Factors Influencing E-learning Technology Among Youth in India: An Extended TAM Model

Abstract

Given the pandemic that hit the world, there was suddenly an increased focus on E-learning. This study aimed at determining the various factors that influence the youth of India to opt for E-learning. This study was conceptualized based on the technology acceptance model and integrating social influence (SI) and perceived enjoyment (PE) as additional constructs to determine the attitude (AT) and behavioural intention (BI) towards E-learning. The study also proposes a basic framework of knowledge for E-learning, a shift from traditional classroom learning, which is not researched adequately in previous literature. The sample for this research study were postgraduate students drawn by non-probability judgment and snowball sampling. Data analysis was performed using structural equation modelling, cross-tabulation and Pearson correlation with the help of SPSS Amos software. The results of the study concluded that AT significantly mediates between perceived usefulness, perceived ease of use, SI, PE, and BI. Additionally, the study determined the classroom and E-learning preferences of students comparatively.

Keywords

E-learning technology acceptance model (TAM)pre & post Pandemic behavioural intention (BI)perceived enjoyment (PE)social influence (SI)

Introduction

In today’s knowledge economy, E-learning embodies an unconventional means of teaching and learning, and various organizations utilize these learning methods for the capacity building of employees and students (Sawang et al., 2013). Therefore, E-learning can be defined as the ‘delivery of training and education via networked interactivity and a range of other knowledge collection and distribution technologies’ (Sawang et al., 2013). It has also been defined as a form of distance education that uses information and communication technology (ICT) and a learning management system (DeRouin et al., 2005).

In today’s technological environment, E-learning platforms are becoming critically important. Due to this sudden technological advancement in education, various E-learning platforms are easily available to students. E-learning is also known as web-based learning and is provided in a convenient and time-saving mode with the help of the Internet. E-learning methodology provides a kind of flexibility to students and learners to use it anytime and anywhere. E-learning systems have become an important tool for imparting a flexible mode of education to college students (Singh et al., 2021). Likewise, an online learning system is an information system through which various learning aids like audio, video and text can be assimilated through electronic mail, interactive sessions, conferences, assignments and quizzes (Lee et al., 2011). According to The Economic Times (2020), E-learning is a system of learning that facilitates network-enabled transfer of skills and knowledge, and the transfer of knowledge can be made available for many audiences simultaneously or at other times.

Further, E-learning provides time adaptability for learning to the students and persuades them to do their research without additional support. It is further inferred that the students feel comfortable browsing and surfing the internet. The young generation lacks imagination because of the exposure to abundant technology; the interplay between technology and learning in the twenty-first century is inseparable (Bhatt, 2021). Therefore, understanding the effect of technology in education that contributes to engagement and, ultimately, learning and innovation is important. Students may assist each other with the use of technology and teach research at universities (Shafieiosgouei et al., 2018).

E-learning systems also facilitate teachers to take live sessions, share audio-visual material and give online instructions and suggestions to the students. Companies like JARO Education and TOPPR offer online E-learning courses to higher education scholars.¹ Even various Massive Open Online Courses are offered through the National Programme on Technology Enhanced Learning and Study Webs of ActivE-learning for Young Aspiring Minds platform. It is interesting to find out varied motivation factors for adopting E-learning platforms. Despite many benefits, E-learning was not so popular among youngsters, but it had become imperative during the pandemic.

Several theories in the field of technology have been used to determine the behaviour intention of students toward using E-learning platforms. Some of the notable theories are technology acceptance model (TAM) (Davis, 1989), theory of planned behaviour (TPB) (Ajzen, 2012), value-based adoption model (Kim et al., 2007), the unified theory of acceptance and use of technology (UTAUT) (Venkatesh & Davis, 2000). However, Venkatesh and Davis (2000) argued that if a superior or co-worker suggests that a particular system might be useful, a person may believe it is useful, resulting in positive intention. Hence, social influence (SI) is considered an important determinant of behaviour intention. Further, Sun and Zhang (2006) argued that perceived enjoyment (PE) is also one of the important antecedents in determining behavioural intention (BI). However, these studies did not explain the SI and PE arising due to the technology interface and only emphasized offline settings. Hence, the present study fills this gap. This study deeply explains SI and PE from the perspectives of E-learning technology. Therefore, the present study develops a comprehensive revised TAM model with a better explanatory power of BI concerning E-learning users.

The widespread popularity of E-learning among students and teachers directed researchers and academicians to understand its antecedents and determinants of adoption of E-learning. Despite all the popularity, school and E-learning dropout ratio is still a matter of concern. Many authors have stressed identifying the causes of these dropouts. Some of the notable contributions in the field of E-learning from eminent authors have been outlined in this study. For example, Nugroho et al. (2019) emphasized that the true value of E-learning lies in student satisfaction, which determines the continuing intention of E-learning.

Further, Samsudeen and Mohamed (2019) indicated that performance expectancy, effort expectancy, SI, work–life balance, hedonic motivation, internet experience and facilitating conditions are critical determinants of BI. These determinants were identified in UTAUT2, proposed by Venkatesh and Davis (2000). In continuation, Rui-Hsin and Lin (2018) suggested that the subjective norm, job relevance, system quality, information quality and service quality predict the behaviour intention towards E-learning. However, only a few studies focus on PE and SI as a determinant in predicting the students behaviour towards E-learning uses (Chang et al., 2014; Kimathi & Zhang, 2019; Park et al., 2012; Samsudeen & Mohamed, 2019). Therefore, this study developed a comprehensive TAM model by including these key constructs, such as PE and SI, to determine the student’s BI towards E-learning usage. This article aims to study the impact of all the above constructs on the attitude (AT) and, finally, the BI of using E-learning by the Indian youth.

This study is based on the following objectives:

To explore the various factors affecting the adoption of E-learning among the youth of India.

To determine the impact of SI and PE on AT under the influence of E-learning technology.

To determine the relative preference of students between E-learning and classroom learning.

Literature Review

In previous studies, various factors have been identified that trigger the adoption of E-learning technology. The success of E-learning depends on the acceptance and usage of the system. Therefore, the successful adoption of E-learning rests on the student’s willingness to adopt this new technology. Therefore, exploring the factors affecting E-learning acceptance that facilitate the online learning experience is pertinent.

Youngsters are heavily exposed to new technological development due to its favourable influence on mentoring and influencing learners (Capece & Campisi, 2011; Cappel & Hayen, 2004). Therefore, E-learning has been broadly pushed into the education system and incorporated into the university curriculum to adopt this new technology (Selim, 2007). Through this new age technology, they ensured that experiential learning was also integrated as one of the important characteristics of E-learning studies (Mondi et al., 2008).

Perceived Usefulness

Perceived usefulness (PU) is defined as ‘the extent to which a person believes that using technology will enhance her/his productivity’ (Schneberger et al., 2007; Venkatesh & Bala, 2008). Ratna and Mehra (2015) stated that PU is significantly associated with perceived ease of use (PEOU), AT and BI. PU also mediates between PEOU and AT. Following past research, results recommend that TAM is a sound hypothetical model (Cheung & Vogel, 2013; Yu et al., 2005), where its legitimacy can stretch out to the E-learning setting. A vast amount of literature demonstrates the relationship between PU, PEOU to AT and BI. Venkatesh and Bala (2008) extended the traditional TAM model and suggested that subjective norms, image, job relevance, output quality and the result can determine the PU of the technology.

