number 3 March 2024.pdf
ILJTER.ORG Volume 23 Number 3 March 2024.pdfILJTER.ORG Volume 23 Number 3 March 2024.pdf
ILJTER.ORG Volume 23 Number 3 March 2024.pdf
 
ILJTER.ORG Volume 23 Number 2 February 2024
ILJTER.ORG Volume 23 Number 2 February 2024ILJTER.ORG Volume 23 Number 2 February 2024
ILJTER.ORG Volume 23 Number 2 February 2024
 
ILJTER.ORG Volume 22 Number 10 October 2023
ILJTER.ORG Volume 22 Number 10 October 2023ILJTER.ORG Volume 22 Number 10 October 2023
ILJTER.ORG Volume 22 Number 10 October 2023
 
ILJTER.ORG Volume 22 Number 06 June 2023
ILJTER.ORG Volume 22 Number 06 June 2023ILJTER.ORG Volume 22 Number 06 June 2023
ILJTER.ORG Volume 22 Number 06 June 2023
 
IJLTER.ORG Vol 22 No 5 May 2023
IJLTER.ORG Vol 22 No 5 May 2023IJLTER.ORG Vol 22 No 5 May 2023
IJLTER.ORG Vol 22 No 5 May 2023
 
IJLTER.ORG Vol 21 No 12 December 2022
IJLTER.ORG Vol 21 No 12 December 2022IJLTER.ORG Vol 21 No 12 December 2022
IJLTER.ORG Vol 21 No 12 December 2022
 
IJLTER.ORG Vol 21 No 10 October 2022
IJLTER.ORG Vol 21 No 10 October 2022IJLTER.ORG Vol 21 No 10 October 2022
IJLTER.ORG Vol 21 No 10 October 2022
 
IJLTER.ORG Vol 21 No 9 September 2022
IJLTER.ORG Vol 21 No 9 September 2022IJLTER.ORG Vol 21 No 9 September 2022
IJLTER.ORG Vol 21 No 9 September 2022
 
IJLTER.ORG Vol 19 No 12 December 2020
IJLTER.ORG Vol 19 No 12 December 2020IJLTER.ORG Vol 19 No 12 December 2020
IJLTER.ORG Vol 19 No 12 December 2020
 
IJLTER.ORG Vol 19 No 10 October 2020
IJLTER.ORG Vol 19 No 10 October 2020IJLTER.ORG Vol 19 No 10 October 2020
IJLTER.ORG Vol 19 No 10 October 2020
 
IJLTER.ORG Vol 19 No 8 August 2020
IJLTER.ORG Vol 19 No 8 August 2020IJLTER.ORG Vol 19 No 8 August 2020
IJLTER.ORG Vol 19 No 8 August 2020
 

Recently uploaded

NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptxNEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
iammrhaywood
 
ZK on Polkadot zero knowledge proofs - sub0.pptx
ZK on Polkadot zero knowledge proofs - sub0.pptxZK on Polkadot zero knowledge proofs - sub0.pptx
ZK on Polkadot zero knowledge proofs - sub0.pptx
dot55audits
 
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumPhilippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
MJDuyan
 
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem studentsRHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Himanshu Rai
 
writing about opinions about Australia the movie
writing about opinions about Australia the moviewriting about opinions about Australia the movie
writing about opinions about Australia the movie
Nicholas Montgomery
 
The basics of sentences session 6pptx.pptx
The basics of sentences session 6pptx.pptxThe basics of sentences session 6pptx.pptx
The basics of sentences session 6pptx.pptx
heathfieldcps1
 
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPLAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
RAHUL
 
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
Nguyen Thanh Tu Collection
 
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
สมใจ จันสุกสี
 
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
MysoreMuleSoftMeetup
 
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
GeorgeMilliken2
 
How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17
Celine George
 
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxBeyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
EduSkills OECD
 
clinical examination of hip joint (1).pdf
clinical examination of hip joint (1).pdfclinical examination of hip joint (1).pdf
clinical examination of hip joint (1).pdf
Priyankaranawat4
 
math operations ued in python and all used
math operations ued in python and all usedmath operations ued in python and all used
math operations ued in python and all used
ssuser13ffe4
 
