Learning Management System in Madrasah: A Study of Teacher Readiness and Acceptance of Technology

: This study aims to analyze the application of learning management systems in the form of technology to support the implementation of learning. Specifically, this study aims to ascertain the relationship between the teacher's technology acceptance model and the technology readiness index. At the elementary school (SD/MI), junior high school (MTs), senior high school (MA), and vocational schools in the city of Bandung, 185 private teachers participated in the survey. The results showed that perceived ease of technology was positively and significantly influenced by optimism and innovation, positively and significantly influenced by insecurity, and negatively or not significantly affected by discomfort. Then, optimism significantly increases people's perception of how helpful technology can be. While innovativeness and inconvenience have little effect on how useful technology is for people, perceived ease of use and insecurity have a small but positive effect. The implications of this research can be explained by the fact that teachers' acceptance of technology in learning can determine their optimism and innovation in learning. Therefore, teachers must be encouraged to prepare various skills and competencies to use technology to support their teaching and learning activities.


INTRODUCTION
Rapid information and communication technology (ICT) changes are now essential in changing education management.To improve the quality of world education, UNESCO, which operates in the fields of education, knowledge, and culture, seeks to plan four pillars of education: (1) learning to know, (2) learning to do, (3) learning to be, and (4) learning to live together.In this case, learning technology is one of the changes in education management.Learning technology, whose current application is utilizing ICT processes and products to solve educational and learning problems, has many benefits and advantages (Rorim Panday, 2020).
As the teacher, the readiness of teachers to use digital tools and technologies methods of network integration in their professional activities (Kamahina, Yakovenko, & Daibova, 2019).Technological developments in learning and other fields encourage educational institutions to implement an effective and efficient learning management system (LMS).The effective use of LMS can create interactive, student-centered learning and meet the needs of diverse students (Veluvali & Surisetti, 2022), so the use of the system (LMS) has a significant relationship with the effectiveness of learning (Chaw & Tang, 2018).Therefore, leaders or managers of educational institutions are obliged to support the effectiveness of learning by providing learning technology facilities.
Facilitating the use of e-learning by the school is one approach to advancing educational technology (Sulisworo & Hidayati, 2021).Online learning is very much needed (Hidayati & Saputra, 2020).However, in the e-learning system, which is now widespread in public, learners (students) are naccessibleree in terms of access, and they face a computer somewhere to study (Hari, Darmanto, & Hermawan, 2013).Although anyone can utilize technology, how it is used relies on the degree of readiness of a person to accept the technology.
During COVID-19, the online learning system using technology in Indonesia was not without problems.The problems faced regarding online learning include: The results of the study show that the problematic aspects of implementing online learning policies include: (a) pedagogical competence and mastery of educator technology; (b) readiness and ability of students; (c) education stakeholder support; and (d) supporting facilities and infrastructure (Rifa'i, 2023).Challenges related to teacher readiness and acceptance of the use of technology in learning based on research from (Andarwulan, Al Fajri, & Damayanti, 2021) show that teachers: 50% do not understand the content of learning, 24% have inadequate technological tools, 67.6% less adaptable in applying technology; 20.4% are less able to buy internet data packages; and 40.4% had difficulty finding an internet signal.This study concluded that teachers were not ready to implement courageous learning policies.
Sulisworo (Sulisworo & Hidayati, 2021) found that the Teachers' technology readiness level causes polarization of technological segmentation at which there are no skeptics and paranoids segments.The extent to which teachers use technology in their classroom instruction has long been a subject of study.Indeed, numerous models explain the influential factors and mechanisms of classroom technology use (Nair & Das, 2012).
In truth, there are still quality gaps in the regions; with online learning made possible by Google, it is believed that it can be accelerated everywhere.This approach can address concerns with teacher quality, undisciplined entry hours, and other technical ones (Mulyani, 2019).To assess teacher technology readiness, the technology readiness index can be used.As for external customers, understanding teacher technology readiness is crucial for making the right choices when designing, implementing, and managing teacher and technology relationships.
Based on the explanation above, studying the crucial factors and looking for the correlation between technological readiness and the acceptance of private teachers regarding understanding the technology readiness index is necessary.This will serve as the cornerstone of a strategy for using digital technology for education, the effectiveness of which can be measured by how much educators and policymakers have accepted it (according to the technology acceptance model).This study estimates the teacher readiness index and technology acceptance to provide a The Model of Influence of Optimism, Innovativeness, Discomfort, and Insecurity on Perceived Ease to Use and Perceived Usefulness.

