International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol. 15, No. 03, 2021 Paper—A Combined Model for Continuous Intention to Use E-Learning System A Combined Model for Continuous Intention to Use E-Learning System https://doi.org/10.3991/ijim.v15i03.18953 Ragad M Tawafak (*), Sohail Iqbal Malik, Roy Mathew, Mohammed Waseem Ashfaque, Jasiya Jabbar, Maryam Nasser AlNuaimi, Abdalla ElDow Al Buraimi University College, Al Buraimi, Oman raghad@buc.edu.om Ghaliya Alfarsi Al Buraimi University College, Oman Universiti Tenaga Nasional, Kajang Selangor , Malaysia Abstract—E-learning systems installed in educational institutions have in- creased the efficiency of educational processes over the years. E-learning, how- ever, has faced many factors affecting the continuous intention of teachers and students to use e-learning, such as student satisfaction, productivity, and aca- demic success. Therefore to improve academic success, there is a need for insti- tutions to enhance their e-learning programs. Thus the primary objective of this thesis is to construct a composite. This research focuses on advancing e-learning to boost students' continuous trying to use e-learning to increase students' under- standing and academic performance. Data were collected using questionnaires returned to determine their e-learning feedback by 295 undergraduates from four universities in Oman, after which (PLS-SEM) used e-learning to assess their on- going trial of using e-learning. The facts demonstrate that the variables are essen- tial for the continuing decision to use e-learning. Keywords—E-learning, PLS-SEM, TAM, ECT 1 Introduction The system of e-learning is for learning that helps, supports learning in the educa- tional sector by using electronic technology [1, 2]. Therefore, it applies to any course offered by an online system, or material, or program. E-learning aims to incorporate new awareness of the technology used in institutions of higher education. The e-learn- ing framework, through web-based technology and e-learning activities, is growing to promote active learning collaboration among students. This practice is used to extract the advantages of enhancing the knowledge delivery measured by the continuing option to use e-learning according to the students' satisfaction, academic success, and effec- tiveness [3]. According to Tawafak [3], it is clear that the technology utilization in iJIM ‒ Vol. 15, No. 03, 2021 113 Paper—A Combined Model for Continuous Intention to Use E-Learning System learning and evaluation allows students to work in limitless time leading to peer assis- tance, instructor feedback, and collaboration that triggers learning and evaluation as a learning outcome for students. Besides, the findings provide teachers with knowledge about their students' learning success and their involvement in the continuous objectives of using e-learning. This use of e-learning apps improves students' confidence and in- creases their understanding and willingness to use technology to facilitate learning and continuous use. Earlier e-learning research performed by Wu [4] argued that the exist- ing e-learning studies are more concerned about the features related to teachers' effi- ciency and their experience as a primary factor without focusing on the continuous in- tention of using the systems. Similarly, studies carried out by Wu, Strang & Tawafak [4, 5, 27] stated that fewer reviews were found to focus on the academic performance of student learning outcomes with student satisfaction, which they used as dependent factors to derive the continuous e-learning system utilization. For the current time, still, there is missing in studies that provide significant results on the effectiveness of e-learning system for both students and teachers at the same time. Besides, the recommendations from Alraimi, Tawafak & Karnouskos [13, 14, 15] to develop a model using the standard theories of model acceptance that evaluate the e- learning system performance. What are the essential elements used and the significant relationships between critical factors? Furthermore, this study highlights the need to enhance an adaptation model by integrating more than two models to improve student satisfaction and performance using e-learning systems. Therefore this study focuses on the ongoing objective of examining positive factors derived from TAM, partial ECT, and the variation of the models proposed by Hone, Huang, Joo & Tawafak [6, 7, 8, 9, 28], using a system of e-learning. On the other hand, this study offers flexibility and independence to teachers and students to choose the most effective e-learning system to promote teaching and to learn through the objec- tives towards Continuous use of e-learning methods. To this end, this study's emphasis and commitment are to focus on the theoretical creation of a combined e-learning model with TAM and partial ECT variables that can be modified to reinforce the continued objective of using the e-learning method in the field of education. This analysis also examines the relationship of nominated models between independent and dependent factors[29]. To note three significant issues, this research subject articulated itself. The first prob- lem is about an ongoing cause, the lack of research Use an e-learning system to use. The second issue is the absence of links between e-learning variables to determine stu- dent satisfaction and the continued objective of increasing academic success by using e-learning systems[8]. The third issue is the need to construct an enhanced paradigm for the continuing alternative of using e-learning systems, articulating the fundamental objectives as follows: i. Identify the variables impacting the ongoing intent to use the method of e-learn- ing. ii. To build a combined model that explores the factors affecting the continuous in- tention of higher education institutions to use the e-learning system. iii. Validating the built model with methods for statistical analysis. 