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Adoption of E-Learning During the COVID-19 Pandemic: The Moderating Role of Age and Gender

IGI Global Scientific Publishing
International Journal of Web-Based Learning and Teaching Technologies
Authors:
  • Birla Global University
  • K L University, Green Fields, Vaddeswaram, Guntur District, A.P- -522 502, INDIA

Abstract

The outbreak of the novel coronavirus disease (COVID-19) has resulted in the complete disruption of the learning ecosystem across the world. The sudden shift from the class room learning to the use of virtual platforms has not only made an unprecedented impact on the learning style of the students, but has also resulted in the problem of adoption of the same. Thus, with the significant surge in the usage of e-learning mechanism, the researchers even tend to predict the continued usage of the digital learning platforms post pandemic due to its accelerated usage and adoption by the learners and teachers as well across the age and gender. Therefore, the present research seeks to study the factors influencing e-learning adoption by the students in the context of the pandemic. Further, it would examine the moderating influence of age and gender for the adoption of e-learning using the UTAUT model with extended constructs like computer anxiety, attitude, and technology anxiety.
DOI: 10.4018/IJWLTT.20220301.oa4

Volume 17 • Issue 2 • March-April 2022
This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium,
provided the author of the original work and original publication source are properly credited.
1



Biswajit Acharjya, Entrepreneurship Development Institute of India, Ahemedabad, India
Simanchala Das, Koneru Lakshmaiah Education Foundation, Vijayawada, India
https://orcid.org/0000-0001-6230-0461

The outbreak of the novel coronavirus disease (COVID-19) has resulted in the complete disruption
of the learning ecosystem across the world. The sudden shift from classroom learning to the use of
virtual platforms has not only made an unprecedented impact on the learning style of the students,
but it has also resulted in the problem of adoption of the same. Thus, with the significant surge in
the usage of e-learning mechanism, the researchers even tend to predict the continued usage of the
digital learning platforms post pandemic due to its accelerated usage and adoption by the learners
and teachers as well across age and gender. Therefore, the present research seeks to study the factors
influencing e-learning adoption by the students in the context of the pandemic. Further, it would
examine the moderating influence of age and gender for the adoption of e-learning using the UTAUT
model with extended constructs like computer anxiety, attitude, and technology anxiety.

E-Learning, Multi-Group Analysis, Novel COVID-19, UTAUT

The novel coronavirus (COVID-19) disease which was first reported in December 2019 in the Wuhan
city of the central Hubei provinces of China (Holsueet al., 2020) created unprecendeted health crisis
across the globe. The World Health Organization, along with the Chinese authorities worked together
to find out the etiological agent and named it as a novel virus (2019 n-Cov). On January 11, China
declared its first COVID 19 death of a 61-year-old man who was exposed to the wet seafood market
(WHO, 2020a). Subsequently, the deadly infection spread across the globe (WHO, 2020b). WHO
declared this deadly virus is a public health emergency on January 30, 2020 (WHO 2020a, 2020b).
As a result of the outbreak of the disease, lockdown was imposed across the world which adversely
affected the normal life halting all the activities. Education was not an exception to this lockdown.
All the educational institutions had come to a standstill due to the closure. According to UNESCO, by
April 2020, 186 countries had imposed nationwide shut down, influencing 73.8% of the total learner
enrolled (UNESCO, April, 2020). Thus, the pandemic forced educational institutions to adopt digital
platforms to reach out to the students. This e-learning, of course, has played crucial role in planning,
delivering and tracking the learning process effectively. But at the same time, its effectiveness depends
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upon digital level efficiency and willingness to adopt and accept the system. However, the e-learning
mode varies from typical classroom situations when it comes to learner satisfaction, motivation, and
interaction (Bignoux and Sund, 2018).
In a developing country like India, the paradigm shift towards e-learning posed serieous challenges
in terms of learning quality and the way content is designed and implemented efficiently. In other
words, e-learning effectiveness depends upon how well contents are curated to an online platform.
The students particularly under-graduates tend to face a lot of problems in adopting e-learning in such
an unprecedented pandemic. The challenges are mostly related to technology, pedagogy, changed
learning styles, constraints of time and cost apart from the other factors. Despite all the challenges, the
learners tend to prefer the online form due to its flexibility and other benefits. Thus, based on these
premises, the present study seeks to explore the factors affecting the adoption of e-learning during
this difficult times. The study also tries to analyze the moderating role of age and gender towards the
adoption of e-learning by the young undergraduates.
The findings of the study are expected to overcome the pertinent challenges in the adoption
of e-learning and would suggest for an alternative model of the same at the university level. As
the universities do not have sufficient time to design the course content and pedagogy to meet the
academic requirements of the students, the learning experience can be improved by making it more
productive, user-friendly and accessible in the course of adoption of e-learning. Furthermore, it is also
expected to continue the e-learning process post pandemic in view of the uncertainties that prevail
today. Thus, a new learning ecosystem is set to emerge where there is a possibility of the adoption of
blended learning even after the pandemic is over. The findings would also suggest the universities to
update their e-learning system to facilitate the adoption of the digital learning platform effectively.
The research starts with a theretical base of UTAUT followed by the review of related works, the
research methodology, empirical analysis, limitations and scope for further research and above all
the conclusions and recommendations.

Technology not only facilitates the design of e-learning content but also helps in developing the
perception and preference of the learners to accept and adopt online pedagogical platforms for thus
making the learning process more interactive, engaging, productive, and useful. As the Technology
Acceptance Model is considered as the most widely used framework for studying the adoption of any
new technology and the attitude and willingness of the learners towards e learning too. This study
adopts the unified theory of acceptance and use of technology (UTAUT) to study the factors that
influence the students to adopt e-learning during an unprecedented situation of COVID-19.