Wound healing PPT
Wound healing PPTWound healing PPT
Wound healing PPT
Jyoti Chand
 
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdfWalmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
TechSoup
 
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
Nguyen Thanh Tu Collection
 
Advanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docxAdvanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docx
adhitya5119
 
Main Java[All of the Base Concepts}.docx
Main Java[All of the Base Concepts}.docxMain Java[All of the Base Concepts}.docx
Main Java[All of the Base Concepts}.docx
adhitya5119
 

Recently uploaded (20)

NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptxNEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
 
ZK on Polkadot zero knowledge proofs - sub0.pptx
ZK on Polkadot zero knowledge proofs - sub0.pptxZK on Polkadot zero knowledge proofs - sub0.pptx
ZK on Polkadot zero knowledge proofs - sub0.pptx
 
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumPhilippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
 
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem studentsRHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
 
writing about opinions about Australia the movie
writing about opinions about Australia the moviewriting about opinions about Australia the movie
writing about opinions about Australia the movie
 
The basics of sentences session 6pptx.pptx
The basics of sentences session 6pptx.pptxThe basics of sentences session 6pptx.pptx
The basics of sentences session 6pptx.pptx
 
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPLAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
 
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
 
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
 
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
 
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
 
How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17
 
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxBeyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
 
clinical examination of hip joint (1).pdf
clinical examination of hip joint (1).pdfclinical examination of hip joint (1).pdf
clinical examination of hip joint (1).pdf
 
math operations ued in python and all used
math operations ued in python and all usedmath operations ued in python and all used
math operations ued in python and all used
 
Wound healing PPT
Wound healing PPTWound healing PPT
Wound healing PPT
 
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdfWalmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
 
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
 
Advanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docxAdvanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docx
 
Main Java[All of the Base Concepts}.docx
Main Java[All of the Base Concepts}.docxMain Java[All of the Base Concepts}.docx
Main Java[All of the Base Concepts}.docx
 

