INTRODUCTION:

Technology Acceptance Model (TAM) is significant in the information systems field. (Lee, Kozar, & and Larsen, 2003). The model has been updated over a period of time with many researchers contributing towards it. The basic TAM is based on two fundamental predictors i.e., a user’s perceived usefulness of the technology that he adopts and the user’s perceived ease of use.

Behaviour intention is defined as the dependent variable which is also vital in the model (Koufaris, 2002) (Wixom & Todd, 2005). Intention to use a technology is further defined by individual attitude and social norms. Social norms are the informal rules of a group that influences a decision to accept or reject behaviour. This model has been widely accepted and popular to understand the end users’ perception and acceptance of a particular technology. To further understand the model, it is important to discuss the theory of reasoned action (Ajzen & Fishbein, 1975)), innovation supporting curve (Rogers, 1995), and theory of planned behaviour (Ajzen, 1991).

Figure1: The widely accepted basic Technology Acceptance Model.

Technology has an impact on higher education. Nowadays, Massive Open Online Courses and E-learning have gained momentum in Universities across the globe. Integrated Computer Technology (ICT) has permitted this change (Bhattacharya & Sharma, 2007). ICT has played an important role in development of learning environment in 21st century (Roy & Jain, 2014). Technology is vital in the growth and expansion of e-learning (Bonk, Wisher, & Lee, 2004). In recent years, innumerable efforts have been made to integrate technology application in the teaching, learning, and evaluation process. For example, use of Facebook Groups by learners for knowledge acquisition with teacher(s) present as online facilitators (Kumar, 2018), or the numerous online learning courses and online certifications offered by various universities (Bonk, Wisher, & Lee, 2004). Students have the benefit of gaining knowledge, learning from experts from different domains. They have the advantage of pursuing an online certification without the expense of travelling and physical availability at a university. So much so that strategists believe that there is a strong trend in approaches integrating e-learning with regular education as a part of an institute’s long term strategy (Allen & Seaman, 2006). (Vaidehi R & Girija T, 2017) focused on how the study of factors influencing user’s acceptance rate of E-Learning Courses will help the institutions plan more efficiently and effectively the adoption and implementation of MOOCs.

1.1. Supporting Theories:

1.1.1.The Innovation Supporting Curve:

Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system. Innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption. (Rogers, 1995) Theory of diffusion of innovation by Everett Rogers has been widely used over the years to analyse and study the adoption of any new technology or innovation among individuals or organisations. Diffusion research includes some key elements that affect the adoption rate. These are - Innovators, Adopters, Communication Channels, Time, and Social System. Based on the varying adoption rate of individuals or organisations, adopters are also categorised into innovators, early adopters, early majority, late majority, and laggards. Given that decisions are not authoritative or collective, each member of the social system faces his/her own innovation decision that follows a 5step process which includes Knowledge awareness, persuasion, decision, implementation, and confirmation.

1.1.2. Theory of Reasoned Action:

Martin Fishbein and Icek Ajzen’s theory of reasoned action (Ajzen & Fishbein, 1975); (Ajzen & Fishbein, 1980); (Fishbein, 1980) is considered one of the most influential theories in the field of psychology. The Theory of Reasoned Action (ToRA or TRA) studies the relationship between attitudes and behaviours in order to justify human action. It is mainly used to predict how individuals will behave based on their pre-existing attitudes and behavioural intentions. Even though it is one of the most cited studies, it is not considered perfect or flawless by several authors (Trafimow, 2009). Subjective Norm can be explained as “the perceived social pressure to perform or not to perform the behaviour” in question (Ajzen, 1991). Figure 2 demonstrates this theory.

Figure 2: Theory of Reasoned Action

1.1.3.Theory of Planned Behaviour:

The Theory of Planned Behaviour (TPB), developed by Ajzen, (Ajzen, 1985) is a descriptive model that had been widely applied in diverse studies on behavioural intentions (Lee, Cerreto, & Lee, 2010); (Ajzen, 2012a); (Ajzen, 2012b); (Fraser, et al., 2010); (Ajzen & Fishbein, 2005); (Yakasai & Jusoh, 2015); (Kataria, Krishna, Tyagi, & Vashishat, 2019). This theory considers human beings are rational (Japutra, Loureiro, Molinillo, & Ekinci, 2019). This theory proposes that behaviour is a reasoned decision determined by intention, which in turn is influenced by one's attitude towards the behaviour (e.g. a positive or negative evaluation of the outcome of a situation), subjective norm (e.g. the perceived social pressure to perform the behaviour), and perceived behavioural control (PBC) (e.g. the perceived ease of control over performing that behaviour) (Emma L.Bird, 2018).