114 http://www.i-jim.org Paper—A Combined Model for Continuous Intention to Use E-Learning System This study is divided into several sections. Section two; pointed to a literature review for all courses used to identify the essential related factors of acceptance models. Be- sides, section three; refers to the definitions of the elements used for this study. Also, Section four; used to highlights the causal relationships between model factors. Section five; provide the framework proposed to develop the combined model. Section six; considered the hypotheses for this research model. Section seven; was designed to ex- plain the findings and path value results. Finally, the study ends with the morals of fundamental research and future recommendation. 2 Literature Review In this section, four different models suggested by Hone, Huang, Tawafak, Joo & Ioannou [6, 7, 8, 9, 10] respectively used to explain the factors that have a direction and in guiding impact on continuous intention to the utilization of a system of e-learning. According to Tawafak [12, 30], the potential of using e-learning technologies continu- ously can enhance collaboration and communication among the students and shared knowledge and ideas to improve their academic performance. However, those studies lacked a theoretical base. There are different studies pointed to use a variety of standard models as TAM, ECT to validate a combined model for e-learning system [8, 20, 21, 26]. On the other hand, still, these studies used limited elements of critical factors. Nev- ertheless, the elements impact and refection provided less essential factors either for teachers or students. The consequence of this acceptance process is establishing an ongoing intention to use an e-learning framework that uses the same acceptance factors for e-learning. Fur- thermore, additional variables, such as the students' happiness and academic perfor- mance[2], the efficacy of the e-learning framework, which involves factors relating to course content[19], is another common factor. The recommendations made by Tawafak & Liu [8, 25, and 31] on how the standard of e-learning can improve as follows: i. Improving the e-learning framework to facilitate the development of the course material of each student, to enhance the continuous intention to access and use e- learning. ii. Using technology-enhanced university learning by rising e-learning adoption to enhance student satisfaction with their academic success is essential for improv- ing continuous e-learning purposes. 2.1 Technology Acceptance Model (TAM) Parameters of smoothness of using technology can indicate social influence pointing to the way of communication and information knowledge progress between students using the TAM model. The previous models on TAM comprise of initial acceptance and satisfaction as outcome factors. Ioannou [10] proposed applying the adopted TAM, which suggested that the relations between system use and benefit outcomes through iJIM ‒ Vol. 15, No. 03, 2021 115 Paper—A Combined Model for Continuous Intention to Use E-Learning System academic performance can achieve in e-learning. In this study [18, 26, 34, 35], the au- thors focus on motivating students towards as an influence of the initially TAM on the constant intention to use without considering any additional regression and feedback of effectiveness of other factors related to the educational process as content satisfaction towards the continuous intention to use the e-learning process. 2.2 Expectation-Confirmation Theory (ECT) The ECT theory was also combined in this study through the factors of usefulness, happiness, and constant use perceived. The peer support factor refers to the type of confirmation that could be predicted from peers to peer discussion on the student's ac- tivities [14, 32] and also look for support by sharing knowledge between students [15, 33]. Teacher support refers to the link and conversation between the teacher and stu- dents to explain the material and discuss assessment feedback [14]. There should be a proper purpose for the use of technology and e-learning. Another study [9, 22, 28, 3, 15] ECT suggested based on these results [6, 9, 22], as well as to strengthen the continuous intent to use the e-learning process [28]. 2.3 Multi-E-learning models Previous studies [7, 8] have shown the model factors for course content to improve an e-learning model [11]. Also, another study by Hone [6], in which the model justified the factors, course content, and effectiveness that significantly influence the continuity of outstanding work. Therefore, their studies targeted the regression of content and use- fulness to teachers and students' benefit more than focusing on continuous intention to use the e-learning process [6, 7, 8, 31]. Each level is essential to react to these instructions. In the first step, e-learning is based on the continuous intent to inspire students as active users using the e-learning platform. The second stage involves teachers' teaching methods in the material offered by the courses and the experience of teachers in the use of integration of teaching tech- nology and assessment towards the continued use of the e-learning process. Also, the level of satisfaction obtained from students with the framework is validated. Also, the productivity and academic performance factors linked to the usability and growth of e- learning for the continuous purpose of using e-learning systems [2] have been estab- lished. The e-learning framework helps teachers increase student comprehension, peer debate, enhance information sharing and the growth of skills [20, 13, 27], and explain how to boost technology outputs to face main challenges. Conversely, other studies improve student learning on internal and external variables such as course materials [13]. Although the study results provided useful information and experience in the field of teaching, the authors of this study did not consider the continued purpose of using the method to be important in their analysis. Their key find- ing on the usefulness of the factor course content is the incorporation of new knowledge to improve educational practice [9] further. The results show a significant outcome that affects the continuous use of the device. In the end, the analysis