Keywords

E-Learning, Sustainability, Technology Acceptance Model (TAM), Structural Equation Modeling (SEM).

Introduction

Universities must keep up with students' expectations, preferences, and standards in today's higher education system, which is constantly changing. As a result, information technology and E-learning platforms are seen as critical components of universities' operations, with these organizations increasingly investing in multimedia systems and devices (Popovici & Mironov, 2015) However, in this technological age, one of the most significant problems facing universities is the integration of groundbreaking E-learning platforms to help and strengthen both teaching and learning (Fischer et al., 2014). Many concepts for the idea of E-learning system have been suggested due to its scope. E-learning, to put it simply, is the use of information and computing technology and programs to create and design learning environments (Horton, 2006). E-learning system, according to Ellaria Engelbrecht, is a term that uses interactive platforms such as the internet, CDs, cell phones, and even television to provide distance learning and teaching (Engelbrecht, 2005). In a summary, E-learning is the process of transmitting information and education through the use of different electronic devices (Koohang & Harman, 2005) and the term is best understood when placed in the light of technology being used to satisfy people's desire to learn and develop (Cohen & Nycz, 2006). E-learning system, according to a more nuanced and inclusive concept is a form of teaching and learning that incorporates electronic tools and mediums with the goal of fostering growth and improving education and training quality (Sangrà et al., 2011). E-learning system may also refer to a system for formal education or a network through which knowledge is disseminated to a wide audience through electronic media. Computers and the internet are the primary components that guarantee the smooth operation of those networks (Babu & Sridevi, 2018). E-learning system has many features that facilitate and foster the learning-teaching process by providing a broad variety of options for exchanging knowledge and downloading documents in various formats. Since it is a web-based framework, no extra resources are needed, and once the content is posted, users can access it at any time (Raheem & Khan, 2020). Given that the evolution and application of programs and technology favored the creation and extension of educational opportunities (Sarikhani et al., 2016), the use of E-learning system in higher education, as well as students' perceptions of its utility, became subjects of concern for many researchers. The Technology Acceptance Model (TAM), which has proven to be useful in assessing and comprehending how students expect to use E-learning system (Almarabeh, 2014), is important in exploring the use of E-learning system. Various multimedia tools are used in the E-learning system in higher education. Many terms, such as Computer-mediated learning (Anaraki, 2004), Web-based teaching, E-learning system, and Learning Management Systems (Costa et al., 2012), have been used to describe online learning over time. In the event of a pandemic, online learning is a viable option (Basilaia & Kvavadze, 2020; Taha et al., 2020). Since it can be accessed anywhere and at any time, online learning is very realistic (Silverman & Hoyos, 2018). Do not, however, believe that implementing online learning can solve all of the problems (Hung & Chou, 2015; Smart & Cappel, 2006). As a result, higher education institutions with little to no familiarity with e-learning or e-learning services face challenges, especially where lecturers are unfamiliar with how to use online applications (Zaharah et al., 2020). There are benefits and drawbacks of implementing online learning in higher education. The benefits of online learning include its flexibility and ability to be used extensively, while the disadvantages include the propensity for plagiarism, internet signal power, and devices that allow it (Arkorful & Abaidoo, 2015). As a result, e-learning system usage for educational sustainability among students is a starting point for assessing and then designing technology integration training to see to what degree they follow and are pleased with using accessible e-learning system is a starting point for assessing and then designing technology integration training. Also, as a result, the aim of this research is to look at students' attitudes toward using e-learning systems and their behavioral intentions to use them for educational sustainability. This research adds to the Technology Acceptance Model (TAM) literature by looking at the relationship between TAM variables and students' attitudes toward using e-learning systems and their behavioral plan to use them in the long run. As a result, the following seven variables were used in this study: computer self-efficacy, subjective norm, perceived enjoyment, perceived usefulness, perceived ease of use, attitude toward use, and behavioral desire to use an e-learning system for educational sustainability. This research could help researchers build and test hypotheses about e-learning systems for educational sustainability, as well as practitioners who design and promote e-learning systems for educational sustainability. The present study's second section covers model construction and hypotheses, the third section covers research methods, and the fourth section covers findings and interpretation, as well as debate and consequences. The final section of the report is the thesis, which discusses prospective research.