IJLTER.ORG Vol 21 No 1 January 2022

  • 1. International Journal of Learning, Teaching And Educational Research p-ISSN: 1694-2493 e-ISSN: 1694-2116 IJLTER.ORG Vol.21 No.1
  • 2. International Journal of Learning, Teaching and Educational Research (IJLTER) Vol. 21, No. 1 (January 2022) Print version: 1694-2493 Online version: 1694-2116 IJLTER International Journal of Learning, Teaching and Educational Research (IJLTER) Vol. 21, No. 1 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically those of translation, reprinting, re-use of illustrations, broadcasting, reproduction by photocopying machines or similar means, and storage in data banks. Society for Research and Knowledge Management
  • 3. International Journal of Learning, Teaching and Educational Research The International Journal of Learning, Teaching and Educational Research is a peer-reviewed open-access journal which has been established for the dissemination of state-of-the-art knowledge in the fields of learning, teaching and educational research. Aims and Objectives The main objective of this journal is to provide a platform for educators, teachers, trainers, academicians, scientists and researchers from over the world to present the results of their research activities in the following fields: innovative methodologies in learning, teaching and assessment; multimedia in digital learning; e-learning; m-learning; e-education; knowledge management; infrastructure support for online learning; virtual learning environments; open education; ICT and education; digital classrooms; blended learning; social networks and education; e- tutoring: learning management systems; educational portals, classroom management issues, educational case studies, etc. Indexing and Abstracting The International Journal of Learning, Teaching and Educational Research is indexed in Scopus since 2018. The Journal is also indexed in Google Scholar and CNKI. All articles published in IJLTER are assigned a unique DOI number.
  • 4. Foreword We are very happy to publish this issue of the International Journal of Learning, Teaching and Educational Research. The International Journal of Learning, Teaching and Educational Research is a peer-reviewed open-access journal committed to publishing high-quality articles in the field of education. Submissions may include full-length articles, case studies and innovative solutions to problems faced by students, educators and directors of educational organisations. To learn more about this journal, please visit the website http://www.ijlter.org. We are grateful to the editor-in-chief, members of the Editorial Board and the reviewers for accepting only high quality articles in this issue. We seize this opportunity to thank them for their great collaboration. The Editorial Board is composed of renowned people from across the world. Each paper is reviewed by at least two blind reviewers. We will endeavour to ensure the reputation and quality of this journal with this issue. Editors of the January 2022 Issue
  • 5. 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
  • 6. 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
  • 7. 1 ©Authors This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). International Journal of Learning, Teaching and Educational Research 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
  • 8. 2 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
  • 9. 3 http://ijlter.org/index.php/ijlter 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
  • 10. 4 http://ijlter.org/index.php/ijlter 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
  • 11. 5 http://ijlter.org/index.php/ijlter 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
  • 12. 6 http://ijlter.org/index.php/ijlter 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,
  • 13. 7 http://ijlter.org/index.php/ijlter 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
  • 14. 8 http://ijlter.org/index.php/ijlter 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
  • 15. 9 http://ijlter.org/index.php/ijlter 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
  • 16. 10 http://ijlter.org/index.php/ijlter 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
  • 17. 11 http://ijlter.org/index.php/ijlter 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