suggested by TAM 116 http://www.i-jim.org Paper—A Combined Model for Continuous Intention to Use E-Learning System [10] and part of the ECT model has merged with the method's continued use of e-learn- ing. Processes in social community services can directly affect student productivity, trust, and performance [36, 27]. For this reason, by the use of three, this study proposed de- veloping a combined model expected acceptance models benefits. In combination with each essential element, these three models can combine to improve the understanding level of higher education students [37, 18]. Furthermore, the integrated model will eval- uate the social acceptance for this new revolution of the learning process to highlight the essential elements used for gaining both student and teacher [38]. The next section explains the factors used for a combined model and declaration of its importance and impact of each element to enhance the continuous intention to use e-learning. 3 Factors Used in This Study There are 34 academic articles from journals and conference proceedings from the open database related to this field. These articles used to assess and identify the most influential factors of continuous intention used by different e-learning systems to ben- efit from the constant choice to use e-learning system. These different types of models work on the enhancement of the e-learning model [14, 39]. They were using the adopted TAM and partial adoption of ECT to aid in universities' continuous intention. The most frequent factors which Perceived utility (PU), perceived ease of use (PEOU), course content (CC), behavioral intention (BI), student satisfaction (SS), academic perfor- mance (AP), effectiveness (EFF), and continuous intention to use (CI). 3.1 Perceived Usefulness (PU) "People's thoughts on how emerging technology can enhance their learning perfor- mance" reflects the findings of both studies have shown that the PU is a crucial factor illustrating the progress of using the e-learning system for educational purposes. 3.2 Ease of Use Perceived (PEOU) PEOU is described as the rate of students feeling that there is no need for effort to use the application of e-learning models (PEOU) attempts to investigate by considering student expectations. 3.3 Course Content (CC) Course content is defined as the contents where students believe the application of e-learning model content can support their needs [7, 8]. The e-learning system allows online technology course content to be uploaded online and downloaded by students who can also access a password and submit their assignments and project outcomes [7, 8]. Factor individual characteristics refer to factor course content from sources [11], [19], [20, 42] to be utilized properly. Wiki chat acts as an e-learning tool to improve iJIM ‒ Vol. 15, No. 03, 2021 117 Paper—A Combined Model for Continuous Intention to Use E-Learning System student learning outcomes—the paper combined using wiki chat and course content effectiveness with the perception of student learning needs. Findings from the study revealed that Wiki chat's course content factor promotes student peer conversation within student groups in a significant result. The indicator from Alraimi, Tawafak [13, 20] shows that course content is used effectively to improve the CI of e-learning. There- fore, e-learning systems such as Wiki chat can increase the speed of accessing and transferring learning materials and simplify the transfer of knowledge from teacher to students in the continuous intention to use through the whole system activation. 3.4 Behavior Intention (BI) The students' behavioral intent is determined by the perceived usefulness [21, 43]. Venkatesh [22] mentioned that behavioral intention clarified students' intention to ei- ther use continuously or adopt the e-learning system. TAM identified PU and PEOU as factors influencing the e-learning system. Besides that, factors influenced by the BI factor CI of the system, according to other studies' findings. Tawafak [21] suggested that any technology can be effectively deployed and based on behavioral intention val- ues, such as the intention to use, which can be why teachers and students utilize tech- nology to influence e-learning effectiveness [18]. 3.5 Academic Performance (AP) AP is defined as performing student activities to improve their grades using the ap- plication model. Similarly, the academic performance factor refers to the students' per- formance of e-system use and technology facilities [2, 1, 7, 8, 21, 11]. Behavioral in- tention influences academic performance with PU and PEOU towards the system use [10]. The improvement of academic performance is one aspect that can affect continu- ous intent to use the e-learning method. Several methods for online communication provided by education technology: Wiki chat and different Google services [19], which aid in managing online courses and try to evaluate continuous use with limited factors of communication. 3.6 Student Satisfaction (SS) Student satisfaction is defined as the course content's satisfaction [1, 2, and 21]. Moreover, happiness refers to the feedback obtained from knowing the students' satis- faction, self-efficacy, and performance [23, 44]. [17] explored the relationship between students' yielded satisfaction aligned with the continuous intention to use computer de- vices with technologies. The strengthens the process of e-learning and promotes contact between instructors, teachers, students, and the students themselves. Student satisfac- tion often offers community suggestions or personal comments to boost students' satis- faction with a lower rating in the assessment. The student satisfaction factor is an essential factor in the educational process that uses several technologies [21]. Schmid [24] re-analyzed the influence of perceived im- pact on e-learning. Their study's findings revealed that technology improved student 118 http://www.i-jim.org Paper—A Combined Model for Continuous Intention to Use E-Learning System learning's average satisfaction based on a university dataset used to measure self-eval- uation effectiveness through