Further, Lee et al. (2003) applied the TAM model to predict group AT towards group decision support system use and determined that the PU and PEOU successfully predicted group AT towards group decision support system. The relationship between PEOU and AT is mediated by PU. Also, AT arbitrated PU, PEOU and BI to use E-learning. Both PU and PEOU e predict the students behavioural intent to use the e-portfolio. PEOU and PU were the most important constructs in TAM (Chen et al., 2011). This is because both of these constructs largely motivated consumers’ adoption or rejection of technologies (Davis, 1989). Both factors, that is, PEOU and PU, directly impact learners’ intention to use E-learning (Calisir et al., 2014).

PU is also strongly associated with PEOU and AT while adopting e-books as a learning material among undergraduate students in Malaysia (Letchumanan & Tarmizi, 2011). Subsequently, Wei Boon et al. (2019) found that PEOU and usefulness positively influenced students’ ATs towards online business simulation games. Simulation integrated with innovative and interactive learning scenarios stimulates learning. These experiential learning activities enhanced their social skills, engaged their critical thinking, acquired needed hands-on experience and improved their self-confidence. Based on the literature, the following hypotheses were derived:

H ₁: PU of E-learning positively influences the AT of the consumer towards E-learning.

H _4: PU of E-learning positively influences the BI of the consumer towards E-learning.

Perceived Ease of Use

PEOU is defined as ‘the extent to which a person believes that using technology will be free of effort’ (Davis, 1989; Schneberger et al., 2007). Investigations using TAM have recommended that a person’s encounters with explicit technology impact views on the ease of use and efficacy of that technology. Davis (1989) also determined the positive correlation between PEOU and PU. The more technology is error-free or easy to use for the consumers, the more they intend to use it (Sharma & Pal, 2020). PEOU was developed as a construct by Davis (1989), which was further elaborated by Venkatesh and Davis (2000) and demonstrated that individual attempts to minimize their efforts in their behaviour, thus supporting a relationship between PEOU with PU. E-learning facilitates learning by giving the students time adaptability, which propels them to do their work without seeking help from others (Salamat et al., 2018).

Further, system consistency, machine self-efficacy and machine playfulness have a major effect on the E-learning system’s PEOU (Salloum et al., 2019; Venkatesh et al., 2003). Besides, the quality of content, PE and accessibility were found to positively impact PEOU and PU of the E-learning system (Sharma et al., 2018; Sharma & Pal, 2020; Venkatesh, 2000; Venkatesh & Agarwal, 2006; Venkatesh & Bala, 2008; Venkatesh et al., 2003). Rui-Hsin and Lin (2018) argued that if a website frequently updates useful information, its usage intention would continually increase. As a result, the police may view E-learning for education and training as good for improving work performance and learning and therefore has a higher E-learning usage intention (Venkatesh & Agarwal, 2006; Venkatesh & Davis, 2000).

Lee (2010) designed a theoretical model after clubbing the findings reported from the TAM, expectation confirmation model, and TPB to envisage the users’ intentions to continue using E-learning. The study shows that users’ satisfaction with the technology positively influences the intention to continue using that technology, followed by subjective norms and PU. The TAM is established on the assumption that ‘both the attitude towards action and subjective norm impact behavioral intention, which in turn affects how people act’ (Shin & Kim, 2008). Thus, this model can be effectively applied to get an insight into understanding particular variables which influence an individual’s decision to use a particular technology.

Stoel and Lee (2003) utilized the TAM framework to demonstrate the influence of student experiences with online learning on adopting web-based technologies. The study shows a positive influence on the intention to use web-based technology through PU and PEOU. Additionally, Selim (2003) indicated that the usefulness of programme webpage and the ease of use demonstrated to be the crucial elements of their approval and usage as a real learning apparatus. Lee et al. (2003) used the TAM model to examine the use of integrated correspondence and engineering design apparatus in a distributed learning environment. Renny et al. (2013) found the role of PEOU in determining consumer ATs toward airline ticket booking. Wei Boon et al. (2019) further advocated the role of PEOU in shaping students’ ATs towards class simulation. These findings were consistent with those (Sharma & Pal, 2020) who proposed that PEOU predicts AT towards learning through social networking sites and is also an important determinant of PU. The following proposition is framed based on the above literature:

H ₂: PEOU of E-learning positively influences the AT of the consumer towards E-learning.

H ₃: PEOU of E-learning positively influences PU of E-learning.

H ₅: PEOU of E-learning positively influences the BI of the consumer towards E-learning.

Perceived Enjoyment

This term can be explained as the activity of using a specific system is perceived to be enjoyable in its own right, aside from any performance consequences resulting from system use’ (Venkatesh, 2000; Venkatesh et al., 2003). PE is a significant predictor of E-learning adoption or acceptance. This significantly influences E-learning’s PEOU and usefulness (Elkaseh et al., 2016; Kanwal & Rehman, 2017). Students’ enjoyment of the system demonstrated that they consider this web-based system useful and easy to use. Punnoose (2012) studied that PE can be considered an important determinant of BI to use E-learning system.

PE helps shape students’ AT towards accepting technology due to subjective feelings such as joy, relaxation, pleasure and holistic experience during learning (Saade et al., 2008). Van Der Heijden (2004) emphasized intrinsic motivation, such as PE during E-learning and its effect on accepting this new learning system. The study reported that PE positively influences student’s intention to use E-learning technology. Venkatesh et al. (2003) stated that users would be considered useful and ease to use if they found the technology they used was interesting. Padilla-Meléndez et al. (2013) indicated the perceived playfulness in a blended learning environment with the gender disparities that exist. The study indicates that gender differences occur in the impact of playfulness on the student’s AT towards technology and its intent. With more technology incorporated into learning, E-learning can be more enjoyable than traditional classroom learning. Hence, if students perceive E-learning as enjoyable, they tend to develop a more favourable AT towards it, which further influences the intention to use the technology. Hence, the influence of PE on PU, ease of use and intention to use E-learning has been explored enough, but the influence of PE on consumers’ ATs has not yet been studied. Therefore, this study explores the same by developing the following hypotheses:

H _6: PE has a positive influence on the AT of consumers towards E-learning.

H _8: PE has a significant influence on the BI of consumers towards E-learning.

Social Influence

Consumer’s SI is a strong predictor of E-learning usage, especially in a university setting as well as SI strongly predicts E-learning usage (Lin & Yang, 2011). SI also affects motivation towards using E-learning in university settings (Hernandez et al., 2011). Therefore, SI improves the consumer’s intention to use E-learning and apply this technology continuously. Al Kurdi et al. (2020) described the role of SI in determining E-learning BI and stated that ‘in some situations, a system may be used by people to meet the needs of other people, rather than to concentrate on their own beliefs and feelings’. Social connections during E-learning play a significant role in using E-learning (Lee & Faulkner, 2011).

Furthermore, a consumer’s acceptance of adopting new technology is influenced by various demographic, organizational and social connections, besides PU and ease of use of the new E-learning systems (Segooa & Kalema, 2016). Therefore, a social connection had a constructive influence over users’ BI to use the new technology (Paola Torres Maldonado et al., 2011). Farahat (2012) found that the student’s perceptions of the social impact of the referent students’ group were recognized as determinants of their intention to pursue E-learning (Yadav et al., 2021). E-learning through social interactions and social media communities enhance female entrepreneurial learning and creativity.

