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This study seeks to explore the effect of fear emotion on students' and teachers' technology adoption during COVID-19 pandemic. The study has made use of Google Meet© as an educational social platform in private higher education institutes. The data obtained from the study were analyzed by using the partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms. The main hypotheses of this study are related to the effect of COVID-19 on the adoption of Google Meet as COVID-19 rise s various types of fear. During the Coronavirus pandemic, fear due to family lockdown situation, fear of education failure and fear of losing social relationships are the most common types of threat that may face students and teachers/educators. These types of fears are connected with two important factors within TAM theory, which are: perceived ease of use (PEOU) and perceived usefulness (PU), and with another external factor of TAM, which is subjective norm (SN). The results revealed that both data analysis techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases.
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Interactive Learning Environments
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Fear from COVID-19 and technology adoption:
the impact of Google Meet during Coronavirus
pandemic
Rana Saeed Al-Maroof , Said A. Salloum , Aboul Ella Hassanien & Khaled
Shaalan
To cite this article: Rana Saeed Al-Maroof , Said A. Salloum , Aboul Ella Hassanien & Khaled
Shaalan (2020): Fear from COVID-19 and technology adoption: the impact of Google Meet during
Coronavirus pandemic, Interactive Learning Environments, DOI: 10.1080/10494820.2020.1830121
To link to this article: https://doi.org/10.1080/10494820.2020.1830121
Published online: 14 Oct 2020.
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Fear from COVID-19 and technology adoption: the impact of
Google Meet during Coronavirus pandemic
Rana Saeed Al-Maroof
a
, Said A. Salloum
b
, Aboul Ella Hassanien
c
and
Khaled Shaalan
d
a
English Language & Linguistics Department, Al Buraimi University College, Al Buraimi, Oman;
b
Research Institute
of Sciences & Engineering, University of Sharjah, Sharjah, UAE;
c
Faculty of Computers & Articial Intelligence, Cairo
University, Cairo, Egypt;
d
Faculty of Engineering & IT, The British University in Dubai, Dubai, UAE
ABSTRACT
This study seeks to explore the eect of fear emotion on studentsand
teacherstechnology adoption during COVID-19 pandemic. The study
has made use of Google Meet© as an educational social platform in
private higher education institutes. The data obtained from the study
were analyzed by using the partial least squares structural equation
modeling (PLS-SEM) and machine learning algorithms. The main
hypotheses of this study are related to the eect of COVID-19 on the
adoption of Google Meet as COVID-19 rise s various types of fear.
During the Coronavirus pandemic, fear due to family lockdown
situation, fear of education failure and fear of losing social relationships
are the most common types of threat that may face students and
teachers/educators. These types of fears are connected with two
important factors within TAM theory, which are: perceived ease of use
(PEOU) and perceived usefulness (PU), and with another external factor
of TAM, which is subjective norm (SN). The results revealed that both
data analysis techniques have successfully provided support to all the
hypothesized relationships of the research model. More interesting, the
J48 classier has performed better than the other classiers in
predicting the dependent variable in most cases.
ARTICLE HISTORY
Received 20 May 2020
Accepted 25 September
2020
KEYWORDS
E-Technology; COVID-19;
Google Meet; fear and TAM
1. Introduction
Universities and colleges have dictated much of their eort to build up a group of virtual teaching
environment supported by necessary resources and platforms. In fact, they are striving to achieve
certain successful results. However, the spread of COVID-19 has left these institutions in a predica-
ment. It has led to bad consequences of emotions, such as fears, worries and feeling of apprehension
among students all over the world. Fear alone negatively aects the psychological status of students
and lead to stigma in some situations. The pandemic nature of COVID-19 has even worsened the
situation leading to psychosocial challenges, such as loss and discrimination (Ahorsu et al., 2020;
Lin, 2020; Pappas et al., 2009). Fear has inuenced the educational institutions resulting in hindering
the teaching and learning process; aecting the concept of e-learning deeply. Fear is manifested in
dierent forms, including fear of security, fear of missing out, fear of failure, fear to take risks, etc.
(Alt & Boniel-Nissim, 2018; Ellahi, 2017; Machů& Morysová, 2016; MORCHID, n.d.). Based on the pre-
vious assumption, it seems that fear may extend its eect to inuence the adoption of technology
during COVID-19 pandemic when most schools, colleges and universities have started implementing
© 2020 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Said A. Salloum ssalloum@sharjah.ac.ae; salloum78@live.com
INTERACTIVE LEARNING ENVIRONMENTS
https://doi.org/10.1080/10494820.2020.1830121
distance learning to lessen the dangerous and malicious eects of Coronavirus. However, most of
universities and colleges have faced certain challenges regarding teachersknowledge and its
implementation through technology, studentsunderstanding and prociency, and the lack of trans-
ferring classroom teaching into virtual classes (Chen & Li, 2011; Li et al., 2018; Liang et al., 2011). The
validation of the eectiveness of technology or virtual class application is highly dependent on the
adoption of the technology as a mean for distance learning. Most adoption studies have shown that
the process of adoption itself is not an easy one as it may inuence many aspects, such as learning
technology, context and strategy. Though technology adoption has been covered by many research-
ers in the previous studies, it is believed that the adoption of innovative ways of teaching, which is
Google Meet, in exceptional circumstance; that is, Coronavirus pandemic has not been investigated
yet. Both Google Play and Apple Store have recently provided all users with Google Meet. The appli-
cation can be accessed and updated automatically from the Store. The freemium strategy that is
found in the App Store has positively aected the number of users (Habes et al., 2020; C. Z. Liu
et al., 2014; McIlroy et al., 2016).
A key extra feature that radically worsens the learning environment is that people have devel-
oped high pressure of fear during the spread of COVID-19 (Lemke & Silverman, 2020) which put
the colleges and universities under the pressure of accounting for two important issues simul-
taneously: choosing an eective e-learning tool and controlling the high fear environment of the stu-
dents. Accordingly, the current study investigates the importance of choosing an eective and
suitable technology that lessens the fear factor during the educational process. Therefore, the fear
element as an external factor was introduced to the TAM model to account for the acceptance of
Google Meet technology. The novelty of the current research lies in the fact that fear factor was
added as an external factor to the well-established TAM model along with the novelty of both the Cor-
onavirus situation and the novelty of Google Meet app that have been also addressed. Google Meet
app is relatively a recent application, and no studies have focused on their role at the higher education
domain. The lack of a clear understanding of the role of fear, which may de crease the opportunities for
using technology for educational purposes, is evident in previous studies.
Keeping all these limitations in mind, the purpose of the current research is to provide a better
educational understanding of the type of technology that can t in the best, whenever fear is a domi-
nant gure in the life of teachers and students. Both teachers and students are following this new
application for the rst time to improve learning outcomes during this critical period.