an e-learning system. Student satisfaction has been pro- posed as necessary in scholarly work by Venkatesh, Davis, and Tawafak & Malik [22, 16, 26, and 45] through the influence of PEOU and PU of TAM. 3.7 Effectiveness (EFF) Effectiveness is defined as the university's assessment scalability to measure the practical assessment of the ongoing intention of using the e-learning system for differ- ent students [19, 1, 2, 11]. Researchers [24] mentioned an increase in the effectiveness and student satisfaction when their learning is facilitated with technology. The results from a combined dataset revealed a rise in the self-evaluation, assessing the significant measure of the students' education for the e-learning system [24]. Hence, it is evident that the use of multiple innovative technologies can improve students' performance with the help of applications that developed students' skills and knowledge and further as- sisted teachers in teaching. This study focuses on increasing effectiveness and satisfac- tion while extending their effects to include the influence on the continuous intention of system use [28]. 3.8 Continuous Intention to Use (CI) The continuous purpose is defined as the ongoing intention of using the e-learning system to enhance their learning abilities [2]. Ongoing intention to use means to justify ECT's satisfaction factor and the behavioral purpose of TAM. Liu and Tawafak [25, 26] explored the usage of video blogs and Wiki to optimize the intention to use systems with student satisfaction in universities continuously. Hone [6] further attempted to in- vestigate how video learning and Face-to-face contact will increase university students' learning output and keep the continuous use of learning activity and make its purpose alive and flourishing. The system used by the authors relied on the use of Web 2.0 technologies to help the teaching process[23, 29]. 4 Extracting Causal Relationship between Factors Accordingly, researchers such as Liu, Eldow, Engelbrecht, MacDonald & Tawafak [25, 18, 1, 2, and 30] believed that the effects of e-learning on assessments and teaching with continuous intention to use vary based on different factors. For instance, some research concentrates on student motivation, appraisal, social impact, and engagement in learning through the continuous intention-to-use model [16, 2, 11]. Other studies are more concerned with how academic success, assistance, and community impact can enhance as the critical reasons for continuous use [11]. Simultaneously, some research- ers are more interested in exploring the perception, experience, circumstances, and at- titude of the ongoing purpose of using the e-learning framework for measuring [22, 7, 8, 31]. Table 1 illustrates the causal link between factors relationship based on the schol- ars of each one. iJIM ‒ Vol. 15, No. 03, 2021 119 Paper—A Combined Model for Continuous Intention to Use E-Learning System Table 1. Causal Relationships from Literature Factors Causal Link Original Theory Recent Studies Perceived usefulness PUà BI TAM [16, 13, 17, 5] PUàSS ECT [5, 8, 13, 39] Perceived ease of use PEOUàPU TAM [16, 4, 10, 21] PEOUàBI TAM [16, 18, 21, 33] PEOUàSS ECT [4, 18, 34, 45] Course Content CCàEFF E-Learning [7, 8, 6, 37, 45] Behavioral Intention BIà AP E-learning [2, 10, 9, 32] Effectiveness EFFà CI E-learning [7, 24, 36, 43] Academic Performance APàCI E-Learning [10, 25, 44, 29] Student Satisfaction SSà CI ECT [24, 9, 21, 35] 5 Research Hypotheses The hypotheses research developed actors identified from the literature. TAM and part of The ECT model are used as a useful theoretical model to analyze students' con- tinuous purpose to use systems for e-learning. Previous studies found that the probabil- ity of success has a positive association with the behavioral goal of consistently using e-learning. Fig. 1. Research Model According to Davis [16], through expressly or implicitly using the model system through TAM factors [35, 13, 17, 40], where PEOU correlated with PU in PEOU has a positive effect PUU through the use of e-learning. BI may directly influence these fac- tors and therefore indirectly influence the CI of e-learning systems. Perceived Ease of Use Perceived Usefulness Behavioral IntentionH1 H2 H3 TAM Student Satisfaction H4 H5 H1 2 Academic Performance Support AssessmentH8 Course Content Effectiveness H7 Continuous Intention H1 3 H1 6 H1 4 H1 5 ECT 120 http://www.i-jim.org Paper—A Combined Model for Continuous Intention to Use E-Learning System These three variables, namely PU, PEOU, and BI, are the critical factors for deter- mining the continuation of an e-learning system. However, these factors are still insuf- ficient to demonstrate the importance of the continuous intention to use e-learning un- less they are related to other hybrid factors. The mixed elements collected from another type of technology tested to identify the positive impact of various factors on continu- ous intention enhancement [6, 7]. Therefore the hypotheses established as below: • H1: A significant influence on perceived ease of use and perceived utility. • H2: A massive effect of perceived ease of use and perceived utility and purpose to behave. • H3: A significant influence on perceived ease of use and behavioral intent. • H4: A considerable effect of perceived utility and student satisfaction. • H5: A significant impact on the perceived ease of student satisfaction. • H6: A significant influence from the content of the productivity course. Another aspect describes how the e-learning system's course content would impact the continuous goal of using the e-learning approach to provide electronic educational resources such as PowerPoint slides, files, and videos. Current revolutionary frame- works focused on e-learning principles that employ advanced, closely connected chat, file, and automated exam technologies to meet the learning and teaching needs of a system's increasingly large number of continuous intentions [26, 33]. The authors' eval- uation based on the usefulness of the robust academic success factor was introduced to promote e-learning on an ongoing basis. Thus, this research