Additionally, the opportunity to compare a group of students and their ATs towards E-learning to use the SI and determine how to predict their behavioural intent to use E-learning was confirmed. Olasina (2019) explored the influence of human and social factors on E-learning acceptance. Therefore, SI is reported to influence BI significantly. According to the research findings, determinants of the TAM are the key factors affecting the adoption of the technology. Moreover, it is established that the subjective norms reflected by peers significantly influence the relationship between AT and intention towards technology. However, Cheung and Vogel’s (2013) findings do not indicate a major impact of the subjective norm portrayed by instructors and mass media on students’ intentions to use the technology. The ability to exchange knowledge on the Google Applications platform in a shared learning environment impacts purpose and action towards it. As SI has not been explored much in the E-learning context, the present study will explore this variable and its influence due to AT.

H ₇: SI has a positive influence on the AT towards using E-learning.

H ₉: SI has a positive influence on the BI towards using E-learning.

Attitude

A learner’s AT towards adopting and accepting modern technology is largely governed by the effectiveness and efficiency of E-learning systems (Chang & Tung, 2007). Similarly, a learner’s AT towards adopting and using the E-learning system is chiefly impacted by how E-learning is successfully implemented as a learning system. On the other hand, in developing countries, the factors affecting E-learning are still undisclosed. Arithra Abdullah et al. (2020) assessed the student’s AT towards advanced cardiac life support and concluded that those who provided E-learning cardiac support training showed encouraging results compared to those who provided offline training and showed a favourable AT towards E-learning. The consumer AT towards E-learning in higher education institutes in Pakistan and stated that male candidates had shown a more favourable AT towards E-learning than female candidates in Pakistan. Also, the studies outlined that if the government provides sufficient monetary support and fulfils the IT infrastructure requirements, electronic media and resources, the candidate’s AT would be much more favourable towards E-learning uses. Phua et al. (2012) employed the TAM model to explore how teachers’ BI to use the Internet as a teaching tool is influenced by four factors: AT towards the Internet, PU, PEOU and enjoyment. The findings reveal that BI of teachers to use the Internet is positively correlated with teacher’s AT towards Internet, PEOU, PU and PE.

Some of the TAM variables affect the academic achievement of e-learners, according to the research findings by Solak & Cakir (2015). Anxiety about E-learning is one significant factor that is known to have a negative impact on academic achievement. PEOU, AT, happiness and self-efficacy have a constructive effect on e-learners’ academic achievement. These results suggest that learners have a positive AT towards technology and are at the point of either accepting or rejecting technology.

The learning expectations of youth and how E-learning can affect them indicate that there is no clear review of how adolescents can use E-learning resources. Much of the research review specifically alludes to Generation Y characteristics and how they want to learn. Choy and Delahaye (2011) stated that most youngsters utilize a surface way to deal with learning and concentrate on results. Most youngsters could be motivated by an organized yet rigorous approach in which the facilitator takes on the role of a motivator and a guide, and many youngsters seem to value emotional understanding rather than a theoretical concept. Chang et al. (2011) studied users’ perceptions of the technology and the correlation of perceptions with the AT and intention to use the technology. The findings demonstrated that AT has the most significant impact on the BI of prospective users. The hypothesis formulated based on the literature is as follows:

H ₁₀: AT of students towards E-learning positively influences BI.

Based on the literature review, a conceptual model (Figure 1) is developed as below:

Figure 1.

Conceptual Model.

Methodology

This research is exploratory and descriptive and has used a quantitative approach. A self-administered survey has been conducted to collect fresh information through a structured, close-ended questionnaire. The constructs and the items in the instrument were adopted from the previous studies to ensure reliability and validity (Table 1). However, some of the items were adopted from studies other than the constructs because the constructs were used in the context of technology adoption in various areas of technology, such as smartphones (Jebarajakirthy et al., 2021), mobile banking (Jebarajakirthy & Shankar, 2021), smart homes (Kim et al., 2007; Sharma & Kuknor, 2022), not necessarily in E-learning. Therefore, the items adopted from the studies relate to E-learning context only. The questionnaire comprises three sections; section 1 comprises general information about participants regarding internet usage and E-learning, the second section entails specific questions about various factors influencing E-learning and the last section includes demographic information about respondents. Primary and secondary data were collected, systematically arranged, filtered, sorted and removed the missing value; finally, the data were analysed using structural equation modelling (SEM) and Pearson correlation. The exploratory factor analysis (EFA) is utilized to explore the underline factors influencing E-learning intention. Each factor extracted from EFA has undergone the reliability test to ensure the reliability of each construct, as the values of Cronbach alpha lie well within the range of acceptance. In addition to that, as a standard procedure, it is suggested to use EFA for adapted/adopted scales. Because confirmatory factor analysis (CFA) can hide true discriminant validity issues, but it cannot be hidden in EFA.

Table 1.

Summary of Constructs and Items of the Questionnaire and Their Sources.

Construct/Items	Source
Perceived Usefulness	Schneberger et al. (2007), Letchumanan and Tarmizi (2011)
E-learning saves my lot of time	Shafieiosgouei et al. (2018)
I find E-learning more useful than physical learning	Stoel and Lee (2003)
I find E-learning more beneficial for my overall career development	Selim (2003)
I am very positive to enrol in E-learning courses	Lee et al. (2003)
Perceived Ease of Use	Schneberger et al. (2007), Letchumanan and Tarmizi (2011), Salloum et al. (2019)
I can easily learn this new E-learning technology	Cheung and Vogel (2013)
This E-learning technology doesn’t take much time to learn	Letchumanan and Tarmizi (2011)
It’s a user-friendly technology	Salloum et al. (2019)
Perceived Enjoyment	Venkatesh (2000), Van Der Heijden (2004)
I enjoying while learning online	Kanwal and Rehman (2017)
I feel excited while learning online	Elkaseh et al. (2016)
I can enjoy the interaction with multiple people at one time during learning	Elkaseh et al. (2016), Saade et al. (2008)
Social Influence	Lin and Yang (2011), Hernandez et al. (2011)
I use this technology because my friends are also using	Lee and Faulkner (2011)
My family members force me to adopt this new system	Paola Torres Maldonado et al. (2011)
I feel socially disconnected while I am not using this new technology	Farahat (2012)
Attitude	Chang and Tung (2007), Chang et al. (2011)
I am very positive towards E-learning tools	Choy and Delahaye (2011)
I find these E-learning tools very impressive
I am very excited to learn through this new technology	Solak and Cakir (2015)
I intend to enrol for at least one course online along with the regular course	Chen et al. (2011)
Behavioural Intention	Phua et al. (2012)
I intend to use and integrate E-learning with my course in future	Cheung and Vogel (2013)
I can predict the use of E-learning blended with classroom learning
I intend to enrol for at least one course online along with the regular course

The study is based on postgraduate students enrolled recently in 2019–2020, and a survey was conducted in various colleges and universities across India. The study’s participants were selected based on non-probability judgmental sampling and snowball sampling to ensure appropriate coverage of participants. The questionnaire was sent to 600 students, of which 348 were finally usable.

Data Analysis

Data analysis consisted of four parts; the first part was based on a basic understanding of participants’ E-learning uses, time spent, enrolment, and others. These students’ characteristics were analysed with the help of cross-tabulation. The second part was to understand the student’s opinion about E-learning and classroom learning and to draw the relationship by Pearson correlation; the third part explored the underlined factors influencing E-learning users’ intention analysed by EFA; the fourth part tested the hypothesis developed by using SEM.

The collected data were further sorted and filtered, and only respondents belonging to the age group of 18 to 25 were observed for further analysis. All respondents belonging to the age group of 18 to 25 were Internet users.