Literature Review

According to the TAM model, emerging technology adoption is determined by four factors: perceived ease of usage (PEOU), perceived utility (PU), attitude toward use (ATU), and behavioral purpose to use (BIU). To analyze the adoption and use of inventions, a variety of models have been used. The Technology Acceptance Model (TAM) is one of the most well-known methods for analyzing users' acceptance of innovations, and it has been used extensively in several studies (Binyamin et al., 2017; Alharbi & Drew, 2014; Binyamin et al., 2017; Mohammadi, 2015). The model has been quoted over 36,000 times, according to Google Scholar. Fred Davis introduced TAM in 1989 as a scientific paradigm focused on the principle of rational behavior (TRA) (Davis 1989). TAM discusses the interaction between consumers and devices in order to estimate the user's technology adoption (Holden & Rada, 2011). Most acceptance models have struggled to integrate psychological and technical frameworks into a single theory; however, TAM is one of the few hypotheses that will do (Holden & Rada, 2011), see Figure 1.

Computer Self-Efficacy (CSE)

CSE is the first and most commonly used vector to extend TAM in the area of e-learning (Abdullah & Ward, 2016). Venkatesh and Davis proposed this element as a PEOU determinant in 1996 (Davis, 1989). CSE is a test that assesses a person's ability to use computer technology (Compeau & Higgins, 1995). As a result, whether a person believes he or she has a high potential to use computer technology, they are more likely to use it. CSE refers to the students' confidence in their abilities to use the e-learning system offered by their institution for the purposes of this report. CSE has been reported to impact students' PEOU and PU of e-learning systems based on TAM in Saudi Arabia (Al-Mushasha, 2013). The TAM3 model (Venkatesh & Bala, 2008) and Venkatesh's model (Venkatesh, 2000). investigated the impact of CSE and hypothesized that CSE has an impact on PEOU. TAM3 (Al-Gahtani, 2016) was used to explain this hypothesis in Saudi Arabia.

Subjective Norm (SN)

SN is the second most commonly used vector to apply TAM in the field of e-learning (Abdullah & Ward, 2016). Scholars exchange the words “external power” and “subjective norm” (Tarhini et al., 2014). This dimension shows how much people believe others believe they can or should not engage in a specific action (Venkatesh & Davis, 2000). In this research, a student is more likely to use an e-learning system whether he or she believes that people who matter to him or her agree that he or she does. It is fair to assume that subjective norms influence technology use in developed countries (Baker et al., 2010). The impact of SN on TAM constructs in e-learning has been examined in the literature, with conflicting results (Tarhini et al., 2014).

Perceived Enjoyment (PE)

PE refers to how enjoyable an activity provided by an e-learning system is viewed, regardless of predicted output outcomes (Van der Heijden, 2004). This construct can be interpreted as a bi-perspective mode of pleasure derived from using an e-learning device with friends and assisting others (Hsu et al., 2009). The PE of students is characterized in this study as the degree to which they enjoy using the e-learning method.

Perceived Usefulness (PU)

The level of belief that using technology can increase one's work efficiency is referred to as PU (Davis, 1989). In this report, PU refers to the extent to which students perceive the use of an e-learning method to be beneficial to their learning. PU has been shown to affect attitudes toward technology and user intentions in recent research (Teo & Zhou, 2014; Al-Rahmi et al., 2018; Alamri et al., 2019 & 2020). Since PU has a direct effect on behaviors, it is believed that it would have an indirect effect on intention to use an e-learning scheme.

Perceived Ease of Use (PEU)

PEU is the degree to which a person believes that using an e-learning device is painless. According to Davis (1989); Venkatesh et al. (2003), when a technology is perceived to be simple to use, people are more inclined to cultivate a favorable outlook about it (Teo & Zhou, 2014). PEU refers to a student's belief that using an e-learning method is both simple and useful in this analysis. Though PU is concerned with the impact of technology on job efficiency, perceived ease is concerned with the impact of technology on performance processes (Davis, 1989).

Attitude towards Use (ATU)

ATU is affected by their classroom (Fabunmi et al., 2007) or their dedication to and recognition of their learning activities (Riaz et al., 2011), according to the literature. PEU and the TAM, according to Davis (1989), affect PU and, when combined, affect consumer approaches to e-learning system use. PEU and PU were considered key cues for interactive course identification in another study (Alalwan et al., 2019; Tan, 2019) PEU has an effect on learners' attitudes toward the BIU e-learning framework and their plans to use it. The BIU for e-learning system use in this study relates to the degree to which students believe that using an e-learning system enriches their learning, which increases their ATU e-learning system use.