aims to establish that by using academic performance as a mediator factor, certain variables have affected the endless choice of e-learning. This research seeks to find a significant link between the academic versions that implicitly suggest the influence of continuous purpose and en- hancement. Researchers such as [18, 43] have used TAM's mediator factor, such as the BI factor, as the main character, based on computer-based web-based survey models, as an effi- cient and widely agreed approach to the ongoing goal of using systems for e-learning. Their analysis showed that academic performance was significantly influenced by the perceived usefulness, ease of use, and user satisfaction. The positive impact on constant motivation, however, was not taken into account by the report. The hypotheses are, therefore defined as follows: • H7: A meaningful impact between academic success and behavioral intent. • H8: A significant impact between academic success and continuous purpose. • H9: A significant influence between efficacy and continuous purpose. • H10: A significant influence between the happiness of students and continuous pur- pose. This research presents ten theories that will discuss the continuous purpose of apply- ing e-learning activities. The system of implementing e-learning should use in the investigation. The study used is TAM, and a portion of ECT models used in postulating the hypotheses. iJIM ‒ Vol. 15, No. 03, 2021 121 Paper—A Combined Model for Continuous Intention to Use E-Learning System 6 Findings and Discussion This section highlights the characteristic findings from the combined model that has been tested in PLS-SEM. These results are characterized by 1) communication skills and 2) incorporation of the e-learning platform, which strengthens the ongoing intent to use e-learning systems. The findings indicate robust frameworks for performance, perceived value, ease of use, students' satisfaction, and behavioral intention variables in communication skills. Based on other examples of technology used, the e-learning system contains differ- ent teaching tools and assessment methods. Similar to the findings of previous research- ers [18, 26], many variables are apparent, such as course material, ease of use perceived, and utility perceived. To improve the accuracy of outcomes in the continuous intention to use, these variables should integrate. 6.1 Participant Demographics This section presents Table 2 offers the demographic profiles of the respondents. Table 2. Participants' demographic characteristics Field Description Number Percent Participant Total 295 100% Major IT 147 47.9% Engineering 71 23.1% LAW 77 25.1% Other 12 0.04% Degree Diploma 99 32.2% High diploma 44 14.3% Bachelor 164 53.4% Gender Male 134 43.6% Female 173 56.4% Financial Assistance/Scholarship Government 191 66.1% Private 98 33.9% Computer skill competency Very High 144 47.4% High 132 43.4% Low 28 9.2% How often do you use the application of e-learning Often 131 43% Sometimes 151 49.5% Never used 23 7.5% Age 18-21 131 42.7% 22-26 125 40.7% Above 26 51 16.6% The questionnaire survey measured based on the Likert's 5-point scale was ranged from 1 to 5, where one strongly disagreed, and five strongly agreed. Then, PLS-SEM used the Smart PLS software to evaluate the results of the survey. The names of six independent factors are shown in Figure 1: 1) Perceived utility, 2) Perceived ease of 122 http://www.i-jim.org Paper—A Combined Model for Continuous Intention to Use E-Learning System use, 3) Items 4, 4, and 5 included in the course material. Figure 1 also shows six con- tingent variables, namely 1) effectiveness, 2) behavioral intention, 3) academic success, 4) student satisfaction, and 5) continuous intent to use. Each one has got a value of 3, 3, 3, 3, and 3, respectively. 6.2 Reliability and convergent validity assessment Assessed reliability using Cronbach's alpha value was measured, where the alpha value of Cronbach for all construction products should be greater than 0.70. The authors tested the measurement scales where all item loadings met 0.7 benchmarks. Also, the Composite Reliabilities (CR) should exceed 0.7, and as indicated by Tawafak [30], the same should be with the Extracted Average Variance (AVE). The results from Table 3 showed the artifacts, their loading, the AVE, Alpha, and CR values that in the measure- ment model, they exceeded the proposed threshold values. Table 3: Loading products and reliability should be greater than 0.50 for each build. Table 3. Loading products and reliability Construct Item Loading Alpha CR (AVE) Academic Performance AP1 1.000 1.000 1.000 1.000 AP2 0.580 AP3 0.702 Behavior Intention BI1 0.937 0.814 0.813 0.689 BI2 0.707 BI3 0.673 Effectiveness EF1 0.630 0.754 0.807 0.680 EF2 0.725 EF3 0.913 Perceived Ease of Use PEOU1 0.832 0.709 0.826 0.704 PEOU2 0.846 Student Satisfaction SS1 0.849 0.813 0.826 0.704 SS2 0.828 Perceived Usefulness PU1 0.765 0.943 0.904 0.703 PU2 0.908 PU3 0.823 PU4 0.851 Course Content CC1 1.000 0.884 1.000 1.000 CC2 0.592 CC3 0.627 CC4 0.638 Continuous Intention to Use CI1 0.873 0.883 0.875 0.700 CI2 0.868 CI3 0.765 iJIM ‒ Vol. 15, No. 03, 2021 123 Paper—A Combined Model for Continuous Intention to Use E-Learning System 6.3 Path value results The following Table explains the accepted parts and the not supported parts of the hypothesis's relationships. (Note: * P<0.001). Table 4. Path coefficient and β results Relationship β P-value Remarks H1-Perceived Ease Of UseàPerceived Usefulness 0.393 0.000 Accepted H2-Perceived UsefulnessàBehavioral Intention 0.293 0.001 Accepted H3-Perceived Ease Of UseàBehavioral Intention 0.302 0.013* Accepted H4-Perceived UsefulnessàStudent Satisfaction 0.326 0.000 Accepted H5-Perceived Ease Of UseàStudent Satisfaction 0.150 0.057* Accepted H6-Course ContentàEffectiveness 0.293 0.000 Accepted H7-Behavioural IntentionàAcademic Performance 0.360 0.000 Accepted H8-Academic PerformanceàContinuous Intention to Use 0.152 0.021* Accepted H9-EffectivenessàContinuous Intention to Use 0.420 0.000 Accepted H10-Student satisfactionàContinuous Intention to Use 0.192 0.000 Accepted H1 leads to a