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DOI: 10.4018/IJWLTT.20220301.oa4
Volume 17 • Issue 2 • March-April 2022
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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
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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
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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.
Volume 17 • Issue 2 • March-April 2022
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Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for E-learning
(GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256.
doi:10.1016/j.chb.2015.11.036
Abdullah, F., Ward, R., Catterall, S., Hill, P., & Wilson, D. (2013). An investigation of the factors that
influence engagement with CPD within e-portfolios used for accredited Higher Education course. University
of Huddersfield.
AbuShanab, E., Pearson, J. M., & Setterstrom, A. J. (2010). Internet banking and customers’ acceptance in
Jordan: The unified model’s perspective. Communications of the Association for Information Systems, 26(1),
23. doi:10.17705/1CAIS.02623
Ain, N., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning management system use:
An extension of UTAUT2. Information Development, 32(5), 1306–1321. doi:10.1177/0266666915597546
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes,
50(2), 179–211. doi:10.1016/0749-5978(91)90020-T
Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation
model. Applied Computing and Informatics, 12(1), 27–50. doi:10.1016/j.aci.2014.09.001
Alenezi, A. R., & Karim, A. (2010). An empirical investigation into the role of enjoyment, computer anxiety,
computer self-efficacy and internet experience in influencing the students’ intention to use e-learning: A case
study from Saudi Arabian governmental universities. Turkish Online Journal of Educational Technology-TOJET,
9(4), 22–34.
Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural
intention to use learning management systems. International Journal of Advanced Computer Science and
Applications, 5(1), 143–155. doi:10.14569/IJACSA.2014.050120
Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift.
Journal of the Association for Information Systems, 8(4), 3. doi:10.17705/1jais.00122
Benbasat, I., & Barki, H. (2007). Quo vadisTAM? Journal of the Association for Information Systems, 8(4), 7.
doi:10.17705/1jais.00126
Bignoux, S., & Sund, K. J. (2018). Tutoring executives online: What drives perceived quality? Behaviour &
Information Technology, 37(7), 703–713. doi:10.1080/0144929X.2018.1474254
Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students’ behavioural intentions to use the
online learning course websites. British Journal of Educational Technology, 39(1), 71–83.
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of
the technology acceptance model for e-learning. Computers & Education, 63(2), 160–175. doi:10.1016/j.
compedu.2012.12.003
Chiu, C. M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: The role of
subjective task value. Information & Management, 45(3), 194–201. doi:10.1016/j.im.2008.02.003
Chuttur, M. (2009). USA Sprouts: Working Papers on Information Systems (vol. 9). Indiana University.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology.
Management Information Systems Quarterly, 13(3), 319–340. doi:10.2307/249008
Demoulin, N. T., & Djelassi, S. (2016). An integrated model of self-service technology (SST) usage in a
retail context. International Journal of Retail & Distribution Management, 44(5), 540–559. doi:10.1108/
IJRDM-08-2015-0122
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and
measurement error. JMR, Journal of Marketing Research, 18(1), 39–50. doi:10.1177/002224378101800104
Gilroy, F. D., & Desai, H. B. (1986). Computer anxiety: Sex, race and age. International Journal of Man-Machine
Studies, 25(6), 711–719. doi:10.1016/S0020-7373(86)80084-0
Volume 17 • Issue 2 • March-April 2022
12
Hall, D. T., & Mansfield, R. (1975). Relationships of age and seniority with career variables of engineers and
scientists. The Journal of Applied Psychology, 60(2), 201–210. doi:10.1037/h0076549
Han, J. H., Kang, S., Oh, I. S., Kehoe, R. R., & Lepak, D. P. (2019). The goldilocks effect of strategic human
resource management? Optimizing the benefits of a high-performance work system through the dual alignment
of vertical and horizontal fit. Academy of Management Journal, 62(5), 1388–1412. doi:10.5465/amj.2016.1187
Hennig, M., & Jardim, A. (1977). Managerial woman. Anchor Press/Doubleday.
