Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 4197-4211 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: anake.kmitl2023@gmail.com Innovative factors affecting performance of local government organizations in Thailand Anake Tunjararuck1, Amnuay Saengnoree2, Thapong Teerawatananond3* 1,2,3College of Innovation and Industrial Management, King Mongkut’s Institute of Technology Ladkrabang, Thailand; anake.kmitl2023@gmail.com (A.T.). Abstract: This dissertation aimed (1) to study factors that have causal influences that affect the performance of municipalities. (2) to study the causal relationship model of innovative organizations, innovative capabilities, service innovation and performance management that influences the performance of municipalities. (3) to develop a linear structural equation model of the influence of service innovation, innovation organization, innovative capabilities and performance management that affects the performance of local government organizations in the category of municipalities in Thailand. The sample group were 320 executives of local government organizations in the category of municipalities in Thailand. Sampling was done in 2 steps: stratified and systematic sampling. This study is a quantitative study, and the research tool was a structured questionnaire and data were analyzed by percentage, mean, standard deviation, skewness, kurtosis, and confirmatory component analysis and structural equation model analysis. The findings of this study demonstrate significant direct effects of innovative capabilities, service innovation and performance management on performance of local government organizations in Thailand. Moreover, it was observed that innovative organizations and innovative capabilities also exerted indirect effects on performance of local government organizations through their influence on performance management, serving as mediator variables. Through the development of a structural equation model, it was determined that innovative capabilities had the greatest overall influence on the performance of local government organizations in the category of municipalities in Thailand, followed by performance management, service innovation and innovation organization, respectively. This research study made executives of local government organizations in category of municipalities in Thailand can be used as a guideline in determining strategies for managing their organization to be able to keep up with changes in the digital world and to ensure that the performance of the local government organization achieves the specified goals in terms of both efficiency and effectiveness. Keywords: Local Government organizations, Operational management, Organizational factors, Performance, Service innovations, Thailand. 1. Introduction There are many different forms of local government in Thailand today that are appropriate to the conditions of each locality. In the situation of changes in the economy, society, and politics, people have become alert and have greater knowledge and understanding of self-government, especially news perception and political participation in various forms. In addition, the development of the country has made local areas progressively more prosperous. Therefore, it is necessary to improve the current Thai local government model to suit the changing economic, social, and political conditions so that local government units can perform their duties fully and efficiently and meet the needs of local people and strengthen people's participation in self-government as much as possible, which is an important goal of decentralization of government. However, in order to change the structural form,powers and duties of 4198 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate any form of local government unit, it is necessary to consider the economic, social, and political suitability as well as the human, financial, and fiscal management capabilities of those localities. Municipal innovation management has a form and method of operations that occur continuously and systematically in stages. The success factors of such innovation management arise from: 1) Policy factors of municipal administrators, 2) Factors in cooperation networks, 3) Factors in participation of people in the area, 4) and factors in the continuity of innovation of the municipality. As for problems and obstacles, as well as important limitations on municipal innovation management, such as obstructing officials from performing their duties Lack of interest in self-improvement. Organizational development and human resource development lead to an innovative organization is necessary to rely on various factors in a process-like manner for efficient driving. This consists of external factors such as customer needs, competitors, and technology that is constantly changing and developing. For internal factors, these include factors at the individual level, group level, and organizational level. These factors are an overall picture of human resource management as a whole. When both external and internal factors are integrated together as driving factors by using human resource development combined with innovative creativity to be the driving force of being innovative, then the organization will be complete as expected. 1.1. Research Objectives 1. To study the factors that have causal influences affecting the performance of local government organizations in municipalities form. 2. To study the causal relationship model of Innovative Organization, Innovative Capability, Service Innovation, and Performance. Management that influences Organizational Performance of local government in municipalities form. 3. To develop a linear structural equation model of the influence of service innovation, Innovative Organization Innovative Capability Performance Management, on the performance of local government organizations. Figure 1. Conceptual framework. 