significant correlation between perceived ease of use and perceived usefulness, where the direction between perceived ease of use and perceived usefulness is defined (β = 0.393, p < 0.000), indicating that the perceived ease of use enhances the perceived usefulness of the acceptance model. It is reinforced by the hypothesis-rele- vant product (β = 0.293, p < 0.001), which means that the perceived utility positively relates to the behavioral intention of using the acceptance model. H2 leads to an influ- ential association between perceived utility and behavioral intent. A significant link between perceived ease of use and behavioral intent is referred to as 3. H3. If an upbeat relationship shows (β = 0.302, p < 0.013), it indicates that the perceived ease of use is positively related to the behavioral intention to use the acceptance model. H4 leads to a substantial positive link between perceived utility and student satisfaction. H5 leads to a strong association between perceived ease of use and students' satis- faction, with values (β = 0.15, p < 0.057) implying that perceived ease of use signifi- cantly affects the acceptance model's student satisfaction. H6 leads to a significant pos- itive association between course content and outcomes, where the positive correlation (β = 0.293, p<0.000) indicates that the course content significantly affects the effective- ness of the acceptance model. H7 leads to a significant correlation between behavioral intention and academic performance (β = 0.360, p<0.000), indicating a positive link between behavioral intention and academic performance, where academic performance is strongly related to behavioral intention. The result of H8 is a significant correlation between academic performance and continuous value intention (β = 0.152, p<0.021), indicating a positive association between academic performance and continuous value intention. 6.4 Predictive relevance Q2and F2 The blindfolding technique conducted using Smart PLS to generate Q2 and F2 values for all independent factors. Thus, all Q2 matters should be above zero, suggesting a 124 http://www.i-jim.org Paper—A Combined Model for Continuous Intention to Use E-Learning System predictive relevance as recommended by Hair et al. (2013). Therefore, the current path model is inferred to have predictive relevance for the independent factors, as presented in Table 4.10. Likewise, F2 values of independent factors above 0.018 indicate a small effect, 0.15 indicates medium effects, and 0.815 indicates there is a large effect on the independent factors and the dependent factors (Hair et al., 2013). The results of the F2 and Q2 values for the model presented in Table 4.10. Table 5. Path coefficient, f2, and Q2 results Relationship Path coefficient F2 Q2 Remarks Perceived Ease Of Use→Perceived Usefulness 0.393 0.150 0.582 Supported Perceived Usefulness→Behavioral Intention 0.293 0.245 0.636 Supported Perceived Ease Of Use→Behavioral Intention 0.302 0.200 0.544 Supported Perceived Usefulness→Student Satisfaction 0.326 0.400 0.145 Supported Perceived Ease Of Use→Student Satisfaction 0.150 0.240 0.372 Supported Course Content→Effectiveness 0.293 0.190 0.815 Supported Behavioral Intention→Academic Performance 0.360 0.236 0.545 Supported Academic Performance→Continuous Intention to Use 0.152 0.190 0.160 Supported Effectiveness Continuous→Intention to Use 0.420 0.323 0.303 Supported Student Satisfaction→Continuous Intention to Use 0.192 0.170 0.118 Supported 7 Conclusion This research provides a theoretical contribution by extending TAM and part of the ECT model constructs and associated items to improve the learning process through the combined model for technology validation. The model also identifies how the course contents act as independent factors to influence the system CI of use. In summary, the findings from this study provide the following significant contributions: This research developed the combined model based on the combined models' factors that directly or indirectly influence CI in a higher educational institution. This contri- bution generated sub-contributions as follows: This research identified the relationships between selected elements. In its essential variables and part of ECT model variables, it also used the TAM model to combine the combined model. This study developed a combined model of continuous intention to use the e-learning system by linking the e-learning system (PU, PEOU, CC) to inter- mediate factors consisting of e-learning systems (BI, AP, EFF, SS). The proposed four new elements based on e-learning CI analogous to both TAM and part of the ECT model used to evaluate the use of the combined model as an e-learning CI model. Lastly, this research-validated the results generated from the modified TAM, part of ECT models by a proposed combined model for continuous intention to use a system. The authors extended the application learning system to a more functional and interac- tive approach to producing efficient learning results for students. iJIM ‒ Vol. 15, No. 03, 2021 125 Paper—A Combined Model for Continuous Intention to Use E-Learning System 8 References [1] Engelbrecht, E. (2003). A look at e-learning models: investigating their value for de-velop- ing an e-learning strategy. Progressio, 25(2), 38-47. [2] MacDonald, C. J., Stodel, E. J., Farres, L. G., Breithaupt, K., & Gabriel, M. A. (2001). The demand-driven learning model: A framework for web-based learning. The Internet and Higher Education, 4(1), 9-30. https://doi.org/10.1016/s1096-7516(01)00045-8 [3] Tawafak R. M., Mohammed M. N, and Arshah R. B. A, "Review on the Effect of Stu-dent Learning Outcome and Teaching Technology in Omani's Higher Education Institu-tion's Ac- ademic Accreditation Process," in International Conference on Software and Computer Ap- plications, 2018. https://doi.org/10.1145/3185089.3185108 [4] Wu, B., & Zhang, C. (2014). An empirical study on continuance intentions towards E-Learn- ing 2.0 systems. Behaviour & Information Technology, 33(10), 1027-1038. https://doi.org/ 10.1080/0144929x.2014.934291 [5] Strang, K. D. (2013). University accreditation and benchmarking: Pedagogy that in-creases student achievement. [6] Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157-168. https://doi.org/10.1016/j.compedu. 