Behavioral Intention to Use (BIU)

BIU is the probability that individuals will engage in the behavior in question is known as behavioral intention to use (BIU) (Al-Rahmi et al., 2019). According to Ajzen (1991), BIU is a clear antecedent of actual behavior, because the stronger an individual's purpose towards a certain behavior, the more likely such behavior will occur (Ajzen, 1991). A significant number of studies have been published that support the connection between BIU and user behavior (Davis, 1989; Al-Rahmi et al., 2019). The vast majority of research on technology adoption in the e-learning world has shown that behavioral intention has a strong positive impact on e-learning system utilization (Alshehri et al., 2019; Salloum & Shaalan, 2018)

Methodology

Many institutions have promoted the use of available e-learning systems for educational sustainability. As a result, through an observational investigation of students' adoption of e-learning system usage for education sustainability, this report aims to create a model of calculation of students' intention to use e-learning system for education sustainability. Undergraduate and postgraduate students who used an e-learning system for education sustainability made up the research group. For objects containing the TAM constructs and demographic features, a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) was used. Respondents were asked to provide input on e-learning system usage for education sustainability, its impact on students' attitudes toward use, and behavioral intention to use an e-learning system for education sustainability in the future using self-administration. To check the quality and efficiency of the calculation model, the data was evaluated using the Statistical Package for the Social Sciences (SPSS) and Partial Least Squares–Structural Equation Modeling (PLS-SEM) using Smart PLS 2.0. Factor loadings were used to ensure construct validity, composite reliability, Cronbach's alpha, and convergence validity for the model's goodness of fit, as recommended by Hair et al. (2012).

Instruments of Measurement

The build elements adopted from previous research confirmed the measurement scales' material validity. There were two sections of the sample questionnaire: Basic demographic data (gender, age, educational level, and specialization) and questionnaire items measuring machine self-efficacy and subjective norm were adapted from [28], as were items measuring perceived enjoyment, perceived usefulness, perceived ease of use, attitude toward use, and behavioral intention to use were adapted from Davis (1989); Al-Rahmi et al. (2019).

Results

The Cronbach's alpha reliability coefficient was found to be 0.891, indicating that the variables that affected attitude toward usage and behavioral intention to use an e-learning system for education sustainability was reliable. Three criteria were used to assess discriminant validity: According to Hair et al. (2012), variable indices must be less than 0.70, the Average Variance Extracted (AVE) of each construct must be equal to or greater than 0.5, and the AVE square root of each construct must be greater than the Inter-Construct Correlations (IC) for a factor. Aside from the above factors, build factor analysis results with factor loadings of 0.70 or greater (Cronbach's alpha 0.70 and composite reliability 0.70) is sufficient (Hair et al., 2012).

Construct Validity of Measurements

Build validity (Alamri et al., 2020) refers to the degree to which individual objects assess the definition for which they were created. This was measured using a systematic analysis of previously reviewed products in the literature. Table 1 lists the items and their loadings, which are required to load into the construct that they were designed to test (Chow & Teicher, 2012).