2. THEORETICAL FRAMEWORK:

2.1. E-Learning in higher Education in India:

Technology has impacted and restructured traditional education practices. Innovative technological systems have paved the way for e-learning (Bhattacharya & Sharma, 2007). Many researchers have attempted to define e-learning. Some of the definitions focus on the availability of infrastructure to support learning on online platforms. E-learning has also been defined as connecting distant learners using electronic means (e.g. computer, mobile phone, etc.) to deliver training, learning or, education material. It creates virtual learning spaces that facilitate learning across different communities (Stockley, 2003). Today, many online learning platforms enable the end-users to participate, learn, and enhance their knowledge base and skills. Institutes and universities have started investing a large sum of their resources to facilitate this learning. Students are vital and an integral part of this process of knowledge transfer. To provide students with high quality services, it is important to understand their attitude towards e-learning, their expectations from such platforms (Mehra & Omidian, 2012) and the parameters that shape their satisfaction, loyalty and perception of the technology (Sharma, 2017). E-Learning has been gaining momentum in developing nations like Bangladesh where government has taken initiatives to integrate ICT and education to improve quality of education (Chowdhury, 2019). Higher education in India has progressed at a rapid phase, but more research like (Pillai, Mathew, Daniel, & Abhilash, 2020) is required into present resources available for creating effective pedagogy of E-Learning systems in India. The student population in higher education in India is humongous, and limitation of shortage of education institutes and faculty can be overcome with help of technology. Increased internet and smartphone penetration in India has made E-learning popular among Youth in higher education (Jayaswal, 2019). The Indian Government has taken many initiatives to enhance e-learning; The National Programme on Technology Enhanced Learning (NPTEL) funded by the Ministry of Human Resource Development (MHRD) launched in 2006 is one such initiative.

2.2. Proposed Model and Hypotheses Formulation:

Technology Accepted Model has evolved over the past 18 years. This progress can be divided into four phases: introduction, validation, extension, and elaboration. (Lee, Kozar, & and Larsen, 2003). The Technology Acceptance Model (TAM) is an information systems theory that models how users come to accept and use a technology. The model suggests that when users are presented with a new technology, several factors influence their decision about how and when they will use it.

2.2.1.Perceived ease-of-use (PEOU):

Davis in 1989, described PEOU as "the degree to which a person believes that using a particular system would be free from effort" (Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology, 1989). Perception is the person’s belief about something based on his past experiences and values. ‘Ease’ typically represents less effort or freedom from hard work. (Radner & Rothschild, 1975). If the users find the application of a product, service, or technology easy and simple, chances of acceptance increases.

2.2.2. Capability (C):

(Gefen, Karahanna, & Straub, 2003) discussed that a user first gets familiar with a new technology, learns about its functions and assesses its capabilities, and then becomes more aware of the system’s potential benefits and its ability to fulfil user expectations/needs. Thus, with an increased understanding of the platform’s capabilities, the relationship between behavioural factors like Perceived Usefulness and Purchase Intentions becomes stronger. E-learning provides a multi-sensory learning environment that may improve learners’ ability to retain information. Several studies examined the positive effect of visual and verbal information delivery quality in the e-learning platform on the learning outcome (Dongsong Zhang, 2006).

2.2.3. Perceived Usefulness (PU):

This was defined by Fred Davis as "the degree to which a person believes that using a particular system would enhance his or her job performance" (Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology, 1989). This follows from the definition of the word Useful: “capable of being used advantageously." Within an organizational context, people are generally reinforced for good performance by raises, promotions, bonuses, and other rewards (Pfeffer, 1984); (Schein, 1980); (Vroom, 1964). A system high in perceived usefulness, in turn, is one for which a user believes in the existence of a positive use-performance relationship. PU is defined as the degree to which a person believes that the use of a system would improve his or her performance.

2.2.4. Intention to Use (IU):

It is defined as a person's perceived likelihood or "subjective probability that he or she will engage in a given behaviour" (Institute of Medicine (US), 2002). (Venkatesh and Davis, A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, 2000) stated that in TAM, ‘perceived usefulness’ and ‘perceived ease of use’ affect an individual’s behavioural intention to use a system. It was observed in some researches that the direct effect of PEOU on IU has increased over time (Venkatesh & Davis, 1996); (Gupta, 2020) and in other research has decreased over time (Davis, Bagozzi, & Warshaw, 1989). Therefore, PU will be considered as a direct determinant of IU, and PEOU as a secondary determining factor while building the hypothetical model for this study.

2.2.5. Trustworthiness (T)

(Gefen, Karahanna, & Straub, 2003) described Trust as “the belief that the trusted individual or company will be ethical, dependable, and act in a socially appropriate manner while fulfilling their expected commitments without taking any unfair advantage”. (Wu, Zhao, Zhu, Tan, & Zheng, 2011) conducted a meta-analysis based on the previous TAM studies and concluded a significant influence of the role of trust on TAM constructs. Trust is considered to be an important factor that influences a user’s behaviour online.

2.2.6. Actual Use (AU):

(Ajzen, The theory of planned behavior, 1991) and (Ajzen & Fishbein, 1975) stated the strong connection between behavioural intention to use a technology and the actual use of technology by the user. This connection has been further used in several pieces of research to predict actual behaviour. For example, (Ahmad, Bhatti, & Hwang, 2019) studied the effect of e-service quality on actual use of e-banking services and the results of the study showed that behavioural intentions positively affect actual usage of the system, and (Uma R & V. Selvam, 2017) studied the consumer’s behaviour to disclose their attitude towards buying organic product foods in future.