Holshue, M. L., DeBolt, C., Lindquist, S., Lofy, K. H., Wiesman, J., Bruce, H., & Diaz, G. (2020). First case
of 2019 novel coronavirus in the United States. The New England Journal of Medicine, 382(10), 929–936.
doi:10.1056/NEJMoa2001191 PMID:32004427
Huang, H. M., & Liaw, S. S. (2005). Exploring users’ attitudes and intentions toward the web as a survey tool.
Computers in Human Behavior, 21(5), 729–743. doi:10.1016/j.chb.2004.02.020
Igbaria, M. (1995). Why do individuals use computer technology? Academic Press.
Igbaria, M., & Chakrabarti, A. (1990). Computer anxiety and attitudes towards microcomputer use. Behaviour
& Information Technology, 9(3), 229–241. doi:10.1080/01449299008924239
Igbaria, M., & Parasuraman, S. (1989). A path analytic study of individual characteristics, computer anxiety and
attitudes toward microcomputers. Journal of Management, 15(3), 373–388. doi:10.1177/014920638901500302
Jong, D., & Wang, T. S. (2009). Student acceptance of web-based learning system. In Proceedings of the 2009
International Symposium on Web Information Systems and Applications (WISA 2009) (p. 533). Academy
Publisher.
Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command
language. Scientific Software International.
Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of Internet-based learning medium: The role of
extrinsic and intrinsic motivation. Information & Management, 42(8), 1095–1104. doi:10.1016/j.im.2003.10.007
Leong, L. Y., Hew, T. S., Ooi, K. B., & Lin, B. (2011). Influence of gender and English proficiency on
Facebook mobile adoption. International Journal of Mobile Communications, 9(5), 495–521. doi:10.1504/
IJMC.2011.042456
Leung, L. (2015). Validity, reliability, and generalizability in qualitative research. Journal of Family Medicine
and Primary Care, 4(3), 324. doi:10.4103/2249-4863.161306 PMID:26288766
Liljander, V., Gillberg, F., Gummerus, J., & Van Riel, A. (2006). Technology readiness and the evaluation and
adoption of self-service technologies. Journal of Retailing and Consumer Services, 13(3), 177–191. doi:10.1016/j.
jretconser.2005.08.004
Lin, H. M., Chen, W. J., & Nien, S. F. (2014). The study of achievement and motivation by e-learning-a case
study. International Journal of Information and Education Technology (IJIET), 4(5), 421–425. doi:10.7763/
IJIET.2014.V4.442
Lin, S. C., Persada, S. F., & Nadlifatin, R. (2014, May). A study of student behavior in accepting the Blackboard
Learning System: A Technology Acceptance Model (TAM) approach. In Proceedings of the 2014 IEEE 18th
international conference on computer supported cooperative work in design (CSCWD) (pp. 457-462). IEEE.
doi:10.1109/CSCWD.2014.6846888
Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the
factors that affect Intention to Use an Online Learning Community. Computers & Education, 54(2), 600–610.
doi:10.1016/j.compedu.2009.09.009
Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology
acceptance. Computers & Education, 52(3), 599–607. doi:10.1016/j.compedu.2008.11.002
Lu, J., Yu, C. S., & Liu, C. (2009). Mobile data service demographics in urban China. Journal of Computer
Information Systems, 50(2), 117–126.
Masrom, M. (2007). Technology acceptance model and e-learning. Technology (Elmsford, N.Y.), 21(24), 81.
Volume 17 • Issue 2 • March-April 2022
13
Meuter, M. L., Ostrom, A. L., Bitner, M. J., & Roundtree, R. (2003). The influence of technology anexiety on
consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899–906.
doi:10.1016/S0148-2963(01)00276-4
Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a
changing work force. Personnel Psychology, 53(2), 375–403. doi:10.1111/j.1744-6570.2000.tb00206.x
Morris, M. G., Venkatesh, V., & Ackerman, P. L. (2005). Gender and age differences in employee decisions
about new technology: An extension to the theory of planned behavior. IEEE Transactions on Engineering
Management, 52(1), 69–84. doi:10.1109/TEM.2004.839967
Motaghian, H., Hassanzadeh, A., & Moghadam, D. K. (2013). Factors affecting university instructors’ adoption
of web-based learning systems: Case study of Iran. Computers & Education, 61, 158–167. doi:10.1016/j.
compedu.2012.09.016
Nanayakkara, C. (2007). A model of user acceptance of learning management systems: A study within tertiary
institutions in New Zealand. International Journal of Learning, 13(12), 223–232. doi:10.18848/1447-9494/
CGP/v13i12/45146
Ngai, E. W., Poon, J. K. L., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM.
