Investigating a theoretical framework for e-learning technology acceptance

Received Mar 31, 2020 Revised Jun 6, 2020 Accepted Jun 17, 2020 E-learning has gained recognition and fame in delivering and distributing educational resources, and the same has become possible with the occurrence of Internet and Web technologies. The research seeks to determine the factors that influence students' acceptance of E-learning and to find out the way these factors determine the students' intention to employ E-learning. A theoretical framework was developed based on the technology acceptance model (TAM). To obtain information from the 270 university students who utilized the E-learning system, a questionnaire was formulated. The results revealed that “social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use” are the strongest and most important predictors in the intention of and students towards E-learning systems. The outcomes offer practical implications for practitioners, lawmakers, and developers in effective E-learning systems implementation to improve ongoing interests and activities of university students in a virtual E-learning atmosphere, valuable recommendations for E-learning practices are given by the research findings, and these may turn out to be as guidelines for the efficient design of E-learning systems.


INTRODUCTION
The use of world wide web (WWW) has expanded to a large extent in the previous two decades, because of which investments are being made by universities and other educational institutes in information systems (like Moodle and Blackboard), which endorse face-to-face [1][2][3] as well remote course delivery [4][5][6]. Even though the internet is viewed as a global technology, it is important to measure the effectiveness of these tools at the domestic level because users are typically employed in local/national contexts [7].
The effective use of E-learning tools is dependent on the perspective of the users, as well as their knowledge and competences with respect to both computer and mobile usage [6,[8][9][10]. It has been found that these significant factors have an impact on the early acceptance [11] of the users of computer technology and their behavior in the future [6,[12][13][14][15] with respect to the use of web-based learning systems [16]. Since the technology acceptance model (TAM) has satisfactory explanatory power and its widespread popularity, it was used in various studies in the technology acceptance and adoption literature in the domain of IS implementation [17], particularly in terms of E-learning [18][19][20][21]. However, there is insufficient evidence to determine if this model is appropriate for E-learning in emerging countries and the Arab nations. Hence, E-learning systems in UAE universities successfully could be gained. This study only aimed at the students engaged in computing degrees in the UAE university system. Several research studies analyzed the effect of specific student features upon the acceptance and implementation on online technology. These studies have been included as part of the E-learning research [47,[63][64][65][66][67][68][69][70][71][72]. Based on the perceived ease of use, the online delivery system may be preferred by the students keeping in mind their ability to make use of the internet and other kinds of electronic communication systems. These students are also interested in individual learning. The academic achievements as part of the online system are also positively influenced by the individual's perception towards the online learning usefulness [73]. Other factors associated with individuals' attributes are the social influence made by the student reference groups and student's outlook concerning online learning, and these may influence their willingness to learn online [74,75]. It is the behavioral intention of the users that has a powerful impact on their actual usage of the technology, which is determined by the previous experience of using this technology [20,[75][76][77]. An integrated theoretical framework that was primarily based on TAM was put forward in this study involving E-learning acceptance and intention to use of university students. The study sought to assess the association between university students' intention to use E-learning and specific attributes like "social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use". In addition, it sought to formulate a general linear structural model of E-learning acceptance of university students, which would have implications for a school manager or an educational practitioner to enhance the adoption of E-learning. Certain descriptive factors of E-learning use and the chosen constructs were also identified.

RESEARCH MODEL AND HYPOTHESIS
Based on TAM, a conceptual model of E-learning technology acceptance was put forward in this study, using previous literature that employed TAM within an educational setting. Furthermore, TAM is expanded in the model by adding further predictor variables, i.e., "Social Influence, Perceived Enjoyment, and Self-efficacy". Figure 2 puts forward the comprehensive conceptual model, while the following sections describe and justify all of the predicted relationships, keeping in view the outcomes of the previous literature.

Social influence (SI)"
The social influence refers to "The degree to which an individual perceives that most people who are important to him think he should or should not use the system" [78,79]. 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 [17,24,80]. Several studies have been carried out regarding the impact of social influence on the learners' e-learning adoption or acceptance [57,[81][82][83][84][85][86][87]. Many studies conducted by many scholars, such as [17,24,57,83,[86][87][88] showed that subjective norm had a significant effect on the perceived usefulness and perceived ease of use of e-learning system. This gives rise to the hypotheses given below: H1a. Social influence (SI) has a positive influence on perceived usefulness (PU)." H1b. Social influence (SI) has a positive influence on perceived ease of use (PEOU)."