RESEARCH METHODS
This study uses a quantitative approach, which is included in this type of correlational research (Donald et al., 2010).This study measures the effect of the independent variable on the dependent variable using modeling analysis using SEM Amos (Byrne, 2010).This study was conducted at private schools in Bandung, Indonesia.This research involved 185 private teacher respondents from Bandung City.The instrument used to measure the technology readiness index (TRI) and TRAM was a questionnaire with Likert scales ranging from 1 to 5 (from disagree to agree from Parasuraman & Colby (2001;2015).The questionnaire was translated from English into Indonesian.Then, it was validated before it was used.While there were four factors of TRI: optimism (OPT, four items), innovativeness (INN, four items), discomfort (DIS, four items), and insecurity (INS, four items), there were two factors of TRAM: perceived ease of use and perceived of usefulness.The research conceptual framework can be seen in Figure 1 below:

RESULTS AND DISCUSSION Validity Testing
Validity testing was done by looking at the value of the loading factor for each indicator.Based on Table 1, it can be concluded that the loading factor value (P) for all of them was above 0.5, meaning that all indicators met the validity criteria.The results of the reliability calculation can be seen in Table 2. Based on the calculation results in Table 2, it can be concluded that the construct reliability values of the six variables are as follows: optimism with 0.98321, innovativeness with 0.970103, discomfort with 0.913338, insecurity with 0.814381, perceived usefulness with 0.990919, and perceived ease of use with 0.986693.The value of the six was more significant than the cut-off value of 0.7, so the indicators had high consistency.
Meanwhile, for the variance extracted, optimism was 0.936063, innovativeness was 0.890263, discomfort was 0.727714, insecurity was 0.52737, perceived usefulness was 0.947888, and perceived ease to use was 0.925139.Since the value of the six was higher than 0.5, the variance extracted from the indicators was more significant for the formation of latent variables.
Testing the Effect of Optimism, Innovativeness, Discomfort, and Insecurity on Perceived Ease to Use and Perceived Usefulness

SEM Assumption Test 1. Univariate and Multivariate Normality
With a sample size of 185 respondents, the normality calculation results are as follows: It may be deduced from the normality test results in Table 3 that the critical ratio (cr) value was more significant than -2.5 cr 2.5 (or rounded to 3).It indicates that the data complied with the normality criteria.The multivariate cr value of 28.006 was significantly higher than 3 (2.5).The next step was to employ ML (maximum likelihood) estimation by performing outlier identification with Mahalanobis because the sample was in the 100-200 range.
In increasing the data normality, the elimination of outlier data was carried out.The measure used was elimination, which was done if p2 <0.05.After elimination, the number of samples was 121.Furthermore, the normality was tested again, and the calculation results were obtained as follows: Based on the normality test results in Table 4, the data can be univariately standard since all cr values were above -2.5 ≤ cr ≤ 2.5, while the multivariate cr value was 12.136, still above 2.5 (3).Since the number of samples was close to 100, and it was impossible to do the second Mahalanobis test, the bootstrapping method was then used (Arbuckel & Wothke, 1999;Boomsma, 2000).

Boollen-Satine Bootstrap
The Bollen-Stine bootstrap results from the research sample are as follows:

Bollen-Stine Bootstrap (Default model)
The model fits better in 199 bootstrap samples.It fits about equally well in 0 bootstrap samples.It fits worse or fails to fit in 1 bootstrap sample.
Testing the null hypothesis that the model is correct, Bollen-Stine bootstrap p = .010 The 200 bootstrap samples yielded findings that one sample was unsuitable (filed).Therefore, the Bollen-Stine test findings generated a probability value (p) of 0.010 (199/121).With a chi-square value of 619.820 and a probability of 0.000 (still below 0.05), these results differed from the initial sample without Bootstrap.Hence, it can be concluded that the fit model was accepted based on the calculation of the Bollen-Satine bootstrap probability value of 0.010 (> 0.05).The following is the Histogram 1 representation of the bootstrap distribution: Based on the structural model analysis results in Figure 2, the feasibility of the model could be tested using several criteria, as presented in Table 5.Based on the eight criteria of model feasibility testing in Table 5 above, three criteria, namely Chi-Square (because it is susceptible to sample size, it requires another test), GFI, and AGFI, were in the marginal category, while the other five criteria, namely RMSEA, CFI, TLI, and NFI, were in a suitable category.It implies that the model proposed in the hypothesis was fit with the data.Furthermore, the model could test research hypotheses based on regression values and correlations or covariances.Based on Table 6, it can be concluded that: a. Optimism and innovativeness had a significant correlation, meaning that the higher a person's optimism, the more innovative, and vice versa.b.Discomfort and innovativeness had a significant negative correlation, meaning that the higher a person's discomfort, the less innovative, or the less innovative a person is, the more uncomfortable.c.Discomfort and insecurity had a significant positive correlation, meaning that the more uncomfortable a person is, the more insecure they feel, and vice versa.d.Optimism and discomfort had a significant negative correlation.It indicates that the higher a person's optimism, the lower the feeling of discomfort, and vice versa.e. Innovativeness and insecurity had a significant negative correlation, meaning that the higher a person's innovativeness, the lower the insecurity, and vice versa.f.Optimism and insecurity had a significant negative correlation, meaning that the higher a person's optimism, the lower his insecurity (feeling insecure), and vice versa.

