1 Introduction

Over the years Technology Enhancement Learning (TEL) services have improved the performance of the academic process of educational institutions, which are installed in both national and international educational systems (Tawafak et al. 2018a, b, c, d, e). In academic teaching and learning development process, online-learning has become one of the most common methods used in the educational learning process (Al-Qirim et al. 2018). However, e-learning process is faced with several issues that affect faculty and student satisfaction on learning continuity (Tawafak et al. 2018a). Thus, there is a need for institutions to evaluate their educational learning process towards improving the academic performance of their students through development the continuous intention to use the application in e-learning (Wang and Hannafin 2005).

At the moment, universities are faced with the challenge of how to add value to student learning and at the same time achieve a top-score in the world ranking community (Wilby et al. 2017). Findings from the historical development of educational institutions through different phases show that these changes are closely linked to differences in each phase, which starts with simple training in traditional face to face learning to virtual learning. Nonetheless, it is necessary for any educational system to reflect the educational needs of technology learning (Wang and Hannafin 2005). Thus, TEL is a new important area for study in high educational institutions. They are growing in popularity as a practice that includes the integration of e-learning to mediate, support active learning and authentic collaboration among students via web-based technologies. Findings from prior studies (Benson 2013; Salajan and Mount 2016) advocated for the use of technologies to support communication between teachers and learners to improve learning. The authors believed that use of technologies for teaching and learning offers flexibility to the learner, increase reading and writing of students, improves the confidence of students concerning how they communicate with their lecturers (Benson 2013; Wang and Hannafin 2005; Salajan and Mount 2016).

These variables help to investigate the differences between the individual’s implementation of e-learning. Accordingly, this research discusses the following points;

  1. i.

    Explores on the influence of educational variables between students, lecturer and course material towards understanding if lecturers and students utilize tools such as YouTube, multimedia and wikis to measure the academic performance, learning enhancement, and student satisfaction.

  2. ii.

    Develop the relationship between academic performance and student satisfaction within the domain of e-learning suggested using e-learning model to examine the significant issues related to student achievements.

However, the authors (Clark 1999; Miri & Ariella 2016) did not address the variables that influence a particular domain. Besides, the studies do not consider the relationship among e-learning model as well as improvement of academic performance and student satisfaction. Furthermore, prior studies that adopted (Huang et al. 2017; Hone and El Said 2016) examined Massive Open Online Courses (MOOC) in their models in an attempt to examine the validity of the main variables with enhancing of continuous intention to use e-learning applications (Albelbisi 2019). Conversely, this research integrates the teaching and learning requirement with the technology acceptance model (TAM) variables that used to develop a model validity and continuity (Davis 1989; Islam 2016). Huang, Zhang & Liu, (2017) and Hone & El said, (2016) their findings suggest that several studies aimed to investigate the learning process involved for course content pedagogy concerning lecturers’ knowledge with technology integration. Moreover, focused on the impact of students, lecturers, and technology on communication towards improving learning procedure involved to complete a course using the MOOC model (Bookstaver et al. 2011). For instance, student’s initial adoption of the e-learning can be assessed based on peers’ suggestions and lecturers support, which may influence the behavior of the student to continuously use e-learning to improve their learning experiences (Lee and Choi 2013). Figure 1 shows the steps of e-learning model techniques.

Fig. 1
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Proposed E-learning model techniques steps

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This paper divided into multi-section: first was the introduction of e-learning applications and its effect on teaching and learning process. Second, shows the research questions and objectives based on the paper target. Third, shows the problem statement that faced the enhancement of UCOM framework. Foruth, the literature review with many parts of existing models and frameworks related to e-learning. Fifth, the methodology where the survey conducted and the type of pilot study. in addition, data collection and model use. Sixth, the discussion of descriptive analysis, reliability, results, and PLS assessment. Seventh, the results, and validity. Besides, the PLS assess and results. Eights, the conclusion from each objective.

2 Research questions and objectives

Based on the previous discussion mentioned in introduction, this paper tries to arise the following research questions:

  1. 1)

    What are the independent variables that influence the teaching and learning process by merging the e-learning model with TAM characteristics?

  2. 2)

    What is the adapted model of e-learning (UCOM) to be implemented for continuous intention to use applications for teaching and learning process?

  3. 3)

    How to assess the accuracy of the adapted model UCOM being implemented for improving the E-Learning continuous intention?

