E-Learning Acceptance: The Role of Task–Technology Fit as Sustainability in Higher Education

The aim of this study was to fill the gap in the literature on e-learning acceptance and its role in the sustainability of learning and the role of task-technology fit (TTF), which influences student satisfaction and academic performance. While researchers have examined e-learning acceptance in a variety of contexts, the role of TTF as a mediating variable in measuring education sustainability has not been explored using the technology acceptance model (TAM). As a result, the goal of this study was to develop a new paradigm by combining two theories: TFF and the TAM. In total, 432 students and researchers from public universities participated in this study. We surveyed students using the structural equation modelling (SEM) approach to learn about their expectations with regard to e-learning adoption. According to the findings, perceived ease of use has a positive impact on perceived enjoyment and usefulness, which in turn has a positive impact on task–technology fit and e-learning use in higher education, resulting in a positive impact on student satisfaction and academic performance as well as sustainability. Finally, the role of task-technology fit and e-learning usage in education sustainability had a positive effect on student satisfaction and learning performance. As a result, the use of e-learning in learning processes should be encouraged as a long-term strategy in higher education institutions.


Introduction
e-learning offers many advantages over conventional learning methods, including greater access to learning materials, faster communication, and academic collaboration [1]. The constant advances in technology have made it impossible to develop a unique concept of e-learning. Previous research has attempted to define e-learning in various ways. Several studies [2,3] have defined e-learning as the use of technology in the learning process, while others [4,5] have defined it as an information system that can assimilate a variety of instructional materials through email, discussion, tasks, quizzes, and live chat sessions. The e-learning paradigm is a development of the 1980s distance learning style of education [6]. In the ongoing global lockdown due to the coronavirus disease of 2019 (COVID- 19) pandemic, e-learning has proved to be the only choice for ongoing learning [7]. Every university in the world has now invested extensively in e-learning, and many traditional classroom-based classes have changed to e-learning [8,9]. Advancements in digital and communication technology (ICT) have resulted in major improvements in all related areas. To ensure its long-term viability, ICT has embraced emerging paradigms such as cloud computing [10,11], Massive Open Online Courses (MOOCs) [12], the internet of things [13], big data [14,15], e-learning [1], social networking [16,17], and blockchain [18,19]. New platforms, products, systems, and facilities have emerged as a result of these ICT-driven developments [20]. This surge in ICT-driven inventions has also benefited education [10]. ICT has ushered in a slew of emerging learning paradigms, including e-learning and mobile learning [21][22][23]. Formal, non-formal, and informal learning are all supported by

Theoretical Model and Hypotheses Development
Davis [39] developed the TAM to explore the relationship between the current application of modern technologies and the intention to use it. The TAM assumes that three main constructs define the individual/organizational knowledge system: perceived usefulness (PU), perceived ease of use (PEOU), and perceived enjoyment (PE). In addition, Goodhue and Thompson [40] suggested the task-technology fit model to emphasize the importance of a good fit between technology and the actual task when it comes to achieving individual performance as a result of technology. Several studies [40,41] have used both contingency and task-technology match theory in the adoption of information systems, implying that the fit between a task and technology is important for information system performance. McGill and Klobas [42] defined task-technology fit as "the degree to which a technology assists a person in executing his or her portfolio of tasks". Thus, in the current research, seven influences on TTF and TAM acceptance of e-learning system usage as sustainability were analyzed as follows: perceived usefulness (PU), perceived ease of use (PEU), perceived enjoyment (PE), social influence (SI), e-learning use as sustainability (MUS), task-technology fit (TTF), student satisfaction (SS), and student academic performance (SAP), see Figure 1.

Perceived Usefulness (PU)
The TAM model is used to examine e-learning systems, and perceived usefulness and ease of use are called independent constructs [30]. Two factors are identified to measure perceived usefulness: perceived ease of use, perceived enjoyment, and helpfulness in completing the task easily. The perceived usefulness and utilization of an e-learning system were shown to be positively correlated with TTF and actual use of online learning in several studies [43,44].