Table 1 Loadings and Cross-Loadings of Items
Factors	Items	ATU	BIU	CSE	PE	PEU	PU	SN
Attitude towards Use	ATU1	0.854489	0.585720	0.094517	0.356804	0.625866	0.621607	0.163071
	ATU2	0.868354	0.534915	0.093805	0.654040	0.417070	0.555444	0.129753
	ATU3	0.841266	0.468622	0.055914	0.602425	0.372779	0.508893	0.008887
	ATU4	0.823617	0.450444	0.066075	0.405158	0.416847	0.583883	0.013183
Behavioral Intention to Use	BIU1	0.452069	0.803486	0.217293	0.297294	0.443521	0.433156	0.296703
	BIU2	0.499467	0.849453	0.285775	0.340338	0.434555	0.577988	0.207029
	BIU3	0.519881	0.845686	0.158930	0.317575	0.587341	0.665157	0.172384
	BIU4	0.550362	0.843148	0.123046	0.320580	0.443348	0.509175	0.160918
Computer Self-Efficacy	CSE1	0.081468	0.230974	0.886301	0.033532	0.272345	0.268148	0.354914
	CSE2	0.044079	0.237678	0.816209	0.076791	0.260064	0.150673	0.494894
	CSE3	0.025947	0.110081	0.859065	0.015642	0.135133	0.238790	0.392306
Perceived Enjoyment	PE1	0.522873	0.385895	0.012291	0.889702	0.254769	0.379052	0.011363
	PE2	0.491143	0.321050	0.027522	0.897108	0.235417	0.345636	0.035830
	PE3	0.544539	0.294872	0.098668	0.856047	0.256969	0.273096	0.098971
Perceived Ease of Use	PEU1	0.542575	0.586688	0.299414	0.233823	0.974260	0.721889	0.262220
	PEU2	0.583842	0.448994	0.140555	0.397367	0.845558	0.749668	0.032480
	PEU3	0.408736	0.536209	0.207712	0.172353	0.830115	0.551454	0.273368
	PEU4	0.378149	0.462753	0.307134	0.168581	0.875392	0.607062	0.183905
Perceived Usefulness	PU1	0.660877	0.633345	0.241988	0.430695	0.696700	0.908846	0.142758
	PU2	0.548527	0.640864	0.233992	0.328033	0.630728	0.918926	0.071483
	PU3	0.631249	0.581858	0.144502	0.318810	0.667321	0.898084	0.101168
	PU4	0.568004	0.520083	0.317188	0.280788	0.701986	0.860687	0.095794
Subjective Norm	SN1	0.016100	0.047942	0.372317	0.035026	0.126771	0.071839	0.792699
	SN2	0.069134	0.312479	0.508708	0.076200	0.160443	0.064045	0.744956
	SN3	0.129045	0.212870	0.319398	0.065180	0.205653	0.130247	0.887879

Convergent Validity of Measurements

The factor loadings of 25 products were considered suitable because they were greater than 0.70, and their composite reliability was over 0.70, varying from 0.851215 - 0.942675. Cronbach's alpha coefficient values ranged from 0.742610 - 0.918775, indicating that the findings were adequate. In terms of AVE, the numbers ranged from 0.657220 - 0.804441. The results of the Confirmatory Factor Analysis (CFA) are mentioned in Hair et al. (2012), see Table 2.

Table 2 Factors Loadings and Confirmatory Factor Analysis Results
Factors	Items	Loadings	AVE	Composite Reliability	Cronbach's Alpha
Attitude towards Use	ATU1	0.854489	0.717566	0.910384	0.869083
	ATU2	0.868354
	ATU3	0.841266
	ATU4	0.823617
Behavioral Intention to Use	BIU1	0.803486	0.698310	0.902478	0.856559
	BIU2	0.849453
	BIU3	0.845686
	BIU4	0.843148
Computer Self-Efficacy	CSE1	0.886301	0.729907	0.890086	0.817270
	CSE2	0.816209
	CSE3	0.859065
Perceived Enjoyment	PE1	0.889702	0.776396	0.912375	0.856438
	PE2	0.897108
	PE3	0.856047
Perceived Ease of Use	PEU1	0.974260	0.779888	0.933842	0.904616
	PEU2	0.845558
	PEU3	0.830115
	PEU4	0.875392
Perceived Usefulness	PU1	0.908846	0.804441	0.942675	0.918775
	PU2	0.918926
	PU3	0.898084
	PU4	0.860687
Subjective Norm	SN1	0.792699	0.657220	0.851215	0.742610
	SN2	0.744956
	SN3	0.887879

Convergent Validity of Measurements

Differences between collections of definitions and their metrics are referred to as discriminant validity. The discriminant validity of all constructs was verified with values greater than 0.50 and meaningful at p=0.001, as required by Fornell & Larcker (1981). Table 3 shows that the AVE square root shared by objects in a single construct should be smaller than the correlations between items in the two constructs (Hair et al., 2012).

Table 3 Discriminant Validity
Factors	ATU	BIU	CSE	PEU	PE	PU	SN
Attitude towards Use	0.943332
Behavioral Intention to Use	0.606682	0.893733
Computer Self-Efficacy	0.063295	0.232352	0.907733
Perceived Ease of Use	0.550250	0.576726	0.268390	0.899878
Perceived Enjoyment	0.588019	0.381989	0.048975	0.282202	0.908864
Perceived Usefulness	0.673141	0.663488	0.260665	0.751991	0.381118	0.887646
Subjective Norm	0.599258	0.244631	0.477303	0.209057	0.051850	0.115690	0.893332

The Structural Model's Analysis

Smart PLS 2.0 was used to test the analysis theories and investigate construct relationships. Figure 1 shows the hypothesis development, Figure 2 shows the path coefficient findings, and Figure 3 shows the path coefficient (T-Values) findings.