3. RESEARCH OBJECTIVE AND SIGNIFICANCE OF STUDY:

This paper attempts to study the concept of TAM in e-learning among undergraduate students in India. E-learning has gained tremendous momentum; this field is expanding at a rapid rate. Students are an important stakeholder in this process. It is important to understand the students’ level of acceptance, perceived usefulness, perceived ease of use, attitude, behaviour, and actual use of e-learning. The study will help to throw light at these aspects which will be useful for academicians, e-content designers, and policy makers in higher education.

The study attempts to answer the following research questions (RQ):

RQ1: What are the components of TAM?

RQ2: What is the influence of TAM on E-Learning?

RQ3: What are the perceptions of students towards E-learning?

Accordingly, the research objectives (O) are:

O1: To study and understand TAM

O2: To analyse the relation between TAM and E-learning

O3: To gauge student perceptions towards E-learning

The paper starts with the introduction and significance of the study, followed by extant literature on the constructs of TAM. Hypotheses have been derived based on the literature review. The researchers have then discussed the sample, research design, research instrument, and methodology used. This is concluded with the results, discussions, limitations and scope for further research.

4. RESEARCH METHODOLOGY:

The researchers have conducted extant literature review by referring to papers published in the journal of high repute. Ebsco host, J Stor, Scopus, Emerald, Google Scholar was used to study the existing literature. The data collected as part of the research is representative for the segment of undergraduate students in India.

The study focused on descriptive research, which provides the study with information to bring conclusions or make decisions for a defined population. Descriptive Cross-Sectional study is the research design, where the factors are measured at a specific point of time for a defined population. The sample survey is a cross-sectional study which was conducted in this study as a representative of a specific population.

A web questionnaire was designed as the research instrument and used as the main tool for data collection. The questionnaire had 31 questions and was filled in by 209 students from undergraduate colleges in India which represents the target group selected for the research study. Reliability and validity of the instrument were checked during the pilot study using Cronbach's Alpha, Average Variance Extracted and Composite Reliability. The pilot study was conducted with a sample size of 30 students. Word, Excel and SPSS were used to process the collected data, test the hypotheses, analyse the result, and achieve the research objectives.

In order to make data fit for statistical analysis and processing, Excel was used for data systemisation. Certain research variables - Perceived Ease of Use of E Learning, Perceived Usefulness of E-Learning, Capability of E-Learning, Intention to Use E Learning, Trustworthiness of E-Learning and Actual Use of E-Learning - were used to group the collected data for consistent and correct analysis.

A series of correlations tests were conducted in SPSS using the Structural Equation Modelling, which helped us understand the relationship between the research variables and the way they influenced the acceptance of E-Learning among the students selected for this research.

4.1. RESEARCH HYPOTHESES:

Based on the Literature Review discussion, the following hypothetical model is proposed for validation using Structural Equation Modelling:

Figure 3: Proposed Model of TAM and E-Learning (Author’s Contribution)

Based on the hypothetical model the following hypotheses (H) are proposed:

H 1: Perceived Ease of Use of E-Learning is positively related to the Perceived Usefulness of E-Learning.

H 2: Capability of E-Learning is positively related to Perceived Usefulness of E-Learning

H 3: Perceived Usefulness of E-Learning is positively related to Intention to Use E-Learning

H 4: Trustworthiness of E-Learning is positively related to Intention to Use E-Learning

H 5: Intention to Use E-Learning is positively related to the Actual Use of E-Learning.

4.2. Scale:

The researchers used a five point Likert scale in the questionnaire to measure the attitudes and perceptions of the respondents. The proposed model of TAM and E-Learning was broadly measured under 6 different categories (constructs) which were further divided into five statements (items) measuring some aspect of that category. Each of these statements was scaled from 1 to 5 (1-Strongly Disagree, 2-Disagree, 3-Neutral, 4- Agree, 5-Strongly Agree) and the mean, i.e., the sum total of all these statements divided by the number of the statements, was taken up for that particular category.

4.3. Sample Considerations:

The sample size (n) for the unlimited population was determined using the below formula:

(z² p(1-p)

n=-------------

e²

Equation 1: Cochran Formula:

Where,

n = Sample Size

p = Population Proportion = 50% or 1/2

e = Margin of error = ±7%

z = z score for the given confidence level of 95% = 1.96

Based on the calculations, sample size of 196 respondents was determined. This means 196 or more surveys/measurements were needed for the research study. The questionnaire was filled in by 209 students from undergraduate colleges in India which represents the target group selected for the research study.

4.4. Structural Equation Modelling:

Structural equation modelling (SEM) is an advanced statistical tool used to study the relationship between multiple variables simultaneously. It involves testing of two models (i) Measurement Model (MM) and (ii) Structural Model (Hair J. F., Black, Babin, Anderson, & Tatham, 1998); (Hughes, Price, & Marrs, 1986)

4.4.1. Confirmatory Factor Analysis:

In this study, Confirmatory Factor Analysis (CFA) was used to test how well the indicators/items of a construct represent the construct. CFA is a multivariate data analysis technique that is used to validate the measurement model of SEM. MM helps specify the items of each construct, and also assess the construct validity and reliability of each construct.