Then the paper suggested the following objectives:

  1. 1)

    To identify the main factors or variables that influence the teaching and learning process using TAM factors.

  2. 2)

    To develop a university communication model (UCOM) for determining the model factor sequence that has continuous intention to use e-learning for teaching and learning process.

  3. 3)

    To validate the developed model based on survey data analyzed using PLS-SEM for determining the accuracy of the teaching and learning process.

3 Problem statement

Information Technology (IT) is significantly important in supporting the daily operation of organizations. Similarly, it is important to review the accreditation of higher educational institutions not just for assigned guiding principle providers and authorization agencies, but also for the College employees and faculty members (Abdullah et al. 2012). Thus, it is required an approach such as technology-enhanced learning (TEL) to help in the accreditation of students, lecturers, and faculties in universities. This TEL activates the technology features influence impact on student satisfaction, course content, support assessment, and academic performance enhancement Huang, Zhang & Liu, (2017) and Hone & El said, (2016). Accreditation standards is working in corporation with teaching and learning process for enhancing student learning outcomes that effect positively with technology development. The enhancment of e-learning application use connected with the influence of student grades improvement (Abdullah et al. 2012; Graffigna et al. 2014). This improvement relies to easy approve with accreditation based on the assessment development and continuous intention to use by feedback forms and satisfactions improvement (Tawafak et al. 2018c).

Researchers such as Cavanagh et al. (2014) mentioned that the capability of institutional systems such as e-learning could be employed to monitor the whole teaching and learning the quality process by linking results with technology educational learning to ensure a complementary integration between academic performance and student satisfaction. These will entail deploying new technologies for online examinations and use of IT technology applications for electronic assessment methods for managing course materials software for classes instead of face to face conversations (Kleebbua and Siriparp 2016).

Furthermore, prior models are based on the perceived ease of use and perceived usefulness as main effective variables to improve the intention to use of e-learning (Salajan and Mount 2012; Barak and Levenberg 2016a, b). But, with changes in the educational domain, there are inadequate methods that can be employed to enhance the benefits of using e-Learning model to help faculties in the assessment of all individual and group student works within a particular cluster. Besides, there are fewer studies that explore how to improve and determine the important variables that significantly influence on the continue of use and acceptance of e-learning concerning satisfaction outcomes.

This study aims to address the following main problem, to develop a model by using the UCOM university communication model as a kind of technology learning adapted tool with Moodle as a common model of open access learning system. The accurate results generated used to compact the integrating theoretical framework to provide an acceptable institutional accreditation that is related to technological improvement and use. This will enable the re-organization of the variables and classify the result through a partial-least-structure (PLS) program to decide whether accept or reject the accuracy of the developed algorithm as recommended by prior studies (Ralph et al. 2015; Janićijević 2015). Table 1 shows the main drawback of existing models and frameworks with continuous intention to use e-learning applications.

Table 1 Existing methods drawbacks
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4 Literature review

There are 11 studies used in this research to explain the type of TEL used or manual coursework use to enhance the continuous use with improvements in teaching and learning process of higher education students in universities. These studies explored the research objective of developing the student learning outcomes and the advantages of getting accreditation approval of universities to enhance the teaching and learning process (Serhani et al. 2019).. Besides, there are some studies shows the benefits of using e-learning application to achieve the needed enhancement of teaching and learning in universities. Table 2 shows comparisons between existing models on their research objectives, the model used, applied active mechanism and the limitation of each study (Tawafak et al. 2019).

Table 2 Comparisons between existing models
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The collaborative work of teaching and learning process with e-learning applications is another challenge for enhancing the continuous intention to use e-learning applications. The teaching and learning process needs to identify the major features used for agreement and the approval of accreditation standards (Abdullah et al. 2012; Graffigna et al. 2014). Therefore, Table 3, shows the major factors of the accreditation process that approved by Oman Academic Authority Accreditation standards, and its impact on e-learning application outcomes and use AlYahya and Abo El-Nasr (2012). These major features include self-evaluation (Schmid et al. 2014), student learning outcomes (Strang 2013), university outcomes (Aldiab et al. (2017), teacher experience (Chmiel et al. 2017), and the assessment method factor (Adwan 2016). Each one has multi features that defined to integrate the collaborative work of teaching and learning process (Ralph et al. 2015; Tawafak et al. 2018ad, e).