Perceived Ease of Use (PEU)
Perceived ease of use is a concept that refers to a learner's perception that a system is simple or easy to use [39,45]. This concept is used in this analysis to refer to the students' views on how to use this system to improve their learning and performance. Other researchers pointed out that this concept is described in terms of the effort put forth in using a particular system [45][46][47].

Perceived Enjoyment (PE)
Perceived enjoyment applies to how learners view various activities or programs as fun to them, regardless of any potential implications [48]. Therefore, in the current research, perceived enjoyment is described as learners' enjoyment as a result of using an elearning system in a way that enriches their learning experiences.

Social Influence (SI)
The degree to which an individual believes important others think he or she can use technology is referred to as social influence [49]. In their analysis comparing universal technological adoption, Im et al. [50] found that social influence played a significant role. According to Yulius et al. [51], an experience cannot improve or degrade social influence relations on e-learning use.

E-Learning Use as Sustainability (EUS)
As a concept, e-learning encompasses a wide variety of applications, learning techniques, and processes [52]. In this approach to using e-learning, Algahtani [53] and Zeitoun [54] demonstrated that the distribution of course materials and descriptions is shared between conventional and e-learning methods in the classroom environment. The use of information and communication technology to allow access to online learning/teaching services is referred to as e-learning. Abbad et al. [55] and Al-rahmi et al. [1] described e-learning as "any learning that is allowed electronically" in its broadest sense.

Perceived Usefulness (PU)
The TAM model is used to examine e-learning systems, and perceived usefulness and ease of use are called independent constructs [30]. Two factors are identified to measure perceived usefulness: perceived ease of use, perceived enjoyment, and helpfulness in completing the task easily. The perceived usefulness and utilization of an e-learning system were shown to be positively correlated with TTF and actual use of online learning in several studies [43,44].

Perceived Ease of Use (PEU)
Perceived ease of use is a concept that refers to a learner's perception that a system is simple or easy to use [39,45]. This concept is used in this analysis to refer to the students' views on how to use this system to improve their learning and performance. Other researchers pointed out that this concept is described in terms of the effort put forth in using a particular system [45][46][47].

Perceived Enjoyment (PE)
Perceived enjoyment applies to how learners view various activities or programs as fun to them, regardless of any potential implications [48]. Therefore, in the current research, perceived enjoyment is described as learners' enjoyment as a result of using an e-learning system in a way that enriches their learning experiences.

Social Influence (SI)
The degree to which an individual believes important others think he or she can use technology is referred to as social influence [49]. In their analysis comparing universal technological adoption, Im et al. [50] found that social influence played a significant role. According to Yulius et al. [51], an experience cannot improve or degrade social influence relations on e-learning use.

E-Learning Use as Sustainability (EUS)
As a concept, e-learning encompasses a wide variety of applications, learning techniques, and processes [52]. In this approach to using e-learning, Algahtani [53] and Zeitoun [54] demonstrated that the distribution of course materials and descriptions is shared between conventional and e-learning methods in the classroom environment. The use of information and communication technology to allow access to online learning/teaching services is referred to as e-learning. Abbad et al. [55] and Al-rahmi et al. [1] described e-learning as "any learning that is allowed electronically" in its broadest sense. They might, however, limit this concept to learning that is facilitated by the use of digital technologies. This technology-assisted e-learning paradigm has created a widespread environment for learning at any time and from any place, promoting the cause of intergenerational schooling for long-term sustainability [56]. In the ongoing global lockdown due to the COVID-19 pandemic, e-learning has proved to be the only choice for replacing traditional face-to-face learning methods. Academic universities all over the world have made substantial improvements in e-learning, and the bulk of conventional lecture classes have been migrated to e-learning. The success of e-learning programs must be assured in order for it to become sustainable for learning [9].

Task-Technology Fit (TTF)
The TTF model is based on the need for a correlation between technology characteristics and task specifications [57]. According to the model, focusing solely on students' expectations of technology is insufficient to estimate its adoption. Students will accept technology if they believe it is effective enough to perform their everyday activities [40]. This theory's paradigm sheds light on the functional aspects of technology use. Since focusing solely on customer expectations of technology is insufficient, the task-technology fit (TTF) model predicts when users will use a technology based on a correlation between performance expectations and technology features [40].