Previous studies on technology acceptance and user acceptance were reviewed to find out the key
variables affecting the adoption of e-learning from the student’s perspective in higher education.
The UTAUT framework was mainly developed to study the adoption of technologies. The original
Technology Acceptance Model (TAM) is the most effective and frequently cited theory in the literature.
But it is believed that it only predicts the success of technology acceptance between 30% to 40%
of cases, which shows a less exploratory power and the less usefulness in the field of acceptance
(Benbasat and Barki,2007; Bagozzi, 2007; Teo,2011; Chuttur, 2009, Venkatesh and Davis, 2000).
In the important study of student’s adoption of web-based learning. TAM was found to explain only
15% of the students’ actual use behaviour putting other constraints on model reliability and validity
(Martins and Kellermanns, 2004). But the UTAUT model has included vital components across the
other user adoption models. Unlike TAM, this model, with the integration of the primary constructs
and the moderating variables, has further improved the analytical efficiency to 70% of the variance
to use the technology (Venkatesh et al., 2003). From the context of e-learning, TAM has relatively
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less capacity to study the individual predisposition to acceptance. For example, social influence
which was not incorporated in TAM is considered as a fundamental driver that defines the adoption
of e-learning. It is believed that learners tend to be influenced by their friends, teachers, relatives, and
colleague. The essential parameters like availability of infrastructure and required resources, and the
social influence may also determine specific individual behaviour to accept or reject the technology.
These parameters have made the model much robust as compared to other theories. Thus, based
on the above research data and the reliability therein, UTAUT was considered to be more suitable
for this study. It explores the adoption of the UTAUT theory with a proposal to add the variables
like technology anxiety and social anxiety for investigating the factors influencing the adoption of
e-learning in the Indian education system. The proposed research framework is presented in figure
1followed by hypotheses formulation.

In continuation of the literature on theoretical framework, this section makes an elaborate attempt for
the formulation of hypothesis. Performance expectancy (PE) is defined as “individual beliefs that a
system use will increase the job performance to complete the various tasks” (Venkatesh et al., 2003).
PE has been introduced to the UTAUT 2 by the prior studies on perceived usefulness, i.e., TAM1,
TAM2, relative advantages – innovation diffusion theory (IDT). It is one of the strongest predictors
of the behavioral intention to adopt the technologies in both involuntary and voluntary settings
(Venkatesh et al., 2003). In the context of e-learning, it is defined as the amount to which students
Figure 1. Proposed Research model
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rely on using e-learning will help to improve their performances in terms of grades and increase the
learning performance (Wang et al., 2009). This strong belief will further strengthen the student’s
behavioral intention to accept e-learning. PE offers students a platform which is user- friendly,
convenient, and easy to handle that in turn helps to improve the learning productivities (Wang et al.,
2009). Thus, this factor helps to understand whether the performance expectancy is useful to in the
adoption of e-learning. A significant positive association was found between the two constructs in
many a studies. Based on the above literature support, the following hypothesis has been proposed.
H1: Performance expectancy influences the behavioral intentions to adopt the e-learning system in
the Indian context.
Similarly, effort expectancy (EE) is explained as the degree of ease associated with the use of the
system” (Venkatesh et al., 2003) and is identical with the perceived ease of use from the technology
acceptance model (Davis et al., 1989). Previous studies have found that EE has a direct relationship
to behavioral intention (BI) (Lin et al., 2014a,b; Venkatesh and Zhang, 2010). After reviewing the
previous studies it is found that views of the students on the adoption of the technologies support
the vital role that EE plays as an essential predictor for the BI (Park, 2009; Cheung and Vogel, 2013;
Al- Gahtani, 2016, Chiu and Wang, 2008). EE as a predictor helps to analyze the belief pattern of
the students to use the e-learning system. It is generally assumed that if a student feels e-learning
pedagogy is user friendly without any technical glitches, he is more likly to be inclined to adopt the
e-learning in their learning environment. Moreover, it would be helpful to get an insight into whether
EE factors influence the e-learning adoption. Therefore, the following hypothesis has been formulated
H2: Effort expectancy has a significant influence on the behavioral intention to adopt e-learning in
the Indian context.
Social influence (SI) is the degree to which a student considers the opinions of peers, groups,
friends, colleagues, relatives, and other family members for using e-learning methods. From the
previous studies on UTAUT, it was found that perception has a significant relationship with the
BI to accept technologies like e-learning (Venkatesh et al., 2003; AbuShanab et al., 2010). Earlier
studies have found that the individual’s intention to adopt e learning technology is generated by social
influences. Thus,, in the literature SI is found to be an essential contributing factor to determine the
BI to adopt e-learning and hence the following hypothesis was formulated.
H3: Social influence has a significant impact on the behavioral intention to adopt e-learning in the
Indian context.
Technology anxiety (TA) is the willingness and abilities of the learners to accept when they
come in contact for the first time with a new technology like e-learning (Venkatesh and Bala, 2008;
Meuter et al., 2003). TA is also found to be helpful in reducing the mental acceptance of automated
technologies. In other words, negative and positive feelings about the technologies are closely related
to their behaviors. TA also might cause discomfort about the technologies (Liljander et al., 2006).
Technology anxiety (TA) is a significant predictor of the user’s intention to adopt technology like
e-learning (Meuter et al., 2003). The user acceptance of e-learning might increase the technology
anxiety while using online platforms and small devices like computers, laptops, mobile phones, tablets
etc.. Although it is convenient and flexible unlike offline mode of attending lectures, there is a lack
of privacy and data security. Thus (TA) has a negative influence on the adoption of e-learning. Based
on the above arguments, the following hypothesis was proposed.