Perceived enjoyment (PE)"
Perceived enjoyment (PE) is defined by [58] as "The extent to which 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" [89]. Interest has been shown by several researchers in enjoyment involved in the studies pertaining to technology acceptance since the intrinsic variables may have a positive impact on the perspective of users [57,70,81,84,86,90]. When a user feels enjoyment while working on a new system, there may be a decline in their perceptions of the effort they are putting in. Therefore, perceived enjoyment signifies a significant factor that describes the adoption or acceptance of e-learning. Earlier studies showed that there is a significant effect of perceived enjoyment [21] on perceived ease of use [42,57,70,91], and perceived usefulness [42,57,70,86,90,91] of e-learning. When students know that they will have an enjoyable experience while working on e-learning systems, there is a greater likelihood that they will have a positive effect on the usefulness and ease of use of such systems [44]. Therefore, the hypotheses given below were formulated: H2a. Perceived enjoyment (PE) has a positive influence on perceived usefulness (PU)." H2b. Perceived enjoyment (PE) has a positive influence on perceived ease of use (PEOU)."

Computer self-efficacy (CSE)"
According to [92][93][94], self-efficacy is "The degree to which an individual believes that he or she has the ability to perform a specific task/job using the computer". Self-efficacy is linked to computer systems in this study (i.e., the confidence shown by the users in their own capacity to employ the e-learning system). It was stated in many studies such as [57, 82-84, 88, 95, 96] that computer self-efficacy (CSE) plays a critical role in identifying the emotions and behavior of a person. Hence, it is claimed that when there are high expectations of efficacy, there are greater possibilities of achieving success in a given task. An analysis of the e-learning literature showed that the most extensively used external determinant of TAM was computer self-efficacy. Different empirical studies showed that there was a significant effect of computer self-efficacy on the perceived usefulness, perceived ease of use of the e-learning system [57, 82-84, 88, 95, 96]. Therefore, the hypotheses given below were formulated: H3a. Computer self-efficacy (CSE) has a positive influence on perceived usefulness (PU)." H3b. Computer self-efficacy (CSE) has a positive influence on perceived ease of use (PEOU)."

Core TAM constructs"
The adoption of new technology such as mobile technology is facilitated by the perceived usefulness and perceived ease of use [42,97,98]. It was stated in the "TAM and other relevant studies that these factors have a significant impact on the behavioral intention to use. The user's perspective regarding the ease of understanding helps to a large extent in identifying the potency and the extent to which the user exhibits adaption to new technology [53,99]." It was shown in a previous study that perceived ease of use (PEOU) has a significant influence on the perceived usefulness [57,70,83,84,88,90,[100][101][102][103][104]. Several studies have been carried out in the past that claim that there is a positive association between the perceived ease of use (PEOU) and the behavioral intention to use E-learning system (BI), direct as well indirect [57,70,83,84,88,90,[100][101][102][103][104]. According to [105], perceived usefulness (PU) will affect their intention to directly or indirectly agree to and adjust to the E-learning system. In the previous, it has been found that perceived usefulness (PU) has a significant positive relationship with the intention to use the E-learning system (BI) [57,70,83,84,88,90,96,[100][101][102][103][104]. This research puts forward the hypotheses given below, in accordance with the given literature, in addition to presenting a significant positive association between BI, PU, and PEOU. H4. Perceived ease of use (PEOU) has a positive influence on perceived usefulness (PU)." H5. Perceived usefulness (PU) has a positive influence on behavioral intention to use social network technology (ELSA)." H6. Perceived ease of use (PEOU) has a positive influence on behavioral intention to use social network technology (ELSA)." The research model given in Figure 2 has been developed in this research using the hypotheses given above.

RESEARCH METHOD 4.1. Procedure
Data collection was carried out by the postgraduate students at the University of Al Fujairah. The questionnaire is the fundamental tool of data collection, were distributed among the students to validate the various research hypotheses and to determine the impact of factors being researched. The questionnaire included 17 items that were distributed under different factors, including "social influence, perceived 6488 enjoyment, self-efficacy, perceived ease of use, perceived usefulness, and behavioral intention to use E-learning system". A total of 270 respondents taking part in the research filled the questionnaire.
On the whole, since 30 surveys were uncompleted, the questionnaires were of no use. Therefore, the total number of complete questionnaires were 270, demonstrating a response rate of 90%. The valid responses that were taken into account and modified into a sample size amounted to 270 responses, according to [106] the estimated sampling size for a population of 460 in 210 respondents. The responses were then examined using the conceptual model. This is acknowledged as a sample size when the structural equation modeling is used for performing the investigation. Therefore, a sample size of 270 is high when compared to the insignificant requirements used for the analysis of the hypotheses in this research [107].

Participants
Once the questionnaires were collected, respondents were classified in accordance with the distinct standards: age, gender, and the use of the E-learning system. The sample size of the study was 270 students, of which 175 were females, and 95 were males (65% and 35% respectively). The ages of 68% of the students were between 18 and 29. The ages of the rest of the 32% of the participants were over 30 years. Out of the 270 students, 265 (98%) were believed to highly competent in computer skills. 92% of the candidates stated that they use the E-learning system every day.