  • 18. 12 http://ijlter.org/index.php/ijlter 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. 9. References Agbozo, G. K., Owusu, I. S., Hoedoafia, M. A., & Atakorah, Y. B. (2017). The effect of work environment on job satisfaction: Evidence from the banking sector in Ghana. Journal of Human Resource Management, 5(1), 12–18. https://doi.org/10.11648/j.jhrm.20170501.12 Ahmad, A., Khan, M. U., Srikanth, A. B., Patel, I., Nagappa, A. N., & Jamshed, S. Q. (2015). Evaluation of workload and its impact on satisfaction among pharmacy academicians in Southern India. Journal of Clinical and Diagnostic Research: JCDR, 9(6), 1–6. https://doi.org/10.7860/JCDR/2015/12921.6023 Akob, M. (2016). Influence workload, work ethic and job satisfaction toward teacher’s performance (Study of Islamic-based school in Makasar- Indonesia). Advanced Research Journal of Management and Business Studies, 5(7), 172–177. Ali, N. H. M., Hassan, S. A., Jailani, O., Zaremohzzabieh, Z., & Lee, Z. J. (2020). The impact of supervisory styles on satisfaction of undergraduate counselling interns in Malaysia. Asian Journal of University Education, 16(3), 138–147. https://doi.org/10.24191/ajue.v16i3.11079
  • 19. 13 http://ijlter.org/index.php/ijlter Alrawahi, S., Sellgren, S. F., Altouby, S., Alwahaibi, N., & Brommels, M. (2020). The application of Herzberg’s two-factor theory of motivation to job satisfaction in clinical laboratories in Omani hospitals. Heliyon, 6(9), 1–9. https://doi.org/10.1016/j.heliyon.2020.e04829 Al-Sada, M., Al-Esmael, B., & Faisal, M. N. (2017). Influence of organizational culture and leadership style on employee satisfaction, commitment and motivation in the educational sector in Qatar. EuroMed Journal of Business, 12(2), 163–188. https://doi.org/10.1108/EMJB-02-2016-0003 Amin, S., Arshad, R., & Ghani, R. A. (2017). Spousal support and subjective career success: The role of work-family balance and career commitment as mediator. Jurnal Pengurusan (UKM Journal of Management), 50, 133–142. https://doi.oeg/10.17576/pengurusan-2017-50-12 Ather, S. M., & Nimlathasan, B. (2006). Association between quality of work life (QWL) and job satisfaction (JS): A study of academic professionals of private universities in Bangladesh. The Chittagong University Journal of Business Administration, 21, 9– 23. https://www.researchgate.net/publication/205019610_Quality_of_Work_life_ QoWL_and_Job_Satisfaction_JS_A_Study_of_Academic_Professionals_of_Privat e_Universities_in_Bangladesh Berberoglu, A. (2018). Impact of organizational climate on organizational commitment and perceived organizational performance: Empirical evidence from public hospitals. BMC Health Services Research, 18(1), 399. https://doi.org/10.1186/s12913-018-3149-z Bibi, P., Ahmad, A., & Majid, A. H. A. (2018). The impact of training and development and supervisor support on employees retention in academic institutions: The moderating role of work environment. Gadjah Mada International Journal of Business, 20(1), 113–131. https://doi.org/10.22146/gamaijb.24020 Blau, G. J. (1985). The measurement and prediction of career commitment. Journal of Occupational Psychology, 58(4), 277–288. https://doi.org/10.1111/j.2044- 8325.1985.tb00201.x Caldana, A. C. F., Eustachio, J. H. P. P., Sampaio, B. L., Gianotto, M. L., Talarico, A. C., & da Silva Batalhão, A. C. (2021). A hybrid approach to sustainable development competencies: The role of formal, informal and non-formal learning experiences. International Journal of Sustainability in Higher Education (ahead of print), 1–24. https://doi.org/10.1108/ijshe-10-2020-0420 Cerci, P. A., & Dumludag, D. (2019). Life satisfaction and job satisfaction among university faculty: The impact of working conditions, academic performance and relative income. Social Indicators Research, 144(2), 785−806. https://doi.org/10.1007/s11205-018-02059-8 Choi, H., & Chiu, W. (2017). Influence of the perceived organizational support, job satisfaction, and career commitment on football referees’ turnover intention. Journal of Physical Education and Sport, 17, 955–959. https://doi.org/10.7752/jpes.2017.s3146 Chong, Y., & Thi, L.-S. (2020). University freshman mentoring effectiveness and scale enhancement. Asian Journal of University Education, 16(4), 181–189. https://doi.org/10.24191/ajue.v16i4.11950 Clarke, M. (2015). Creating a supportive working environment in European higher education [technical report]. Education International Research Institute. de Lourdes Machado-Taylor, M., Meira Soares, V., Brites, R., Brites Ferreira, J., Farhangmehr, M., Gouveia, O. M. R., & Peterson, M. (2016). Academic job satisfaction and motivation: Findings from a nationwide study in Portuguese