Table 3 Major factors of accreditation and outcomes of E-learnings
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Table 3, shows the derived model variables of independent and dependent variables suggested to be used in the developed model. Moreover, Table 4, shows the attributes of each variable recommended with positive effects and the references connected to each variable that shows the strength of these variables in enhancing continuous intention to use e-learning for teaching and learning process.

Table 4 Moderator independent and dependent derived variables
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5 Methodology

A mixed-method consisting of a cooperative case study and a simultaneous frame strategy for data analysis was used in this paper since numerical and qualitative data were collected. Thus, University Communication Model (UCOM) was developed to achieve the aims of this paper.

The methodology of this study is based on three sub-phases which includes the analysis, development, and evaluation (ADV). In the analysis phase, different articles were analyzed as a literature review. Thus, research journal and conference proceedings related to e-learning assessment methods were analysed to identify the gap and limitations, in prior MOOC and MOODLE models. Besides, the technologies used to test the validity by using the TAM model. Whereas, in the second phase the UCOM model was proposed based on the usage of TAM with the using of e-learning techniques that were deployed by adding functions for selecting of measurements to support lecturers teaching needs and students’ interest. The third phase involves the validation of the data using the Smart Partial Least Square-Structural Equation Model (PLS-SEM) program to analyze and measure the role of technologies in the improvement of teaching and learning process of continuous intention to use e-learning model (Ringle et al. 2005; Henseler et al. 2014).

Based on the description of technological, governmental, and environmental characteristics, the ease of use may or may not be the most important part of the model implementation, although, it could also be one of the factors that influence the perceived usefulness. The issues of supervising and control within specific groups such as IT is required to be addressed by the auditors. Thus, the IT auditors repeatedly depend on a well-structured IT governance framework (specifically, COBIT) to improve the control of the development process.

Therefore, this study describes how the proposed e-learning framework can be deployed to promote the use of communication tools in educational methods, organization, and business requirements. The survey sent to 392 students and only 307 samples are received with answers. Then 295 only have full responding of this survey. The web-based applications such as shared online documents (Liu 2016), Wikis and discussion forums (Ioannou et al. 2015); Bookstaver et al. 2011), and use of mobile applications such as WhatsApp, Instagram, or any other social media (AlYahya and Abo El-Nasr 2012; Mohammadi et al. 2012), appear to provide easy linking and sharing that can be used by inter-organizational or in educational teams.

5.1 Pilot study

A pilot test was conducted before the final version of the questionnaire was distributed to the respondents. Sekaran and Bougie (2016) have suggested that prior to collecting data; applicable statistics from the original study is to be calculated to ascertain reliability. The pilot questionnaires were sent to the participants via email. Out of all questionnaires distributed, 44 questionnaires were responded to be usable responses representing a response rate of 80%. The internal consistency of the items was measured by using Cronbach ‘s alpha analysis. Since the Cronbach ‘s alpha fell within the acceptable range (0.83 to 0.88) which it’s above (0.7) the reliability of the scale was confirmed.

The participant that was selected for data collection received a preliminary declaration stating that the analysis is voluntary and that their anonymity will be guaranteed if they chose to complete the study. To ensure no difficulty was experienced in the survey, a pilot test was carried out. The feedback created from the pilot test was integrated into the survey before it was launched for the main survey. Likewise, a panel of experts consisting of English department members checked the grammar errors, while qualified and expert members of the IT department checked the understanding and meaning of the terms used in the survey.

The data was collected through surveys based on a population that comprises of participants from different universities. Then, SPSS was used to analyze the results from different universities. After which Smart PLS program was used to verify the accuracy and validity of factors (Henseler et al. 2014). The data were collected in two approaches first by selecting appropriate respondents (those that are literate, motivated, and amenable). The literacy level was not a problem since the prospective respondents were registered in graduate education. Next, the amenability is reliant on personality, therefore, the survey was designed to be accomplished in a timeframe of approximately 10 min. The data were confined through both the hard copy distribution-based data survey with an online survey link to be filled by participants. The technique is utilized with BUC sections.

5.2 Ethical data collection

An intensive data collection process was employed during the first academic semester of 2018–19 and it comprises of 1 month to complete. Then many procedures of testing were followed collection data to sign the main study findings. In the first step of collecting data, the questionnaire was sent to some selected students to test the questions understandable level and the clear idea from each question before distributes between samples. To promote and ensure anonymity, the starting statement and survey connection were emailed to the participants from each university or college as their given names and emails were not requested by the field researcher. The survey results were forwarded directly to the researcher with no personal attributes except the name of the institution included in the survey questions (as an optional answer).