Construct Validity is “the degree to which a test measures what it claims to be measuring” (Cronbach & Meehl, Construct Validity in Psychological Tests, 1955). According to (Campbell & Fiske, 1959), there are two parameters to assess the construct validity – (i) Convergent Validity, i.e., the degree to which measures of constructs are related to each other and (ii) Discriminant Validity, i.e., the degree to which indicators of different constructs are unrelated to each other. The size of factor loading is an important indicator of convergent validity. Factor loadings that are significant with loading values above 0.5 indicate convergent validity (Hair J. F., Black, Babin, Anderson, & Tatham, 2006). Further, the latent constructs were validated through Average Variance Extracted (AVE) and Composite Reliability. AVE value of 0.5 and above indicates validity (Fornell & Larcker, 1981). Discriminant Validity was measured by comparing the AVE for each construct and correlation between the constructs. The square root of the AVE greater than or equal to the correlation indicates Discriminant Validity (Fornell & Larcker, 1981).

The reliability of the instrument was checked using Cronbach’s Alpha. Cronbach’s alpha is the measure of internal consistency within items’ latent constructs. A Cronbach value of 0.7 and more indicates reliability (Cronbach, 1951). Composite Reliability (CR), another estimate of reliability, was also measured in addition to Cronbach’s Alpha. A CR value of 0.7 or above is acceptable (Fornell & Larcker, 1981).

4.4.2. Descriptive Statistics:

Data systemisation was done using Excel; questions/items were grouped under 6 variables/constructs based on the hypothetical TAM model developed for the purpose of the study. Descriptive statistics was applied to the data to measure Mean and Standard Deviation. A series of correlations tests were conducted in SPSS using the Structural Equation Modelling to derive the Spearman’s Correlation Coefficient, which helped us understand the relationship between the research variables and the way they influenced the acceptance of E-Learning among the students selected for this research.

5. DATA ANALYSIS, RESULTS AND INTERPRETATION:

5.1. Participants’ Biographical Details:

The questionnaire was filled by 209 respondents. The data as depicted by Figure 4 and Figure 5 show that the majority of the students that participated in the survey are female. In addition, around 86.6% of the students fall in the age of 18 to 20.

Figure 1: Gender Data

Figure 2: Age Data

5.2. Factor Loading Values:

Confirmatory factor analysis is a way of testing how well the indicators/items of a construct represent the construct. The researcher’s hypothesized model included six constructs with five items representing each construct. SPSS has been used to conduct the Factor Analysis and extract factor loading values from the Rotated Component Matrix.

The size of factor loading is an important indicator of convergent validity. Convergent validity is used to measure the degree to which two measures of constructs are related to each other. According to (Hair J. F., Black, Babin, Anderson, & Tatham, 2006), factor loadings that are significant with loading values above 0.5 indicate convergent validity. Table 1 displays the construct, items of construct, and their factor loading values.

Table 1: Confirmatory Factor Analysis – Factor Loading Values

Construct	Items of Construct	Factor Loading
Trustworthiness	T1	0.750
	T2	0.737
	T3	0.701
	T4	0.655
	T5	0.562
Perceived ease-of-use	PEOU1	0.776
	PEOU2	0.761
	PEOU3	0.735
	PEOU4	0.656
	PEOU5	0.545
Intention to Use	IU1	0.729
	IU2	0.725
	IU3	0.652
	IU4	0.638
	IU5	0.596
Perceived Usefulness	PU1	0.727
	PU2	0.694
	PU3	0.625
	PU4	0.606
	PU5	0.553
Actual Use	AU1	0.790
	AU2	0.709
	AU3	0.696
	AU4	0.644
	AU5	0.563
Capability	C1	0.669
	C2	0.617
	C3	0.604
	C4	0.557
	C5	0.523

As can be noted from the table above, the factor loading value for all the constructs is above 0.5 which is an indication of construct validity.

5.3. Questionnaire Reliability and Validity:

The latent constructs were validated through Average Variance Extracted (AVE) and Composite Reliability. AVE value of 0.5 and above indicates validity (Fornell & Larcker, 1981). The reliability of the instrument was checked using Cronbach’s Alpha. Cronbach’s alpha is the measure of internal consistency within items’ latent constructs. A Cronbach value of 0.7 and more indicates reliability (Cronbach, 1951). Composite Reliability (CR), another estimate of reliability, was also measured in addition to Cronbach’s Alpha. A CR value of 0.7 or above is acceptable (Fornell & Larcker, 1981).

Table 2 shows the values of Alpha, AVE and CR for each construct.

The Alpha for all construct is above 0.70. CR value is all above 0.7 for each construct. Hence, the reliability of the instrument is supported. AVE is below the threshold mark of 0.5 but we can accept 0.4. This is because according to (Fornell & Larcker, 1981) if AVE is in the range 0.4 to 0.5 and CR is higher than 0.6, the convergent validity of the instrument is still supported.