  • 20. 14 http://ijlter.org/index.php/ijlter higher education. Studies in Higher Education, 41(3), 541–559. https://doi.org/10.1080/03075079.2014.942265 De Sousa Sabbagha, M., Ledimo, O., & Martins, N. (2018). Predicting staff retention from employee motivation and job satisfaction. Journal of Psychology in Africa, 28(2), 136–140. https://doi.org/10.1080/14330237.2018.1454578 Dorenkamp, I., & Ruhle, S. (2019). Work-life conflict, professional commitment, and job satisfaction among academics. The Journal of Higher Education, 90(1), 56–84. https://doi.org/10.1080/00221546.2018.1484644 Duffy, R. D., England, J. W., Douglass, R. P., Autin, K. L., & Allan, B. A. (2017). Perceiving a calling and well-being: Motivation and access to opportunity as moderators. Journal of Vocational Behavior, 98, 127–137. https://doi.org/10.1016/j.jvb.2016.11.003 Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71(3), 500–507. https://doi.org/10.1037/0021-9010.71.3.500 Escardíbul, J.-O., & Afcha, S. (2017). Determinants of the job satisfaction of PhD holders: An analysis by gender, employment sector, and type of satisfaction in Spain. Higher Education, 74(5), 855–875. https://doi.org/10.1007/s10734-016-0081-1 Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447. https://doi.org/10.1108/IntR-12-2017-0515 Gendron, T., Welleford, E. A., Pelco, L., & Myers, B. J. (2016). Who is likely to commit to a career with older adults? Gerontology & Geriatrics Education, 37(2), 208–228. https://doi.org/10.1080/02701960.2014.954042 Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584. https://doi.org/10.1108/EJM-10-2018-0665 Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069 Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013- 0128 Halder, N. (2018). Investing in human capital: Exploring causes, consequences and solutions to nurses’ dissatisfaction. Journal of Research in Nursing, 23(8), 659–675. https://doi.org/10.1177/1744987118807251 Hall, D. T., Yip, J., & Doiron, K. (2018). Protean careers at work: Self-direction and values orientation in psychological success. Annual Review of Organizational Psychology and Organizational Behavior, 5, 129–156. https://doi.org/10.1146/annurev- orgpsych-032117-104631 Hee, O. C., Ong, S. H., Ping, L. L., Kowang, T. O., & Fei, G. C. (2019). Factors influencing job satisfaction in the higher learning institutions in Malaysia. International Journal of Academic Research in Business and Social Sciences, 9(2), 10–20. https://doi.org/10.6007/IJARBSS/v9-i2/5510 Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
  • 21. 15 http://ijlter.org/index.php/ijlter Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 Herzberg, F.I., Mausner, B., & Snyderman, B. (1959). The motivation to work (2nd ed.). John Wiley. Herzberg, F. I. (1966). Work and the Nature of Man. Wold. Houston, D., Meyer, L. H., & Paewai, S. (2006). Academic staff workloads and job satisfaction: Expectations and values in academe. Journal of Higher Education Policy and Management, 28(1), 17–30. https://doi.org/10.1080/13600800500283734 Hsiao, J.-M., & Lin, D.-S. (2018). The impacts of working conditions and employee competences of fresh graduates on job expertise, salary and job satisfaction. Journal of Reviews on Global Economics, 7, 246–259. https://ideas.repec.org/a/lif/jrgelg/v7y2018p246-259.html Imondi, P. J. N. (2011). The influence of workload on performance of teachers in public primary schools in Kombewa Division, Kisumu West District, Kenya (Master’s dissertation). University of Nairobi. Indarti, S., Fernandes, A. A. R., & Hakim, W. (2017). The effect of OCB in relationship between personality, organizational commitment and job satisfaction on performance. Journal of Management Development, 36(10), 1283–1293. https://doi.org/10.1108/JMD-11-2016-0250 Inegbedion, H., Inegbedion, E., Peter, A., & Harry, L. (2020). Perception