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H4: Technology anxiety has a significant negativeinfluence on the behavioral intention to accept the
e-learning in the Indian context.
Computer anxiety (CA) is an essential factor for the acceptance of new technology like e-learning.
Venkatesh et al., (2003) defines it as “evoking anxious or emotional reactions when it comes to
performing the behavior.” CA is an emotional reaction that results from a fear of negative output
after using the computer. Igbaria and Parasuraman (1989) define CA as the “tendency of individuals
to be uneasy, apprehensive, or faithful about the future or current uses of computer. To Alenezi et
al. (2010) CA plays a crucial role in the adoption of e-learning in the setting of higher education.
Abdullah and Ward (2016) found that 59% of students expressed about the negative effect of CA on
the e-learning context. In previous studies fear of the implementations of the computer was associated
with data hacking and leaking which posed a challenge for the adoption of e-learning (Igbaria and
Chakrabarti,1990; Gilroy and Desai,1986). Computer anxiety is associated with negative opinions
about computers. Based on the above premises, the following hypothesis was formulated
H5: Computer anxiety has a significant negative influence on the behavioral intention to accept
e-learning in the Indian context.
Facilitating condition (FC) implies for an environmental or physical setting under which a
person performs various activities (Salloum and Shaalan, 2018). FC is defined as the presence of
resources and infrastructure to support the usage and adoption of e-learning in a given institutional
setting. (Venkatesh et al., 2003). In the e-learning context, resources like excellent, fast, seamless
net connectivity, availability of a computer, laptop or tablet, and other necessary infrastructure
resources directly or indirectly support the success of accepting e-learning. Thus student’s perception
depends on the availability of supportive products and services to adopt e-learning culture. Thus, the
presence of external resources are needed to enhance the implementation of its behavior (Ajzen,1991).
Generally, lack of complete information, timely assistance, and insufficient resources could obstruct
the students in the adoption of e-learning (Nanayakkara,2007). This study assumes that FC determines
student’s perception to use e-learning. Training on usage, infrastructure availability increase the skills,
abilities, and knowledge of e-learning systems (Salloum and Shaalan, 2018). Teo (2010) studied
that the influence of FC on the adoption of the technologies tend to affect the adoption of e-learning
(Sharma et al., 2016; Tarhini et al., 2017a). Thus, based on the above logic the following hypothesis
has been formulated.
H6: Facilitating condition has a significant influence on the user behavior to accept the e-learning
in the Indian context.
Attitude towards using technology in e-learning explains the degree to which the person has an
attitude, which influences the behavioral intention to use directly. The attitude towards using in the
context of the e-learning system affects the behavioural intention to use (Stoel and Lee,2003; Lee et
al., 2005; Roca and Gagne, 2008; Liu et al., 2009). From the previous literature, it shows that ‘attitude
towards using’ realizes successful implementations (Huang and Liaw, 2005). Generally, attitude
towards using it directly affects the behavioral intention to use.(Alharbi and Drew,2014). Therefore,
the following hypothesis was proposed based on the aforesaid literature support.
H7: Attitude towards using influences the behavioral intention to accept e-learning in the Indian
context.

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Behavioural intention (BI) refers to an individual’s intention to use the technology for various
tasks. An individual’s intention to use e-learning applications rather then to use the conventional mode
of learning. Previous studies have validated and tested the actual uses in the e-learning context and
have been affected by the intention to use the system (Chang and Tung, 2008; Tarhini et al., 2014,
Tarhini et al., 2017a, 2017b; Liu et al., 2010). Individual commitment also strengthens behavioral
intentions (Ngai et al., 2007). Various studies have found that behavioral intention influences the
actual uses systems (Motaghian et al., 2013; Davis, 1989; Wang and Wang,2009). As aligned with
the previous studies, this study also established a relationship between (BI) to the actual use in the
context of e-learning adoption. In other words, it is a precursor of the behavior to use. Therefore, the
following hypothesis was developed.
H8: Behavioural intention to use significantly influences the behaviour to accept e-learning in the
Indian context.
Age moderates the relationship between PE, EE, SI and FC on the BI (Venkatesh et al., 2003; Lu
et al., 2009). The influences of PE was found to be more strong in the case of youngsters unlike EE
and SI, which had a stronger effect for the older people (Venkatesh et al., 2003). It is found that older
people tend to give more importance to support and help in the context of a job, which is known as
FC (Hall and Manfield, 1975). Younger students are more worried about increasing the performance
during the time of using the technologies, unlike older students whose primary concern is the ease
of using e-learning and its applications. Age and gender have a combined effect on the relationship
between BI and FC. The differences in gender in the task orientation and focus on instrumentality
would become higher with age (Morris et al., 2005). As students grow older, the differences in
gender roles would be found significant. Thus, older girls’ studentsare likely to focus on FC. Previous
literature shows that gender differences in the context of (FC) become high with the increase in age
(Venkatesh et al., 2003; Morris et al., 2005).
Age and gender are theorized to play a moderating role in the effect of PE on BI. In other words,
the effect of PI on BI moderated by age and gender would be stronger for youngers (Venkatesh et
al., 2003). Similarly, older users face difficulty in processing the complex and new information that
influences their learning of the latest technologies (Plude and Hoyer, 1985; Morris et al., 2005). These
difficulties might be reduced to the memory and cognitive capabilities linked with the ageing process
Male students tend to give maximum efforts to achieve their goals despite the inherent constiraints.
But in case of female students, they tend to emphasis to maximise their efforts involved to attain
their objectives (Rotter and Portugal, 1969; Henning and Jardim, 1977; Venkatesh and Morris,2000).
Similarly, male students tend to depend less on the FC when accepting the new technologies like
e-learning but female students focus more on supporting external factors. Therefore, the following
hypothesis was formulated.
H9: Age and gender moderate the influences of performance expectancy, effort expectancy, social
influence, and facilitating conditions on the behavioral intentions to accept e-learning in the
Indian context.