2016.03.016 [7] Huang, L., Zhang, J., & Liu, Y. (2017). Antecedents of student MOOC revisit inten-tion: Moderation effect of course difficulty. International Journal of Information Man-agement, 37(2), 84-91. https://doi.org/10.1016/j.ijinfomgt.2016.12.002 [8] Tawafak, Ragad M., Awanis BT Romli, and Ruzaini Bin Abdullah Arshah. "Continued In- tention to use UCOM: Four factors for integrating with a technology acceptance model to moderate the Satisfaction of Learning." IEEE Access 6 (2018): 66481-66498. https://doi. org/10.1109/access.2018.2877760 [9] Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among stu-dents' self-determination, technology acceptance, satisfaction, and continuance intention to use K- MOOCs. Computers & Education. https://doi.org/10.1016/j.compedu.2018.01.003 [10] Ioannou, A., Brown, S., & Artino, A. R. (2015). Wikis and forums for collaborative prob- lem-based activity: A systematic comparison of learners' interactions. The Internet and Higher Education, 24, 35-45. https://doi.org/10.1016/j.iheduc.2014.09.001 [11] Goodhue, D.L., Thompson, R.L., 1995. Task-technology fit and individual perfor-mance. MIS Q. 19 (2), 213–236. https://doi.org/10.2307/249689 [12] Tawafak, Ragad M., et al. "Technology enhancement learning reflection on improving stu- dents' satisfaction in Omani universities." Advanced Science Letters 24.10 (2018): 7751- 7757. https://doi.org/10.1166/asl.2018.13012 [13] Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continu-ance: The role of openness and reputation. Computers & Education, 80, 28-38. https://doi.org/10. 1016/j.compedu.2014.08.006 [14] Tawafak, R. M., Romli, A., Malik, S. I., Shakir, M., & Alfarsi, G. M. (2019). A sys-tematic review of personalized learning: Comparison between E-learning and learning by course- work program in Oman. International Journal of Emerging Technologies in Learn-ing (iJET), 14(09), 93-104. https://doi.org/10.3991/ijet.v14i09.10421 [15] Karnouskos, S. (2017). Massive open online courses (MOOCs) as an enabler for competent employees and innovation in the industry. Computers in Industry, 91, 1-10. https://doi.org/ 10.1016/j.compind.2017.05.001 [16] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. https://doi.org/10.2307/249008 126 http://www.i-jim.org Paper—A Combined Model for Continuous Intention to Use E-Learning System [17] Lee, C., Yeung, A. S., & Ip, T. (2017). University English language learners' readiness to use computer technology for self-directed learning. System, 67, 99-110. https://doi.org/ 10.1016/j.system.2017.05.001 [18] Eldow, A., Shakir, M., Talab, M. A., Muttar, A. K., & TAWAFAK, R. M. (2006). Literature Review of Authentication Layer for Public Cloud Computing: A Meta-Analysis. [19] Junglas, I., Abraham, C., & Watson, R. T. (2008). Task-technology fit for mobile lo-catable information systems. Decision Support Systems, 45(4), 1046-1057. https://doi.org/10.1016/ j.dss.2008.02.007 [20] Tawafak, Ragad M., et al. "Framework design of university communication model (UCOM) to enhance continuous intentions in teaching and e-learning process." Education and Infor- mation Technologies (2019): 1-27. https://doi.org/10.1007/s10639-019-09984-2 [21] Tawafak, Ragad M., Awanis BT Romli, and Maryam Alsinani. "E-learning system of UCOM for improving student assessment feedback in Oman higher education." Educa-tion and Information Technologies 24.2 (2019): 1311-1335. https://doi.org/10.1007/s10639-018- 9833-0 [22] Venkatesh, V., and Davis, F.D. (2000). A theoretical extension of the technology ac- ceptance model: Four longitudinal field studies. Management of Science. 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926 [23] De Wever, B., Hämäläinen, R., Voet, M., & Gielen, M. (2015). A wiki task for first-year university students: The effect of scripting students' collaboration. The Internet and Higher Education, 25, 37–44. https://doi.org/10.1016/j.iheduc.2014.12.002 [24] Schmid, R. F., Bernard, R. M., Borokhovski, E., Tamim, R. M., Abrami, P. C., Surkes, M. A., & Woods, J. (2014). The effects of technology use in postsecondary education: A meta- analysis of classroom applications. Computers & Education, 72, 271-291. https://doi.org/10. 1016/j.compedu.2013.11.002 [25] Liu, L. (2005). Evaluating online learning applications: Development of quality-related models. International Journal of Information and Communication Technology Education (IJICTE), 1(3), 21-35. https://doi.org/10.4018/jicte.2005070102 [26] Tawafak, Ragad M., Sohail Iqbal Malik, and Ghaliya Alfarsi. "Development of Framework from Adapted TAM with MOOC Platform for Continuity Intention." Devel-opment 29.1 (2020): 1681-1691. [27] Tawafak, R. M., Romli, A. M., & Alsinani, M. J. (2019). Student assessment feedback ef- fectiveness model for enhancing teaching method and developing academic perfor-mance. International Journal of Information and Communication Technology Education (IJICTE), 15(3), 75-88. https://doi.org/10.4018/ijicte.2019070106 [28] Tawafak, R. M., Romli, A. B., bin Abdullah Arshah, R., & Almaroof, R. A. S. (2018). As- sessing the impact of technology learning and assessment method on academic per-for- mance. EURASIA Journal of Mathematics, Science and Technology Education, 14(6), 2241-2254. https://doi.org/10.29333/ejmste/87117 [29] Tawafak, F. R. M. The Comprehension Theories Of Continuous Intention To Use Tel With E-Learning Models In Educations. Table of Content Id Title Author Page No., 41. [30] Tawafak, R. M., Romli, A., & Arshah, R. A. (2019, August). E-learning prospect on im- proving academic performance in Omani Universities. In IOP Conference Series: Mate-rials Science and Engineering (Vol. 551, No. 1, p. 012033). IOP Publishing. https://doi.org/10. 