of workload balance and employee job satisfaction in work organisations. Heliyon, 6(1), 1–9. https://doi.org/10.1016/j.heliyon.2020.e03160 Janib, J., Rasdi, R. M., Omar, Z., Alias, S. N., Zaremohzzabieh, Z., & Ahrari, S. (2021). The relationship between workload and performance of research university academics in Malaysia: The mediating effects of career commitment and job satisfaction. Asian Journal of University Education, 17(2), 85–99. https://doi.org/10.24191/ajue.v17i2.13394 Kenny, J. (2018). Re-empowering academics in a corporate culture: An exploration of workload and performativity in a university. Higher Education, 75(2), 365–380. https://doi.org/10.1007/s10734-017-0143-z Kim, S. J., Song, M., Hwang, E., Roh, T., & Song, J. H. (2020). The mediating effect of individual regulatory focus in the relationship between career commitment and job satisfaction. European Journal of Training and Development, 45(2/3), 166−180. https://doi.org/10.1108/EJTD-02-2020-0030 Laihonen, H., & Mäntylä, S. (2017). Principles of performance dialogue in public administration. International Journal of Public Sector Management, 30(5), 414–428. https://doi.org/10.1108/IJPSM-09-2016-0149 Liu, H.-L., & Lo, V. (2018). An integrated model of workload, autonomy, burnout, job satisfaction, and turnover intention among Taiwanese reporters. Asian Journal of Communication, 28(2), 153–169. https://doi.org/10.1080/01292986.2017.1382544 Liu, J., Yu, W., Ding, T., Li, M., & Zhang, L. (2019). Cross-sectional survey on job satisfaction and its associated factors among doctors in tertiary public hospitals in Shanghai, China. BMJ Open, 9(3), 1–10. http://dx.doi.org/10.1136/bmjopen- 2018-023823 Marsaditha, P. H. (2017). The influence of work load, job satisfaction, and working environment towards woman work life balance (Case study in Pt Hasta Ayu Nusantara Jakarta) (PhD thesis). President University, West Java, Indonesia.
  • 22. 16 http://ijlter.org/index.php/ijlter Mudrak, J., Zabrodska, K., Kveton, P., Jelinek, M., Blatny, M., Solcova, I., & Machovcova, K. (2018). Occupational well-being among university faculty: A job demands- resources model. Research in Higher Education, 59(3), 325–348. https://doi.org/10.1007/s11162-017-9467-x Naseer, S., Raja, U., Syed, F., & Bouckenooghe, D. (2018). Combined effects of workplace bullying and perceived organizational support on employee behaviors: Does resource availability help? Anxiety, Stress, & Coping, 31(6), 654–668. https://doi.org/10.1080/10615806.2018.1521516 Newman, A., Nielsen, I., Smyth, R., Hirst, G., & Kennedy, S. (2018). The effects of diversity climate on the work attitudes of refugee employees: The mediating role of psychological capital and moderating role of ethnic identity. Journal of Vocational Behavior, 105, 147–158. https://doi.org/10.1016/j.jvb.2017.09.005 Osifila, G. I., & Aladetan, T. A. (2020). Workload and lecturers’ job satisfaction in Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria. Journal of Education and Learning (EduLearn), 14(3), 416–423. https://eric.ed.gov/?id=EJ1266299 Prieto, I. M., & Pérez-Santana, M. P. (2014). Managing innovative work behavior: The role of human resource practices. Personnel Review, 43(2), 184–208. https://doi.org/10.1108/PR-11-2012-0199 Purba, S. D. (2017). Career management dan subjective career success: Dapatkah meningkatkan kepuasan kerja wanita karir? [Career management and subjective career success: Can women’s job satisfaction improve their career?]. MIX: Jurnal Ilmiah Manajemen, 7(1), 113–131. Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1617–1643. https://doi.org/10.1080/09585192.2017.1416655 Sabagh, Z., Hall, N. C., & Saroyan, A. (2018). Antecedents, correlates and consequences of faculty burnout. Educational Research, 60(2), 131–156. https://doi.org/10.1080/00131881.2018.1461573 Singh, J. K., & Jain, M. (2013). A study of employees’ job satisfaction and its impact on their performance. Journal of Indian Research, 1(4), 105–111. Song, S. L., Barker, K., & Kerbyson, D. (2013). Unified performance and power modeling of scientific workloads. Proceedings of the 1st International Workshop on Energy Efficient Supercomputing, 4, 1–8. https://doi.org/10.1145/2536430.2536435 Suifan, T. S., Abdallah, A. B., & Al