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Analysis of Acceptance of Vocational High School e-Report in Temanggung District Using the UTAUT Model (Unified Theory of Acceptance and Use of Technology)

This research examines the abuse of access rights of users of the VHS e-Report application which are not in accordance with the rules set by the Government. Therefore, this study aims to: (1) find the factors that affect the acceptance rate of the application of the e-Report of Vocational High Schools (VHS); and (2) find factors that do not affect the acceptance rate of implementing VHS e-Report. This research uses quantitative methods with an ex-post facto approach. This research was conducted at 4 Vocational High Schools in Temanggung Regency, namely Jumo State Vocational School, Dr Sutomo Temanggung Vocational School, Bhumi Phala Parakan Vocational School, and Vocational High School 17 Parakan. The population of all VHS was 432 respondents, while the sample is selected by proportional random sampling of 204 respondents. The data collection instrument used an online (google form) and offline (handout) questionnaire. The research questionnaire was compiled based on the UTAUT (Unified Theory of Acceptance and Use of Technology) model variables which have been adjusted to the conditions of the research field. The data analysis technique uses quantitative descriptive analysis with the PLS-SEM (Partial Least Square-Structural Equation Model) method which applies a bootstrap model to test the hypothesis. The results of this study concluded that there were: (1) factors that had a positive effect on the acceptance rate of VHS e-Report, namely business expectations, social influence, facilitation conditions, and behavioral intentions. The most influential factor was the intention to behave with the value of the path coefficients 0.412; and (2) factors that did not affect the acceptance level of the VHS e-Report application were performance expectations, age, and regulations. The least influential factor was the performance expectancy which was moderated by age with the path coefficients value of -0.188. The study on the acceptance of the VHS e-Report application in Temanggung Regency can be used as a recommendation in determining how to increase the use of the VHS e-Report application in accordance with access rights.


Introduction
The digital era guides the educational assessment process from conventional to internet-based information technology systems. Information and communication technology are all forms of technology that play a role in recording, processing, storing, transmitting, manipulating, and receiving information [1]. One level of education in Indonesia that implements this system is Vocational High School (VHS). VHS is a vocational education that teaches about the readiness of workers to enter the world of work to have abilities, skills, and habits in meaningful and productive work [2]. The information technology system for an internet-based assessment process to support and improve education services is called the VHS e-Report application [3].
The VHS e-Report application was developed directly by the Directorate of Vocational Education with the primary function to make it easier to compile reports on student assessment results in the form of reports per assessment, competency achievement reports, report cards, and leggers according to user access rights. It is hoped that with the VHS e-Report application, teachers and education personnel can carry out their duties. Even so, the use of the VHS e-Report application is still not by the access rights that have been determined, especially in the Temanggung area, Indonesia. This has resulted in not achieving the primary goal of making the VHS e-Report application. Thus, it is necessary to analyze the factors that influence the acceptance of the VHS e-Report application in the Temanggung area.
To determine the acceptance of technology, researchers have used various theories such as Theory of Reasoned Action/TRA, Theory Acceptance Model/TAM, Motivational Model/MM, Theory of Planned Behavior/TPB, TAM + TPB, Model of PC Utilization/MPCU, Innovation Diffusion Theory/IDT, and Social Cognitive Theory/SCT, Unified Theory of Acceptance and Use of Technology (UTAUT/UTAUT2) [4]. The VHS e-Report application can be measured using the UTAUT model of technology acceptance theory. The UTAUT model was chosen because it can provide a better comprehend of the factors or variables that influence behavioral intention to use a technology [5].
Research on the acceptance of VHS e-Report application technology using the UTAUT model is developed based on factors that are adjusted to field conditions then, these factors are adopted into UTAUT variables. Also, the variables chosen for the UTAUT model must be adjusted to the literature in order to achieve significant results in receiving a VHS e-Report application. Thus, the final result of the UTAUT model development in this research aims to: (1) looking for any factors that affect the acceptance rate of the VHS e-Report application; (2) finding factors that do not affect the acceptance rate of the VHS e-Report application; (3) determine the factors that most influence the acceptance rate of the VHS e-Report application; and (4) determine the factor that does not most affect the acceptance rate of the VHS e-Report application.