4199 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate 2. Research Methodology This study employs Quantitative Research. The population is administrators of local administrative organizations in the type of municipalities, which are as follows: (1) city municipalities (2) city municipalities (3) sub-district municipalities. Samples is the mayor or deputy mayor or municipal secretary who is an executive of a local government organization of the type municipality. Determination of the sample size uses the criteria for determining the number of samples of the Structural Equations Model (SEM) analysis to estimate parameters using the Maximum Likelihood method according to the proposal of Hair, Black, Babin & Anderson (2010). Statistical analysis of structural equation types samples should be approximately 10-20 times of the observed variable. The researcher therefore used a maximum sample size of 20 times. There are 16 variables so sample size is 320. The research instrument was Structured Questionnaire. Data were analyzed with Descriptive Statistics, Inference Statistic, and Multivirate with the IBM SPSS package, and in the Structural Equations Modeling (SEM) analysis, analysis was done with the IBM SPSS AMOS package. 3. Research Results 3.1. Confirmatory Factor Analysis Results Table 1. Observed correlation values. Variables in innovative organization components. From Table 1, it is found that Pearson's product moment correlation coefficient consists of 3 Observed Variables. The results show that the correlation between Observed all 3 pairs of variables have values significantly different from zero at the .01 level, with positive correlation values and the correlation coefficient is between 0.64 and 0.73, which should not exceed 0.80, therefore it is not severe, indicating that all Observed Variables in this model are related to each other no more than the specified value and in the same direction. Table 2. Component weight validity, average variance extraction of innovative organization variables. From Figure 2 and Table 2, it is found that the Confirmatory Factors Analysis model of Innovative Organization (IO) variables is valid because the model is good. It fits the empirical data well, with every Goodness of Fit Index passing the good criteria, including Chi-Square = 0.00, df = 0, p = 0.00, that is, the R2 value is significantly different from zero. The values are as follows: RMSEA = 0.00 and the value is close to 0. The GIF index value = 1.00 has a value close to 1 and c2 / df = 0.00, which is less than 3 and The Model is Saturated, the Fit is Perfect with Latent Variables. Innovative Organization consists Latent Variables AVE CR Observed Variables Standard Component Weight R2 Innovative Organization 0.72 0.88 Organizational Culture (IO1) 0.71 0.50 Working Environment (IO2) 0.91 0.83 Employee Behavior (IO3) 0.91 0.83 4200 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate of Observed Variables. There are 3 variables with Average Variance Extraction (AVE) equal to 0.72 and Composite Reliability (CR) equal to 0.88. Each Observed Variable has a Standard Component Weight value between 0.71–0.91, suitable for further input into the structural equation. The reliability coefficient of Observed Variables All values, measured by R2, indicate the shared variance of the Observed. Variables and Innovative Organization is at a medium to high level (R2 is between 0.50 and 0.83). 2) Validity inspection results according to the structure of the Innovative Capability model, as shown in Table 3-4. Table 3. Correlation value of observed variables in the components of innovative capability. From Table 3, it was found that Pearson's product moment correlation coefficient consists of 3 observed variables. The results found that the correlation between observed all 3 pairs of variables have values significantly different from zero at the .01 level, with positive correlation values. and the correlation coefficient is between 0.54 and 0.67, which should not exceed 0.80, therefore it is not severe, indicating that all Observed Variables in this model are related to each other no more than the specified value and in the same direction. Table 4. Component weight validity and average variance extraction of innovative capability variables. From Figure 3 and Table 4, it is found that the Confirmatory Factors Analysis model of Innovative Capability (IC) variables is valid because the model is good. It fits the empirical data well, with every Goodness of Fit Index passing the good criteria, including Chi-Square = 0.00, df = 0, p = 0.00, that is, the R2 value is significantly different from zero. The values are as follows: RMSEA = 0.00 and RMR = 0.00 have values close to 0, index values GIF = 1.00 and AGIF = 1.00 have values close to 1, and R2 / df = 0.00, which has a value less than 3 and The Model is Saturated, the Fit is Perfect. Latent Variables Innovative Capability consists of 3 Observed Variables with Average Variance Extraction (AVE) equal to 0.84 and Composite Reliability (CR) equal to 0.94. Each Observed Variable has a Standard Component Weight value between 0.90–0.94, suitable for entering into the structural equation. next The reliability coefficient of Observed All variables, as measured by R2, indicate a high level of covariance between Observed Variables and Innovative Capability (R2 ranges from 0.81 to 0.88). Validity inspection result according to the structure of the Service Innovation model as shown in Table 5-6 Observed Variables Correlation Value IC1 IC2 IC3 IC1 1 IC2 0.62** 1 IC3 0.54** 0.67** 1 Remarks: **p<.01 Latent Variables AVE CR Observed Variables Standard Component Weight R2 Innovative Capability 0.84 0.94 Learning Capability (IC1) 0.90 0.81 Innovative Organization Capability (IC2) 0.92 0.84 Innovation Strategic Capability (IC3) 0.94 0.88 4201 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate Table 5. Correlation value of observed variables in the components of service innovation. From Table 5, it was found that Pearson's product moment correlation coefficient consists of 3 observed variables. The results found that the correlation between observed all 3 pairs of variables have values significantly different from