5.4. Descriptive Statistics:

Table 3 shows the Mean and Standard Deviation of the responses for each construct.

Table 3: Descriptive Statistics

Construct	Mean	Standard Deviation
Perceived Usefulness	3.35215311	0.943402015
Capability	3.257416268	0.886303377
Perceived ease-of-use	3.413397129	0.894541374
Intention to Use	3.342583732	0.966633245
Trustworthiness	3.645933014	0.887192642
Actual Use	3.210526316	0.999078523

To test the hypothesis model, correlation between different construct have to be calculated, Usually, Pearson correlation is used to identify correlation between variables. But, Pearson’s Correlation is applicable when data is Continuous data like Sales data or Marketing Expenditure. For this study, Likert Scale has been used to measure the respondents’ opinions and perception. Thus, data collected is ordinal. Spearman’s Correlation Coefficient was used to understand the relationship between the research variables identified in the hypotheses model and the way they influenced the acceptance of E-Learning among the undergraduate students selected for this research.

Hypotheses 1: Perceived Ease of Use of E-Learning is positively related to the Perceived Usefulness of E-Learning.

The researchers used a five point Likert scale, ranging from “1 strongly disagree” to “5 strongly agree”, in the questionnaire to measure the attitudes and perceptions of the respondents regarding Perceived Ease of Use of E-Learning (PEOU). The construct was further divided into five items measuring some aspect of that construct. The five items were: “Perceived ease-of-use [It is easy to operate the e-learning system.] Perceived ease-of-use [The e-learning system is flexible to interact with.] Perceived ease-of-use [I consider that the e-learning system is easy to use] Perceived ease-of-use [It is easy to raise doubts in an e-learning system] Perceived ease-of-use [E-learning is user-friendly and convenient.]”. The items were averaged to produce a scale mean score for the construct as shown in Table 4.

The researchers used a five point Likert scale, ranging from “1 strongly disagree” to “5 strongly agree”, in the questionnaire to measure the attitudes and perceptions of the respondents regarding Perceived Usefulness (PU). The construct was further divided into five items measuring some aspect of that construct. The five items were: “Perceived Usefulness [E-Learning helps me get a better insight into a course.] Perceived Usefulness [Using E-Learning material enhances my understanding and learning.] Perceived Usefulness [I find the E-Learning courses useful] Perceived Usefulness [E-Learning courses are useful in increasing my qualification] Perceived Usefulness [I believe E-Learning courses add value]”. The items were averaged to produce a scale mean score for the construct as shown in Table 4.

The data corresponding to the PEOU and PU was entered into SPSS and correlation was analysed between the two variables using the Spearman’s Correlation Coefficient. Table 4 shows the result of the analysis.

Table 4: The correlation between PEOU and PU using Spearman’s Correlation

		PEOU	PU
Perceived Ease of Use of E-Learning (PEOU)	Correlation Coefficient	1.000	.555^**
	Sig. (2-tailed)		0.000
	N	209	209
Perceived Usefulness (PU)	Correlation Coefficient	.555^**	1.000
	Sig. (2-tailed)	0.000
	N	209	209

Note. **Correlation is statistically significant at .01 level.

As can be noted from the table above, the resulting correlation is direct and positive; the PEOU positively influences the PU based on the perception and opinions of students. These results confirm the first research hypothesis according to which Perceived Ease of Use of E-Learning is positively related to the Perceived Usefulness of E-Learning.

Hypotheses 2: Capability of E-Learning is positively related to Perceived Usefulness of E-Learning

The researchers used a five point Likert scale, ranging from “1 strongly disagree” to “5 strongly agree”, in the questionnaire to measure the attitudes and perceptions of the respondents regarding Capability (C). The construct was further divided into five items measuring some aspect of that construct. The five items were: “Capability [E-learning courses provide a lot of information] Capability [E-learning courses are robust] Capability [E-learning courses keep the learners engaged] Capability [The language in E-learning courses is good] Capability [E-learning courses are well designed]”. The items were averaged to produce a scale mean score for the construct as shown in Table 4.

The data corresponding to the C and PU was entered into SPSS and correlation was analysed between the two variables using the Spearman’s Correlation Coefficient. Table 5 shows the result of the analysis.

Table 5: The correlation between C and PU using Spearman’s Correlation

		C	PU
Capability (C)	Correlation Coefficient	1.000	.733^**
	Sig. (2-tailed)		0.000
	N	209	209
Perceived Usefulness (PU)	Correlation Coefficient	.733^**	1.000
	Sig. (2-tailed)	0.000
	N	209	209

Note. **Correlation is statistically significant at .01 level.

As can be noted from the table above, the resulting correlation is direct and positive; the C positively influences the PU based on the perception and opinions of students. These results confirm the second research hypothesis according to which Capability of E-Learning is positively related to Perceived Usefulness of E-Learning.