Research Method
This type of research uses the ex-post facto method with a quantitative approach. Research using the expost-facto method aims to examine the events that have occurred, by tracing backward to determine the factors that influence them. This method was chosen because ex-post facto did not control the independent variables. After all, the indication had occurred in systematic empirical findings.
The study was conducted in four Vocational High School (VHS) that have used the VHS e-Report application that as a student assessment process in the Temanggung Regency, Central Java, Indonesia. These include state VHS Jumo, VHS Dr. Sutomo Temanggung, VHS Bhumi Phala Parakan, and VHS 17 Parakan. The research was conducted in March-August 2020, with a population of 432 respondents. Research respondents consisted of five categories with access rights to use the VHS e-Report application, namely admin, curriculum assistant, teacher, administrator, and homeroom teacher. The research data were obtained from a sample of 204 respondents using the formula from Isaac and Michael [6]. Determination of the research sample using proportional random sampling technique, which is a way of taking samples from each sub-population by considering the size of the sub-populations as shown in Table 1. The data collection technique used a questionnaire that consisted of two types, namely online (google form) and offline (handout). The questionnaire contains statements related to the variables that are the focus of the study and are thought to affect the acceptance of applications. The variables in the research questionnaire were arranged based on the UTAUT theory [7] and adapted to field conditions. The data analysis technique uses quantitative descriptive analysis with the PLS-SEM (Partial Least Square-Structural Equation Model) method, which applies a bootstrapping model to test the hypothesis [8]. The program used to analyze this hypothesis is SmartPLS version 3.0 (Trial-Full Version).

Research Model
The modified Unified Theory of Acceptance and Use of Technology (UTAUT) method was used in this study, with the exogenous variables being performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC); endogenous variables behavioral intention (BI), use behavior (UB); and moderator age (A), regulation (R). The moderator variable is adjusted to the problems in the field to show a stronger or weaker relationship between latent variables [8]. The model framework is prepared based on the conditions of the VHS in the Temanggung area. The framework of UTAUT model of this research look in Figure 1.

Hypothesis
The hypotheses compiled refer to Figure 1. In this study, several hypotheses emerged that would serve to prove the relationship between variables, so that it can be explained what are the variables that affect the acceptance of the VHS e-Report application in the Temanggung area Table 2. manifest variables or indicators. The model is analyzed first in testing the outer loadings until it meets the rule of thumb criteria. The design of the initial measurement model can be seen in Figure 2.

Outer Loadings
The outer loading test explains the absolute correlation between latent variables and each indicator using the indicator reliability criteria >0.7. Indicators or manifest variables of each latent variable that do not meet the reliability criteria of the indicator will be eliminated (invalid) from the measurement model. The final test results of the outer model using SmartPLS are shown in Figure 3 and Table 3.   Table 3

. Outer Loadings Test Results
Based on the results of data processing using SmartPLS in Figure 2, there are several indicators do not meet the criteria for the value of outer loadings >0.7. Indicators that do not meet these criteria, namely FC2 = 0.461, BI4 = 0.500, UB3 = 0.512, SI6 = 0.613, and SI2 = 0.624, so they must be eliminated from the outer model that has been designed. Also, variable indicators with an outer loadings value >0.7 have a high level of validity, thus meeting the indicator reliability criteria shown in Figure 3.

Convergent Validity Testing
Convergent validity testing explains that a set of indicators represents one latent variable and the underlying latent variable. This test can be demonstrated using the criteria for an average variance extracted value (AVE) >0.5. The results of testing the convergent validity of the outer model using SmartPLS are shown in Table 4.
Based on the results of data processing using SmartPLS in Table 4, it can be seen that all AVE values are >0.5, so that they meet the convergent validity requirements. This means that each latent variable already represents a manifest variable or indicators in its block.