Table 4, the relationship between Computer Self-Efficacy -> Perceived Usefulness (H1) (β = 0.109796, T= 1.176651, p <0.001), thus, hypothesis 1 was accepted. Next hypothesis 1 the relationship between Computer Self-Efficacy -> Perceived Ease of Use (H2) (β = 0.209974, T= 2.390059, p <0.001) thus, hypothesis was accepted. The hypothesis 3 the relationship between Subjective Norm -> Perceived Usefulness (H3) (β = 0.090385, T= 1.173009, p <0.001), thus, hypothesis was accepted. Also, the relationship between Subjective Norm -> Perceived Ease of Use (H4) (β = 0.094992, T= 1.044504, p <0.001), thus, hypothesis was accepted. Next hypothesis 5 the relationship between Perceived Enjoyment -> Perceived Usefulness (H5) (β = 0.186011, T= 2.343403, p <0.001), thus, hypothesis was accepted. Similarly, the relationship between Perceived Enjoyment -> Perceived Ease of Use (H6) (β = 0.266993, T= 2.783814, p <0.001), thus, hypothesis was accepted. The relationship between Perceived Ease of Use -> Perceived Usefulness (H7) (β = 0.688926, T= 10.260074, p <0.001), thus, hypothesis was accepted. And the relationship between Perceived Ease of Use -> Attitude towards Use (H8) (β = 0.101388, T= 0.869676, p <0.001), thus, hypothesis was accepted. Also, the relationship between Perceived Ease of Use -> Behavioral Intention to Use (H9) (β = 0.150581, T= 1.184832, p <0.001), thus, hypothesis was accepted. Additionally, relationship between Perceived Usefulness -> Attitude towards Use (H10) (β = 0.596898, T= 5.293028, p <0.001), thus, hypothesis was accepted. Similarly, the relationship between Perceived Usefulness -> Behavioral Intention to Use (H11) (β = 0.361404, T= 2.283493, p <0.001), thus, hypothesis was accepted. Finally, the relationship between Attitude towards Use -> Behavioral Intention to Use (H12) (β = 0.280549, T= 2.353268, p <0.001), thus, hypothesis was accepted.

Table 4 Hypotheses Testing
Path and Hypotheses	Path coefficient	Standard Error	T-values	Results
Computer Self-Efficacy -> Perceived Usefulness (H1)	0.109796	0.093312	1.176651	Accepted
Computer Self-Efficacy -> Perceived Ease of Use (H2)	0.209974	0.087853	2.390059	Accepted
Subjective Norm -> Perceived Usefulness (H3)	0.090385	0.077054	1.173009	Accepted
Subjective Norm -> Perceived Ease of Use (H4)	0.094992	0.090945	1.044504	Accepted
Perceived Enjoyment -> Perceived Usefulness (H5)	0.186011	0.079376	2.343403	Accepted
Perceived Enjoyment -> Perceived Ease of Use (H6)	0.266993	0.095909	2.783814	Accepted
Perceived Ease of Use -> Perceived Usefulness (H7)	0.688926	0.067146	10.260074	Accepted
Perceived Ease of Use -> Attitude towards Use (H8)	0.101388	0.116581	0.869676	Accepted
Perceived Ease of Use -> Behavioral Intention to Use (H9)	0.150581	0.127091	1.184832	Accepted
Perceived Usefulness -> Attitude towards Use (H10)	0.596898	0.112771	5.293028	Accepted
Perceived Usefulness -> Behavioral Intention to Use (H11)	0.361404	0.158268	2.283493	Accepted
Attitude towards Use -> Behavioral Intention to Use (H12)	0.280549	0.119217	2.353268	Accepted