A structured questionnaire was administered on randomly selected sample respondents in the state
of Andhra Pradesh, India. A total of 500 respondents mostly undergraduate students belonging to
different age categories were selected for the survey based on convenience sampling out of which
89 responses were rejected for not filling the responses correctly. The survey instrument of (PE),
(EE), (SI), (BI) and (FI) were adopted from Venkatesh et al., (2012). The scale of attitude towards

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using the e-learning was adopted from Masrom, (2007) and the scale of use behavior was taken from
Samsudeen and Mohamed (2019). The scale measurement of (CA) was adopted from Abdullah et
al. (2016) and (TA) scale was used from Yoon and Han, (2013). All the constructs were measured
using a 5-point Likert scale starting from 1= strongly disagree to 5= strongly agree. For analysing
the data smart PLS- 2.0 was applied for the study.

From the demographic composition of the respondents, it is observed that 70.80% of the respondants
are male, and 29.10% are female. Similarly, it is found that 15.57% belonged to the age group of
17-20 years, 24.87% of the resondants are aged between 21-23 years 19.70% of the respondents are
between 24-26 years old and 18.97% of the total respondents are aged above 27 years.
Further a two-method approach was used. The measurement model was developed with the help
of confirmatory factor analysis (CFA). The CFA was used for conducting the convergent and the
discriminant validity. For calculating the moderating variables, we have applied multigroup analysis.
Before proceeding for further analysis, there is a need to evaluate the content validity of the scale
items measurements. There is also a need to calculate the criterion validity to check how best the
predictor would predict a dependent variable.

Reliability explains the consistency of the items using construct measurement (Leung et al., 2015).
Cronbach alpha (α) values must be greater than 0.70 (Bernstein and Nunnally, 1994).The construct
reliability, which is based on the calculation of the actual loading of each construct, must be higher
than 0.70, and the average variance extraction (AVE) much be higher than 0.50. While construct
validity intends to validate the scale measurement. (Hew and Leong, 2011) convergent validity explains
the constructability to provide similar results. In the convergent validity, the composite reliability
(CR) must be higher than 0.70, and each factor loading and (AVE) must be higher than 0.50 (Fornell
and Larcker, 1981). The average variance extraction must be higher than the correlation coefficient.
From Table 1, it is observed that Cronbach alpha and composite reliability of all the construct
are greater than 0.70 hence it met the criteria for reliability. Further the factor loading of all the
items are greater than 0.5 except item 2 of the construct EE and item 3of the construct UB. But the
factor loding values of these two items are nearly equal to 0.5 hence accepted for convergent validity.
Further AVE value for all the construct are greater than 0.5 that met the criteria for discriminant
validity. As convergent validity, construct reliability, and discriminant validity threshold are met
for all the construct. Hence we further test the significance through the bootstrapping method of
structural equation modelling. The path analysis based on the t-statistics to verify the significance
of the hypotheses is presented in Table2.
Table 2 descriptions: From Table 2 it is observed that, the t – statistics values for the hypothesis
H2 and H4 are less than 1.96 at 0.05 level of significance, hence not accepted that means effort
expectancy and technology anxiety does not have significant impact towards behavioural intention.
Similarly all other hypothesis such as H1, H3, H5, H6, H7 and H8 the value of t – statistics are greater
than 2.56 at 0.01 level of significance. Hence the relationship among these variable of interest are
considered significant and we accept the proposed hypothesis.

The moderator effect was calculated and analysed using Joreskog and Sorbom (1993). In table 3, the
moderating effect of (PE), (EE), (SI) and (FC) are represented.
From Table 3 it is clear that t- statistics for the hypothesis H9b is greater than 1.96 and hence
accepted at 0.01 level of significance (t=2.08) and rest all are not accepted. Hence gender is only

Volume 17 • Issue 2 • March-April 2022
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Table 1. Construct reliability and convergent validity
Latent constructs Factor loading Cronbach
alpha
Composite
reliability
Average
variance
extraction
Effort
expectancy
Item 1
Item 2
Item 3
Item 4
.901
.455
.513
.500
.7366 .7328 .5420
Social
influences
Item 1
Item 2
Item 3
.891
.728
.661
.7642 .8072 .5865
Technology
anxiety
Item 1
Item 2
Item 3
.861
.728
.661
.8056 .8853 .7202
Computer
anxiety
Item 1
Item 2
Item 3
Item 4
.859
.725
.799
.508
.7173 .8196 .5401
Facilitating
conditions
Item 1
Item 2
Item 3
Item 4
.853
.575
.718
.508
.7494 .8278 .5634
Attitude Item 1
Item 2
Item 3
Item 4
.893
.905
.898
.848
.9088 .9361 .7856
Performance
expectancy
Item 1
Item 2
Item 3
Item 4
.769
.799
.929
.930
.8803 .9185 .7393
Behavioral
intentions
Item 1
Item 2
Item 3
.921
.920
.869
.8877 .9304 .8169
Uses Behavior Item 1
Item 2
Item 3
.904
.928
.485
.7130 .8244 .6286
Table 2. Hypotheses result by using SEM
Hypotheses Regression path T-statistics Supported
H1 PE BI 3.160 Yes
H2 EE BI .677 No
H3 SI BI 2.519 Yes
H4 TA BI .934 No
H5 CA BI 9.436 Yes
H6 FC BI 3.346 Yes
H7 Attitude BI 5.203 Yes
H8 BI Usage behavior 13.492 Yes

Volume 17 • Issue 2 • March-April 2022
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having a moderating effect between effort expectancy and behavioural intention. Further moderating
test of age between the variable of intrest are presented in Table-4
From Table 4 it is clear that t- statistics for the hypothesis H10a is greater than 1.96 and hence
accepted at 0.01 level of significance (t=2.18) and rest all are not accepted. Hence age is only having
a moderating effect between performance expectancy and behavioural intention.