Table 2 describes the time youngsters spend on the Internet daily, concluding that 59.8% of respondents lie between 3 and 5 hours of usage daily, categorized under average internet users. Total of 20.4% of respondents use the Internet for less than 2 hours, and 13.2% use it for 6 to 10 hours a day; only 6.6% use it for more than 10 hours to be considered heavy Internet users. Therefore, the majority of respondents are medium or average Internet users.

Table 2.

Average Time Spent on the Internet by Respondents.

		Frequency	Per cent	Valid Per cent	Cumulative Per cent
Valid	Less than 2 hours	71	20.4	20.4	20.4
	3 to 5 hours	208	59.8	59.8	80.2
	6 to 10 hours	46	13.2	13.2	93.4
	More than 10 hours	23	6.6	6.6	100.0
	Total	348	100.0	100.0

Table 3 demonstrates that out of 348 respondents, a total of 267 respondents utilized the Internet for E-learning. Moderate users or average users of the Internet account for 159, heavy users constitute only 17 and light users add to 50 respondents using the Internet for E-learning. Therefore, we can say that students use the Internet wisely for learning purposes, and heavy users may utilize it for purposes other than E-learning.

Table 3.

Internet Usage and E-learning.

		E-learning		Total
		Yes	No	Total
Time_spend_Internet	Less than 2 hours	50	21	71
	3 to 5 hours	159	49	208
	6 to 10 hours	41	5	46
	More than 10 hours	17	6	23
Total		267	81	348

Table 4 depicts that out of the total respondents, 267 spend time in E-learning, 151 respondents comprising 56.6% enrolled for an online course and the rest 116 (43.4%) respondents did not enrol for any online course but are still learning through YouTube videos, etc.

Table 4.

E-learning Versus E-learning Course Enrolment.

		E-learning Course		Total
		Yes	No	Total
E-learning	Yes	151	116	267
		56.6%	43.4%	100.0%
		100.0%	58.9%	76.7%
		43.4%	33.3%	76.7%
Total		151	197	348
		43.4%	56.6%	100.0%
		100.0%	100.0%	100.0%
		43.4%	56.6%	100.0%

As per Table 5, 90.7% of respondents enrolled for an E-learning course go for a certification course, and only 5.3% and 2.6% choose diploma and degree courses as online courses. Therefore, most youngsters choose online certification courses as part of their E-learning.

Table 5.

Online Course Enrolment and Type of Course Student Enrolled In.

			Which_course				Total
			Diploma	Certificate	Degree	Doctorate	Total
E-learning course	Yes	Count	8	137	4	2	151
		% within E-learning course	5.3%	90.7%	2.6%	1.3%	100.0%
		% within Which_course	100.0%	100.0%	100.0%	100.0%	100.0%
		% of Total	5.3%	90.7%	2.6%	1.3%	100.0%
Total		Count	8	137	4	2	151
		% within E-learning course	5.3%	90.7%	2.6%	1.3%	100.0%
		% within Which_course	100.0%	100.0%	100.0%	100.0%	100.0%
		% of total	5.3%	90.7%	2.6%	1.3%	100.0%

Table 6 depicts that out of 139 respondents who went for a certificate course, only 61 (44%) paid the fee, while out of 8 respondents who enrolled for the diploma course, 6 paid the fee (77%), and 2 (50%) out of 4 respondents who enrolled for degree course paid the fee. Therefore, it can be concluded that most respondents are interested in paying a fee for the diploma, followed by a degree course. Hence, respondents enrolled for certificate courses because of the programme’s short duration and free availability. However, the diploma and degree courses were not available for free.

Table 6.

Types of Online Courses and Fees Paid for the Course.

Course * Fee_Paid Cross Tabulation
			Fee_Paid		Total
			Yes	No	Total
E-learning Course	Diploma	Count	6	2	8
	Diploma	% of total	4.00%	1.30%	5.30%
	Certificate	Count	59	78	137
	Certificate	% of total	39.1%	51.70%	90.7%
	Degree	Count	2	2	4
	Degree	% of total	1.30%	1.30%	2.60%
Total		Count	69	82	151
Total		% of total	45.70%	54.30%	100.00%

Correlation Analysis

The correlation will be applied to establish a relationship between the choice of E-learning and classroom learning and to conclude by comparing both learning methods.

From Table 7, it can be interpreted that respondents agree that classroom learning is better to help them learn better and provide a learning environment and personal interaction with the instructor. However, respondents agree that E-learning should go hand in hand with classroom learning, so it should blend both methods for better learning. Respondents do not agree that E-learning is better than classroom learning. Therefore, youngsters appreciate E-learning along with classroom learning, but E-learning alone is not enough to provide a better learning environment.

From the correlation in Table 8, it can be understood that the respondents who thought classroom learning is better to have a significant correlation with E-learning, but the direction of the relationship is negative. Therefore, there is the existence of a negative correlation. On the other hand, there is no significant association between respondents who thought E-learning should be parallel to the classroom learning at the 0.01 significance level. There is a significant positive relationship between E-learning as a better option, and E-learning, which can be used along with classroom learning, signifying that those respondents who agree that E-learning is better also agree that it can be parallel with classroom learning. Hence, we can conclude that solely E-learning is not considered a better learning method but can rather be blended with classroom learning. However, classroom learning can still be considered an important and better learning method.

Table 7.

Descriptive Analysis.

Descriptive Statistics
	Mean	Std. Deviation	N
Classroom teaching is better for learning	4.2040	.86313	348
E-learning should be parallel with classroom learning	3.8908	.94800	348
E-learning is better than classroom learning	2.8793	1.08823	348

Table 8.

Correlation Analysis of Respondent’s Acceptability to Choose Learning Methods.

Correlations
		Classroom teaching is better for learning	E-learning should be parallel with classroom learning	E-learning is better than classroom learning
Classroom teaching is better for learning	Pearson correlation	1	−.026	−.317^**
	Sig. (2-tailed)		.635	.000
	N	348	348	348
E-learning should be parallel with classroom learning	Pearson correlation	−.026	1	.350^**
	Sig. (2-tailed)	.635		.000
	N	348	348	348
E-learning is better than classroom learning	Pearson correlation	−.317^**	.350^**	1
	Sig. (2-tailed)	.000	.000
	N	348	348	348

Note: **Correlation is significant at the 0.01 level (2-tailed).

Exploratory Factor Analysis

Factor analysis is a method of data reduction. It does so by seeking underlying unobservable (latent) variables that are reflected in the observed variables (manifest variables).

Table 9 reveals the appropriateness of the sample to conduct EFA. The value of the KMO test is 0.886, which lies within the range of acceptability, hence the sample size is very good to perform EFA. Bartlett’s test of sphericity tests the hypothesis that the correlation matrix is an identity matrix; hence, it also lies under the acceptability range. Therefore, the test passes the assumptions before conducting EFA.

Table 9.

Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test.

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy		.886
Bartlett’s test of sphericity	Approx. Chi-square	2325.015
	Df	120
	Sig.	.000

Table 10 indicates that six factors were extracted by PCA; only those with an eigenvalue of more than 1 were considered. The cumulative variance explained by all four factors is 82.94%.

Table 10.

Total Variance Explained.

Total Variance Explained
Component	Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
Component	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %
1	6.232	38.953	38.953	2.941	18.384	18.384
2	1.653	10.332	49.285	2.801	17.504	35.888
3	1.164	7.275	56.560	2.561	16.008	51.897
4	1.030	6.440	63.000	1.777	11.103	63.000
5	1.10	6.030	69.030	1.532	10.159	73.159
6	1.08	5.880	74.910	1.168	9.785	82.944

Note: Extraction method: Principal component analysis (PCA).