Regression Test
Finding out whether there was an influence of the independent (exogenous) variable on the dependent (endogenous) variable can be seen in Table 7.The results of the hypothesis testing may be summarized in the following statement in light of the regression analysis in the Table 8: Based on the hypothesis testing results on several variables in the table above, most variables influenced perceptions of the ease of technology and the benefits of technology.The variables that influenced and did not affect the two perceptions are described as follows: 1. User insecurity had no significant positive effect on the perceived ease of technology.2. User optimism significantly positively affected the perceived ease of technology.
3. The innovativeness of users had a significant positive effect on the perceived ease of technology.4. User discomfort had a significant negative effect (0.05) on the perceived ease of technology. 5. User optimism had a significant positive effect on the perceived usefulness of technology.6.The innovativeness of users had no significant negative effect on the perception of the usefulness of technology.7. User discomfort had no significant negative effect on the perceived usefulness of technology.8. User insecurity had no significant positive effect on the perception of the usefulness of technology.9. Perceived ease of use of technology had a significant positive effect on the perceived usefulness of technology.

H1: User optimism affects the perception of the usefulness of technology
The user optimism variable consisted of four indicators.Based on the study results, the user optimism variable had a positive and significant effect on the perceived usefulness of technology, with a value of 0.617.The results of this study indicate that the hypothesis was accepted, i.e., user optimism influenced the perception of the usefulness of technology.
The research results agree that optimism positively influenced the perceived benefits of using this technology (Pradana, 2021).However, it differs from the opinion that optimism has no significant effect on perceived benefits (Andayani & Ono, 2020).Based on the research results and discussion, it can be concluded that user optimism had a positive and significant effect on the perception of the usefulness of technology.The higher the optimism of technology users, the higher the perception of the benefits of technology.Technology users in this research were teachers.A teacher with high optimism would affect his perception of the use of technology.Optimism is also a teacher's view of the benefits of the technology used, where these benefits are used in the learning process and other activities.

H2: The innovativeness of users influences the perception of the usefulness of technology
Four indications made up the user innovativeness variable.According to the study's findings, the user's innovativeness variable had a -0.089 value and had a negative, inconsequential impact on people's opinions of technology's value.The results of this study suggest that it was not true that innovativeness affected people's perceptions of how valuable technology was.
Accordingly, innovative improvements could raise perceptions of the advantages of using technology.The results are consistent with those that found that innovativeness influenced perceptions of the usefulness of technology (Nahzdifah et al., 2022).Users will be more inclined to utilize technology if there is a high level of innovation (Harianja et al., 2023).Based on the findings and analysis of the research, it can be said that consumers' innovativeness had a negative and negligible impact on how beneficial they perceived technology to be.Technology may be used in life and the classroom by someone with high levels of inventiveness.

H3: User discomfort influences the perception of the usefulness of technology.
The user discomfort variable encompassed four indicators.Based on the study results, the user discomfort variable had a negative and insignificant effect on the perception of the usefulness of technology, with a value of -0.128.The results of this study indicate that the hypothesis was rejected, i.e., user discomfort affected the perception of the usefulness of technology.
The research results corroborate the statement that discomfort did not affect usefulness (Rosmayanti et al., 2018).The results of this study are not in line with the statement, which states that the discomfort of technology users had a significant effect on the benefits of technology (Rifai et al., 2019).Based on the research results and discussion, it can be concluded that user discomfort had a negative and insignificant effect on the perception of the usefulness of technology.Thus, the discomfort felt by the teacher had no effect on his perception of understanding that the technology used has benefits for its users.