Student Satisfaction (SS)
Wang [58] analyzed adult respondents in a study to learn more about student satisfaction with e-learning, and his results revealed key TAM variables. According to [59], the relationship between the above TAM variables and students' satisfaction with their e-learning use was investigated. As a result, it is useful to look at their effect on TAM variables like student satisfaction and e-learning use and see how they affect academic performance.

Student Academic Performance (SAP)
The ultimate goal of e-learning implementation is to improve learning pedagogy and academic performance [60,61]. Other studies have been conducted to assess the progress of e-learning adoption and its effect on student academic performance [9,62,63]. The current research will aim to investigate the essence and extent of the relationship between e-learning use as sustainability and student academic performance.

Research Methodology
The study's key goal was to provide a clear and understandable conceptual model for assessing e-learning acceptance as sustainability and its determinants. The proposed model and questionnaire were conceptualized, validated, and examined using a multistage testing design. To begin, the research was exploratory, with participants testing 25 previously used items to measure student academic performance and extracting eight constructs to determine the acceptability of e-learning as sustainability. Second, the research was empirical, using structural equation modelling (SEM) suggested by [64] for empirically evaluating the proposed conceptual model of e-learning usage as sustainability. In this research data, appropriate statistical tests to validate findings and show significance in the results were used, see Appendix A. The sample size for the review was calculated using Roscoe's rule of thumb in the analysis of [65]. As a result, multiplying 10 by 25 items generated a sample size of 250 participants for this research. The SPSS kit program was then used to import 432 participants. This study's sample includes postgraduate and undergraduate university students who are active users of an e-learning system for sustainability.

Measurement Methodology
The scales used to calculate the constructs were adapted and adopted from commonly used validated scales from previous experiments to develop the instruments. Basic demographic management (gender, age, educational level, and specialization) and questionnaire items measuring perceived usefulness, perceived ease of use, perceived enjoyment, and using an e-learning system for sustainability were adapted from the sample questionnaire [39]. Student satisfaction was adapted from [66], task-technology fit was adapted from [67], student academic performance was adapted from [68], and social influence was adapted from [69], see Appendix A.

Analysis and Findings
The Cronbach's alpha reliability parameter was 0.915, meaning that the factors that influenced e-learning system use as well as educational sustainability, which in turn affected student academic performance, were reliable. Discriminant validity was assessed using three criteria: variable indices must be less than 0.70, each construct's average variance extracted (AVE) 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, according to [64]. Aside from the above variables, build factor analysis results with factor loadings of 0.70 or higher (Cronbach's alpha 0.70 and composite reliability 0.70 are acceptable) [64], see Table 1.

Measurement of Construct Validity
The degree to which individual objects meet the purpose for which they were developed [70] is referred to as construct validity. This was determined by a thorough examination of previously tested items in the literature. Table 2 shows the items and their loadings that must be loaded into the model they were developed to evaluate [71].

Measurement Validity That Is Convergent
Since the factor loadings of 25 items were greater than 0.70 and their composite reliability was greater than 0.70, ranging from 0.869 to 0.943, they were found acceptable. Cronbach's alpha coefficient values ranged from 0.774 to 0.909, suggesting satisfactory performance. The numbers for AVE ranged from 0.707 to 0.847. Hair et al. [64] mentions the findings of the confirmatory factor analysis (CFA), see Table 3.

Measurement Validity That Is Convergent
Discriminant validity refers to the differences between sets of definitions and their measures. Both constructs' discriminant validity was confirmed with values greater than 0.50 and significant at p = 0.001, as expected by the study [64]. The AVE square root shared by objects in a single construct should be smaller than the correlations between items in the two constructs [64], as seen in Table 4.
measures. Both constructs' discriminant validity was confirmed with values greater than 0.50 and significant at p = 0.001, as expected by the study [64]. The AVE square root shared by objects in a single construct should be smaller than the correlations between items in the two constructs [64], as seen in Table 4.