The main purpose of the study seeks to check the appropriateness of the extended UTAUT model
in the context of e-learning of the under-graduates in the Indian education system. By applying the
extended model, it examined the relationship between the perception of students towards e-learning
and use behavior by the Indian students. The results of this study confirmed that all the antecedents
are are found to have 53.3% of cumulative variance for the user behaviour. Almost all the constructs
are well supported based on the proposed conceptual model. The findings reaffirm the applicability
of the UTAUT to measure the acceptance and adoption of e-learning in India. This study contributes
to the field of e-learning acceptance by adopting the most critical factors after validating the adoption
model in a particulare-learning environment. This study confirms that (PE), (SI), (CA), (FC), attitude,
and (BI) were found to be significant for determining the e-learning adoption corroborating the
previous studies by Samsudeen and Mohamed (2019) and Ain and Kaur (2015). Age moderates the
relationship between (PE) and (BI) with 95% of the confidence interval, which implies that the effect
of younger is stronger than the older age category students. Gender moderates the relationship between
(EE) and (BI), which supports the previous studies done by Wang, (2016). At 95% of confidence
interval, the relationship between (EE) and (BI) is significantly different from male to female which
indicated that the effect of the male is stronger than female category students.

This study contributes to the UTAUT model in several ways. The demographic role of age and gender
in the adoption of e-learning in the Indian context was well analysed in the study using the theoretical
Table 3. Moderator effect- Gender: Multigroup analysis
Hypotheses Path Male Female T-statistics P values Significant
H9a PE BI .26 .25 .01 .99 No
H9b EE BI .29 .28 2.08 .03 Yes
H9c SI BI .78 .75 .29 .76 No
H9d FC BI .83 .349 .39 .72 No
Table 4. Moderator effect- Age: Multigroup analysis
Hypotheses Path Younger Older T-statistics P
values
Significant
H10a PE BI .34 .28 2.18 .03 Yes
H10b EE BI .07 .65 .19 .84 No
H10c SI BI .49 .83 .34 .72 No
H10d FC BI .49 .83 .29 .51 No

Volume 17 • Issue 2 • March-April 2022
10
model. It also contributes to the body of knowledge of e-learning adoption. From the theoretical
context, the proposed research model is well validated in the Indian context about the use behavior
and (BI) of e-learning adoptions. This study has extended UTAUT by adding three constructs viz.
attitude, (TA), and (CA) and demographic variables like age and gender The findings of the study also
provides an insight to the management, faculty and researchers as well. The study findings may identify
the priority areas where e -learning can be adopted and successfully implemented without technical
glitches. Moreover, the study would help in understanding the modalities in the adoption of e-learning
for improving the learning process during the difficult times. With the study of student’s perception
towards e learning process, the university administration could improve the existing installations and
future deployments can be customized based on needs and expectation of the students. At the same
time, the educational institutions have to ensure that the e -learning platforms achieve productivity,
efficiency, and performance of students during this period of pandemic.

The present study, of course, suffers from certain limitations as well. With regard to sampling, the
study selected samples from a few universities and colleges in a particular State of India and has
not collected data representing the entire student population of the country which may not provide
enough reflection of the attitude towards the acceptance of e-learning. Future research should focus
on the universities and colleges in the entire country for more generalisation of the findings. Future
studies should address the behaviour towards other e-learning technologies like WebCT. This study
has examined the acceptance of e-learning in a specific milieu of compulsive learning especially
during the period of lockdown due to the pandemic. Thus future research should focus on the study
of attitude of the students during the period of normalcy to accept e-learning with regard to voluntary
usage. Further, as the study was conducted using quantitative method, future research can be conducted
with a qualitative approach or a mixed approach for better generalisation. Alternative frameworks
and theories can also be used for investigating the behavioural intention of the students to accept
e-learning. Future research may include other constructs like trust, culture, experiences as well for
the study of e-learning acceptance.. As the present study is student-centric, the future studies may be
conducted from the faculty perspective to get an understanding of the factors affecting the successful
implementation of virtual learning.

Based on the previous studies on UTAUT, our research has tried to examine the factors affecting the
adoption of e-learning in a specific context. It has also examined the moderating role of age and gender
for the study of behavioral intention. In the first place, the study has validated, tested, and extended
the UTAUT for the Indian e-learning ecosystem by incorporating important constructs like computer
anxiety, technology anxiety, and attitude on the UTAUT nomological structure. Secondly, the findings
indicated that the factors like performance expectancy, social influence, computer anxiety, facilitating
condition, attitude, and above all behavioral intention were significant. Thirdly, with regard to gender,
the male category, moderated the relationship between effort expectancy the behavioral intentions,
unlike the female category. Fourthly, the effect of age, especially younger category moderated the
relationship between performance expectancy and behavioral intention. Finally, the findings of
the study would be helpful for the e-learning experts in developing robust user friendly and highly
customised applications for effective adoption of e-learning by the students and instructors as well.