Table 11 summarizes the EFA result. According to that, seven factors were extracted through PCA, and the first factor, PU, explains the usability or the utility of this new technology from the student’s point of view. The second factor, PEOU, describes the complexity and the ease with which students can use this technology. The third factor, titled PE, explained the students’ interesting and enjoyable experience; the fourth factor explicated the SI such as family, friends and acquaintances on E-learning uses; the fifth factor expounded the student’s AT towards using E-learning, and the last factor enlightened about the BI of students to use this innovative technology finally.

Table 11.

Summary of EFA.

Name of the Factor	Eigenvalue	% of Variance	Cumulative %	Items	Loadings
Perceived usefulness	6.23	38.95	38.95	E-learning saves me a lot of time	0.874
				I find E-learning more useful than physical learning	0.879
				I find E-learning more beneficial for my overall career development	0.867
				I am very positive about enrolling in E-learning courses	0.887
Perceived ease of use	1.65	10.33	49.28	I can easily learn this new E-learning technology	0.908
				This E-learning technology doesn’t take much time to learn	0.902
				It’s a user-friendly technology	0.899
Attitude	1.16	7.27	56.56	I am very positive towards E-learning tools	0.831
				I find these E-learning tools very impressive	0.897
				I am very excited to learn through this new technology	0.961
Behavioural intention	1.13	6.44	63	I intend to use and integrate E-learning with my course in future	0.845
				I can predict the use of E-learning blended with classroom learning	0.853
				I intend to enrol for at least one course online along with the regular course	0.821
Perceived enjoyment	1.1	6.03	69.03	I am enjoying learning online	0.798
				I feel excited while learning online	0.712
				I can enjoy interacting with multiple people at one time during learning	0.752
Social influence	1.03	5.5	80.41	I use this technology because my friends are also using	0.785
				My family members force me to adopt this new system	0.777
				I feel socially disconnected when I am not using this new technology	0.712

Confirmatory Factor Analysis

Once the factor structure was identified using the EFA, CFA was run to assess the scale’s reliability and validity. Based on Table 12, it can be stated that: (a) all the factor loadings are above 0.7; (b) the values of Cronbach’s alpha for all the constructs are above 0.7; (c) all the constructs are exhibiting composite reliability (CR) above 0.7; (d) and all the values of average variance extracted (AVE) for all the constructs are above 0.5. The values of CR and AVE calculated for the scale developed meet the threshold value of 0.7 and 0.5, respectively (Fornell & Larcker, 1981).

Table 12.

Reliability and Convergent Validity.

Construct	Items	Factor Loading	Cronbach’s Alpha	CR	AVE
PU	PU_1	0.874	0.905	0.93	0.769
	PU_2	0.879
	PU_3	0.867
	PU_4	0.887
PEOU	PEOU_1	0.908	0.911	0.93	0.815
	PEOU_2	0.902
	PEOU_3	0.899
AT	AT1	0.831	0.905	0.926	0.807
	AT2	0.897
	AT3	0.961
BI	BI_1	0.845	0.896	0.878	0.705
	BI_2	0.853
	BI_3	0.821
PE	PE_1	0.798	0.909	0.799	0.57
	PE_2	0.712
	PE_3	0.752
SI	SI_1	0.785	0.87	0.802	0.574
	SI_2	0.777
	SI_3	0.712

It is important to identify the difference between the construct identified using the discriminant validity, as shown in Table 13.

Table 13.

Discriminant Validity.

	ATT	BI	PE	PEoU	PU	SI
AT	0.898
BI	0.366	0.84
PE	0.213	0.174	0.755
PEOU	0.206	0.255	0.015	0.903
PU	0.161	0.275	0.067	0.205	0.877
SI	0.179	0.11	0.095	0.011	0.098	0.758

The diagonal values in the above table, marked in bold, represent the square root of the AVE, which is higher than the inter-construct correlation, suggesting the fulfilment of the validity requirement, as suggested in the literature (Fornell & Larcker, 1981). The other method to assess the discriminant validity is using the heterotrait–monotrait (HTMT) ratio. As suggested in the literature (Henseler et al., 2015), the maximum threshold for the HTMT value is 0.9, which is satisfied, as shown in Table 14, suggesting discriminant validity has been established between constructs.

Table 14.

Heterotrait–Monotrait Ratio (HTMT).

	ATT	BI	PE	PEoU	PU	SI
AT
BI	0.398
PE	0.233	0.188
PEOU	0.221	0.274	0.040
PU	0.174	0.297	0.077	0.221
SI	0.192	0.118	0.108	0.050	0.106

Common Method Bias

There is a possibility of common method bias, as the data were collected using a single instrument for data collection (Podsakoff et al., 2003). Two methods were used in assessing the common method bias. First, the standard regression weights were measured and compared with and without common latent factors. It was found that the difference in the standard regression weight is not more than 0.2. Second, a more common approach for evaluating the common method bias when using partial least square is checking variance inflation factor (VIF) values. All the inner VIF values were less than 2, and outer VIF values were less than 3, both being less than the threshold value of 3.3 (Kock, 2015), indicating that the model is not affected by the common method bias.

Structural Model Fitness Assessment

As per Mishra et al. (2020), the way towards building up the structural model’s legitimacy follows the overall rules obtained for the measurement model. Another SEM-evaluated covariance framework is processed and is not the same as the measurement model. This is so because this model accepts that all developments correspond, yet the connections between certain builds are considered zero in the structural model. Along these lines, the chi-square goodness of fit (GOF) for the measurement model will not be exactly the GOF for the structural model for practically all regular SEM models.

The model fit lists give a decent model fit to the structural model. The goodness of fit index (GFI) acquired is 0.960. The adjusted goodness of fit index is 0.947. The normed fit index, comparative fit index, Tucker–Lewis Index are 0.945, 1.000, 1.000, respectively. RMSEA is 0.000 and root mean square residual is 0.013. Hence, the proposed structural model is deemed fit.

Testing Structural Relationship

After assessing the structural relationships for hypothesis testing, the summary of the results prepared and summarizes in Table 15, student’s AT towards using E-learning is influenced by PU and PEOU. AT, too, is influenced by PE and SI. However, PU, PEOU and SI do not display any direct influence on BI.

Table 16 explains the proportion of the dependent variables explained by an independent variable. The present model predicts 8.4% AT towards E-learning uses and 29.9% BI.

Table 17 presented the summary of the hypothesis testing result. According to that, out of 10 hypothesis, 7 hypothesis were accepted and 3 hypothesis were not accepted.

Table 15.

Unstandardized Regression Weights.

			Estimate	S.E.	C.R.	P
AT	←	PU	.154	.074	2.077	.038
AT	←	PE	.118	.055	2.126	.034
AT	←	PEOU	.162	.051	1.228	.020
AT	←	SI	.183	.072	2.536	.011
BI	←	AT	.642	.093	6.911	***
BI	←	SI	.037	.080	.459	.646
BI	←	PU	–.049	.082	.591	.554
BI	←	PEOU	.064	.057	1.127	.260
PU	←	PEOU	.163	.054	3.012	.003
BI	←	PE	.167	.05	3.312	.001

Table 16.

Square Multiple Correlations.

	Estimate
PU	.026
AT	.084
BI	.299

Table 17.

Summary of Hypothesis Testing Result.