The respondents were asked to reveal their evaluation point averages, institution attended, and demographic notes in the order of gender, major, degree, age, scholar, knowledge of computer use, and how often is UCOM used. Answering these questions was voluntary to the respondents of the survey. In the second step, a letter that explains, in brief, the details of the study and a copy of designed questionnaire were sent to the management office of each selected university to facilitate obtaining data with permission to conduct this survey at their university. In the final step, after getting approval, the questionnaire link was uploaded online to respondents by email to be distributed between students and faculties asking them to fill out the questionnaire. In addition, more hard copies are distributed between common students in the university out of class timing to obtain more responses.

5.3 UCOM model design

In this sub-section, we propose new complementary factors for our model of Learning Management System (LMS) continuance that links the Technology Acceptance Model (TAM) in the theoretical background with our adopted important measurement. The factors identified from the literature based on knowledge development and the effects of assessment method acceptance are used in the educational process. The relationships between these constructs are explained by the model design in Fig. 2. Finally, it is noticed that previous studies provide knowledge that helps with continued intention to use the learning system. However, this learning is based on factors such as student satisfaction, behavior intention, support assessment and effectiveness to confirm the continued intention. However, fewer studies address details of determining learning assessment and academic performance.

Fig. 2
figure 2

UCOM model factors test (Tawafak et al. 2018d, 2018e)

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This study moves in the path of drawing the characteristics of UCOM as same as MOOC technology to solve the complexity of course assessment depend on course content, teacher subject knowledge level, and interactivity. In addition, academic performance results of grade scores and student satisfaction feedback with student effectiveness and support assessment were considered. The model is different from the traditional teaching model that is fixed on time and real class, in which students and teachers can directly connect online or outside of course time (Lee and Lehto 2013; Huang et al. 2017).

This model derived from TAM model variables presented by Davis (1986) and the other features add from of course content, teacher subject knowledge and interactivity derived from Huang et al. (2017) and Eom et al. (2006), while effectiveness and continuance intention to use derived from Hone and El Said (2016), and student satisfaction with continuance intention derived from Joo et al. (2018). This model produces support assessment as a new variable added to increase model performance. In addition, the model variables structure built in more types of connections for better significant values using PLS-SEM program.

This model aurges the following hypothesis:

  • H1: There is a significant positive relationship between perceived ease of use and perceived usefulness.

  • H2: There is a significant positive relationship between perceived usefulness and behavioral intention.

  • H3: There is a significant relationship between perceived ease of use and behavioral intention.

  • H4: There is a significant positive relationship between perceived usefulness and Student Satisfaction.

  • H5: There is a significant relationship between perceived ease of use and Student Satisfaction.

  • H6: There is a significant relationship between technology integration and behavior intention.

  • H7: There is a significant relationship between course content and effectiveness.

  • H8: There is a significant relationship between course content and support assessment.

  • H9: There is a significant relationship between teacher subject knowledge and effectiveness.

  • H10: There is a significant relationship between interactivity and support assessment.

  • H11: There is a significant relationship between interactivity and effectiveness.

  • H12: There is a significant relationship between behaviour intention and academic performance.

  • H13: There is a significant relationship between academic performance and continued intention to use.

  • H14: There is a significant relationship between effectiveness and continued intention to use.

  • H15: There is a significant relationship between support assessment and continued intention to use.

  • H16: There is a significant relationship between student satisfaction and continued intention to use.

In the context of UCOM, the model attempts to test the dependent variables of UCOM that is applied to test the relationship between student perception (perceived ease of use (PEOU)) and perceived usefulness (PU) of behavioral intention (BI) as recommended by Al-Maroof and Al-Emran (2018). These relationships influence the factors of interactivity, teacher subject knowledge (TSK) and course content (CC) on the effectiveness (Huang et al. 2017). Both relationships demonstrate an extra effect on support assessment, academic performance and student satisfaction on continued intention to use. In addition, technology integration (TI) with PEOU and PU affect behavior intention and student satisfaction which is derived from the main factors of TAM combined the additional factors from other adapted models from prior studies (Lee, Hsieh, & Chen 2013; Hone and El Said 2016; Joo et al. 2018).