Discussion

Based on a survey and study of e-learning studies, the researcher created a systematic model. Studies that used TAM to look at the influence of seven variables, including computer self-efficacy, subjective norm, perceived enjoyment, perceived usefulness, perceived ease of use, attitude toward use, and behavioral intention to use an e-learning system for education sustainability. Many scholars have performed studies on the use and implementation of e-learning programs (Almaiah et al., 2016, 2018 & 2019). However, observational studies focusing exclusively on Saudi Arabia were scarce (Almaiah et al., 2017 & 2019a). While the majority of Saudi Arabian universities effectively adopted the e-learning scheme, the percentage of teachers and students who used it was low (Selim, 2007). This inspired the current research, which aimed to fill a gap in the literature on the use of e-learning systems for long-term educational sustainability. This was undertaken in order to figure out what aspects influenced students' decision to use the university's e-learning scheme. The results of this study will be addressed in this section, and professionals, scholars, and educators will gain valuable insight into the reasons that improve the use of e-learning programs in universities for educational sustainability. The results about model characteristics specifically showed that e-learning system authors, programmers, and purchasers use for educational sustainability. Sustainability of online learning in higher education and task-technology-fit (TTF) and compatibility on students' satisfaction and success have an effect on its use in higher education (Almaiah et al., 2015a). As a result, consumer expectations and principles should be considered to ensure that the device addresses student needs. This perceived fit between system features and student needs has the potential to improve e-learning system use and education sustainability. According to Almaiah et al. (2015b & 2019b, all of the hypotheses were endorsed and positively linked to educational sustainability, confirming important links between online learning and student interactivity and utility. Indirectly, considerations such as machine self-efficacy, subjective standard, and presumed satisfaction influence students' behavioral intention to use an e-learning system for education sustainability. In terms of the study's implications, it reinforces the well-known importance of belief structures in e-learning system usage for education sustainability in Saudi Higher education, as measured by perceived usefulness, perceived ease of use, and attitude toward using e-learning systems for education sustainability. The results also revealed the role of faculty in explaining how students should use e-learning systems for education sustainability to learn course material, as attitude toward e-learning systems for education sustainability improves behavioral intention to use one.

Conclusion

This study contributes to the body of knowledge about the use and implementation of e-learning systems. The factors that influence students' actual use of e-learning systems in Saudi Arabia were identified using an adapted TAM model. To begin, the findings demonstrate a connection between the TAM model and the use of an e-learning system for educational sustainability. Second, the findings show that perceived utility and perceived ease of use both add to the decision to use e-learning platforms for education sustainability, according to the TAM model constructs. Finally, this indicates that machine self-efficacy, subjective norm, and perceived enjoyment affect perceived usefulness, perceived ease of use, and attitude toward use, as well as behavioral intention to use an e-learning system for educational sustainability. As major determinants of e-learning environment for educational sustainability, this research used eight novel TAM model characteristics. However, mixed findings in the literature suggest that the association between machine self-efficacy, subjective standard, and perceived pleasure and TAM model variables should be investigated further. In light of the study design's limitations and the quantitative methodology selected, subsequent experiments might use interview methods to address these concerns. Furthermore, prospective scholars should investigate these areas by using this model and cross-validate them across cultures by including cultural aspects.

Acknowledgement

The author extends his appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number IFT20183.