Hypotheses 3: Perceived Usefulness of E-Learning is positively related to Intention to Use E-Learning

The researchers used a five point Likert scale, ranging from “1 strongly disagree” to “5 strongly agree”, in the questionnaire to measure the attitudes and perceptions of the respondents regarding Intention to Use (IU). The construct was further divided into five items measuring some aspect of that construct. The five items were: “Intention to Use [I intend to use e-learning courses for additional information about a course.] Intention to Use [I intend to use e-learning for training in order to improve my performance.] Intention to Use [I intend to use e-learning for training on a regular basis.] Intention to Use [I intend to use e-learning to explore new courses] Intention to Use [I intend to use e-learning to learn from universities across the globe]”. The items were averaged to produce a scale mean score for the construct as shown in Table 4.

The data corresponding to the PU and IU was entered into SPSS and correlation was analysed between the two variables using the Spearman’s Correlation Coefficient. Table 6 shows the result of the analysis.

Table 6: The correlation between PU and IU using Spearman’s Correlation

		PU	IU
Perceived Usefulness (PU)	Correlation Coefficient	1.000	.676^**
	Sig. (2-tailed)		0.000
	N	209	209
Intention to Use (IU)	Correlation Coefficient	.676^**	1.000
	Sig. (2-tailed)	0.000
	N	209	209

Note. **Correlation is statistically significant at .01 level.

As can be noted from the table above, the resulting correlation is direct and positive; the PU positively influences the IU based on the perception and opinions of students. These results confirm the third research hypothesis according to which Perceived Usefulness of E-Learning is positively related to Intention to Use E-Learning.

Hypotheses 4: Trustworthiness of E-Learning is positively related to Intention to Use E-Learning

The researchers used a five point Likert scale, ranging from “1 strongly disagree” to “5 strongly agree”, in the questionnaire to measure the attitudes and perceptions of the respondents regarding Trustworthiness (T). The construct was further divided into five items measuring some aspect of that construct. The five items were: “Trustworthiness [I believe the content of the e-learning course is relevant] Trustworthiness [I believe the content of the e-learning course is updated] Trustworthiness [I believe the instructor of the e-learning course is qualified] Trustworthiness [I believe the university offering the e-learning course is authentic] Trustworthiness [I believe the content of e-learning course is accurate]”. The items were averaged to produce a scale mean score for the construct as shown in Table 4.

The data corresponding to the T and IU was entered into SPSS and correlation was analysed between the two variables using the Spearman’s Correlation Coefficient. Table 7 shows the result of the analysis.

Table 7: The correlation between T and IU using Spearman’s Correlation

		T	IU
Trustworthiness (T)	Correlation Coefficient	1.000	.631^**
	Sig. (2-tailed)		0.000
	N	209	209
Intention to Use (IU)	Correlation Coefficient	.631^**	1.000
	Sig. (2-tailed)	0.000
	N	209	209

Note. **Correlation is statistically significant at .01 level.

As can be noted from the table above, the resulting correlation is direct and positive; the T positively influences the IU based on the perception and opinions of students. These results confirm the fourth research hypothesis according to which Trustworthiness of E-Learning is positively related to Intention to Use E-Learning.

Hypotheses 5: Intention to Use E-Learning is positively related to the Actual Use of E-Learning.

The researchers used a five point Likert scale, ranging from “1 strongly disagree” to “5 strongly agree”, in the questionnaire to measure the attitudes and perceptions of the respondents regarding Actual Use (AU). The construct was further divided into five items measuring some aspect of that construct. The five items were: “Actual Use [I use E-learning quite frequently] Actual Use [I use e-learning courses offered from different universities] Actual Use [I complete the e-learning course] Actual Use [I explore different e-learning courses] Actual Use [I have received e-learning completion certification]”. The items were averaged to produce a scale mean score for the construct as shown in Table 4.

The data corresponding to the IU and AU was entered into SPSS and correlation was analysed between the two variables using the Spearman’s Correlation Coefficient. Table 8 shows the result of the analysis.

Table 8: The correlation between IU and AU using Spearman’s Correlation

		IU	AU
Intention to Use (IU)	Correlation Coefficient	1.000	.666^**
	Sig. (2-tailed)		0.000
	N	209	209
Actual Use (AU)	Correlation Coefficient	.666^**	1.000
	Sig. (2-tailed)	0.000
	N	209	209

Note. **Correlation is statistically significant at .01 level.

As can be noted from the table above, the resulting correlation is direct and positive; the IU positively influences the AU based on the perception and opinions of students. These results confirm the fifth research hypothesis according to which Intention to Use E-Learning is positively related to the Actual Use of E-Learning.

6. CONCLUSION AND DISCUSSION:

Research revealed that undergraduate students in India have positive perception of E-learning. This is because of the technological advancement in the teaching, learning and evaluation process which have provided students the opportunity to pursue online certifications, gain knowledge and learn from experts without the expense of traveling and physical availability (Allen & Seaman, 2006).