The Analysis of the Structural Model
To validate the research hypotheses and examine construct relationships, Smart PLS 2.0 was used. The hypothesis is seen in Figure 1, the path coefficient findings in Figure 2, and the path (T-Values) findings in Figure 3.    Table 5 summarizes the results of the study, including all partnerships. For the relationship between perceived usefulness -> e-learning use as sustainability (H1) (β = 0.502, SE = 0.100, t = 1.659, p < 0.001), the hypothesis was accepted. For the relationship between perceived usefulness -> tasktechnology fit (H2) (β = 0.180, SE = 0.098, t = 1.900, p < 0.001), the hypothesis was accepted. For the relationship between perceived ease of use -> e-

Discussion and Implications
Theoretically, this study improved understanding of how to use e-learning as a source of educational sustainability by developing a research model focused on the role of tasktechnology fit (TTF) as a source of sustainability in higher education. In the study model, determinant TTF and TAM variables include perceived usefulness, perceived ease of use, perceived enjoyment, social influence, e-learning use as sustainability, task-technology fit, student satisfaction, and student academic performance. As a result, the research model identifies TTF and TAM variables as having the largest impact on student satisfaction and academic performance by using e-learning as a sustainability for education strategy. Therefore, the study's findings strongly support the perceived usefulness variable, which confirms hypotheses one and two, indicating that perceived usefulness has a positive impact on e-learning usage as sustainability in education and task-technology fit. To put it another way, when an e-learning system is beneficial and acceptable (Fit), the higher perceived usefulness contributes to increased use of the e-learning system as sustainability in education, and hence technology is fit. The significance of perceived usefulness in the field of e-learning has been examined by a number of scholars. Therefore, the results of this study confirm previous relationships between factors [72][73][74][75]. In addition, the study's findings strongly support the perceived ease of use variable, which confirms hypotheses three and four, indicating that perceived ease of use has a positive impact on e-learning usage as sustainability in education and task-technology fit. To put it another way, when an e-learning system is easy to use and acceptable (Fit), the higher perceived ease of use contributes to increased use of the e-learning system as sustainability in education, and hence technology is fit. The significance of perceived ease of use in the field of e-learning has been examined by a number of researchers. Thus, the results of this study confirm previous relationships between factors [55,[76][77][78]. Similarly, the study's findings strongly support the perceived enjoyment variable, which confirms hypotheses five and six, indicating that perceived enjoyment has a positive impact on e-learning usage as sustainability in education and task-technology fit. To put it another way, when an e-learning system is enjoyable and acceptable (Fit), the higher perceived enjoyment contributes to increased use of the e-learning system as sustainability in education, and hence technology is fit. The significance of perceived enjoyment in the field of e-learning has been examined by a number of researchers. Thus, the results of this research confirm previous findings [79][80][81][82]. Additionally, the findings in this research strongly support the social influence variable, which confirms hypotheses seven and eight, indicating that social influence has a positive impact on e-learning usage as sustainability in education and task-technology fit. To put it another way, when an e-learning system affects social influence and is acceptable (Fit), the higher social influence contributes to increased use of the e-learning system as sustainability in education, and hence technology is fit. The significance of social influence in the field of e-learning has been examined by previous researchers. Therefore, the results of this research confirm previous relationships between factors [83][84][85][86]. Further, this research shows the findings strongly support the task-technology fit variable, which confirms hypotheses nine, ten, and eleven, indicating that task-technology fit has a positive impact on e-learning usage as sustainability in education, student satisfaction, and student academic performance. To put it another way, when an e-learning system is affected by task-technology fit and is acceptable, the higher task-technology fit contributes to increased use of the e-learning system as sustainability in education, student satisfaction, and student academic performance. The significance of social influence in the field of e-learning has been examined by previous researchers. Therefore, the results of this research confirm previous relationships between factors [87,88]. Moreover, the findings in this research strongly support the e-learning system use as sustainability variable, which confirms hypotheses twelve and thirteen, indicating that e-learning system use as sustainability has a positive impact on student satisfaction and student academic performance. To put it another way, when an e-learning system is affected by e-learning system use as sustainability and is acceptable, the higher e-learning system use contributes to increased use of the e-learning system as sustainability in education, student satisfaction, and student academic performance. The significance of social influence in the field of e-learning has been examined by previous researchers. The results of this research therefore confirm previous relationships between factors [9,89,90]. Finally, the study's findings strongly support the student satisfaction variable, which confirms hypothesis fourteen, indicating that student satisfaction has a positive impact on e-learning usage as sustainability in education and student academic performance. To put it another way, when an e-learning system is beneficial and acceptable (enjoyable, easy to use, and useful), the higher student satisfaction contributes to increased use of the e-learning system as sustainability in education, and hence affects student academic performance. The significance of student satisfaction in the field of e-learning has been examined by a number of scholars. Therefore, the results of this study confirm previous relationships between factors [33,[91][92][93][94]. Based on the model and the results, this analysis has a number of implications. The first implication has to do with the value of agreed constructs. The positive relationship between perceived usefulness, perceived ease of use, perceived enjoyment, and social influence is particularly important in the role of technology fit of e-learning use as a source of learning sustainability. Second, faculty can demonstrate how to use technology by providing students with teaching resources that can help them learn how to use it, keeping in mind that e-learning should be seen as both easy to use and useful. Third, students should be educated on the many advantages of using technology, as well as provided with course material or other learning objectives related to long-term learning sustainability, which in turn increases student satisfaction and student academic performance. While this research shows that statistical support is available, there are several limitations to this research. Since the respondents in this sample are all from the same institution, subsequent research would need more respondents from a variety of majors. Since there was no qualitative evidence in the sample, it was based on students' expectations, which could differ from teachers' perceptions. It is advised that subsequent experiments be duplicated in other nations.