The aim of the use of the technology acceptance model (TAM) presented by Davis (1986) was to explain user acceptance behavior for computer technology. Davis employed constructs related to the perception of usefulness and ease of use. In this study, the author increases the constructs to six measures, as shown in Fig. 2 and the variables used in Table 5. The TAM is adapted to measure the level of support assessment and evaluate the academic performance improvement using the UCOM model. Therefore, TAM is used as the background for the effectiveness of the adapted technologies’ enhancement of learning that uses many factors to influence their decision (Al-Maroof and Al-Emran 2018).

Table 5 Variables items and citation
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5.4 Data analysis techniques

Finally, the study employed structured equation modeling using PLS software that is based on a set of data collection used to evaluate all the questions with different variables. However, before embarking on the data analysis, missing data, outlier, normality and multicollinearity issues were addressed, to cleaned the data before further data analysis was employed. The data analysis procedure starts in the classes where UCOM was used during the course, and it shows the importance of UCOM in-class learning and in improving self-motivation in relation to the level of satisfaction and agreement of assessment support and discussion announced through class meeting and UCOM chat course.

Additionally, students utilized the e-learning model as a supplementary tool for the education process to improve student learning outcomes and increase the confidence of students. Furthermore, students were informed that some of the uploaded material in the model will be included in the final exam to evaluate the involvement of students with this model. Then, the integrative mixture between materials was employed between different universities that depend on the specific course that matched between two program studies. Even when students present their assignments, the connection between different groups to discuss and moderate the answers also involves faculties to moderate the assessment process. Ultimately, 392 questionnaires were distributed, and only 307 replies were collected, with 12 records having missing answers.

The analyzed result helped to assess the interest of students to add more updated communication tools that encourage teamwork among students. This, in turn, can motivate lagging students to learn and add learning skills when they know the weakness of their evaluation by the faculty and moderator through the sequence of assessments. This process can help in controlling can monitoring students’ outputs in finalizing the impact related to AP and SLO in a short amount of time and efficient values. This is supposed to reduce the wasted time and update the applications, which are easy to access, use, and develop by the users of this technique.

6 Discussion

6.1 Descriptive analysis

Descriptive analysis of the data is carried out to analyze the demographics of the respondents and the frequency, mean, and standard deviations of the variables. The descriptive analysis was performed to examine the influence of independent variables on the dependent values in the proposed model as seen in Fig. 2.

6.2 Skill reliability analysis

The measures employed in this study were supported by earlier research and were modified for this current study. Similarly, the instrument employed in this study has been applied in a similar context based on an analogous population. Also, the scales used to conduct the reliability analysis is computed based on the Cronbach‘s alpha value from the questionnaire items in assessing the reliability of the constructs which reflect the consistency of the respondent’s replies in terms of the items Cronbach‘s alpha measure which is above the required benchmark suggesting high-reliability measurements.

6.3 Information results and impact

Kitchenham and Pfieeger (2002) stated that it is required to carry out pre-testing of a survey instrument to ensure that the consistency and strength of the instrument are valid. Thus, the data was collected based on the 5-point Likert scale, as suggested by Kitchenham and Pfleeger (2002) to reduce the time of answering the survey questions. The employed scale showed how to balance validity and reliability. This scale is similar to the approach utilized by where the author utilized online survey such as (Google application for design and evaluation surveys). This ensures that respondents can be navigated to the survey via a specified hyperlink (Fricker and Matthias 2002).

6.4 Using PLS to assess the measurement and structural models

A two-step approach was adopted to assess the research model based on the assessment of the measurement model and assessment of the structural model. The guidelines used to assess both measurement and structural models of this study are deliberated in the next subsections. The measurement model determines the link between the independent variables, and dependent variable and assesses the variables indicators values based on the confirmatory factor analysis (CFA) technique. Next, the measurement model’s validity can be evaluated by testing the convergent validity and discriminant validity.

The convergent validity assesses to what extent the constructs measures are different from the other constructs in the model. The value of the convergent validity measure is based on a merged or percentage of variance. Several techniques are employed to measure the relative quantum of convergent validity among item measures. Accordingly, Hair et al. (2013) suggested the use of factor loadings, composite reliability, and Average Variance Extracted (AVE) in measuring the convergent validity, where Factor loadings ≥0.5, and preferably ≥0.70, show high convergent validity. Whereas, composite reliability estimates ≥0.70 show enough convergence or internal consistency. The AVE exhibits the indicators total variance accounted for by the latent construct and the value for the AVEs should be ≥0.5. Thus, when the values are higher than the minimum recommended score for factor loading, composite reliability, and AVE it signifies that the instrument items are valid and reliable.