RESULTS AND DISCUSSIONS 5.1. Measurement model analysis
Before checking for important connections in the structural model, the measurement method must show a reasonable level of reliability and validity [108,109]. The measurement model is made up of correlations between the latent factors and the variables identified that underlie that construct.

Convergent validity
The Cronbach's alpha scores, as shown in Table 1, are greater than 0.7, and the construct's composite reliabilities differ from the range of 0.722 to 0.898. Therefore, the condition for explicating at least 50 percent of this variance extracted between a set of the elements underlying the latent structure meets all the AVE (Average Variance extracted) values between 0.553 and 0.786. As a consequence, the convergent validity of the measurements for the constructs of buildings is considered. The following [110] metrics are used as indicators to measure the relative value of convergent validity, which are factors loadings, variance

Discriminant validity
As is shown by Table 2 and Table 3, the criteria for discriminant validity have been fulfilled as all the AVE values in the measurement model are larger than their squared correlation, among the constructs [108,109,111].

Structural model analysis (SEM)
With the final goal to examine the proposed hypotheses, the relationships that the theoretical constructs for the structural model have with each other were assessed via a structural equation model that had SEM-PLS with the maximum likelihood estimation [6,24,25,42,[112][113][114][115]. As appeared in Table 4 and Figure 3, the model had a high predictive power [116], that's, the percentage of the variance within perceived usefulness, perceived ease of use, and e-learning system acceptance are nearly 70%, 70%, and 71%, separately . All hypotheses were found to be significant. Based on the data analysis hypotheses H1a , H1b, H2a, H2b, H3a, H3b, H4, H5, and H6 were supported by the empirical data. The results showed that perceived usefulness (PU) significantly influenced social influence (SI) (β=0.723, P<0.001), , and H3b respectively. Furthermore, E-learning system acceptance (ELSA) was significantly influenced by perceived usefulness (PU) (β=0.256, P < P<0.001) and perceived ease of use (PEOU) (β=0.519, P < P<0.05) which support hypotheses H5 and H6. A summary of the hypotheses testing results is shown in Table 5.

CONCLUSION
The objective of this research is to extract the critical factors associated with E-learning acceptance or adoption within the UAE. The critical factors were subjected to a statistical analysis which showed social influence (SI), perceived enjoyment (PE), and computer self-efficacy (SE) maintain a positive and strong effect upon, perceived ease of use (PEOU) and perceived usefulness (PU). Additionally, behavioral intention to use the E-learning system (ELSA) is being significantly affected by the perceived usefulness (PU) and perceived ease of use (PEOU). Various other research studies assessed the positive influence of social influence, perceived enjoyment, and computer self-efficacy on perceived ease of use, perceived usefulness, and E-learning acceptance. The administration at the university is engaged in creating an E-learning infrastructure and to carry out assessments. The research is engaged in extracting and analyzing the learner perspectives influential factors keeping in mind the acceptance and adoption of E-learning systems. Thorough information can be gained through external factor research results. The recommendations and the information can be shared with practitioners, developers, policymakers, and designers so that they may effectively adopt the e-learning systems.
The research model presents a framework that can be used by the university administration so that they may create the E-learning infrastructure and analyze the readability of the students. When the E-learning practice is being planned or evaluated, the online platforms and university administration should consider these factors. The social factors are also significant which are related to the students' adoption of E-learning and they have been presented as part of the research outcomes. Furthermore, the E-learning culture should be deployed so that the entire E-learning process is understood and the E-learning adoption is supported. This culture should be present within the student community, the readability of the students towards E-learning must be developed and evaluated, students should be provided with internet access free of cost and a server should be of high capabilities, the computer labs should be built using appropriate E-learning facilities and all university students should be able to access these facilities throughout the day. Furthermore, it is possible to strengthen E-learning through planning and to carry out events and implementing an E-learning culture amongst the students and relatives. The positive attitude towards E-learning can also be developed by conducting training courses as it would help state E-learning usefulness and ease perceptions. Hence, E-learning is practiced efficiently only after enhancing behavioral intention.
A strict research process is being followed within this research, but there is a specific limitation that needs to be considered so that future research can benefit. The response rate attained from the students was quite low. The research results have been extracted using the UAE population, which makes use of the E-learning systems. Hence, when generalizing the results, the cross-cultural difference amongst provinces should be kept in mind. The research also analyzed the intention of the learners to adopt the E-learning systems, and much importance has also been given to the role played by the faculty in stating the adoption intention. Future studies would indicate the role played by the faculty for E-learning system adoption.