1088/1757-899x/551/1/012033 [31] Tawafak, R. M., Malik, S. I., & Alfarsi, G. (2020). Development of Framework from Adapted TAM with MOOC Platform for Continuity Intention. Development, 29(1), 1681- 1691. iJIM ‒ Vol. 15, No. 03, 2021 127 Paper—A Combined Model for Continuous Intention to Use E-Learning System [32] Papadakis, S. (2018). Evaluating pre-service teachers' acceptance of mobile devices with regards to their age and gender: a case study in Greece. International Journal of Mobile Learning and Organisation, 12(4), 336-352. https://doi.org/10.1504/ijmlo.2018.10013372 [33] Tawafak, R. M., Romli, A. B., & Arshah, R. B. A. (2019, February). E-learning Model for Students' Satisfaction in Higher Education Universities. In 2019 International Confer-ence on Fourth Industrial Revolution (ICFIR) (pp. 1-6). IEEE. https://doi.org/10.1109/icfir. 2019.8894778 [34] Papadakis, S., Kalogiannakis, M., Orfanakis, V., & Zaranis, N. (2017). The Appropri- ateness of Scratch and App Inventor as Educational Environments for Teaching Introduc- tory Programming in Primary and Secondary Education. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 12(4), 58-77. https://doi.org/10.4018/ ijwltt.2017100106 [35] Vidakis, N.; Barianos, A.; Trampas, A.; Papadakis, Stamatios; Kalogiannakis, M. and Vass- ilakis, K. (2019). Generating Education in-Game Data: The Case of an Ancient Thea-tre Serious Game.In Proceedings of the 11th International Conference on Computer Sup-ported Education - Volume 1: CSEDU, ISBN 978-989-758-367-4, pages 36-43. https://doi.org/ 10.5220/0007810800360043 [36] Papadakis, S.; Vaiopoulou, J.; Kalogiannakis, M.; Stamovlasis, D. Developing and Ex-plor- ing an Evaluation Tool for Educational Apps (ETEA.) Targeting Kindergarten Chil-dren. Sustainability, 2020, 12, 4201. https://doi.org/10.3390/su12104201 [37] Papadakis, S., Kalogiannakis, M., Sifaki, E., & Vidakis, N. (2017). Access Moodle Using Smart Mobile Phones. A Case Study in a Greek University. In Proceedings of the 6th Inter- national Conference and Second International Conference (ArtsIT 2017, DLI 2017), ‘Inter- activity, Game Creation, Design, Learning, and Innovation’. Heraklion, Crete, Greece, Oc- tober 30–31, 2017. https://doi.org/10.1007/978-3-319-76908-0 [38] ALFarsi, G., Jabbar, J., Tawafak, R. M., Iqbal, S., Alsidiri, A., Alsinani, M., & bte Sulaiman, H. (2020). Mobile Application System Supported BUC Students Services and Learning. https://doi.org/10.3991/ijim.v14i09.12053 [39] Tawafak, R. M., Alfarsi, G., AlNuaimi, M. N., Eldow, A., Malik, S. I., & Shakir, M. (2020, April). Model of Faculty Experience in E-Learning Student Satisfaction. In 2020 Interna- tional Conference on Computer Science and Software Engineering (CSASE) (pp. 83-87). IEEE. https://doi.org/10.1109/csase48920.2020.9142071 [40] Tawafak, R. M., AlSideir, A., Alfarsi, G., Al-Nuaimi, M. N., Malik, S. I., & Jabbar, J. (2019). E-learning Vs. Traditional Learning for Learners Satisfaction. E-learning, 29(3), 388-397. [41] Jabbar, J., Malik, S. I., AlFarsi, G., & Tawafak, R. M. The Impact of WhatsApp on Employ- ees in Higher Education. In Recent Advances in Intelligent Systems and Smart Applications (pp. 639-651). Springer, Cham. https://doi.org/10.1007/978-3-030-47411-9_34 [42] Alfarsi, G., Sulaiman, H., Tawafak, R. M., Malik, S., Jabbar, J., & Alsidiri, A. (2019). A Study of Learning Management System with E-Learning. [43] Tawafak, R., Romli, A., Malik, S., & Shakir, M. (2020). IT Governance Impact on Ac- ademic Performance Development. International Journal of Emerging Technologies in Learning (iJET), 15(18), 73-85. https://doi.org/10.3991/ijet.v15i18.15367 [44] Syaifudin, Y. W., Yunhasnawa, Y., Pramitarini, Y., Setiawan, A., Rohadi, E., & Sapu-tra, P. Y. (2020). A Proposed Framework of Campus-Oriented Online Text Messaging System. International Journal of Interactive Mobile Technologies, 14(16). https://doi.org/10.3991/ ijim.v14i16.11454 128 http://www.i-jim.org Paper—A Combined Model for Continuous Intention to Use E-Learning System [45] Malik, S., Al-Emran, M., Mathew, R., Tawafak, R., & AlFarsi, G. (2020). Comparison of E-Learning, M-Learning and Game-based Learning in Programming Education–A Gen- dered Analysis. International Journal of Emerging Technologies in Learning (iJET), 15(15), 133-146. https://doi.org/10.3991/ijet.v15i15.14503 [46] Tatnall, A. (2020). Editorial for EAIT issue 2, 2020. Education and Information Technolo- gies, 25(2), 647-657. https://doi.org/10.1007/s10639-020-10135-1 [47] Lezzar, F., Benmerzoug, D., & Kitouni, I. (2020). IoT for Monitoring and Control of Water Quality Parameters. International Journal of Interactive Mobile Technologies, 14(16). https://doi.org/10.3991/ijim.v14i16.15783 [48] Utomo, B., Wibowo, A. T., Ridwan, M., Izzuddin, M. A., Gumelar, A. B., & Arifin, S. (2020). Enhanced of Attendance Records Technology used Geospatial Retrieval based on Crossing Number. International Journal of Interactive Mobile Technologies, 14(16). https://doi.org/10.3991/ijim.v14i16.13911 9 Authors Ragad M Tawafak has Ph.D. Degree in computer science, 2020 Malaysia. She is an expert in E-Learning, Mobile Application, Knowledge Management, etc. Ghaliya Alfarsi: has MSc. Degree in computer science, 2015, Oman. She is an ex- pert in E-Learning, Mobile Application, Knowledge Management, etc. Jasiya Jabbar is an expert in E-Learning, Mobile Application, Knowledge Manage- ment, etc. Sohail Iqbal Malik has Ph.D. Degree in computer science, 2016 Australia. Also, he is an expert in E-Learning, Mobile Application, Knowledge Management, etc. Roy Mathew is a Ph.D. in Computer science, 2016, India. He is an expert in E- Learning, Mobile Application, Knowledge Management, etc. Mohammed Waseem Ashfque has is post-graduation in Computer Science from Dr. Babasaheb Ambedkar Marathwada University, India. His research domain is Arti- ficial intelligence, Machine learning, and Deep Learning. He has sound experience in teaching at various colleges and Universities. Maryam Nasser AL-Nuaimi has completed her Ph.D. degree in 2019 from Sul- tanQaboos University. Her research interests e-learning in higher education. Abdalla Eldow has Ph.D. Degree in computer science, 2013, Sudan. Also, he is an expert in E-Learning, Mobile Application, Knowledge Management, etc. Article submitted 2020-09-30. Resubmitted 2020-12-06. Final acceptance 2020-12-06. Final version pub- lished as submitted by the authors. iJIM ‒ Vol. 15, No. 03, 2021 129