Janini, M. (2018). The impact of transformational leadership on employees’ creativity: The mediating role of perceived organizational support. Management Research Review, 41(1), 113–132. https://doi.org/10.1108/MRR-02-2017-0032 Tight, M. (2010). Are academic workloads increasing? The post-war survey evidence in the UK. Higher Education Quarterly, 64(2), 200–215. https://doi.org/10.1111/j.1468-2273.2009.00433.x Toropova, A., Myrberg, E., & Johansson, S. (2021). Teacher job satisfaction: The importance of school working conditions and teacher characteristics. Educational Review, 73(1), 71–97. https://doi.org/10.1080/00131911.2019.1705247 Wolf, P., Harboe, J., Sudbrack Rothbarth, C., Gaudenz, U., Arsan, L., Obrist, C., & Van Leeuwen, M. (2021). Non-governmental organisations and universities as transition intermediaries in sustainability transformations building on grassroots initiatives. Creativity and Innovation Management, 30(3), 596–618. https://doi.org/10.1111/caim.12425 Xie, B., Zhou, W., Huang, J. L., & Xia, M. (2017). Using goal facilitation theory to explain the relationships between calling and organization-directed citizenship behavior
  • 23. 17 http://ijlter.org/index.php/ijlter and job satisfaction. Journal of Vocational Behavior, 100, 78–87. https://doi.org/10.1016/j.jvb.2017.03.001 Zhang, J., Wu, Q., Miao, D., Yan, X., & Peng, J. (2014). The impact of core self-evaluations on job satisfaction: The mediator role of career commitment. Social Indicators Research, 116(3), 809–822. https://doi.org/10.1007/s11205-013- 0328-5
  • 24. 18 ©Authors This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). International Journal of Learning, Teaching and Educational Research 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
  • 25. 19 http://ijlter.org/index.php/ijlter 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
  • 26. 20 http://ijlter.org/index.php/ijlter 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
  • 27. 21 http://ijlter.org/index.php/ijlter 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.
  • 28. 22 http://ijlter.org/index.php/ijlter 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
  • 29. 23 http://ijlter.org/index.php/ijlter 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
  • 30. 24 http://ijlter.org/index.php/ijlter 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
  • 31. 25 http://ijlter.org/index.php/ijlter 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
  • 32. 26 http://ijlter.org/index.php/ijlter 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
  • 33. 27 http://ijlter.org/index.php/ijlter 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.
  • 34. 28 http://ijlter.org/index.php/ijlter 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-
  • 35. 29 http://ijlter.org/index.php/ijlter 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. 8. References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. Al-Emran, M., & Granić, A. (2021). Is it still valid or outdated? A bibliometric analysis of the technology acceptance model and its applications from 2010 to 2020. In M. Al- Emran & K. Shaalan (Eds.), Recent advances in technology acceptance models and theories (Vol. 335, pp. 1–12). Springer. https://doi.org/10.1007/978-3-030-64987- 6_1 Almaiah, M. A., & Al Mulhem, A. (2019). 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, 24(2), 1433–1468. Al-Mamary, Y., Al-nashmi, M., Shamsuddin, A., & Hassan, Y. A. G. (2018). Development of an Integrated Model for Successful Adoption of Management Information Systems in Organizations. Progress in Machines and Systems, 7(1), 1–27. Amadin, F. I., Obienu, A. C., & Osaseri, R. O. (2018). Main barriers and possible enablers of Google apps for education adoption among university staff members. Nigerian Journal of Technology, 37(2), 432–439. Andrade, C. (2019). Describing research design. Indian Journal of Psychological Medicine, 41(2), 201–202. Bardakcı, S., & Alkan, M. F. (2019). Investigation of Turkish preservice teachers’ intentions to use IWB in terms of technological and pedagogical aspects. Education and Information Technologies, 24(5), 2887–2907. Botero, G. G., Questier, F., Cincinnato, S., He, T., & Zhu, C. (2018). Acceptance and usage of mobile assisted language learning by higher education students. Journal of Computing in Higher Education, 30(3), 426–451. Butler-Adam, J. (2018). The fourth industrial revolution and education. South African Journal of Science, 114(5–6), 1–1. https://doi.org/10.17159/sajs.2018/a0271 Butt, J. (2020). A strategic roadmap for the manufacturing industry to implement industry 4.0. Designs, 4(2), 1–31. Cha, K., & Kwon, S. (2018). Understanding the adoption of e-learning in South Korea: Using the extended Technology Acceptance Model approach. KEDI Journal of Educational Policy, 15(2). Chao, C.