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Volume 17 • Issue 2 • March-April 2022
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Teo, T. (2011). Assessing the cross-cultural validity study of the E-learning Acceptance Measure (ElAM): A
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e-learning in the workplace. International Journal on E-Learning, 12(4), 425–438.
... Effort expectancy, performance expectancy, social influence, and facilitating conditions all contribute to behavioral intentions in e-learning (Tewari et al., 2023). UTAUT constructs also significantly determine behavioral intentions to use mobile learning systems (Acharjya & Das, 2022;Naveed, et al., 2020;Welch et al., 2020). ...
... Past research has shown limited effects of age and gender moderating UTAUT relationships. Acharjya and Das (2022) find just one out of four hypothesized moderating effects by gender, and one out of four by age, significant. ...
Article
This paper explores the application of the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) to a course management system in student learning. Through a TAM-aligned UTAUT model, it examines the influences of performance expectancy, effort expectancy, perceived enjoyment, social influence, and facilitating conditions on student attitude toward using a learning management system. The hypothesized relationships in the model were tested via Canvas. Results provide evidence that the TAM-aligned UTAUT model is applicable to examining factors influencing learner attitude and behavioral intentions in the use of technology-supported learning management systems.
... While using F Anova, Tussardi et al. (2021) found that age, gender and experience had a significant effect on the relationship between PE and BI, SI and BI, and EE and BI. Also, Acharjya and Das (2021) found that gender moderated the relationship between effort expectancy and behavioural intentions more significantly in males than females, while age, particularly among younger individuals, moderates the relationship between performance expectancy and behavioural intention in the adoption of eLearning during COVID-19. ...
Article
The onset of COVID-19 dealt a severe blow to the education sector in Uganda, leading to the mandatory closure of all learning institutions to mitigate the spread of the virus. Makerere University, a venerable institution in the region, was not exempt from this directive. The initial uptake of e-learning was sluggish due to various factors, including inadequate infrastructure, digital illiteracy among both students and some faculty members, and, most notably, a lack of preparedness in utilizing digital eLearning technologies. The objective of this study was to examine the factors that impacted students’ embrace of eLearning at Makerere University during the COVID-19 pandemic. Employing a quantitative approach, the study utilized questionnaires structured based on the nine constructs of the Unified Theory of Acceptance and Use of Technology model. Between August and December 2021, a questionnaire was distributed to 374 students from two colleges. Structured equation modelling was employed to assess 16 factors that were hypothesized to have an impact on adoption. Effort expectancy emerged as the most robust predictor. Likewise, the behaviour of utilizing eLearning technologies was predominantly impacted by facilitating conditions. Utilizing the UTAUT methodology, this study’s theoretical significance arises from our effort to broaden the existing literature on the utilization of video conferencing software (Zoom) in conjunction with a learning management system (MUELE) during the challenging period of COVID-19, an area that has not been extensively explored. The results offer insights into the embrace and approval of both systems within the framework of a developing nation. This study delivers valuable perspectives for developers of eLearning systems, emphasizing the importance of creating user-friendly platforms that enhance the learner experience. This includes the incorporation of intuitive designs and intelligent features such as chatbots and AI-driven tutoring systems, which adapt to the unique needs of students.
... Integrated learning, which includes online courses with face-to-face teaching, is a promising method for this pedagogical request. This system uses classical and up-to-date educational systems in order to develop a state-of-the-art and remote learning method [9][10][11][12] . ...
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Higher Education (HE) is transforming towards embracing global pedagogical standards, particularly emphasizing student-centered learning models. In conjunction with these progressive initiatives, the incorporation of the Internet is aimed at enhancing course flexibility for both university instructors and students. Blended Learning (BL), a synthesis of online and face-to-face instruction, emerges as a methodology capable of leveraging the advantages inherent in both traditional classroom learning and online learning environments. The research being examined discovers the HE sector's application of globally recognized educational ethics and student-focused teaching. Online access has better-quality course flexibility. This type of education, which participates in classroom teaching with Online Education (OE), is being verified for communication skills. The research uses Moodle and a predictable informative setting to deal with several modes of education. The 49 HE pupils shared in a pre and post-test. Combining teaching improves communication skills, increasing the relationship between students and educators for a more practical education practice.
... This attempt promises to address DE laboratory class problems for Argentine Higher Education (AHE). IVLs are incorporated into the learning program to provide DE students with laboratory training and avoid remote locations or physical obstacles to their learning [11][12][13][14][15] . This research developed and tested an IVL for DE-focused AHE institutions. ...
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p>The adoption of distance education (DE) in Argentine universities has long been in practice and was accelerated by the COVID-19 pandemic. The effectiveness of online learning (OL) in such situations has contributed a lot, but it has limitations, especially in delivering practical laboratory-based education. Interactive virtual laboratories (IVLs) are being designed within the framework of this attempt in order to address these types of issues. The purpose of these virtual laboratories is to provide learners who have recently graduated from DE courses in science and engineering with the potential to get hands-on training. The layout and creation of an IVL application that attempts to recreate face-to-face laboratory experiences within an online environment is the subject of examination among the researchers of the current research. This method of HE focuses on integrating college-level teaching with real-world use to provide learners with a complete education subject to their physiological limitations. The program’s goal is to address immediate challenges and provide an architecture for Argentina’s higher education (AHE) system’s constant evolution.</p
... Commencement of the new academic year in mid-July 2020, educational institutions initiated the adoption of standardized electronic learning (e-learning) methods to address challenges encountered during the early phase of transition (Acharjya & Das, 2022). ...