In terms of academic research adoption model, TAM has been successfully used widely in the lit-
erature as a model for adoption of technology. It has been used to measure the userswillingness to
accept and use a technology (Q. Liu et al., 2020; Tsai et al., 2020). Hence, the current study makes use
of TAM with two external factors: namely, fear and subjective norms to understand teachers and stu-
dentswillingness to accept Google Meet. The technology acceptance model (TAM) is used to inves-
tigate the perceptions of two groups, namely: teachers and students regarding the use of Google
Meet in the Coronavirus pandemic. Fear during the predicament of COVID-19 and its direct relation
with the TAM model has never been dealt with before. Our model, therefore, focuses on the dierent
forms of fear that teachers and students may have especially when there is a big threat from Coro-
navirus pandemic. By doing so, it is believed that this research paper will provide technological and
educational input to both teachers and application developer of how to implement and develop
new technologies in the time of disease lockdown. Understanding the conditions behind the adop-
tion of Google Meet during Coronavirus pandemic may lead to highlight certain educational issues
that are unique and can appear only in such exceptional circumstances. It may add theoretically and
practically to the related literature in the eld of technology adoption.
2. Literature review and knowledge gap
Previous adoption studies have focused on dierent forms of fear emotion. Anxiety, for instance, is
considered a crucial factor in many research studies that tackle the adoption of technology and
2R. S. AL-MAROOF ET AL.
anxiety. Part of the educational sector, anxiety is an outstanding factor that aects students adoption
of technology. In addition to anxiety, the lack of skills and experience may add to the lack of interest in
the usage of technology. Another distinguished factor is the fear of the technology itself, which works
with the anxiety and literacy to decrease the chances of adopting technology properly. Therefore, tea-
chers and educators should pay attention to the psychological aspect and prepare students to be
ready to accept the technology. The lack of preparedness and technical readiness is another causal
of fear within the educational sector and both of them have a negative inuence on the adoption
of technology (Mac Callum & Jerey, 2014; Nchunge et al., 2012; Thatcher & Perrewe, 2002). Edu-
cational sector is not an exception and other domains also show fear of technology adoption.
Within the health sector, patients main concern is related to health anxiety which refers to the
patientsapprehension or fear from any results that indicate severe illness. Studies in the medical
sector, therefore, put more emphasis on the negative eects of anxiety and perceived risk on using
technology (Kamal et al., 2020; Meng et al., 2020). Within the banking sector, rather dierent types
of fear are found that stem from customersattitude and perception towards technology itself. As
far as mobile payment is concerned, most customers are afraid of using their data. Other studies
have shown that customersfear of being a fraud in addition to lack of experience and trust have
inuenced the adoption of mobile banking negatively (Bailey et al., 2020; Makttoofa, Khalidb, & Abdul-
lahc, n.d.). Finally, within the household, it seems that fear of technology is the dominant factor behind
the lack of interest in using the technology along with the increase in the number of family task. Table 1
shows the main forms of fear in dierent sectors along with the model adopted.
Most recent studies have tackled the issues of fear and technology acceptance as well. Most of
these studies tend to rely on the TAM model and other models. The mainstream of research
focuses on the eect of technology acceptance due to fear of technology itself. Most users have
given dierent justications regarding the fear to use technology. Some of them have conrmed
the fact that it is a matter of self-condence. Whenever a human works, he is supposed to make
errors and this fact amplies the fear factor (Gresham, 2020). Others claim that they do not prefer
to use technology because it is time-consuming and they are not be able to accomplish their tasks
on time (Appavoo, 2020). Other acceptance studies have focused on the eect of fear of breaching
the privacy of data which adds more emphasis on security and privacy awareness (Distler et al., 2020).
In light of the rapid spread of COVID-19, the universities and colleges found themselves under the
pressure of building up a safe teaching environment where the internet was the main facilitator.
However, choosing the best e-learning platform with eective pedagogies has been regarded as a
big challenge. Therefore, this study tries to pave the way to the innovative element of fear from
COVID-19 within a specic technology, which is Google Meet, to pinpoint the groundbreaking eect
of fear from the disease in a selected educational environment. The study adopted the TAM model
which has been proved to be widely-spread in research that focuses on technology adoptions and it
wasregardedasaninuential and eective tool in previous studies (Baby & Kannammal, 2020).
Recent studies (Al-Azawei et al., 2017;Mugoetal.,2017; Scherer et al., 2019) have made it a well-estab-
lished. The paper is intended to implement a variant ofthe TAM model and introduce an external factor
to the model that will support the research questions and hypotheses. The addition of the fear of COVID-
19 sets our model apart from other previous studies and contributes to the novelty of the paper.
3. Theoretical model and research model
3.1. TAM
The validation of an external factor on personal belief is one of the xed goals that TAM model tends
to measure. It is, therefore, the most powerful model that can explain how individuals tend to accept
technology especially within the educational institutions (Fred D Davis, 1989; V Venkatesh & Bala,
2008; Wong et al., 2012). TAM considers perceived usefulness (PU) and perceived Ease of use
(PEU) as the common dominant element that can measure two dierent perceptions. This fact
INTERACTIVE LEARNING ENVIRONMENTS 3
can aect directly usersbehavioral intention (BI). Accordingly, attention should be paid to PU as it is
used as a tool to measure the degree to which an individual tends to evaluate technology as a useful
tool and likely to accept or adopt that technology. PEU, on the other hand, refers to the degree to which
an individual believes that using technology is attainable and easy to manage (Fred D Davis, 1989).
Based on the previous assumption, when users perceive technology to be easy to use, they will be
more likely to have positive attitudes towards that technology; hence, the usersperceptions of its
usefulness are evident. Similarly, when users perceive technology as useful, they will be more likely
to have a positive attitude to adopt technology. To apply the previous assumptions to the current
model, the following hypotheses are proposed:
H1: Perceived Ease of use (PEU) would predict the intention of using Google Meet (AGM).
H2: Perceived usefulness (PU) would predict the intention of using Google Meet (AGM).
H3: Perceived Ease of use (PEU) would predict the Perceived usefulness (PU).
3.2. Subjective norm
Subjective norm (SN) is considered as a tool to measure individualsperception that the availability
of other individuals who share the same attitude will or will not perform the same behavior towards
technology. SN has strengthened the TAM model socially as it enables the TAM to account to users
behaviors among a group of users (Fishbein & Ajzen, 1975). This study has considered SN as an exter-
nal factor that can account for studentsintention to adopt the technology of GM in a group of class-
mate meetings.
The eect of SN on behavioral intention; specically on PU and PEOU, has been used extensively
in many literature studies on technology acceptance or adoption (Song & Kong, 2017; V Venkatesh &
Bala, 2008; Viswanath Venkatesh & Davis, 2000; Wong et al., 2012). One of the recent studies that
Table 1. The main forms of fear in dierent sectors along with the model adopted.