Hypotheses	Statement	Path Coefficient	P Value	Supported/Not Supported
H ₁	The perceived usefulness of E-learning positively influences the attitude of the consumer towards E-learning	0.154	0.038	Supported
H ₂	Perceived ease of use of E-learning positively influences the attitude of the consumer towards E-learning	0.162	0.020	Supported
H ₃	Perceived ease of use of E-learning positively influences perceived usefulness of E-learning	0.163	0.003	Supported
H ₄	Perceived usefulness of E-learning has a positive influence on behavioural intention	0.049	0.554	Not supported
H ₅	Perceived ease of use of E-learning has a positive influence on behavioural intention	0.064	0.260	Not supported
H ₆	Perceived enjoyment has a positive influence on the attitude of consumers towards E-learning	0.118	0.034	Supported
H ₇	Social influence has a positive influence on the attitude towards using E-learning	0.183	0.011	Supported
H ₈	Perceived enjoyment significantly influences the behavioural intention with respect to E-learning	0.167	0.002	Supported
H ₉	Social influence has a positive influence on the behavioural intention towards using E-learning	0.037	0.646	Not supported
H ₁₀	Attitude of students towards E-learning positively influences behavioural intention	0.534	***	Supported

Note: *Paths H₁, H₂, H₃, H₆, H₇ significant at 0.05 significance level, and path H₁₀ at 0.01 significance level.

Results and Discussion

This study reported that SI does not directly influence BI toward using E-learning technology; these results are similar to the study of Mtebe and Raisamo (2014). However, it is seen that SI positively influences students ATs towards E-learning, and AT affects the student’s behaviour towards using E-learning technology (Tai et al., 2012), which is consistent with the result of this study. Hence, AT acts as a mediator between SI and BI. The study also concluded that PE significantly influences the AT towards E-learning technology; therefore, the more students enjoy the technology and the virtual interaction, the more likely they are to develop a positive AT towards this innovative technology which is consistent with the findings of Saade et al. (2008). The study supported the previous finding that defines the positive influence of PU and PEOU on AT but did not support the direct influence of PU and PEOU on students’ BI to use E-learning. The rationale behind these results is many researchers’ wide acceptance of AT as a mediator between PU, PEOU, SI, PE and BI (Ibrahim et al., 2017; Tai et al., 2012).

Additionally, in recent years AT formation and development have gained tremendous importance in determining the actual behaviour, hence becoming a very strong mediator between the dependent constructs of TAM (PU, PEOU, PE, SI) and the independent variable (BI). Lastly, as stated by Ajzen (1991), Ajzen and Fishbein (1980), Fishbein (2008), the individual develops a favourable or unfavourable opinion about the technology based on its expected outcomes and other’s opinion that determines the BI. Hence, AT is considered a strong predictor for determining students’ BI for using E-learning technology.

Finally, the study found out that postgraduate students are moderate Internet users who use the Internet for learning. Most of them enrolled in a certificate programme and preferred free certification rather than paying for that. However, those enrolled in an online degree programme are more likely to pay the fee. Students have more intent to use a blend of online and classroom learning due to limitations associated with both learning methods separately and do not consider E-learning as a potential replacement for classroom learning due to social factors associated with classroom learning. These results and stated that the classroom preference of the students did not change despite COVID-19 pandemic.

Theoretical Implications

The study contributes to the existing literature on E-learning behaviour, and TAM. The theoretical contribution of this study lies mainly in three ways. First, the study contributes to the existing literature about E-learning behaviour by targeting various factors influencing E-learning behaviour in using E-learning platforms. The previous literature on E-learning behaviour was mainly focused on system quality, user learning experiences and building learners’ confidence to predict BI (Mailizar et al., 2021; Rajeh et al., 2021). This study uniquely focuses on the PE and the SI on students’ behaviour towards E-learning.

Second, this study supported and contributed to the TAM literature by incorporating the SI and PE which is crucial in determining the consumer intention towards uses of E-learning. Also, the study has researched a few critical points in understanding the consumer intention to enrol in online programmes, fee payment, engagement with online classes and their future intention to use the E-learning platform.

Third, it is found from the study that AT plays a crucial role while determining the effects of SI and PE and mediated the relationship. However, in previous research studies, the direct effect of SI and PE was reported on BI and ignored on the mediation of AT, which has been explored in this research.

Lastly, the study validates the TAM model over UTAUT and IS models for predicting BI towards E-learning since AT is valid as an important construct to predict BI towards E-learning, and its effects are much stronger than the direct effects of other constructs. Also, AT acts as a mediator to enhance the effect of SI and PE on BI.

Managerial Implications

The study has several managerial implications as it details the future challenges and opportunities for E-learning companies. The students do not consider E-learning a complete substitute for classroom learning and therefore prefer to enrol only for free certifications while they are ready to pay for degree and diploma courses. Therefore, it makes great sense for E-learning companies to focus on degree and diploma courses, and the certifications could be offered as value addition. E-learning companies should design appropriate strategies to engage and make the E-learning platform more interactive and enjoyable for the student community. In the long run, this strategy would ensure that the students get immersed in E-learning, paving the way for greater involvement in this form of education. E-learning companies should understand that SI plays a major role in shaping the BI mediated via AT. Hence, E-learning companies should focus on obtaining recommendations and positive word of mouth from existing students to grow their business.

Conclusion

Different studies researched the TAM and found it robust. However, new factors have been incorporated over the period of time, and the extended TAM model has been defined under different circumstances. TAM has also been explored in the E-learning context. However, the TAM model has yet to explore factors such as SI and PE. Therefore, this research incorporated these two factors under the present E-learning environment and extended the TAM model. The study concluded that in addition to the traditional TAM model, SI and PE develop the student’s favourable AT towards E-learning, positively influencing the BI to use E-learning technology. Hence, AT acts as a mediator between SI, PU and BI. Finally, the study suggested that E-learning can provide satisfaction and a great learning experience when blended with the classroom learning environment.

Future Scope of the Study

There is immense scope for future studies to be carried out on pre-primary and primary school Principals, teachers and other stakeholders to study the E-learning execution and implementation for small children. Like colleges and universities, schools are also encouraging their students to explore E-learning. Technological factors such as the long-term implications of computer screen time also need to be discussed in future research.

Limitations of the Study

This research is based on only postgraduate students considered more tech-savvy and who can easily accept the new technology. Therefore, the findings cannot generalize all categories of students studying at different levels.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Note

References

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. 10.1016/0749-5978(91)90020-T

Ajzen

(2012). The theory of planned behavior. In Van Lange

P. A. M.

, Kruglanski

A. W.

& Higgins

E. T.

(Eds.), Handbook of theories of social psychology: Volume 1 (pp. 438–459). Sage Publications. 10.4135/9781446249215.n22

Ajzen

, & Fishbein

(1980). Understanding attitudes and predicting social behaviour. Prentice Hall.