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Objective: The objective of this study is to examine the impact of schools and key stakeholders in the socio-educational environment, particularly in assisting high school teachers during remote learning amid the COVID-19 pandemic. Method: This study employs a case study methodology with a qualitative approach, utilizing interview and observation tools for data mining. The research employed purposive sampling as a data mining method, and an interactive model was used for data analysis. Results and Discussion: The research findings indicate that teachers encountered social and educational challenges throughout the pandemic and the use of remote learning. Significant discoveries pertaining to this matter can be classified into two categories. Firstly, teachers saw profound disruptions due to the closure of schools during the pandemic. The rapidly evolving circumstances pose significant difficulties for teachers in the socio-educational domain, particularly due to the transition from in-person instruction to online learning and the substantial surge in technology-driven education. In this scenario, teachers find themselves facing a challenging predicament. They must simultaneously navigate the transition to digital learning while also effectively facilitating interactive learning with their students. The second discovery pertains to significant stakeholders inside schools and their respective roles in upholding the significance of educational institutions throughout the pandemic. The individuals encompassed in this group are teachers, principal, students, and parents. Research Implications: Despite the dire circumstances of the pandemic, the decision to close schools does not diminish their crucial function as institutions that are vital for ensuring the continuity of education and the involvement of key stakeholders. Originality/Value: This study offers a teacher's viewpoint on examining many factors that contribute to preserving the crucial function of schools during the pandemic. The topic of the COVID-19 pandemic, which encompasses environmental background, institutions, actors, education, and social factors, has not been extensively examined in scientific studies.
... context of how businesses are evolving in the face of digital transformation (Kurniawati et al., 2021). As AI and machine learning reshape talent acquisition practices, businesses in the e-commerce sector are compelled to become more adaptable and foster innovative workplace environments (Acharjya & Das, 2022). The importance of digital connectivity and its role in promoting economic growth is significant, considering Indonesia's e-commerce industry's rapid expansion. ...
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This research explores the impact of Artificial Intelligence (AI) and Machine Learning (ML) on talent acquisition within Indonesia's growing e-commerce sector. While acknowledging the transformative potential of these technologies, the study uncovers significant challenges in their integration into existing talent acquisition processes. These challenges include issues related to data quality, model accuracy, and system adaptability. The study emphasizes the difficulty of acquiring talent with expertise in AI and ML, given the increasing demand for skilled workers in the e-commerce boom. It explores strategies employed by companies to address this talent gap, such as upskilling existing staff and seeking external expertise. The research provides a nuanced understanding of AI and ML applications in the Indonesian e-commerce landscape, highlighting both benefits and obstacles. The insights derived from the study aim to offer actionable guidance for e-commerce firms, HR professionals, and researchers navigating the evolving landscape of AI, ML, and talent acquisition in Indonesia's digital marketplace.
... In PLS-SEM, validity and reliability tests on each construct are verified using the CFA technique 66,67 . As seen in Table 2, all item loadings exceed the minimum criterion of 0.7, hence the construct has a good agreement. ...
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Teacher innovative behavior is one of the vital factors, affecting student engagement, addresses diverse needs, promotes critical thinking, fosters lifelong learning, and contributes to educational research and development. By encouraging and supporting teacher innovation, we may can ensure that education remains relevant, effective, and impactful in preparing students for the future. Teacher innovative behavior is also needed to improve the mathematics skills of elementary school students, and it is important to determine the predictors that significantly affecting Teacher innovative behavior. Therefore, this study aimed to develop a model that predicted possible factors affecting mathematics teachers' innovative behavior based on Social Cognitive Theory (SCT). Data were collected from 132 elementary school teachers in China to verify the model, and the analysis was conducted using a structural equation modelling approach. Theoretically, 10 of the 15 hypotheses were found to be significant. The results showed that facilitating conditions and self-efficacy significantly affect mathematics teachers' innovative behavior. Meanwhile, Technological, Pedagogical and Content Knowledge (TPACK) knowledge, Social Influences, Rewards, Work engagement and anxiety did not show any effect. The contribution developed a model and provided new knowledge about the factors affecting elementary school teachers' innovative behavior. Practically, this could be used to improve teachers' innovative behavior.
... The outcome demonstrates inconsistency with certain previous works, which have illustrated the important influence of the EE factor on the intention to utilise technology [39]. Given that the parcel locker is a new method of delivery in comparison to standard home delivery, this finding was surprising, even though it was consistent with those of several other studies [40] and [21]. The facilitating conditions findings were consistent with those of other researchers [41] and [42] who found this was not a significant factor affecting individuals' intentions to utilise technology. ...
Article
Researchers have been intrigued by parcel lockers for last-mile delivery services, prompting them to investigate the matter more. This study examines factors affecting consumers’ intention to use parcel lockers through the Unified Theory of Acceptance and the Use of Technology (UTAUT). This study proposed the mediating role of performance expectancy in the relationship between social influence and effort expectancy with the intention of adopting parcel locker services. An online structured questionnaire was employed and managed to collect data from 444 respondents. The non-probability purposive sampling technique was chosen as the sampling technique, while the SmartPLS version 4.0 analysed research data. The data found that performance expectancy and compatibility over consumers' intention strongly exerted the intention to use parcel lockers. For the mediator factor, the analysis uncovered evidence that performance expectancy can effectively mediate the relationship between social influence, effort expectancy, and intention to adopt parcel lockers. The research demonstrated the significance of the UTAUT model in pinpointing the reason for the parcel locker's adoption intention in Malaysia. The research findings could provide meaningful information to logistics businesses, courier companies, and relevant government bodies to design and implement strategies to enhance the acceptance and usage of parcel lockers as the last delivery option compared to home delivery.
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Purpose The COVID-19 pandemic provided unprecedented impetus to the evolution of the e-learning learning ecosystem by compelling students to adopt e-learning systems. This paper aims to use the UTAUT model to provide insight into the differences in factors influencing the adoption of e-learning systems before and after the pandemic. Design/methodology/approach This longitudinal study uses two surveys conducted among graduate students in the city of Bengaluru in India. One prior to the start of the COVID-19 pandemic and a second in its aftermath. PLS-SEM is used to analyze both data sets to draw insights into the constructs that influence Behavioral intention to adopt e-learning systems. The moderating effect of gender is also analyzed. Findings Pre COVID-19, Facilitating Conditions, Performance Expectancy and Effort Expectancy (quadratic behavior) were dominant factors influencing the adoption of e-learning technologies. Post pandemic, Performance Expectancy and Social Influence are drivers of e-learning adoption. Effort Expectancy and Facilitating Conditions grouped as Ease of Use is a significant driver of e-learning adoption post pandemic. Gender is found to not have a moderating influence. Originality/value The unique longitudinal study of the differences in factors influencing students’ intention to adopt e-learning pre- and post-COVID-19 can prove useful to policy makers in the higher education sector. Academics can use the post-pandemic e-learning model’s findings in multiple contexts such as generational cohorts, educational contexts and social contexts.
Article