No. Sectors Authors Date Forms of fear Technology Samples Models
1. Educational
Institutions
(Mac Callum &
Jerey, 2014)
2014 Anxiety ICT Educators
and
Students
TAM
(Thatcher &
Perrewe, 2002)
2002 Anxiety IT Technology Students CSE
(Nchunge et al.,
2012)
2012 Fear of
preparation
and support
Electronic mode
of teaching
Teachers and
students
TAM
2. Health
Institutions
(Bhattacherjee &
Hikmet, 2007)
2007 Fear of
Technology
Healthcare
information
technology (HIT)
Physicians TAM & UTAUT
(Meng et al.,
2020)
2020 Fear of severe
illness results
Mobile Patients cognitive and
aective trust
(Kamal et al.,
2020)
2020 Anxiety and fear
of risk
Telemedicine People from
Pakistan
TAM with
inclusion of
additional
constructs
3. Banking
Sector
(Johnston &
Warkentin,
2010)
2010 Fear Appeals Fear Appeals
System
Customers PMT
(Bailey et al.,
2020)
2020 Fear of losing
personal data
Mobile Payment
(MP)
Bank
Customers
TAM
(Makttoofa et al.,
n.d.)
2020 Fear of hacking,
phishing being
fraud
Mobile Banking Bank
Customers
TAM
4. Household
Sector
(Brown &
Venkatesh,
2005)
2005 Fear of
Technology
Computers American
households
MATH
4R. S. AL-MAROOF ET AL.
have made use of TAM and SN as an external factor is by (Huang et al., 2020) where it is emphasized
that there is a close relation between the external factors and other embedded factors of TAM in
dierent previous studies. However, it seems that the external factor of SN has not been
implemented deeply and eciently in these studies.
Therefore, this study has intended to make a connection between SN as an external factor and
another external factor that has deeply aect the individual during the spread of Coronavirus
which is perceived fear (PF) that is explained in the following section. The hypothesis that can be
formed is that
H4: Subjective norm (SN) would predict the intention of using Google Meet (AGM).
3.3. Perceived fear
A novel coronavirus disease has started to appear in December 2019 and was found rst in China.
Then, it eventually spread all over the world. Based on recent studies, it has been shown that the
most common reaction that is deeply-perceived during this period is the feeling of fear. Fear
scores the highest in the scale of Health Anxiety Inventory (HAI) (Nicomedes & Avila, 2020).
Though studies have claimed that the feeling of fear can be positively perceived whenever there
is a real danger, fear of Coronavirus has become chronic and burdensome. The fear from COVID-
19 has many forms, such as the feel of uncertainty, health anxiety, the risk for loved ones and it
has raised two essential issues: the high degree of worries and the high possibility of being
aected by the disease (Ahorsu et al., 2020; Gerhold, 2020).
The current study has intended to investigate the relation between technology adoption using
TAM and the external factor of Perceived Fear (PF). Therefore, this study is an attempt to overcome
the limitations of TAM model, which is an implementation of external factors that are context-
specic (Tarhini et al., 2015), by exploring the eect of perceived fear (PF) on the TAM model,
namely: PU and PEOU along with another external factor which is SN. Based on this assumption,
it is hypothesized that:
H5: Perceived Fear (PF) would predict the intention of using Google Meet (AGM).
H6: Perceived Fear (PF) would predict the Perceived Ease of use (PEU).
H7: Subjective norm (SN) would predict the Perceived usefulness (PU).
H8: Perceived Fear (PF) would predict the Subjective norm (SN).
The proposed research model relies on these hypotheses, as illustrates in Figure 1. The theoretical
model is rst given the form of a structural equation model, and then it is assessed by using machine
learning methods.
4. Research methodology
Technology during COVID 19 is approaching closer to eectively facilitate the process of teaching.
Coined by the fact the GM was one of the inuential approaches to replace the face to face teaching
environment within the breakdown period, GM helps in contextualizing many educational practices
including implementing the curriculum, designing an eective lesson plan, giving oral presentations
from both teachers and students, giving feedback individually or in the group and following up with
students whose achievement was not up to the required standard due to COVID 19 predicament.
4.1. Context and subjects
The students who were studying at The British University in Dubai (BUiD) in United Arab Emirates
participated in this study. Online surveys were used to collect recent data between April and May
INTERACTIVE LEARNING ENVIRONMENTS 5
2020. There was no compensation given to the participants who volunteered to ll out the surveys.
This research collects the data by employing the convenience sampling approach. The survey was
appropriately lled by 450 students in total out of 500 surveys circulated, having 90% of the response
rate. Among these 180 were males and 270 females. The detailed information regarding the respon-
dents can be seen in Table 2.
4.2. Study instrument
There are two parts in the research instrument of this study. The demographic data of the partici-
pants is gathered in the rst part, while feedback relating to the factors in the conceptual model
is gathered in the second one. A 5-point Likert scalewas used to measure the items in the
second part. A detailed image of the collected items instrument has been shown in Table 3. The
items employed to measure the PEOU and PU were adapted from (Fred D Davis, 1989). The con-
structs and their underlying items are mentioned in the Appendix.
4.3. Pre-test of the questionnaire
A pilot study was conducted to measure the reliability of the questionnaire items before carrying out
the nal survey. 50 students were randomly chosen from the target population to carry out this
study. The internal reliability of the constructsitems was estimated through the Cronbachs
alpha. In the opinion of (Nunnally & Bernstein, 1978), a reliability coecient of 0.70 or greater is
thought to be acceptable. Table 4 presents that the Cronbachs alpha values for all the constructs
were higher than 0.7 in this study. Thus, all the constructs were reliable and can therefore be
employed in the nal study.
Figure 1. The study model.
6R. S. AL-MAROOF ET AL.
The ve measurement scales of the questionnaire are reliable as per the aforementioned table,
thus the study can integrate these.
5. Findings and discussion
5.1. Data analysis
Two distinct techniques are used by this study to evaluate the developed theoretical model. Keeping
in mind, the rst technique, the partial least squares-structural equation modeling (PLS-SEM) is
employed by this study via SmartPLS tool (Ringle et al., 2015). As the PLS-SEM gives simultaneous
assessment for measurement as well as structural model, which yields more precise results;
hence, the PLS-SEM is used in this study (Barclay et al., 1995). Concerning the second technique,
the machine learning algorithms are employed by this study via Weka toolkit to predict the depen-
dent variables in the conceptual model (Arpaci, 2019).
5.2. Measurement model assessment
The testing of the reliability and validity helps to analyze the measurement model (Hair Jr et al.,
2016). The Cronbachs alpha and composite reliability (CR) measures were employed to test the
reliability 0.70 are the suggested values for all of these measures (Hair Jr et al., 2016). The values
of both measures are believed to be satisfactory; and hence, the reliability is established as per
the results in Table 5.
Evaluation of the convergent and discriminant validities is recommended by (Hair Jr et al., 2016)
keeping validity testing in mind. The testing of average variance extracted (AVE) and factor loadings
was done for convergent validity. The values of AVE should be 0.50 should be the values of AVE
(Fornell & Larcker, 1981), while 0.70 should be the values of factor loadings (Hair et al., 2010).
Table 2. Summary of studentsdemographic characteristic.