7 Results

7.1 Discriminant validity

A discriminant validity measure is another test carried out the measure the extent to which a construct is truly different from other constructs. A high discriminating validity shows that a concept is specific and highlights some effects overlooked by other measures. To assess discriminating validity, latent constructs correlations matrices were applied where the square roots of the AVEs along the diagonals are indicated. Correlational statistics between constructs are shown in the lower-left off-diagonal elements in the matrix. Thus, discriminant validity is realized when the diagonal elements (square roots of AVEs) exceed the off-diagonal elements (correlations between constructs) in the same row and column as suggested by Fornell and Larcker (1981).

7.2 Assessment of structural model

The structural model’s characteristic is measured by studying R2 determination coefficients, regression estimates, and statistical significance. The R2 value assesses the amount of prognostic power and shows the extent of divergence, justified by its antecedent variables in the model. The model’s R2 values should be high enough to reach a minimum level of explanatory power. Accordingly, considered R2 values of 0.67 as significant, 0.33 as reasonable, and 0.19 as, poor. Another measure carried out in the assessment of the structural model is the path coefficient value which measures how strong the link between the independent variable and dependent variable. To assess if the path coefficients are significant the value should be higher than 0.100 within the model and be substantive at the 0.05 level of significance at least. Table 6. Shows the VIF values that number range between (above of 1.3 and less than 3.9). Table 6, shows the inner cross between factors are matching with the limitations and all positive reflection between column crossing and factors itself. In addition, Table 7, shows the results of the value for each relationship created between factors and hypothesis. Theses values explained in original sample use, sample Mean (M), sample Standard Deviation (STDEV), T-test Statistics (STDEV) and P values.

Table 6 Inner VIF values
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Table 7 Indirect effect: Mean, SD, T Value and P Value
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Table 7, shows, P values were weak in the following relationships, Technology-Integration - > Behavior-intention - > Academic Performance give P valuea (0.387). Perceived Ease of use - > Behavior-intention - > Academic Performance - > Continue-Intention to Use have P value (0.118). Technology Integration - > Behavior-intention - > Academic Performance - > Continue Intention to Use, p value (0.475). Teacher Subject Knowledge - > Effectiveness - > Continue Intention to Use, p value is (0.227). Course Content - > Support Assessment - > Continue Intention to Use, p value = (0.136). Therefore, the model shows low performance with these paths.

Table 8, shows in the first part the initial Eigen values with percent of variance and cumulative for each component, while the second part shows the Extractionsums of squared loadings with percent of variance and cumulative percent for all components.

Table 8 Common method bias test
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Table 9, explains the comparisons between the current values achieved from this study and the best results achieved from the previous works. The compare used to check the positive results in testing Alpha, CR, and AVE.

Table 9 Validity and powerful of current model vs. Existing models with alpha, CR, AVE
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Table 10 depicts the summarized description of the research objectives, research questions and related hypotheses based on the final effect and decision.

Table 10 Summarized research objectives, research questions, and hypotheses
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8 Conclusion

The three objectives are accomplished successfully in this thesis. For objective 1, there are many papers used to analyst the selected variables that have a high effect on the continuity intention of E-Learning. Then a lot of finalized papers was used to derive the clear need to enhance the teaching and e-learning framework of the currently available models and applications seen in section 3, literature review.

In objective 2, this dissertation derived a proposed adapted model that considers the TAM model as a basic and standard model. Many independent variables added to the original model to increase the validity of model continue intention to use and to enhance the assessment method of the relations between variables proposed in section 4, sub-section 4.3, with Fig. 2.

The third objective accomplished by a survey distributed between four selected universities in Oman with 200 and 95 responses participates in filling all 13-partition related to the proposed model seen in Appendix Table 11 the survey previously checked and revised by an expert faculty and most of the questions used in adapted form from previously cited papers. The results analyst by PLS_SEM program and the reliability, validity, and normality evaluated in section 6 with effective values in mean and standard deviation. The cross leading between variables and the Alpha, CR, and AVE, also confirmed the validity of the model values from the other works.