Conclusion and Future Works
According to the results, perceived usefulness, perceived ease of use, perceived enjoyment, and social influence are all significant factors affecting the role of technology fit in e-learning use as a way of learning sustainability. Moreover, students' satisfaction has been shown to have an effect on their academic performance. The study's findings may encourage developers of e-learning systems that are used for sustainability learning to understand these factors that influence user satisfaction and academic performance. Furthermore, this research can lead in the right direction for cultivating student satisfaction in order to enable them to use e-learning as a sustainability learning strategy. TTF and the TAM were validated in the current research in an educational context, giving further insight into students' future perceptions of the use of e-learning systems as a source of learning sustainability. The hypotheses of the proposed paradigm of e-learning usage for sustainability in higher education were adopted in this study. The study found that combining TTF and the TAM will positively influence the study's outcome, and that the role of task-technology fit (TTF) has a positive impact on e-learning acceptance for long-term sustainability in higher education. More research into the relationships between the complexity of e-learning systems and the links between e-learning systems and other education systems such as massive open online courses (MOOCs) and learning management systems (LMS) is necessary in higher education. Follow-up research is also needed to investigate the roles of observability and trialability in e-learning, especially in terms of adaptation and sustainability in higher education.

Conflicts of Interest:
The authors declare no conflict of interest.

PU1
Using the e-learning system improves my course performance.

PU2
Using the e-learning system improves my productivity in courses.

PU3
I find the e-learning system useful for my studies.

PEU1
I find the e-learning system easy to use.

PEU2
My interaction with the e-learning system is clear and understandable.

PEU3
It would be easy for me to find the required information for using e-learning.

PE1
I find the e-learning system process enjoyable.

PE2
The actual process of using the e-learning system is pleasant.

PE3
I have fun using the e-learning system.

SI1
My instructors think that I should participate in e-learning system activities.

SI2
My peers think that I should participate in e-learning system activities.

SI3
The management of my university thinks that I should use e-learning system activities.

EUS1
I use the e-learning system frequently.

EUS2
I tend to use the e-learning system frequently.

EUS3
I spend a lot of time exploring within the e-learning system.

TTF1
I think that using e-learning is well suited for the way to learn.

TTF2
E-learning is a good tool to provide the way I like to study tasks.

TTF3
Using e-learning fits well for the way I like to study tasks.

SS1
The e-learning system is effective for gathering knowledge.

SS2
The e-learning system is efficient for the construction of knowledge.

SS3
The e-learning system is efficient for the exchange of knowledge.

SS4
I am satisfied with using the e-learning system as a learning tool.

SAP1
I feel the e-learning system helps me improve my creativity.

SAP2
I feel the e-learning system helps me improve my knowledge and information.

SAP3
I feel the e-learning system helps me improve my experiences and performance.