Variables No. of Respondents Percent
Gender:
Male 179 40
Female 271 60
Age:
18 to 29 years 253 56
30 to 39 years 99 22
40 to 49 years 73 16
50 to 59 years 25 6
Departments:
English Language & Literature Department 66 15
Business Administration & Accounting Department 78 17
IT Department 69 15
Law Program 237 53
Level of education:
Diploma 67 15
Bachelors degree 188 42
Master 195 43
Table 3. Constructs and their sources.
Constructs Number of items Source
Adoption of GM 2 (Fred D Davis, 1989; Rai & Selnes, 2019; V Venkatesh et al., 2003)
Subjective norm 4 (Moore & Benbasat, 1991; Rai & Selnes, 2019)
Perceived fear 4 Developed in this study
Perceived ease of use 4 (Fred D Davis, 1989; Johnston & Warkentin, 2010; V Venkatesh et al., 2003)
Perceived usefulness 4 (Fred D Davis, 1989; Johnston & Warkentin, 2010; V Venkatesh et al., 2003)
INTERACTIVE LEARNING ENVIRONMENTS 7
According to the results in Table 5, the convergent validity is conrmed as the values of both
measures were true. Testing the Heterotrait-Monotrait ratio (HTMT)of correlations was rec-
ommended by (Henseler et al., 2015) for discriminant validity. < 0.85 should be the values of
HTMT. According to the readings in Table 6, the discriminant validity is conrmed as all the
values were true.
5.3. Hypotheses testing and coecient of determination
The structural equation modeling (SEM) approach (Fred D Davis et al., 1992) was used to test the
aforementioned nine hypotheses together. The variance described (R
2
value) by each path and
every hypothesized connections path signicance in the research model were assessed. The stan-
dardized path coecients and path signicances are demonstrated in Figure 2 and Table 6.
Table 6 shows that the R
2
values for adoption of Google Meet, subjective norm, perceived fear,
perceived Ease of use, and perceived usefulness ranged between 0.578 and 0.633. Therefore,
these constructs appear to have Moderate predictive power (Liu et al., 2005). Based on the
dataanalysishypothesesH1,H2,H3,H4,H5,H6,H7,andH8weresupportedbytheempirical
data. The results showed that adoption of Google Meet (AGM) signicantly inuenced per-
ceived Ease of use (PEU) (β= 0.313, P< 0.001), perceived usefulness (PU) (β= 0.440, P<
0.001), subjective norm (SN) (β= 0.584, P< 0.05), and perceived fear (PF) (β= 0.584, P<0.05)
supporting hypothesis H1, H2, H4, and H5 respectively. Perceived Usefulness (PU) was deter-
mined to be signicant in aecting perceived Ease of use (PEU) (β= 0.553, P< 0.001), and
subjective norm (SN) (β= 0.250, P< 0.05) supporting hypothesis H3 and H7 respectively.
Finally, perceived fear (PF) has signicant eects on perceived Ease of use (PEU) (β= 0.469,
P< 0.001), and subjective norm (SN) (β= 0.658, P< 0.001) respectively; hence H6, and H8 are
supported (Tables 7 and 8).
Table 4. Cronbachs alpha values for the pilot study (Cronbachs alpha 0.70).
Construct Cronbachs alpha
Adoption of GM 0.883
Subjective norm 0.865
Perceived fear 0.896
Perceived ease of use 0.789
Perceived usefulness 0.852
Table 5. Convergent validity results which assures acceptable values (Factor loading, Cronbachs alpha, composite reliability
0.70 & AVE >0.5).
Constructs Items Factor loading Cronbachs alpha CR AVE
Adoption of GM AGM1 0.769 0.805 0.810 0.771
AGM2 0.815
Subjective norm SN1 0.836 0.828 0.799 0.700
SN2 0.847
SN3 0.708
SN4 0.778
Perceived fear PF1 0.868 0.798 0.835 0.705
PF2 0.804
PF3 0.800
PF4 0.816
Perceived ease of use PEU1 0.830 0.847 0.770 0.799
PEU2 0.884
PEU3 0.798
Perceived usefulness PU1 0.879 0.780 0.785 0.600
PU2 0.830
PU3 0.862
PU4 0.801
8R. S. AL-MAROOF ET AL.
4.4. Hypotheses testing using machine learning algorithms
The machine-learning classication algorithms are used by this research through the application of a
variety of methodologies, consisting Bayesian networks, decision trees, if-then-else rules, and neural
networks, to predict the associations in the proposed theoretical model (Arpaci, 2019). Weka (ver.
3.8.3) was employed to test the predictive model depending on various classiers, consisting Bayes-
Net, AdaBoostM1, LWL, Logistic, J48, and OneR (Frank et al., 2009). Keeping the results in Table 9 in
mind, it can be seen that J48 performs better than the other classiers in calculating the adoption of
Google Meet (AGM). The J48 predicted AGM having an accuracy of 73.03% for the 10-fold cross-vali-
dation. Thus, H1, H2, H4, and H5 were supported. This classier had an improved performance with
regard to the TP rate (.904), precision (.815), and recall (.905) than the other classiers.
It was also highlighted by the results that the J48 had an improved classication performance as
compared to the other classiers in predicting the PU, as illustrated in Table 10. The J48 predicted the
PU by the attributes of subjective norm and perceived Ease of use having an accuracy of 89.19%, and
thus, H3 and H7 as well were supported.
Table 6. HeterotraitMonotrait Ratio (HTMT).
AGM SN PF PEU PU
AGM
SN 0.300
PF 0.315 0.228
PEU 0.487 0.493 0.600
PU 0.500 0.533 0.550 0.692
Note: AGM, Adoption of Google Meet; SN, subjective norm; PF, Perceived fear; PEOU, perceived ease of use; PU, perceived
usefulness.
Figure 2. Hypotheses testing results (signicant at P** < = 0.01, P* < 0.05).
INTERACTIVE LEARNING ENVIRONMENTS 9
As illustrated in Table 11, the results identify that both OneR and J48 classiers gave improved
performance as compared to the other classiers in predicting the perceived Ease of use (PEOU)
by perceived fear (PF). The OneR and J48 classiers predicted the satisfaction of having an accuracy
of 88.11%. Consequently, H6 is supported.
Moreover, the results highlighted that Logistic gave an improved performance as compared to
the other classiers in predicting the subjective norm (SN) by perceived fear (PF), as per Table 12.
The Logistic classier predicted the actual use having an accuracy of 85.39%. Consequently, H8 is
supported.
5. Discussion
Recent studies are investigating the eect of coronavirus pandemic on modern technology,
especially the ones that are related to teaching and learning. Technology has proven to be a
useful tool and a captive road. In fact, it has achieved a kind of victory over the disease itself and
paves the way to a new approach in teaching (Kumar et al., 2020). The present study focuses on
the eect of COVID-19 on the teaching process via GM. The model of the study puts more emphasis
on the perceived fear factor (PF), which has an extraordinary eect on measuring the inuence of
COVID-19 over a group of teachers and students. Likewise, there is an interest to investigate the
eect of the pandemic not only on Google Meet but also on other teaching-based technologies
that have been used during this period. Therefore, this is study is an attempt to ll this gap and
opens the door to future work.