Al Kurdi

, Alshurideh

, & Salloum

S. A.

(2020). Investigating a theoretical framework for e-learning technology acceptance. International Journal of Electrical and Computer, Engineering (IJECE), 10(6), 6484. 10.11591/ijece.v10i6.pp6484-6496

Arithra Abdullah

, Nor

, Baladas

, Tg Hamzah

T. M. A.

, Tuan Kamauzaman

T. H.

, Md Noh

A. Y.

, & Rahman

(2020). E-learning in advanced cardiac life support: Outcome and attitude among healthcare professionals. Hong Kong Journal of Emergency Medicine, 27(6), 328–333. 10.1177/1024907919857666

Bhatt

(2021). Assessing the significance and impact of artificial intelligence and machine learning in Placement of Advertisements [Conference]. 2021 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), Marrakech, Morocco, pp. 1–6. 10.1109/ICTMOD52902.2021.9739228

Calisir

, Altin Gumussoy

, Bayraktaroglu

A. E.

, & Karaali

(2014). Predicting the intention to use a web-based learning system: Perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515–531. 10.1002/hfm.20548

Capece

, & Campisi

(2011). Technological change and innovation behaviour in high level education: An international comparison between Italian and Portuguese samples. Knowledge and Process Management, 18(1), 67–74. 10.1002/kpm.362

Cappel

J. J.

, & Hayen

R. L.

(2004). Evaluating e-learning: A case study. Journal of Computer Information Systems, 44(4), 49–56. 10.1080/08874417.2004.11647595

10.

Chang

S.-C.

, & Tung

F.-C.

(2007). An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1). 10.1111/j.1467-8535.2007.00742.x

11.

Chang

Y.-J.

, Chen

C.-H.

, Huang

W.-T.

, & Huang

W.-S.

(2011). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of English learning using augmented reality [Conference]. 2011 IEEE International Conference on Multimedia and Expo, Barcelona, pp. 1–6. 10.1109/ICME.2011.6012177

12.

Chang

, Wong

S. F.

, & Park

M.-C.

(2014). Determinants of user satisfaction in internet use among socio-economically advantaged and disadvantaged groups: The role of digital access and government policy 1 [Conference]. 25th European Regional Conference of the International Telecommunications Society (ITS), Belgium, Brussels, pp. 1–12.

13.

Chen

S.-C.

, Li

S.-H.

, & Li

C.-Y.

(2011). Recent related research in technology acceptance model: A literature review. Australian Journal of Business and Management Research, 1(9). 10.52283/NSWRCA.AJBMR.20110109A14

14.

Cheung

, & Vogel

(2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers and Education, 63, 160–175. 10.1016/j.compedu.2012.12.003

15.

Choy

, & Delahaye

(2011). Partnerships between universities and workplaces: Some challenges for work-integrated learning. Studies in Continuing Education, 33(2), 157–172. 10.1080/0158037X.2010.546079

16.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. 10.2307/249008

17.

DeRouin

R. E.

, Fritzsche

B. A.

, & Salas

(2005). E-learning in organizations. Journal of Management, 31(6), 920–940. 10.1177/0149206305279815

18.

Elkaseh

A. M.

, Wong

W. K.

, & Fung

C. F.

(2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192–199.

19.

Farahat

(2012). Applying the technology acceptance model to online learning in the Egyptian Universities. Procedia—Social and Behavioral Sciences, 64, 95–104. 10.1016/j.sbspro.2012.11.012

20.

Fishbein

(2008). Reasoned action, theory of. The International Encyclopedia of Communication. 10.1002/9781405186407.WBIECR017

21.

Fornell

, & Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. 10.1177/002224378101800104

22.

Henseler

, Ringle

C. M.

, & Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. 10.1007/s11747-014-0403-8

23.

Hernandez

, Montaner

, Sese

F. J.

, & Urquizu

(2011). The role of social motivations in e-learning: How do they affect usage and success of ICT interactive tools? Computers in Human Behavior, 27, 2224–2232. 10.1016/j.chb.2011.07.001

24.

Ibrahim

M. A.

, Kim-Soon

, Ahmad

A. R.

, & Sirisa

N. M. X.

(2017). Mediating roles of attitude on intention to use m-learning among students at Malaysian technical universities. Advanced Science Letters, 23(4), 2795–2798. 10.1166/ASL.2017.7657

25.

Jebarajakirthy

, Maseeh

H. I.

, Morshed

, Shankar

, Arli

, & Pentecost

(2021). Mobile advertising: A systematic literature review and future research agenda. International Journal of Consumer Studies, 45(6), 1258–1291. 10.1111/IJCS.12728

26.

Jebarajakirthy

, & Shankar

(2021). Impact of online convenience on mobile banking adoption intention: A moderated mediation approach. Journal of Retailing and Consumer Services, 58(October 2020), 102323. 10.1016/j.jretconser.2020.102323

27.

Kanwal

, & Rehman

(2017). Factors affecting e-learning adoption in developing countries—Empirical evidence from Pakistan’s higher education sector. IEEE Access, 5, 10968–10978. 10.1109/ACCESS.2017.2714379

28.

Kim

H. W.

, Chan

H. C.

, & Gupta

(2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems. 10.1016/j.dss.2005.05.009

29.

Kimathi

F. A.

, & Zhang

(2019). Exploring the general extended technology acceptance model for e-learning approach on student’s usage intention on e-learning system in University of Dar es Salaam. Creative Education, 10(01), 208–223. 10.4236/ce.2019.101017

30.

Kock

(2015). Common method bias in PLS-SEM. International Journal of E-Collaboration, 11(4), 1–10. 10.4018/ijec.2015100101

31.

Lee

M. C.

(2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers and Education, 54(2), 506–516. 10.1016/j.compedu.2009.09.002

32.

Lee

, & Faulkner

(2011). The roles of extrinsic factors in a community of inquiry model of e-learning. E-Learning and Digital Media, 8(1), 58–67. 10.2304/elea.2011.8.1.58

33.

Lee

Y. H.

, Hsieh

Y. C.

, & Ma

C. Y.

(2011). A model of organizational employees’ e-learning systems acceptance. Knowledge-Based Systems, 24(3), 355–366. 10.1016/j.knosys.2010.09.005

34.

Lee

, Kozar

K. A.

, & Larsen

K. R. T.

(2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(1), 752–780. 10.17705/1cais.01250

35.

Letchumanan

, & Tarmizi

R. A.

(2011). E-book utilization among mathematics students of Universiti Putra Malaysia (UPM). Library Hi Tech, 29(1), 109–121. 10.1108/07378831111116949

36.

Lin

W.-C.

, & Yang

S. C.

(2011). Exploring students’ perceptions of integrating Wiki technology and peer feedback into English writing courses. English Teaching: Practice and Critique, 10(2), 88–103.

37.

Mailizar

, Burg

, & Maulina

(2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 1. 10.1007/S10639-021-10557-5

38.

Mishra

, Pathak

, & Sharma

B. K.

(2020). Impact of work culture, employees’ relationship with their managers, and training program on employees’ satisfaction. International Journal of Advanced Science and Technology, 29(9S), 5145–5159.

39.

Mondi

, Woods

, & Rafi

(2008). A “uses and gratification expectancy model” to predict students’ “perceived e-learning experience.” Educational Technology and Society, 11(2), 241–261.

40.

Mtebe

J. S.

, & Raisamo

(2014). Challenges and instructors’ intention to adopt and use open educational resources in higher education in Tanzania. International Review of Research in Open and Distance Learning, 15(1), 249–271. 10.19173/irrodl.v15i1.1687

41.

Nugroho

M. A.

, Setyorini

, & Novitasari

B. T.

(2019). The role of satisfaction on perceived value and e-learning usage continuity relationship. Procedia Computer Science, 161, 82–89. 10.1016/j.procs.2019.11.102

42.

Olasina

(2019). Human and social factors affecting the decision of students to accept e-learning. Interactive Learning Environments, 27(3), 363–376. 10.1080/10494820.2018.1474233

43.

Padilla-Meléndez

, Rosa Del Aguila-Obra

, & Garrido-Moreno

(2013, April). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306–317. 10.1016/j.compedu.2012.12.014

44.