Discriminant Validity Testing
Discriminant validity testing serves as an additional concept that explains that a combined set of indicators is expected not to be unidimensional (single). One alternative way of testing discriminant validity is using the Fornell-Larcker criterion. The Fornell-Larcker criterion statistically means that the AVE value of each latent variable must be greater than R 2 with all latent variables. The results of testing the discriminant validity of the outer model using SmartPLS are shown in Table 5.
Based on the results of data processing using SmartPLS in Table 5, it can be seen that the comparison of all AVE values for each variable is greater than the R 2 (BI and UB), so that all variables are declared valid.

Reliability Testing
Testing construct reliability or latent variables is measured using two methods, namely composite reliability, and Cronbach Alpha. Composite reliability testing describes the measurement of internal consistency for each construct or latent variable with a value of ≥ 0.6. The results of testing the composite reliability outer model using SmartPLS are shown in Table 6.
Tests carried out then used the Cronbach alpha method this, measurement explains the excellent reliability for each construct or latent variable with a value of ≥ 0.7. The results of the Cronbach Alpha outer model test using SmartPLS are shown in Table 7. Based on the results of data processing using SmartPLS in Table 6, it can be seen that all composite reliability values are ≥ 0.6, so that the internal consistency measurement for all variables is above the standard value (valid). Also, Table 7 shows that all Cronbach Alpha values are ≥ 0.7, so that all indicators in the model reflect excellent reliability.

Inner Model Analysis 3.2.1. Inner Model Design (Structural Model)
The structural model design (inner model) is a regression model that connects the exogenous latent variables, moderator variables, and endogenous latent variables. The structural model design (inner model) can be seen in Figure 4.

R 2 Value
Testing the R 2 value explains the combined effect of each endogenous latent variable and its indicators on the exogenous latent variable and its indicators in the structural model. The test used the criteria R 2 ≥ 0.19 in the weak category, R 2 ≥ 0.33 in the moderate category, R 2 ≥ 0.67 with the substantial category, and R 2 ≥ 0.7 with the strong category. The results of testing the R 2 inner model using SmartPLS are shown in Table 8. Based on the results of data processing using SmartPLS in Table 8, it can be seen that the value of the first R 2 (BI) means that the influence of latent variables PE, EE, SI, and moderator variables A and R along with all manifest variables or indicators on BI latent variables is 0.549. Furthermore, the value of the 2nd R 2 (UB) means that the influence of the latent variables BI and FC, along with all manifest

Path Coefficients
Testing path coefficients explains the effect of one latent variable on other latent variables. According to Hass and Lehner [8], it is stated that the path coefficient values that are in the range of -0.1 to 0.1 are considered insignificant, values greater than 0.1 are significant and directly proportional, and smaller values. Of -0.1 is a significant value and inversely proportional. The results of testing the inner model path coefficients using SmartPLS are shown in Table 9.
According to the results of data processing using SmartPLS in Table 9, there are path coefficients that have a significant and proportional effect, namely A-BI, A_EE-BI, BI-UB, EE-BI, FC-UB, PE-BI, R-BI, R_EE-BI, and SI-BI.

Hypothesis Test
The research hypothesis testing is done by looking at the path coefficients that are positive or negative, and the t-value resulting from the analysis using the SmartPLS application, which is compared with the t-table value. The research hypothesis is accepted or rejected based on the test conditions, as in Table  10 [8] [9]. Based on Table 10, hypothesis testing can be done using the t-table value of 1.653 with a significance level of 5%, as presented in Table 11.