For the rst three hypotheses, the focus was on the factors of PEU and PU. The hypotheses seem
to be consistent with previous studies, that is, the fact that usefulness of Google Meet is governed by
its of ease of use seems to be agreed upon by much prior research where colleges and universities
were encouraged to focus on the factors of usefulness and ease of use (Martín-García et al., 2019;
Raque et al., 2020). For the fourth hypothesis, where SN factor was investigated, the results have
shown that peers opinions have an eective role in creating an inuential educational environment.
The results seem to be in line with the previous studies, which have revealed a positive role of peers
in the e-learning environment (Knabe, 2012; Nadlifatin et al., 2020). Finally, the hypotheses that were
related to perceived fear (PF) have suggested that the novelty of this factor seems to be unique and it
lies behind the innovation of the study.
Table 7. R
2
of the endogenous latent variables.
Constructs R
2
Results
AGM 0.620 Moderate
PEU 0.633 Moderate
PU 0.581 Moderate
SN 0.578 Moderate
Note: AGM, Adoption of Google Meet; SN, subjective norm; PF, Perceived fear; PEOU, perceived ease of use; PU, perceived
usefulness.
Table 8. Summary of hypotheses tests at P** = <0.01, P*<0.05 Signicant at P** = <0.01, P* < 0.05.
H Relationship Path t-value P-value Direction Decision
H1 PEU -> AGM 0.313 12.058 0.002 Positive Supported**
H2 PU -> AGM 0.440 18.297 0.000 Positive Supported**
H3 PEU -> PU 0.553 13.450 0.001 Positive Supported**
H4 SN -> AGM 0.675 10.605 0.000 Positive Supported**
H5 PF -> AGM 0.584 3.229 0.030 Positive Supported*
H6 PF -> PEU 0.469 22.108 0.000 Positive Supported**
H7 SN -> PU 0.250 5.835 0.011 Positive Supported*
H8 PF -> SN 0.658 19.005 0.000 Positive Supported**
Note: AGM, Adoption of Google Meet; SN, subjective norm; PF, Perceived fear; PEU, perceived ease of use; PU, perceived
usefulness.
10 R. S. AL-MAROOF ET AL.
The proposed model was tested in this study by a corresponding approach employing PLS-SEM
and machine learning classication algorithms. The complementary multi-analytical approach is
used to further contribute to the information systems (IS) literature as this study is among the
small eorts done for the application of machine learning algorithms in predicting the actual use
of Google Meet application. Signicantly, it is to be observed that to predict a dependent variable
and to validate a conceptual model depending on the extension of an existing theory, PLS-SEM
can be employed (Al-Emran et al., 2018). Similarly, to predict a dependent variable depending on
independent variables, supervised machine learning algorithms (i.e. possessing a pre-dened depen-
dent variable) can be employed (Arpaci, 2019). Furthermore, another point of interest is that various
classication algorithms with dierent methodologies like decision trees, Bayesian networks, associ-
ation rules, neural networks, and ifthen-else rules have been used in the study. Particularly, it has
been indicated by the results that J48 (a decision tree) gave an improved performance as compared
to other classiers in many scenarios. It is also important to state that the decision tree (nonpara-
metric) was employed for classifying continuous (numerical) as well as categorical variables by divid-
ing the sample into homogeneous sub-samples depending on the highly important independent
Table 9. Predicting AGM by PEOU, PU, SN, and PF.
Classier CCI
1
(%) TP
2
Rate FP
3
Rate Precision Recall F-Measure
BayesNet 88.33 .883 .336 .710 .883 .699
Logistic 88.22 .882 .325 .721 .882 .711
LWL 82.32 .823 .310 .696 .883 .688
AdaBoostM1 83.18 .831 .316 .622 .832 .619
OneR 86.16 .862 .329 .701 .862 .700
J48 90.45 .904 .726 .815 .905 .802
1
CCI: Correctly Classied Instances,
2
TP: True Positive,
3
FP: False Positive.
Table 10. Predicting PU by PEOU and SN.
Classier CCI
1
(%) TP
2
Rate FP
3
Rate Precision Recall F-Measure
BayesNet 80.29 .802 .286 .710 .803 .700
Logistic 79.23 .792 .254 .689 .792 .688
LWL 77.37 .773 .263 .704 .774 .770
AdaBoostM1 81.16 .811 .327 .700 .812 .810
OneR 82.01 .820 .364 .736 .820 .816
J48 89.19 .891 .549 .786 .892 .880
Table 11. Predicting PEOU by PF.
Classier CCI1 (%) TP
2
Rate FP
3
Rate Precision Recall F-Measure
BayesNet 81.16 .811 .201 .732 .812 .730
Logistic 81.22 .812 .206 .698 .812 .690
LWL 79.56 .795 .200 .678 .800 .673
AdaBoostM1 80.33 .803 .389 .787 .803 .780
OneR 88.11 .881 .569 .796 .881 .792
J48 88.11 .881 .578 .785 .881 .782
Table 12. Predicting SN by PF.
Classier CCI1 (%) TP
2
Rate FP
3
Rate Precision Recall F-Measure
BayesNet 80.20 .802 .333 .740 .802 .739
Logistic 80.20 .802 .370 .752 .802 .750
LWL 79.77 .797 .329 .769 .800 .762
AdaBoostM1 81.14 .811 .381 .788 .811 .783
OneR 84.41 .844 .450 .790 .844 .783
J48 85.39 .853 .605 .799 .854 .792
INTERACTIVE LEARNING ENVIRONMENTS 11
variable (Arpaci, 2019). On the contrary, the signicant coecients with substitutes from the sample
to pull a large number of sub-samples randomly was tested through the PLS-SEM (a nonparametric
procedure).
6. Conclusion
Results of the current study seem to be in line with previous studies regarding the importance of
TAM variables (F. D Davis, 1989; Teo, 2012; V Venkatesh & Bala, 2008). It seems that studentsinten-
tion to accept technology is higher when there are no other sources available except GM technology
as a tool in studying during the spread of COVID-19. The results that are related PU and PEU are con-
sistent with previous studies as it was found that both PU and PEU signicantly aect students
acceptance of GM, which puts more emphasis on the importance of them as indicators for students
intention to use GM during especial atmosphere which is the spread of COVID-19. Moreover, PEU
signicantly inuences PU, which implies that whenever technology is evaluated as easy, it has
the implicit indication that it is useful.
Regarding subjective norm (SN), the results illustrate that there is a strong relationship between
subjective norm and studentsacceptance of GM. It is suggested that studentsacceptance of GM is
signicantly inuenced by their classmatesreactions, existence, and behavior inside classes via GM.