References

1. Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in human behavior, 56, 238-256.
2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179– 211.
3. Alalwan, N., Al-Rahmi, W.M., Alfarraj, O., Alzahrani, A., Yahaya, N., & Al-Rahmi, A.M. (2019). Integrated three theories to develop a model of factors affecting students’ academic performance in higher education. Ieee Access, 7, 98725-98742.
4. Alamri, M.M., Almaiah, M.A., & Al-Rahmi, W.M. (2020). Social media applications affecting Students’ academic performance: A model developed for sustainability in higher education. Sustainability, 12(16), 6471.
5. Alenazy, W.M., Al-Rahmi, W.M., & Khan, M.S. (2019). Validation of TAM model on social media use for collaborative learning to enhance collaborative authoring. Ieee Access, 7, 71550-71562.
6. Al-Gahtani, S.S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50.
7. Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155.
8. Almaiah, M.A., Alamri, M.M., & Al-Rahmi, W.M. (2019). Analysis the effect of different factors on the development of Mobile learning applications at different stages of usage. IEEE Access, 8, 16139-16154.
9. Almaiah, M.A., Jalil, M.A., & Man, M. (2016). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3(4), 453-485.
10. Almarabeh, T. (2014). Students' Perceptions of E-Learning at the University of Jordan. International Journal of Emerging Technologies in Learning, 9(3), 31-35.
11. Al-Mushasha, N.F.A. (2013). Determinants of e-learning acceptance in higher education environment based on extended technology acceptance model. In 2013 Fourth International Conference on e-Learning" Best Practices in Management, Design and Development of e-Courses: Standards of Excellence and Creativity" (pp. 261-266), IEEE.
12. Al-Rahmi, W.M., Aldraiweesh, A., Yahaya, N., & Kamin, Y.B. (2018). Massive open online courses (MOOCS): Systematic literature review in Malaysian higher education. International Journal of Engineering & Technology, 7(4), 2197-2202.
13. Al-Rahmi, W.M., Alias, N., Othman, M.S., Ahmed, I.A., Zeki, A.M., & Saged, A.A. (2017). Social Media Use, Collaborative Learning and Students' academic Performance: A Systematic Literature Review of Theoretical Models. Journal of Theoretical & Applied Information Technology, 95(20).
14. Al-Rahmi, W.M., Othman, M.S., & Yusuf, L.M. (2015). Effect of engagement and collaborative learning on satisfaction through the use of social media on Malaysian higher education. Research Journal of Applied Sciences, Engineering and Technology, 9(12), 1132-1142.
15. Al-rahmi, W.M., Othman, M.S., & Yusuf, L.M. (2015). Using social media for research: The role of interactivity, collaborative learning, and engagement on the performance of students in Malaysian post-secondary institutes. Mediterranean Journal of Social Sciences, 6(5), 536.
16. Al-Rahmi, W.M., Yahaya, N., Alamri, M.M., Aljarboa, N.A., Kamin, Y.B., & Moafa, F.A. (2018). A model of factors affecting cyber bullying behaviors among university students. IEEE Access, 7, 2978-2985.
17. Al-Rahmi, W.M., Yahaya, N., Alamri, M.M., Alyoussef, I.Y., Al-Rahmi, A.M., & Kamin, Y.B. (2019). Integrating innovation diffusion theory with technology acceptance model: Supporting students’ attitude towards using a massive open online courses (MOOCs) systems. Interactive Learning Environments, 1-13.
18. Al-Rahmi, W.M., Yahaya, N., Aldraiweesh, A.A., Alturki, U., Alamri, M.M., Saud, M.S.B., Kamin, Y.B., Aljeraiwi, A.A., & Alhamed, O.A. (2019). Big data adoption and knowledge management sharing: An empirical investigation on their adoption and sustainability as a purpose of education. IEEE Access, 7, 47245-47258.
19. Alshehri, A., Rutter, M.J., & Smith, S. (2019). An implementation of the UTAUT model for understanding students' perceptions of learning management systems: A study within tertiary institutions in Saudi Arabia. International Journal of Distance Education Technologies (IJDET), 17(3), 1-24.
20. Anaraki, F. (2004). Developing an effective and efficient eLearning platform. International Journal of the computer, the internet and management, 12(2), 57-63.
21. Arkorful, V., & Abaidoo, N. (2015). The role of e-learning, advantages and disadvantages of its adoption in higher education. International Journal of Instructional Technology and Distance Learning, 12(1), 29-42.
22. Babu, G.S., & Sridevi, K. (2018). Importance of E-learning in Higher Education: A study. International Journal of Research Culture Society, 2(5), 84–88.
23. Baker, E.W., Al-Gahtani, S.S., & Hubona, G.S. (2010). Cultural impacts on acceptance and adoption of information technology in a developing country. Journal of Global Information Management (JGIM), 18(3), 35-58.
24. Basilaia, G., & Kvavadze, D. (2020). Transition to online education in schools during a SARS-CoV-2 coronavirus (COVID-19) pandemic in Georgia. Pedagogical Research, 5(4), 1-9.
25. Binyamin, S., Rutter, M., & Smith, S. (2017). The students’ acceptance of learning management systems in Saudi Arabia: A case study of King Abdulaziz University. In Proceedings of the 11th Annual International Conference of Technology, Education and Development (INTED2017) (pp. 9324-9333).