Paola Torres Maldonado

, Feroz Khan

, Moon

, Korea

, & Jeung Rho

(2011). E-learning motivation and educational portal acceptance in developing countries. Online Information Review, 35(1), 1468–4527. 10.1108/14684521111113597

45.

Park

S. Y.

, Nam

M. W.

, & Cha

S. B.

(2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605. 10.1111/J.1467-8535.2011.01229.X

46.

Phua

P. L.

, Wong

S. L.

, & Abu

(2012). Factors influencing the behavioural intention to use the internet as a teaching-learning tool in home economics. Procedia—Social and Behavioral Sciences, 59, 180–187. 10.1016/j.sbspro.2012.09.263

47.

Podsakoff

P. M.

, MacKenzie

S. B.

, Lee

J.-Y.

, & Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. 10.1037/0021-9010.88.5.879

48.

Punnoose

A. C.

(2012). Determinants of intention to use e-learning based on the technology acceptance model. Journal of Information Technology Education: Research, 11(1), 301–337. 10.28945/1744

49.

Rajeh

M. T.

, Abduljabbar

F. H.

, Alqahtani

S. M.

, Waly

F. J.

, Alnaami

, Aljurayyan

, & Alzaman

(2021). Students’ satisfaction and continued intention toward e-learning: A theory-based study. Medical Education Online, 26(1). 10.1080/10872981.2021.1961348

50.

Ratna

P. A.

, & Mehra

(2015). Exploring the acceptance for e-learning using technology acceptance model among university students in India. International Journal of Process Management and Benchmarking. 10.1504/IJPMB.2015.068667

51.

Renny, Guritno

, & Siringoringo

(2013). Perceived usefulness, ease of use, and attitude towards online shopping usefulness towards online airlines ticket purchase. Procedia—Social and Behavioral Sciences, 81, 212–216. 10.1016/j.sbspro.2013.06.415

52.

Rui-Hsin

, & Lin

C. T.

(2018). The usage intention of e-learning for police education and training. Policing, 41(1), 98–112. 10.1108/PIJPSM-10-2016-0157

53.

Saade

, Tan

, & Nebebe

(2008). Impact of motivation on intentions in online learning: Canada vs China. Issues in Informing Science and Information Technology, 5, 137–147. 10.28945/3212

54.

Salamat

, Ahmad

, Bakht

M. I.

, & Saifi

I. L.

(2018). Effects of e-learning on students’ academic learning at university level. Asian Innovative Journal of Social Sciences & Humanities, 2(2), 1–12.

55.

Salloum

S. A.

, Qasim Mohammad Alhamad

, Al-Emran

, Abdel Monem

, & Shaalan

(2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445–128462. 10.1109/ACCESS.2019.2939467

56.

Samsudeen

S. N.

, & Mohamed

(2019). University students’ intention to use e-learning systems: A study of higher educational institutions in Sri Lanka. Interactive Technology and Smart Education, 16(3), 219–238. 10.1108/ITSE-11-2018-0092

57.

Sawang

, Newton

, & Jamieson

(2013). Increasing learners’ satisfaction/ intention to adopt more e-learning. Education & Training, 55(1), 83–105. 10.1108/00400911311295031

58.

Schneberger

, Amoroso

D. L.

, & Durfee

(2007). Factors that influence the performance of computer-based assessments: An extension of the technology acceptance model. Journal of Computer Information Systems, 48(2), 74–90. 10.1080/08874417.2008.11646011

59.

Segooa

M. A.

, & Kalema

B. M.

(2016). A contextualized model for virtual learning in higher institutions. International Journal of Technology Diffusion, 7(4), 60–81. 10.4018/ijtd.2016100104

60.

Selim

H. M.

(2003). An empirical investigation of student acceptance of course websites. Computers and Education. 10.1016/S0360-1315(02)00142-2

61.

Selim

H. M.

(2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers and Education, 49(2), 396–413. 10.1016/j.compedu.2005.09.004

62.

Shafieiosgouei

, Nourdad

, Hassantofighi

, & Shafieioskouei

(2018). The effect of e-learning on learning and interest in school attendance among elementary school students. 122–138. 10.5281/zenodo.1215138

63.

Sharma

B. K.

, & Kuknor

S. C.

(2022). Smart homes adoption in India—Value-based adoption approach. 1–6. 10.1109/ICTMOD52902.2021.9739353

64.

Sharma

B. K.

, Mishra

, & Arora

(2018). Does Social Medium Influence Impulse Buying of Indian Buyers? Journal of Management Research, 18(1).

65.

Sharma

B. K.

, & Pal

V. K.

(2020). Application of TAM model in teaching and learning—A behavioural study on social media. International Journal of Management Practice, 13(4), 478–497. 10.1504/IJMP.2020.108306

66.

Shin

D. H.

, & Kim

W. Y.

(2008). Forecasting customer switching intention in mobile service: An exploratory study of predictive factors in mobile number portability. Technological Forecasting and Social Change, 75(6), 854–874. 10.1016/j.techfore.2007.05.001

67.

Singh

, Sinha

, & Bhatt

(2021). A study on teachers’ perception towards online teaching with reference to high schools in Indore. Shodh Sanchar Bulletin, 11(41), 6–11.

68.

Solak

, & Cakir

(2015). Language learning strategies of language e-learners in Turkey. E-Learning and Digital Media, 12(1), 107–120. 10.1177/2042753014558384

69.

Stoel

, & Lee

K. H.

(2003). Modeling the effect of experience on student acceptance of web-based courseware. Internet Research, 13(5), 364–374. 10.1108/10662240310501649

70.

Sun

, & Zhang

(2006). Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems, 7(9), 24. 10.17705/1jais.00100

71.

Tai

D. W. S.

, Zhang

, Chang

, Chen

, & Chen

(2012). A meta-analytic path analysis of e-learning acceptance model. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 6(5), 760–763. 10.5281/zenodo.1081747

72.

The Economic Times. (2020). What is “e-learning”? https://economictimes.indiatimes.com/definition/e-learning

73.

Van Der Heijden

(2004). User acceptance of hedonic information systems. MIS Quarterly: Management Information Systems, 28(4), 695–704. 10.2307/25148660

74.

Venkatesh

(2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. 10.1287/isre.11.4.342.11872

75.

Venkatesh

, & Agarwal

(2006). Turning visitors into customers: A usability-centric perspective on purchase behavior in electronic channels. 10.1287/Mnsc.1050.0442, 52(3), 367–382. 10.1287/MNSC.1050.0442

76.

Venkatesh

, & Bala

(2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. 10.1111/J.1540-5915.2008.00192.X

77.

Venkatesh

, & Davis

F. D.

(2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. 10.1287/MNSC.46.2.186.11926

78.

Venkatesh

, Morris

M. G.

, Davis

G. B.

, & Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425. 10.2307/30036540

79.

Wei Boon

, Anak Kok

, & Aziz

(2019). Perceived usefulness, perceived ease of use, and attitude towards using front office tasks simulator: A preliminary study in community college. Leadership & Social Sciences, 15, 31–43.

80.

Yadav

, Sinha

, & Bhatt

(2021). Impact of Covid-19 on students’ perception of e-learning concerning online value-added courses. Shodh Sarita, 8(29), 135–140.

81.

, Ha

, Choi

, & Rho

(2005). Extending the TAM for a t-commerce. Information & Management, 42(7), 965–976. 10.1016/J.IM.2004.11.001