The relationship between SN and studentsacceptance of GM is in line with previous studies such as
(Song & Kong, 2017; V Venkatesh & Bala, 2008; Viswanath Venkatesh & Davis, 2000; Wong et al., 2012)
where [Country Y] students are seen to be highly aected with the behavior of their classmate that
may add the sense of security and comfort in attending classes during the pandemic period. Stu-
dents are more intrinsically motivated to use GM whenever the same class is shared with a group
of his or her colleagues. Furthermore, SN is signicantly aected by the variables PEU and PU. The
results have shown that peersand instructorsattitude and availability may promote GM as a
tool for learning through the pandemic period, they are more willing to perceive it as useful, free
of eort and enjoyable. These ndings seem to be consistent with the previous study by (El-Gayar
et al., 2011) where it was conrmed that feedback from instructors and peers can highly inuence
studentsattitude towards perceived eectiveness of the technology.
The fear factor that appears due to the spreading of COVID-19 represents one of the crucial
hypotheses in the current study. COVID-19 is a kind of pandemic that has aected human popu-
lations severely. The possibility of transmission is very high, causing the lockdown and stay-at-
home strategy (Zhang et al., 2020). This study has adopted a model that is considered to be prom-
ising for future research as it sheds light on the eect of COVID-19 during the pandemic period.
Based on the results obtained from the study, the fear factor is evident in this period, but GM has
been proven to be a successful tool to lessen the fear of instructors and peers. Accordingly, the vari-
ables PEU and PU are signicantly aected by perceived fear (PF). The responses have shown that the
PF is evident during the pandemic period, but the fact that GM has a high degree of PEU and PU has
reduced the fear factor and encourages students to attend the scheduled classes.
6.1. Implications for research
This study is one of the earliest attempt to: (1) theoretically integrate the notion of fear within a
unied model of TAM, and (2) empirically test the eect of COVID-19 on the users of Google
Meet application, and (3) explore the impact of the Coronavirus pandemic on usersability to use
the Google Meet easily and usersattitude towards the usefulness of Google Meet. Previous research
has tackled the importance of fear from dierent perspectives, such as fear of technology (Bhatta-
cherjee & Hikmet, 2007), and they come up with the implication that negative perception may
aect directly or indirectly the ease of use and perceived usefulness. This implies that our impli-
cations coincide with Bhattacherjee & Hikmets implications, and fear will negatively aect the
12 R. S. AL-MAROOF ET AL.
usage of technology. Therefore, we demonstrate empirically that the perceived fear in the time of
disease should be considered as a dominant factor in any adoption model.
Management has to focus on the ndings of the study where the peersopinions aect positively
the educational environment during the COVID-19. It creates an extra-social oriented factor that
lessens the fear factor and adds a high social level of intimacy. In addition, based on the current
ndings it seems that most technology usersaccount for the higher usage of technology on its use-
fulness. Thus, colleges and universities have to adopt useful technology where ease of use is a sig-
nicant factor.
In a global context, educational stakeholders should consider the eect of a fear factor during the
spread of COVID-19 in developing a positive correlation between the usefulness of the technology
and the controlling the fear factor which means that students have reacted eectively and positively
to the used technology; thus, educators from all around the world should create a real functioning
learning environment that guarantees the implementation of good pedagogies and lessen the fear
factor.
6.2. Limitations and future research
It is important to report several major limitations. Firstly, much caution is needed while generalizing
the ndings to the rest of the institutes in United Arab Emirates or other countries. There are two
reasons behind it: (a) mainly focusing on just one institute for data collection, and (b) for choosing
the respondents, a convenient sampling approach is used. To increase the practicality of results gen-
eralization, more research upon these issues is required. Secondly, the only concentration of the
study was on assessing the adoption of Google Meet application by students and instructors. For
the evaluation of the educatorsactual use of Google Meet application, more attempts in the
future are much needed so as to attain deeper insights into the inuencing factors and obtain a con-
clusion a precise image of the implementation of these systems.
6.3. Recommendations
Google Meet is considered as a safe environment in online teaching, and it is highly recommended
during the pandemic outbreak. It is considered as a potential solution in teaching during the shut-
down period. The availability of GM has given all teachers and peers the self- sensing of security and
an immediate communication tool when the city of Dubai is the contamination status. Google Meets
has several advantages over other means of communication. First of all, it is an application on smart-
phones and laptops. This fact helps The British University in Dubai (BUiD) students to join classes
easily using their own smartphones. The second important factor is that the links that are provided
within each class time can be used several times which enables the students to be connected with
their teachers any time during the day. The last crucial factor is that students are more condent and
the feeling of fear is reduced to its minimum level.
Disclosure statement
No potential conict of interest was reported by the author(s).
ORCID
Rana Saeed Al-Maroof http://orcid.org/0000-0003-4408-7710
Said A. Salloum http://orcid.org/0000-0002-6073-3981
Aboul Ella Hassanien http://orcid.org/0000-0002-9989-6681
Khaled Shaalan http://orcid.org/0000-0003-0823-8390
INTERACTIVE LEARNING ENVIRONMENTS 13
References
Ahorsu, D. K., Lin, C.-Y., Imani, V., Saari, M., Griths, M. D., & Pakpour, A. H. (2020). The fear of COVID-19 scale:
Development and initial validation. International Journal of Mental Health and Addiction,1.https://doi.org/10.
1007/s11469-020-00270-8
Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the eect of learning styles in a blended e-learning
system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology,
33(2). https://doi.org/10.14742/ajet.2741
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018, September 13). PLS-SEM in information systems research: A
Comprehensive methodological reference. In 4th International Conference on Advanced Intelligent systems and
Informatics (AISI 2018) (pp. 644653). Springer.
Alt, D., & Boniel-Nissim, M. (2018). Links between adolescentsdeep and surface learning approaches, problematic inter-
net use, and fear of missing out (FoMO). Internet Interventions,13,3039. https://doi.org/10.1016/j.invent.2018.05.002
Appavoo, P. (2020). Acceptance of technology in the classroom: A qualitative analysis of mathematics teacherspercep-
tions. In S. C. Satapathy, V. Bhateja, B. L. Nguyen, G.-N. Nguyen, & D- N. Le (Eds.), Frontiers in intelligent computing:
Theory and applications (pp. 355364). Springer.
Arpaci, I. (2019). A hybrid modeling approach for predicting the educational use of mobile cloud computing services in
higher education. Computers in Human Behavior,90, 181187. https://doi.org/10.1016/j.chb.2018.09.005
Baby, A., & Kannammal, A. (2020). Network path analysis for developing an enhanced TAM model: A user-centric e-learn-
ing perspective. Computers in Human Behavior,107,17. https://doi.org/10.1016/j.chb.2019.07.024
Bailey, A. A., Pentina, I., Mishra, A. S., & Ben Mimoun, M. S. (2020). Exploring factors inuencing US millennial consumers
use of tap-and-go payment technology. The International Review of Retail, Distribution and Consumer Research,30(2),
143163. https://doi.org/10.1080/09593969.2019.1667854
Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (pls) approach to casual modeling: Personal
computer adoption and use as an illustration.