-M. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology, 10, 1–14. Chick, R. C., Clifton, G. T., Peace, K. M., Propper, B. W., Hale, D. F., Alseidi, A. A., & Vreeland, T. J. (2020). Using technology to maintain the education of residents during the COVID-19 pandemic. Journal of Surgical Education, 77(4), 729–732. https://doi.org/10.1016/j.jsurg.2020.03.018 Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [PhD Thesis]. Massachusetts Institute of Technology.
  • 36. 30 http://ijlter.org/index.php/ijlter Elshafey, A., Saar, C. C., Aminudin, E. B., Gheisari, M., & Usmani, A. (2020). Technology acceptance model for Augmented Reality and Building Information Modeling integration in the construction industry. Journal of Information Technology in Construction, 25, 161–172. https://doi.org/10.36680/j.itcon.2020.010 Farella, M., Arrigo, M., Taibi, D., Todaro, G., Chiazzese, G., & Fulantelli, G. (2020). ARLectio: An Augmented Reality Platform to Support Teachers in Producing Educational Resources. Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020), 2, 469–475. https://doi.org/10.5220/0009579104690475 Farjon, D., Smits, A., & Voogt, J. (2019). Technology integration of pre-service teachers explained by attitudes and beliefs, competency, access, and experience. Computers & Education, 130, 81–93. https://doi.org/10.1016/j.compedu.2018.11.010 Ferguson, R., Coughlan, T., Egelandsdal, K., Gaved, M., Herodotou, C., Hillaire, G., Jones, D., Jowers, I., Kukulska-Hulme, A., Mcandrew, P., Misiejuk, K., Ness, J., Rienties, B., Scanlon, E., Sharples, M., Wasson, B., Welle, M., & Whitelock, D. (2019). Innovating Pedagogy 2019 (No. 7). The Open University. Francisco, K., & Swanson, D. (2018). The supply chain has no clothes: Technology adoption of blockchain for supply chain transparency. Logistics, 2(1), 1–13. https://doi.org/10.3390/logistics2010002 Ghauri, P., Grønhaug, K., & Strange, R. (2020). Research methods in business studies. Cambridge University Press. Gómez-Trigueros, I. (2020). Digital teaching competence and space competence with TPACK in social sciences. International Journal of Emerging Technologies in Learning (IJET), 15(19), 37–52. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage. Harmandaoğlu Baz, E., Cephe, P. T., & Balcıkanlı, C. (2019). Understanding EFL pre- service teachers’ behavioral intentions to use cloud applications. E-Learning and Digital Media, 16(3), 221–238. Henderson, M., Selwyn, N., & Aston, R. (2017). What works and why? Student perceptions of ‘useful’digital technology in university teaching and learning. Studies in Higher Education, 42(8), 1567–1579. https://doi.org/10.1080/03075079.2015.1007946 Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. Henson, R., Stewart, G., & Bedford, L. (2020). Key challenges and some guidance on using strong quantitative methodology in education research. Journal of Urban Mathematics Education, 13(2), 42–59. https://doi.org/10.21423/jume-v13i2a382 Ibili, E., Resnyansky, D., & Billinghurst, M. (2019). Applying the technology acceptance model to understand maths teachers’ perceptions towards an augmented reality tutoring system. Education and Information Technologies, 24, 2653–2675. https://doi.org/10.1007/s10639-019-09925-z Ismail, A. A., & Hassan, R. (2019). Technical competencies in digital technology towards industrial revolution 4.0. Journal of Technical Education and Training, 11(3), 55–62. Jang, H., & Koh, J. (2019). 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, 11(1), 87–103. Kaliisa, R., Palmer, E., & Miller, J. (2019). Mobile learning in higher education: A comparative analysis of developed and developing country contexts. British Journal of Educational Technology, 50(2), 546–561.