Students from diverse backgrounds often face challenges that are not in parallel with the evolution of equitable assessments in higher education. Thus, this study employed the UTAUT model with the inclusion of additional constructs, namely self-efficacy and technology awareness, to examine the intention to use online assessments among diverse students in Malaysian higher education institutions. A quantitative study was conducted using an online survey. A total of 411 responses were collected from undergraduates in both public and private universities in Malaysia and analysed using partial least square-structural equation modelling. The main findings indicated that performance expectancy and resource-facilitating conditions have a significant impact on behavioural intention. This study also indicated that self-efficacy has a significant effect on performance expectancy and effort expectancy. In addition, this study highlighted that technology awareness positively moderates the relationship between effort expectancy, resource-facilitating conditions, and behaviour intention. This paper is expected to contribute to research on the use of online assessments focusing on self-efficacy and technology awareness among students with diverse backgrounds.
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The main objective of this article is to study the factors that affect university students’ acceptance of E-learning systems. To achieve this objective, we have proposed a new model that aims to investigate the impact of innovativeness, quality, trust, and knowledge sharing on E-learning acceptance. Data collection has taken place through an online questionnaire survey, which was carried out at The British University in Dubai (BUiD) and University of Fujairah (UOF) in the UAE. There were 251 students participated in this study. Data were analyzed using SmartPLS and SPSS. The Structural Equation Modelling (SEM) has been used to validate the proposed model. The outcomes revealed that knowledge sharing and quality in the universities have a positive influence on E-learning acceptance among the students. Innovativeness and trust were found not to significantly affect the E-learning system acceptance. By identifying the factors that influence the E-learning acceptance, it will be more useful to provide better services for E-learning. Other implications are also presented in the study.
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Studies of learning and student satisfaction in the context of online university programmes have largely neglected programmes catering specifically to business executives. Such executives have typically been away from higher education for a number of years, and have collected substantial practical experience in the subject matters they are taught. Their expectations in terms of both content and delivery may therefore be different from non-executive students. We explore perceptions of the quality of tutoring in the context of an online executive MBA programme through participant interviews. We find that in addition to some of the tutor behaviours already discussed in the literature, executive students look specifically for practical industry knowledge and experience in tutors, when judging how effective a tutor is. This has implications for both the recruitment and training of online executive MBA tutors.
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In this study the perceived value construct of the Unified Theory of Acceptance and Use of Technology (UTAUT2) is investigated in the context of a learning management system (LMS), in which the construct is redefined from its original price value conceptualization. It was found that many researchers simply ignore the price value construct when applying the UTAUT2 model in technology use studies in the educational context. This study extends the UTAUT2 framework by integrating the learning value construct and provides fresh insight about predictors of students’ intentions towards LMS and its use. A quantitative research approach was employed by utilizing a closed-ended questionnaire to collect data from Malaysian university students who were users of LMS. Probability proportional stratified sampling was employed to select an appropriate sample. The results indicated a good measurement and structural model fit and suggested the significant influence of performance expectancy, social influence and learning value on students’ intention towards LMS and also confirmed the influence of facilitating conditions and behavioral intention on LMS use. The extended UTAUT2 framework helps in understanding students’ perceived value in the LMS context. Furthermore, this study will help institutions to consider the factors for successful implementation of an LMS in an academic setting.
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Cloud computing is a recent and significant development in the domain of network applications with a new information technology perspective. This study attempts to develop a hybrid model to predict motivators influencing the adoption of cloud computing services by information technology (IT) professionals. The research proposes a new model by extending the Technology Acceptance Model (TAM) with three external constructs namely computer self-efficacy, trust, and job opportunity. One of the main contributions of this research is the introduction of a new construct, Job Opportunity (JO), for the first time in a technology adoption study. Data were collected from 101 IT professional and analyzed using multiple linear regression (MLR) and neural network (NN) modeling. Based on the RMSE values from the results of these models NN models were found to outperform the MLR model. The results obtained from MLR showed that computer self-efficacy, perceived usefulness, trust, perceived ease of use, and job opportunity. However, the NN models result showed that the best predictor of cloud computing adoption are job opportunity, trust, perceived usefulness, self-efficacy, and perceived ease of use. The findings of this study confirm the need to extend the fundamental TAM when studying a recent technology like cloud computing. This study will provide insights to IT service providers, government agencies, academicians, researchers and IT professionals.
Article
An outbreak of novel coronavirus (2019-nCoV) that began in Wuhan, China, has spread rapidly, with cases now confirmed in multiple countries. We report the first case of 2019-nCoV infection confirmed in the United States and describe the identification, diagnosis, clinical course, and management of the case, including the patient's initial mild symptoms at presentation with progression to pneumonia on day 9 of illness. This case highlights the importance of close coordination between clinicians and public health authorities at the local, state, and federal levels, as well as the need for rapid dissemination of clinical information related to the care of patients with this emerging infection.
Article
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.
Article
In this study, we examine the effects of individual-level culture on the adoption and acceptance of e-learning tools by students in Lebanon using a theoretical framework based on the Technology Acceptance Model (TAM). To overcome possible limitations of using TAM in developing countries, we extend TAM to include subjective norms (SN) and quality of work life constructs as additional constructs and a number of cultural variables as moderators. The four cultural dimensions of masculinity/femininity (MF), individualism/collectivism, power distance and uncertainty avoidance were measured at the individual level to enable them to be integrated into the extended TAM as moderators and a research model was developed based on previous literature. To test the hypothesised model, data were collected from 569 undergraduate and postgraduate students using e-learning tools in Lebanon via questionnaire. The collected data were analysed using the structural equation modelling technique in conjunction with multi-group analysis. As hypothesised, the results of the study revealed perceived usefulness (PU), perceived ease of use (PEOU), SN and quality of work life to be significant determinants of students’ behavioural intention (BI) towards e-learning. The empirical results also demonstrated that the relationship between SN and BI was particularly sensitive to differences in individual-cultural values, with significant moderating effects observed for all four of the cultural dimensions studied. Some moderating effects of culture were also found for both PU and PEOU, however, contrary to expectations the effect of quality of work life was not found to be moderated by MF as some previous authors have predicted. The implications of these results to both theory and practice are explored in the paper.