Bhattacherjee,A.,&Hikmet,N.(2007). Physiciansresistance toward healthcare informationtechnology: A theoretical model
and empirical test. European Journal of Information Systems,16(6), 725737. https://doi.org/10.1057/palgrave.ejis.3000717
Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and exten-
sion incorporating household life cycle. MIS Quarterly, 399426. https://doi.org/10.2307/25148690
Chen, E., & Li, Z. (2011, July 2628). On the application of multimedia technology in foreign language teaching and
learning in Chinas colleges: Challenges, problems and implications. In 2011 International Conference on
Multimedia technology (pp. 595597). IEEE.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS
Quarterly,13(3), 319340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace.
Journal of Applied Social Psychology,22(14), 11111132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Distler, V., Lallemand, C., & Koenig, V. (2020). How acceptable is this? How user experience factors can broaden our
understanding of the acceptance of privacy trade-os. Computers in Human Behavior,106,110. https://doi.org/
10.1016/j.chb.2019.106227
El-Gayar, O., Moran, M., & Hawkes, M. (2011). Studentsacceptance of tablet PCs and implications for educational insti-
tutions. Journal of Educational Technology & Society,14(2), 5870. https://www.jstor.org/stable/jeductechsoci.14.2.58
Ellahi, A. (2017, December 1314). Fear of using technology: Investigating impact of using social networking sites
in business education. In 2017 IEEE 15th student Conference on research and development (SCOReD) (pp. 234237). IEEE.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Elsevier.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with Unobservable variables and measurement
Error. Journal of Marketing Research,18(1), 3950. https://doi.org/10.2307/3151312
Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I. H., & Trigg, L. (2009). Weka-a machine learning
workbench for data mining. In O. Maimon & L. Rokach (Eds.), Data mining and knowledge discovery handbook
(pp. 12691277). Springer.
Gerhold, L. (2020). COVID-19: Risk perception and coping strategies.
Gresham, J. (2020). Manufacturing trends in automated inspection equipment: Linking technology with business
change management using the technology acceptance model. Northcentral University.
Habes, M., Alghizzawi, M., Ali, S., SalihAlnaser, A., & Salloum, S. A. (2020). The relation among Marketing ads, via Digital
Media and mitigate (COVID-19) pandemic in Jordan. International Journal of Advanced Science,29(7), 12326
12348. http://sersc.org/journals/index.php/IJAST/article/view/27927
Hair, J. F., Black Jr, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Pearson Prentice Hall.
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling
(PLS-SEM). Sage.
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), 115135. https://doi.org/10.
1007/s11747-014-0403-8
14 R. S. AL-MAROOF ET AL.
Huang, F., Teo, T., & Zhou, M. (2020). Chinese studentsintentions to use the internet-based technology for learning.
Educational Technology Research and Development,68(1), 575591. https://doi.org/10.1007/s11423-019-09695-y
Johnston, A. C., & Warkentin, M. (2010). Fear appeals and information security behaviors: An empirical study. MIS
Quarterly, 549566. https://doi.org/10.2307/25750691
Kamal, S. A., Shaq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended tech-
nology acceptance model (TAM). Technology in Society,60, 101212. https://doi.org/10.1016/j.techsoc.2019.101212
Knabe, A. (2012). Applying Ajzens theory of planned behavior to a study of online course adoption in public relations
education.
Kumar, A., Gupta, P. K., & Srivastava, A. (2020). A review of modern technologies for tackling COVID-19 pandemic.
Diabetes & Metabolic Syndrome: Clinical Research & Reviews,14(4), 569573. https://doi.org/10.1016/j.dsx.2020.05.008
Lemke, G., & Silverman, G. J. (2020). Blood clots and TAM receptor signalling in COVID-19 pathogenesis. Nature Reviews
Immunology,20,12. https://doi.org/10.1038/s41577-020-0354-x
Li, L., Chen, Y., Li, Z., Li, D., Li, F., & Huang, H. (2018, August 811). Online virtual Experiment teaching platform for
Database technology and application. In 2018 13th International Conference on Computer Science & education
(ICCSE) (pp. 15). IEEE.
Liang, Y., Zheng, T., & Wang, M. (2011, July 2628). English audio-visual teaching mode and its teaching environment
construction Henan institute of science and technology as the example. In 2011 International Conference on
Multimedia technology (pp. 30503053). IEEE.
Lin, C.-Y. (2020). Social reaction toward the 2019 novel coronavirus (COVID-19). Social Health and Behavior,3(1), 1.
https://doi.org/10.4103/SHB.SHB_11_20
Liu, C. Z., Au, Y. A., & Choi, H. S. (2014). Eects of freemium strategy in the mobile app market: An empirical study of
Google play. Journal of Management Information Systems,31(3), 326354. https://doi.org/10.1080/07421222.2014.
995564
Liu, Q., Geertshuis, S., & Grainger, R. (2020). Understanding academicsadoption of learning technologies: A systematic
review. Computers & Education, 103857. https://doi.org/10.1016/j.compedu.2020.103857
Liu, S.-H., Liao, H.-L., & Peng, C.-J. (2005). Applying the technology acceptance model and ow theory to online e-learn-
ing usersacceptance behavior. E-Learning,4(H6), H8.
Mac Callum, K., & Jerey, L. (2014). Comparing the role of ICT literacy and anxiety in the adoption of mobile learning.
Computers in Human Behavior,39,819. https://doi.org/10.1016/j.chb.2014.05.024
Machů, E., & Morysová, D. (2016). Analysis of the emotion of fear in gifted children and its use in teaching practice.
Procedia-Social and Behavioral Sciences,217, 222228. https://doi.org/10.1016/j.sbspro.2016.02.071
Makttoof, N., Khalid, H., & Abdullah, I. (n.d.). The eect of individual factors on the adoption of mobile banking within
banks in Iraq. International Journal of Innovation, Creativity and Change,7390.
Martín-García, A. V., Martínez-Abad, F., & Reyes-González, D. (2019). TAM and stages of adoption of blended learning in
higher education by application of data mining techniques. British Journal of Educational Technology,50(5), 2484
2500. https://doi.org/10.1111/bjet.12831
McIlroy, S., Ali, N., & Hassan, A. E. (2016). Fresh apps: An empirical study of frequently-updated mobile apps in the
Google play store. Empirical Software Engineering,21(3), 13461370. https://doi.org/10.1007/s10664-015-9388-2
Meng, F., Guo, X., Zhang, X., Peng, Z., & Lai, K.-H. (2020, January 710). Examining the role of technology anxiety and
health anxiety on elderly userscontinuance intention for mobile health services use. Proceedings of the 53rd
Hawaii International Conference on System Sciences.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an infor-
mation technology innovation. Information Systems Research,2(3), 192222. https://doi.org/10.1287/isre.2.3.192
Morchid, N. (n.d.). The current state of technology acceptance: A comparative study.