6.1. STUDY LIMITATIONS:

(Straub & Karahanna, 1995) stated that Actual System Usage can be divided into two independent sub constructs, self-reported system usage and computer-recorded system usage. The responses collected under the parameter Actual Usage is solely based on users/respondent's estimates. Considering humans being bad at making judgements about their own behaviour, their self-reported usage data may be an overestimate of how much they do use the e-learning services if compared with the data recorded through E-learning platform progress page, i.e., the computer recorded data.

6.2. CONCLUSION:

India has one of the highest student population in higher education and as per the study, they have a positive attitude towards e-learning. This shows the scope of e-learning platforms in developing nation like India and innovative technological developments in traditional education system. Institutes and universities should focus on developing the e-learning infrastructure to meet the students’ expectation from such platforms. The initiatives taken by Indian Government to enhance e learning, like the National Programme on Technology Enhanced Learning (NPTEL) funded by the Ministry of Human Resource Development (MHRD) launched in 2006, also display the rate at which higher education is changing in India.

In the end, we can state that the research study conducted in this paper demonstrated that there is positive relationship between the research variables identified from the hypothetical Technology Acceptance Model (TAM). The research helped to understand the components of TAM, analyse and identify the relation between TAM and E learning, and evaluate students’ perceptions, attitude and opinion towards E learning in India.

1.1.

1.2.

1.3.

1.4.

1.5.

1.5.1.

1.5.2.

7. REFERENCES:

1. Ahmad, S., Bhatti, S. H., and Hwang, Y. (2019, August 29). E-service quality and actual use of e-banking: Explanation through the Technology Acceptance Model. Information Development, 36(4), 503-519.

2. Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. Action Control, 11-39.

3. Ajzen, I. (1991, December). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), pg 179-211.

4. Ajzen, I. (2012a). Attitudes and persuasion. (K. Deaux, and M. Snyder, Eds.) The Oxford Handbook of Personality and Social Psychology (1st edn), 367-393.

5. Ajzen, I. (2012b). The theory of planned behavior. (P. A. Lange, A. Kruglanski, and E. T. Higgins, Eds.) Handbook of theories of social psychology, 1, 438-459.

6. Ajzen, I., and Fishbein, M. (1980). Understanding attitudes and predicting social behavior. New Jersey: Prentice-Hall.

7. Ajzen, I., and Fishbein, M. (2005). The influence of attitudes on behavior. (D. Albarracín, B. Johnson, and M. P. Zanna, Eds.) The handbook of attitudes, 173-221.

8. Ajzen, I., and Fishbein, M. A. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research (Vol. 27).

9. Allen, I. E., and Seaman, J. (2006). Making the Grade: Online Education in the United States. USA: Sloan Consortium (Sloan-C).

10. Bhattacharya, I., and Sharma, K. (2007, August). India in the knowledge economy - An electronic paradigm. International Journal of Educational Management, 21(6), 543-568.

11. Bonk, C. J., Wisher, R. A., and Lee, J.-Y. (2004). Moderating Learner-Centered E-Learning: Problems and Solutions, Benefits and Implications. USA.

12. Campbell, D. T., and Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81-105.

13. Chowdhury, D. (2019). Role of E-Learning on Education: A Review of Literature. Asian Journal of Management, 10(3), 273-278.

14. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334.

15. Cronbach, L. J., and Meehl, P. (1955). Construct Validity in Psychological Tests. Psychological Bulletin, 52(4), 281-302.

16. Davis, F. D. (1989, September). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

17. Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989, August). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003.

18. Dongsong Zhang, L. Z. (2006). Instructional video in e-learning: Assessing the impact of. Information and Management, 43, 15-27.

19. Emma L. Bird, J. P. (2018, September). Predicting walking and cycling behaviour change using an extended Theory of Planned Behaviour. Journal of Transport and Health, 10, 11-27.

20. Fishbein, M. (1980). A theory of reasoned action: some applications and implications. Nebraska Symposium on Motivation, 27, 65-116.

21. Fornell, C., and Larcker, D. F. (1981, f). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.

22. Fraser, R., Johnson, K., Hebert, J., Ajzen, I., Copeland, J., Brown, P., et al. (2010). Understanding employers’ hiring intentions in relation to qualified workers with disabilities: Preliminary findings. Journal of Occupational Rehabilitation, 20, 420-426.

23. Gefen, D., Karahanna, E., and Straub, D. W. (2003, August). Inexperience and Experience with Online Stores: The Importance of TAM and Trust. IEEE Transactions on Engineering Management, 50(3), 307-321.

24. Gupta, N. (2020). The Effect of Technology Acceptance on Online Customers’ Repurchase Intention. Asian Journal of Management, 11(3), 343-348.

25. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., and Tatham, R. L. (1998). Multivariate data analysis (5th edition). 5(3).

26. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., and Tatham, R. L. (2006). Mutivariate Data Analysis (Vol. 6). Pearson Prentice Hall.

27. Hughes, M. A., Price, R. L., and Marrs, D. W. (1986). Linking theory construction and theory testing: Models with multiple indicators of latent variables. The Academy of Management Review, 11(1), 128-144.