26. Binyamin, S., Rutter, M., & Smith, S. (2017). Factors influencing the students' use of learning management systems: A case study of King Abdulaziz University. In International Conference on e-Learning (pp. 289-297). Academic Conferences International Limited.
27. Chow, Y.S., & Teicher, H. (2012). Probability theory: independence, interchangeability, martingales. Springer Science & Business Media, Berlin, Germany, 2012.
28. Cohen, E., & Nycz, M. (2006). Learning objects and e-learning: An informing science perspective. Interdisciplinary Journal of E-Learning and Learning Objects, 2(1), 23-34.
29. Compeau, D.R., & Higgins, C.A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 189-211.
30. Costa, C., Alvelos, H., & Teixeira, L. (2012). The use of Moodle e-learning platform: a study in a Portuguese University. Procedia Technology, 5, 334-343.
31. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
32. Engelbrecht, E. (2005). Adapting to changing expectations: Post-graduate students’ experience of an e-learning tax program. Computers & Education, 45(2), 217-229.
33. Fabunmi, M., Brai-Abu, P., & Adeniji, I.A. (2007). Class factors as determinants of secondary school student’s academic performance in Oyo State, Nigeria. Journal of Social Sciences, 14(3), 243-247.
34. Fischer, H., Heise, L., Heinz, M., Moebius, K., & Koehler, T. (2014). E-Learning trends and hypes in academic teaching. methodology and findings of a trend study, International association for development of the information society.
35. Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
36. Hair, J.F., Sarstedt, M., Ringle, C.M., & Mena, J.A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433.
37. Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343-367.
38. Horton, W. (2006). E-Learning by design. Pfeiffer: San Francisco, CA, USA, 2006; ISBN -13.
39. Hsu, M.K., Wang, S.W., & Chiu, K.K. (2009). Computer attitude, statistics anxiety and self-efficacy on statistical software adoption behavior: An empirical study of online MBA learners. Computers in Human Behavior, 25(2), 412-420.
40. Hung, M.L., & Chou, C. (2015). Students' perceptions of instructors' roles in blended and online learning environments: A comparative study. Computers & Education, 81, 315-325.
41. Koohang, A., & Harman, K. (2005). Open source: A metaphor for e-learning. Informing Science, 8, 75-86.
42. Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374.
43. Popovici, A., & Mironov, C. (2015). Students’ perception on using e-Learning technologies. Procedia-Social and Behavioral Sciences, 180, 1514-1519.
44. Raheem, B.R., & Khan, M.A. (2020). The role of e-Learning in COVID-19 crisis. International Journal of Creative Research Thought (IJCRT), 8(3), 3135-3138.
45. Riaz, A., Riaz, A., & Hussain, M. (2011). Students’ acceptance and commitment to e-learning: Evidence from Pakistan. Journal of Educational and Social Research, 1(5), 21-30.
46. Salloum, S.A., & Shaalan, K. (2018, September). Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. In International Conference on Advanced Intelligent Systems and Informatics (pp. 469-480). Springer, Cham.
47. Sangrà, A., Vlachopoulos, D., Cabrera, N., & Bravo, S. (2011). Towards an inclusive definition of e-learning. Barcelona: E Learn Centre, UOC Open University Catalonia. Barcelona, Spain, 2011.
48. Sarikhani, R., Salari, M., & Mansouri, V. (2016). The impact of e-learning on university students’academic achievement and creativity. Journal of Technical Education and Training, 8(1), 25-33.
49. Selim, H.M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396-413.
50. Silverman, J., & Hoyos, V. (2018). Distance learning, e-learning and blended learning in mathematics education. Springer.
51. Smart, K.L., & Cappel, J.J. (2006). Students’ perceptions of online learning: A comparative study. Journal of Information Technology Education: Research, 5(1), 201-219.
52. Taha, M.H., Abdalla, M.E., Wadi, M., & Khalafalla, H. (2020). Curriculum delivery in Medical Education during an emergency: A guide based on the responses to the COVID-19 pandemic. Med Ed Publish, 9.
53. Tan, P.J.B. (2019). An empirical study of how the learning attitudes of college students toward English e-tutoring websites affect site sustainability. Sustainability, 11(6), 1748.
54. Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153-163.
55. Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: a structural equation modeling approach. Journal of Computing in Higher education, 26(2), 124-142.
56. Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 695-704.
57. Venkatesh, V. (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.
58. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
59. Venkatesh, V., & Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
60. Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478.
61. Zaharah, Z., Kirilova, G.I., & Windarti, A. (2020). Impact of corona virus outbreak towards teaching and learning activities in Indonesia. SALAM: Jurnal Sosial dan Budaya Syar-i, 7(3), 269-282.