Mugo, D. G., Njagi, K., Chemwei, B., & Motanya, J. O. (2017). The technology acceptance model (TAM) and its application
to the utilization of mobile learning technologies. Journal of Advances in Mathematics and Computer Science,20,
18. https://doi.org/10.9734/BJMCS/2017/29015
Nadlifatin, R., Miraja, B., Persada, S., Belgiawan, P., Redi, A. A. N., & Lin, S.-C. (2020). The measurement of University stu-
dentsintention to use blended learning system through technology acceptance model (TAM) and theory of planned
behavior (TPB) at developed and developing regions: Lessons learned from Taiwan and Indonesia. International
Journal of Emerging Technologies in Learning (IJET),15(9), 219230. https://doi.org/10.3991/ijet.v15i09.11517
Nchunge, D. M., Sakwa, M., & Mwangi, W. (2012). Users perception on ICT adoption for education support in schools: A
survey of secondary school teachers in Thika district Kenya. International Journal of Humanities and Social Science,2
(10), 1729.
Nicomedes, C. J., & Avila, R. M. (2020). An analysis on the panic of Filipinos during COVID-19 pandemic in the Philippines.
Nunnally, J. C., & Bernstein, I. H. (1978). Psychometric theory. McGraw-Hill.
Pappas, G., Kiriaze, I. J., Giannakis, P., & Falagas, M. E. (2009). Psychosocial consequences of infectious diseases. Clinical
Microbiology and Infection,15(8), 743747. https://doi.org/10.1111/j.1469-0691.2009.02947.x
Raque, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library
applications with an extended technology acceptance model (TAM). Computers & Education,145, 103732. https://doi.
org/10.1016/j.compedu.2019.103732
INTERACTIVE LEARNING ENVIRONMENTS 15
Rai, R. S., & Selnes, F. (2019). Conceptualizing task-technology t and the eect on adoptionA case study of a digital
textbook service. Information & Management,56(8), 103161. https://doi.org/10.1016/j.im.2019.04.004
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. SmartPLS.
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation
modeling approach to explaining teachersadoption of digital technology in education. Computers & Education,128,
1335. https://doi.org/10.1016/j.compedu.2018.09.009
Song, Y., & Kong, S.-C. (2017). Investigating studentsacceptance of a statistics learning platform using technology
acceptance model. Journal of Educational Computing Research,55(6), 865897. https://doi.org/10.1177/
0735633116688320
Tarhini, A., Hone, K., & Liu, X. (2015). A cross-cultural examination of the impact of social, organisational and individual
factors on educational technology acceptance between B ritish and L ebanese university students. British Journal of
Educational Technology,46(4), 739755. https://doi.org/10.1111/bjet.12169
Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the technology
acceptance model and theory of planned behavior. Interactive Learning Environments,20(1), 318. https://doi.org/10.
1080/10494821003714632
Thatcher, J. B., & Perrewe, P. L. (2002). An empirical examination of individual traits as antecedents to computer anxiety
and computer self-ecacy. MIS Quarterly, 381396. https://doi.org/10.2307/4132314
Tsai, T.-H., Lin, W.-Y., Chang, Y.-S., Chang, P.-C., & Lee, M.-Y. (2020). Technology anxiety and resistance to change behav-
ioral study of a wearable cardiac warming system using an extended TAM for older adults. Plos One,15(1), 1
24. https://doi.org/10.1371/journal.pone.0227270
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on interventions. Decision
Sciences,39(2), 273315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal eld
studies. Management Science,46(2), 186204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unied view.
MIS Quarterly,27(3), 425478. https://doi.org/10.2307/30036540
Wong, K.-T., Teo, T., & Russo, S. (2012). Inuence of gender and computer teaching ecacy on computer acceptance
among Malaysian student teachers: An extended technology acceptance model. Australasian Journal of
Educational Technology,28(7), 11901207. https://doi.org/10.14742/ajet.796
Zhang, S. X., Wang, Y., Rauch, A., & Wei, F. (2020). Unprecedented disruption of lives and work: Health, distress and life
satisfaction of working adults in China one month into the COVID-19 outbreak. Psychiatry Research, 112958. https://
doi.org/10.1016/j.psychres.2020.112958
Appendix
Instrument development
Adoption of Google Meet (AGM)
AGM1: I highly recommend GM to use it in the pandemic time.
AGM2: I am willing to use GM with my classmates to enrich my study.
Perceived usefulness (PU)
PU1: Using GM will lessen my fear from Coronavirus.
PU2: Using GM will increase my understanding in virtual class.
PU3: Using GM will make more productive as student in the disease time.
PU4: Overall, I nd GM is useful in my study during the lockdown period.
Perceived Ease of use (PEU)
EPU1: Learning through GM is easy.
EPU2: I can become skillful in GM though I am under the lockdown pressure.
EPU3: I am using GM easily to do what I am supposed do (Homework, assignment).
EPU4: Overall, the GM is easy to use.
Perceived Fear (PF)
PF1: I cant concentrate on my class through GM because of COVIC-19.
PF2: GM reduces my fear.
PF3: GM provides a chance to be away from the lockdown.
PF4: GM provides chances of learning instead of being afraid.
Subjective Norm (SN)
SN1: I am not afraid of COVID-19 when I join virtual classes via GM.
SN2: I feel that GM reduces my fear and my classmatesfear.
SN3: I feel that GM is more useful with my classmates.
SN4: I feel that GM is easy to use with my classmates.
16 R. S. AL-MAROOF ET AL.
... For example, fear is the mechanism by which individuals protect themselves from dangerous situations [39]. During the rapid spread of COVID-19 around the world, fear of losing health and life, fear of being fired or unemployed, and fear of declining economic situations all bring psychological distress to people [40][41][42]. Due to the COVID-19 pandemic, with an increasing number of studies confirming that fear has a positive correlation with consumer behavior [43], fear may drive and accelerate the adoption of technologies or techniques, such as mobile payment, that can minimize these fears [44]. Therefore, we hypothesize that fear influences an individual's intention to engage in health-focused behavior, which leads consumers to use contactless mobile payment. ...
... In contrast, this is consistent with previous research findings that dissatisfaction with cash payment pushes consumers away from the original choice [25,32], as well as how people may choose to use mobile payment because of perceived fear [40,44]. In addition, among the three factors of PPM, the conversion intention brought by mooring has the greatest influence in our study. ...
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... Although there have been numerous studies integrating the TAM and IS frameworks to study the BI to adopt mobile wallets, the integrated framework in this research provides a greater understanding of CUI during the course of COVID-19. Fear of contracting COVID-19 will have a major impact on technology adoption (Al-Maroof et al. 2020), while Rafdinal and Senalasari (2021) established that if the mobile wallet applications are useful, they will result in easy adoption during a pandemic. The significant impact of PU in adopting mobile wallets and their CUI during the pandemic is established in the study adhering to the findings of Daragmeh et al. (2021). ...
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... B-schools in Republic of India are formulating themselves to overwhelm with shortrange tasks confronted due to COVID-19 and transmuting themselves through longstanding approaches (Rana et al. 2020). The phase of learning was continued without any disruption during COVID19 and there was continuous upgradation of skills of teachers and students by the adoption of the online education system (Jain and Lamba 2020). ...
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