Computers & Education, 48(2), 250–267. doi:10.1016/j.compedu.2004.11.007
Nunnally, J. C. (1994). Psychometric theory (3rd ed.). Tata McGraw-Hill Education.
Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’
behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150–162.
Plude, D. J. (1985). Attention and performance: Identifying and localizing age deficits. Aging and Human
Performance, 47-99.
Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A
self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604. doi:10.1016/j.
chb.2007.06.001
Rotter, G. S., & Portugal, S. M. (1969). Group and individual effects in problem solving. The Journal of Applied
Psychology, 53(4), 338–341. doi:10.1037/h0027771
Salloum, S. A., Al-Emran, M., Shaalan, K., & Tarhini, A. (2019). Factors affecting the e-learning acceptance: A
case study from UAE. Education and Information Technologies, 24(1), 509–530. doi:10.1007/s10639-018-9786-3
Samsudeen, S. N., & Mohamed, R. (2019). University students’ intention to use e-learning systems. Interactive
Technology and Smart Education, 16(3), 219–238. doi:10.1108/ITSE-11-2018-0092
Sharma, S. K., Al-Badi, A. H., Govindaluri, S. M., & Al-Kharusi, M. H. (2016). Predicting motivators of cloud
computing adoption: A developing country perspective. Computers in Human Behavior, 62, 61–69. doi:10.1016/j.
chb.2016.03.073
Stoel, L., & Lee, K. H. (2003). Modeling the effect of experience on student acceptance of web‐based courseware.
Internet Research, 13(5), 364–374. doi:10.1108/10662240310501649
Tarhini, A., Al-Busaidi, K. A., Mohammed, A. B., &Maqableh, M. (2017). Factors influencing students’ adoption
of e-learning: a structural equation modeling approach. Journal of International Education in Business.
Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behaviour in
developing countries: A structural equation model. Computers in Human Behavior, 41, 153–163. doi:10.1016/j.
chb.2014.09.020
Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level
cultural values on users’ acceptance of E-learning in developing countries: A structural equation modeling of
an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328. doi:10.1080/
10494820.2015.1122635
Teo, T. (2010). A path analysis of pre-service teachers’ attitudes to computer use: Applying and extending the
technology acceptance model in an educational context. Interactive Learning Environments, 18(1), 65–79.
doi:10.1080/10494820802231327
Volume 17 • Issue 2 • March-April 2022
14
Teo, T. (2011). Assessing the cross-cultural validity study of the E-learning Acceptance Measure (ElAM): A
structural equation modeling approach. Academic Press.
Venkatash, V., & Davis, F. D. (2000). A Theoretical Extension of Technology Acceptance Model: Four
Longitudinal Studies. Management Science, 46(2), 186–204.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions.
Decision Sciences, 39(2), 273–315.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology:
Toward a unified view. Management Information Systems Quarterly, 425–478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending
the unified theory of acceptance and use of technology. Management Information Systems Quarterly, 157–178.
Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs. China. Journal
of Global Information Technology Management, 13(1), 5–27.
Wang, Y., Wu, M., & Wang, H. (2009). Investigating the determinants and age and gender differences in the
acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–119. doi:10.1111/j.1467-
8535.2007.00809.x
WHO. (2020a). Pneumonia of Unknown Cause – China. https://www.who.int/csr/ don/05-january-2020-
pneumonia-of-unkown-cause-china/en/
WHO. (2020b). Coronavirus Disease 2019 (COVID-19) Situation Report – 46. https:// www.who.int/docs/
default-source/coronaviruse/situation-reports/20200306-sitrep-
WHO. (2020c). Rolling Updates on Coronavirus Disease (COVID-19). WHO.
Yoo, S. J., & Han, S. H. (2013). The effect of the attitude towards e-learning: The employees’ intention to use
e-learning in the workplace. International Journal on E-Learning, 12(4), 425–438.
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