Performance Expectancy (PE) has a positive and significant effect on Behavioral Intention (BI)
The first hypothesis concludes that the proposed H1 is rejected. The inner model displays performance expectancy (PE) on behavioral intention (BI), resulting in a path coefficient value of 0.172> 0.1 and a t-statistic of 1.442 where the worth is smaller than the t-

Age moderates the positive and significant role of Performance Expectancy on Behavioral
The conclusion of the second hypothesis testing (H2) is rejected. The inner model displays age (A) as moderating performance expectancy (PE) on behavioral intention (BI), resulting in a path coefficient value of -0.188 <-0.1 and a t-statistic value of 1.383 where the value is smaller than the t-table 1.653. Thus, the results of hypothesis testing indicate that age (A) moderates performance expectancy (PE) and does not affect behavioral intention (BI) for the acceptance of VHS e-Report application in Temanggung Regency and is insignificant when applied to the population according to previous research results [14].

Regulation moderates the positive role of Performance Expectancy and is significant on
Behavioral Intention The conclusion of the third hypothesis testing (H3) is rejected. The inner model displays regulation (R) moderating performance expectancy (PE) on behavioral intention (BI), resulting in a path coefficient value of -0.181 <-0.1 and the t-statistic value of 1.844 where the value is greater than the t-table 1.653. Thus, the results of hypothesis testing indicate that regulation (R) moderating performance expectancy (PE) does not affect behavioral intention (BI) on acceptance of VHS e-Report application in Temanggung Regency, but is significant when applied to the population according to the results of previous studies [15].

Effort Expectancy has a positive and significant effect on Behavioral Intention
The conclusion of the fourth hypothesis testing (H4) is accepted. The inner model displays effort expectancy (EE) on behavioral intention (BI), resulting in a path coefficient value of 0.251> 0.1 and a t-statistic value of 1.672 where the value is greater than the t-table 1.653. Thus, the results of hypothesis testing indicate that effort expectancy (EE) affects behavioral intention (BI) on the acceptance of VHS e-Report application in Temanggung Regency and is significant when applied to the population according to the results of previous studies [16] [14] [10].

Age moderates the positive and significant role of Effort Expectancy on Behavioral Intention
The conclusion of the fifth hypothesis testing (H5) is rejected. The inner model displays age (A) moderating effort expectancy (EE) on behavioral intention (BI), resulting in a path coefficient value of 0.206> 0.1 and a t-statistic value of 1.302 where the value is smaller than the t-table 1.653. Thus, the results of hypothesis testing indicate that age (A) moderates effort expectancy (EE) influencing behavioral intention (BI) on the acceptance of VHS e-Report application in Temanggung Regency, but it is not significant when applied to the population according to the results of previous studies [14].

Regulation moderates the positive and significant role of Effort Expectancy on Behavioral
Intention The conclusion of the sixth hypothesis testing (H6) is rejected. Inner model displays regulation (R) moderating effort expectancy (EE) on behavioral intention (BI), resulting in a path coefficient value of 0.197> 0.1 and a t-statistic value of 1.481 where the value is smaller than the t-table 1.653. Thus, the results of hypothesis testing indicate that regulation (R) moderates effort expectancy (EE) influencing behavioral intention (BI) on acceptance of VHS e-Report application in Temanggung Regency, but it is insignificant when applied to the population according to the results of previous studies [15].

Social Influence has a positive and significant effect on Behavioral Intention
The conclusion of the seventh hypothesis testing (H7) is accepted. The inner model displays social influence (SI) on behavioral intention (BI), resulting in a path coefficient value of 0.248> 0.1 and a tstatistic value of 3.006, where the value is greater than the t-table 1.653. Thus, the results of hypothesis testing indicate that social influence (SI) affects behavioral intention (BI) on the acceptance of VHS e-Report application in Temanggung Regency and is significant when applied to the population by the results of previous studies [14] [17] [18].