H4: User insecurity affects the perception of the usefulness of technology.
Four indicators made up the user insecurity variable.According to the study's findings, the user's insecurity variable, with a value of 0.094, had no appreciable beneficial impact on the perceived usefulness of technology.The findings of this study imply that the hypothesis that user insecurity affects perceptions of the value of technology was accepted.
The findings are consistent with a prior study, which revealed that perceived advantages of the technology employed were positively impacted by insecurity (Hadisuwarno & Bisma, 2020).It may be inferred from the research findings and discussion that user insecurity had a favorable but negligible impact on how valuable people considered technology.In connection with this, consumers will not utilize technology as much if they feel insecure about how it will affect their security.Thus, user insecurity affects how users or teachers use technology.Since it stores information about how people use technology, the level of security is essential (Afolo & Dewi, 2022).
H5: User optimism affects the perceived ease of technology.
The user optimism variable comprised four indicators.Based on the study results, the user optimism variable had a significant positive effect on the perceived ease of technology, with a value of 0.330.The results of this study indicate that the hypothesis was accepted, namely, that the user's optimism influenced the perceived ease of technology.
The research results were obtained, which also found that optimism positively influenced assessing the ease of use of technology (Wahyuni et al., 2020).Optimism has a positive and significant effect on perceived ease of use (Panday et al., 2019).Based on the research results and discussion, it can be concluded that user optimism influenced the perceived ease of technology.The higher the optimism that technology users have, the higher the perception of the ease of use of the technology.Hence, a teacher with high innovation will have the perception that new technology is easy to use.

H6: Innovativeness of users affects the perceived ease of technology.
There were four indications for the user innovativeness variable.According to the study's findings, the user's innovativeness variable, with a value of 0.370, had a favorable and substantial impact on how easily technology was regarded.The findings of this study indicate that the hypothesis, according to which the user's inventiveness impacted the perception of technological ease, was accepted.
The research findings concur with a study that discovered innovativeness affected user-friendly technology (Nahzdifah et al., 2022).In a different research, innovativeness did not significantly impact the perceived ease of utilizing technology (Andayani & Ono, 2020).It is clear from the research's findings and analysis that consumers' inventiveness impacted how easily they regarded technology to be used.The perception of technology's usability increases with consumers' level of innovation.H7: User discomfort influences the perceived ease of technology.
The user discomfort variable consisted of four indicators.Based on the study results, the variable user discomfort had a negative and significant effect on the perceived ease of technology, with a value of -0.198.The results of this study suggest that the hypothesis was rejected; namely, discomfort affected the perception of convenience.
The research results showed that discomfort negatively and significantly affected the ease of technology use (Faizani & Indriyanti, 2021).Innovative people have minimal obstacles in mastering new technology (Hadisuwarno & Bisma, 2020).Based on the research results and discussion, it can be concluded that user discomfort had a negative but significant (0.05) effect on the perceived ease of technology.The higher the discomfort of technology users, the lower the perception of the convenience of the technology.
H8: User insecurity affects the perceived ease of technology.
User insecurity encompassed four indicators.According to the study's findings, the variable user insecurity, which had a value of 0.032, had no discernible beneficial influence on how easily people viewed using technology.The findings of this study demonstrate that the hypothesis, according to which user insecurity had an impact on how easily technology was perceived, was approved.
Users who feel insecure about technology can still feel the ease of technology for several reasons, including not being used to using technology and users feeling that technology is challenging to use and insecure (Rifai et al., 2019).If all technology users think that technology can maintain data confidentiality, users are interested in the ease of use of this technology (Dewi, 2019).Based on the research results and discussion, it can be concluded that user insecurity had a positive but insignificant effect on the perceived ease of technology.
H9: The user's perceived ease of technology influences the perceived usefulness of technology.
The user's perceived ease of technology had six indicators.Based on the study results, the variable perceived ease of use of technology had a positive and significant effect on the perceived usefulness of technology, with a value of 0.528.The results of this study imply that the hypothesis was accepted; in other words, the user's perceived ease of technology influenced the perceived usefulness of technology.
The research results are consistent with the statement that perceived ease of technology influences perceptions of the usefulness of technology (Widaningsih & Mustikasari, 2022).The higher a teacher's perception of technology, the higher the utilization of this technology (Hudayati et al., 2021).It denotes that if the teacher has the perception that technology is easy to use, it will affect his perception of the benefits of using technology.Based on the research results and discussion, it can be concluded that the user's perceived ease of technology positively and significantly affected its perceived usefulness.The higher the perception of the convenience of technology, the higher a person's perception of the benefits of the technology.

CONCLUSION
Based on the analysis of research data and the discussion carried out in this study, it can be concluded that of the four variables, some influenced the perceived ease of technology and the perceived usefulness of technology.Optimism and innovativeness positively and significantly affected the ease of technology.Hence, the higher the teacher's optimism and innovativeness, the higher the ease of technology he will feel.Conversely, the higher the discomfort of a teacher will further reduce the perception of the ease of technology, or discomfort has a significant adverse effect on the perception of the ease of technology.Meanwhile, the insecurity variable had an insignificant positive effect on the perceived ease of technology.
Only optimism had a significant positive effect when associated with the benefits of technology.Meanwhile, innovativeness and discomfort did not affect

Figure 2 .
Figure 2. The Model of Influence of Optimism, Innovativeness, Discomfort, and Insecurity on Perceived Ease to Use and Perceived Usefulness