28. Institute of Medicine (US). (2002). Committee on Communication for Behavior Change in the 21st Century: Improving the Health of Diverse Populations. In Speaking of Health: Assessing Health Communication Strategies for Diverse Populations. Washington (DC): National Academies Press (US).

29. Japutra, A., Loureiro, S. M., Molinillo, S., and Ekinci, Y. (2019, April). Travellers' mindsets and theory of planned behaviour. Tourism Management Perspectives, 30, 193-196.

30. Jayaswal, P. (2019). Critical Success Factors Affecting E-learning Quality for Management Students in Delhi-NCR. Research Journal of Humanities and Social Sciences, 10(2), 346-350.

31. Kataria, Y. S., Krishna, H. G., Tyagi, V. K., and Vashishat, T. (2019). Consumer Buying behavior of Organic food Products in India Through the Lens of Planned Behavior Theory. Research Journal of Humanities and Social Sciences, 10(1), 60-67.

32. Koufaris, M. (2002, June). Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior. Information Systems Research, 13(2),

33. Kumar, M. V. (2018). Teacher as online facilitator in a Face book group for Knowledge acquisition. Research Journal of Humanities and Social Sciences, 9(1), 245-247.

34. Lee, J., Cerreto, F. A., and Lee, J. (2010, January). Theory of Planned Behavior and Teachers' Decisions Regarding Use of Educational Technology. Educational Technology and Society, 13(1), 152-164.

35. Lee, Y., Kozar, K. A., and and Larsen, K. R. (2003). The Technology Acceptance Model: Past, Present, and Future. Communications of the Association for Information Systems, 12(50), 752-780.

36. Mehra, V., and Omidian, F. (2012, January). Development an instrument to measure university students' attitude towards e-learning. Turkish Online Journal of Distance Education, 13(1), 34-51.

37. Pfeffer, J. (1984, December). Organizations and Organization Theory. Administrative Science Quarterly, 29(4), 640-643.

38. Pillai, S. V., Mathew, L. S., Daniel, A., and A. V. (2020). Technology enabled online learning in a Digital age. Asian Journal of Management, 11(3), 266-274.

39. Radner, R., and Rothschild, M. (1975). On the allocation of effort. Journal of Economic Theory, 10(3), 358-376.

40. Rogers, E. (1995). Diffusion of Innovations.

41. Roy, N., and Jain, Y. (2014). Education through ICT Technology: Need of an hour. Asian Journal of Management, 5(2), 246-259.

43. Sharma, R. L. (2017). The Study of Customer Behaviour and Its Impact on Customer Satisfaction, Loyalty and Service Quality Perception in E-Banking Services in Jammu Division. Asian Journal of Management, 8(2), 241-245.

44. Stockley, D. (2003). E-learning Definition and Explanation. Retrieved November 2020, from derekstockley: http://www.derekstockley.com.au/elearning-definition.html

45. Straub, D., and Karahanna, E. (1995, August). Measuring System Usage: Implications for IS Theory Testing. Management Science, 41(8), 1328-1342.

46. Trafimow, D. (2009, August). The Theory of Reasoned Action. Theory and Psychology, 19, 501-518.

47. Uma R, and V. Selvam. (2017). Consumer’s Attitude towards Organic Food Products buying Decision in Vellore City: Structural Equation Modeling – Using Partial Least Square (SEM-PLS) approach. Research Journal of Pharmacy and Technology, 10(12), 4333-4338.

48. Vaidehi R, and Girija T. (2017). Factors Influencing Diffusion and Continuance of Massive Open Online Course (MOOC). Asian Journal of Management, 8(3), 731-733.

49. Venkatesh, V., and Davis, F. D. (1996, September). A Model of the Antecedents of Perceived Ease of Use: Development and Test. Decision Sciences, 27(3), 451-481.

50. Venkatesh, V., and Davis, F. D. (2000, February 2). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.

51. Vroom, V. (1964). Work and motivation. 331.

52. Wixom, B. H., and Todd, P. A. (2005, March 1). A Theoretical Integration of User Satisfaction and Technology Acceptance. Informations Systems Research, 16(1).

53. Wu, K., Zhao, Y., Zhu, Q., Tan, X., and Zheng, H. (2011, December). A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31(6), 572-581.

54. Yakasai, A., and Jusoh, W. (2015, November). Testing the Theory of Planned Behavior in Determining Intention to use Digital Coupon among University Students. Procedia Economics and Finance, 31, 186-193.

Received on 07.01.2021 Modified on 10.02.2021

Asian Journal of Management. 2021; 12(3):243-252.

DOI: 10.52711/2321-5763.2021.00037

Construct	Number of Items	Cronbach's Alpha Value	Average Variance Extracted	Composite Reliability
Perceived Usefulness	5	0.906	0.41	0.78
Capability	5	0.876	0.40	0.73
Perceived ease-of-use	5	0.886	0.49	0.83
Intention to Use	5	0.897	0.45	0.80
Trustworthiness	5	0.912	0.47	0.81
Actual Use	5	0.861	0.47	0.81