Age moderates the positive and significant role of Social Influence on Behavioral Intention
The conclusion of the eighth hypothesis testing (H8) is rejected. The inner model displays age (A) as moderating social influence (SI) on behavioral intention (BI), resulting in a path coefficient value of -0.1 ≤ -0.058 ≤ 0.1 and the t-statistic value of 0.577 where the value is smaller than the t-table 1.653. Thus, the results of hypothesis testing indicate that age (A) moderates social influence (SI) does not affect behavioral intention (BI) on acceptance of VHS e-Report application in Temanggung Regency and is insignificant when applied to the population according to previous research results [14].

Regulation moderates the positive role of Social Influence and is significant on Behavioral
Intention The conclusion of the ninth hypothesis testing (H9) is rejected. The inner model displays regulation (R) moderating social influence (SI) on behavioral intention (BI), resulting in a path coefficient value of -0.1 ≤ 0.073 ≤ 0.1 and the t-statistic value of 0.791 where the value is greater than the t-table 1.653. Thus, the results of hypothesis testing indicate that regulation (R) moderating social influence (SI) does not affect behavioral intention (BI) on the acceptance of VHS e-Report application in Temanggung Regency, but is significant when applied to the population according to the results of previous studies [15].

Facilitating Conditions have a positive and significant effect on Use Behavior
The conclusion of the tenth hypothesis testing (H10) is accepted. The inner model showing facilitating conditions (FC) to use behavioral (UB) resulted in a path coefficient value of 0.390> 0.1 and a t-statistic value of 5.442 where the value is greater than the t-

Age moderates the positive role of Facilitating Conditions and is significant towards Use
Behavior The conclusion of the eleventh hypothesis testing (H11) is rejected. Inner model displays age (A) moderating facilitating conditions (FC) to use behavioral (UB), resulting in a path coefficient value of 0.041 <0.1 and a t-statistic value of 0.741 where the value is smaller than the t-table 1.653. Thus, the results of hypothesis testing indicate that age (A) moderating facilitating conditions (FC) does not affect use behavioral (UB) on the acceptance of VHS e-Report application in Temanggung Regency and is insignificant when applied to the population according to the results of previous studies [24] [25].

Behavioral Intention has a positive and significant effect on Use Behavior
The conclusion of the twelfth hypothesis testing (H12) is accepted. The inner model displays behavioral intention (BI) to use behavioral (UB), resulting in a path coefficient value of 0.412> 0.1 and a t-statistic value of 4.868 where the value is greater than the t-table 1.653. Thus, the results of hypothesis testing indicate that behavioral intention (BI) affects use behavioral (UB) on the acceptance of VHS e-Report application in Temanggung Regency and is significant when applied to the population according to the results of previous studies [18] [19] [21].

Conclusion
Based on the research results of the analysis of acceptance of vocational high school e-report in Temanggung district using the UTAUT model (unified theory of acceptance and use of technology), the conclusions are several findings, namely (1) The factors that have a positive effect on the acceptance rate of the VHS e-Report application in Temanggung Regency are positive, namely effort expectancy, social influence, facilitating conditions, and behavioral intention. The most influential factor is the intention to behave with a path coefficient of 0.412; and (2) factors that do not affect the acceptance rate of the VHS e-Report application in Temanggung Regency, namely performance expectancy, age, and regulation. The least influential factor was performance expectancy which was moderated by age with a path coefficient value of -0.188.
The study of acceptance of the VHS e-Report application in Temanggung Regency can be improved by building trust in each user, that using the VHS e-Report application will facilitate the process of sending students. To build this trust, it is necessary to provide motivation through socialization of the use of VHS e-Report application according to access rights by paying attention to the age division of users so that each range can clearly understand the socialization. In addition, firm and clear regulations need to be stipulated in the use of applications as well as socialization that does not come out of the provisions that have been regulated by the Directorate of Vocational Education. The implication is that users of the VHS e-Report application who have access rights will more easily understand the regulations and use of applications in the student production process, and directly increase the level of use of the VHS e-Report application in the Temanggung Regency area.