zero at the .01 level, with positive correlation values. and the correlation coefficient is between 0.52 and 0.60, which should not exceed 0.80, therefore it is not severe, indicating that all Observed Variables in this model are related to each other no more than the specified value and in the same direction. Table 6. Component weight validity and average variance extraction of service innovation variable. From Figure 4 and Table 6, it is found that the Confirmatory Factors Analysis model of the Service Innovation (SI) variable is valid because the model is good. It fits the empirical data well, with every Goodness of Fit Index passing the good criteria, including Chi-Square = 0.00, df = 0, p = 0.00, that is, the R2 value is significantly different from zero. The values are as follows: RMSEA = 0.00 and RMR = 0.00 have values close to 0, index values GIF = 1.00 and AGIF = 1.00 have values close to 1, and R2 / df = 0.00, which has a value less than 3 and The Model is Saturated, the Fit is Perfect. Latent Variables, Service Innovation consists of 3 Observed Variables with Average Variance Extraction (AVE) equal to 0.90 and Composite Reliability (CR) equal to 0.97. Each Observed Variable has a Standard Component Weight value between 0.73–0.81, suitable for further input into the structural equation. As for the reliability coefficient of all Observed Variables, which is measured by R2, which indicates the covariance of Observed Variables and Service Innovation is at a high level (R2 is between 0.87 and 0.93)ใ Validity inspection result according to the structure of the Performance Management model as shown in Table 7-8. Table 7. Correlation value of observed variables in the components of performance management. From Table 7, it was found that the Pearson's product moment correlation coefficient value consisted of 4 observed variables. The results found that the correlation between the observed variables Observed Variables Correlation Value SI1 SI2 SI3 SI1 1 SI2 0.60** 1 SI3 0.59** 0.52** 1 Remarks: **p<.01 Latent Variables AVE CR Observed Variables Standard Component Weight R2 Service Innovation 0.90 0.97 New Service Technology (SI1) 0.93 0.87 New Service Concept (SI2) 0.96 0.93 Relationship with clients (SI3) 0.95 0.91 Observed Variables Correlation Value PM1 PM2 PM3 PM4 PM1 1 PM2 0.61** 1 PM3 0.68** 0.69** 1 PM4 0.67** 0.69** 0.61** 1 Remarks: **p<.01 4202 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate All 6 pairs of variables have values significantly different from zero at the .01 level, with positive correlation values. and the correlation coefficient is between 0.61 and 0.69, which should not exceed 0.80, therefore it is not severe, indicating that all Observed Variables in this model are related to each other no more than the specified value and in the same direction. Table 8. Component weight validity and average variance extraction of performance management variable. From Figure 5 and Table 8, it is found that the Confirmatory Factors Analysis model of Performance Management (PM) variables is valid because the model is good. The fit with the empirical data is good, with the Goodness of Fit Index passing all good criteria, including Chi-Square value = 0.00, df = 0, p = 0.996, that is, the R2 value is different from zero and is not statistically significant. The values are as follows: RMSEA = 0.00 and RMR = 0.00 have values close to 0, index values GIF = 1.00 and AGIF = 1.00 have values close to 1, and R2 / df = 0.00, which has a value less than 3 and The Model is Saturated, the Fit is Perfect. Latent Variables. Performance Management consists of 4 Observed Variables with an Average Variance Extraction (AVE) equal to 0.88 and Composite Reliability (CR) equal to 0.97. Each Observed Variable has a Standard Component Weight value between 0.90– 0.96, suitable for use in the equation. Next structure As for the reliability coefficient of all Observed Variables, which is measured by R2, which indicates the covariance of Observed Variables. Variables and Performance Management is at a high level (R2 is between 0.82 and 0.92). Validity inspection result according to the structure of the Organizational model. Performance as shown in Table 9-10. Table 9. Correlation value of observed variables in the components of organizational performance. Note: Remarks: **p<.01. From Table 9, it was found that the Pearson's product moment correlation coefficient value consisted of 3 observed variables. The results found that the correlation between the observed variables, all 3 pairs of variables have values significantly different from zero at the .01 level, with positive correlation values and the correlation coefficient is between 0.61 and 0.69, which should not exceed 0.80, therefore it is not severe, indicating that all Observed Variables in this model are related to each other no more than the specified value and in the same direction. Table 10. Latent Variables AVE CR Observed Variables Standard Component Weight R2 Performance Management 0.88 0.97 Goal (PM1) 0.90 0.82 Assessment (PM2) 0.93 0.86 Reward (PM3) 0.95 0.91 Human Resource Development (PM4) 0.96 0.92 Observed Variables Correlation Value OP1 OP2 OP3 OP1 1 OP2 0.64** 1 OP3 0.69** 0.61** 1 4203 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate Component weight validity and average variance extraction of organizational performance variable From Figure 6 and Table 10, it is found that the Confirmatory Factors Analysis model of Organizational Performance (OP) variables is valid because the model is good. The fit with the empirical data is good, with every Goodness of Fit Index passing the good criteria, including the Chi-Square value = 0.00, df = 0, p = 0.00, that is, R2 is significantly different from zero. As follows: RMSEA = 0.00 and RMR = 0.00 have values close to 0, GIF index = 1.00 and AGIF = 1.00 have values close to 1, and R2 / df = 0.00 which has a value less than 3 and The Model is Saturated, the Fit is Perfect Latent. Variables. Organizational Performance consists of 3 Observed Variables with Average Variance Extraction (AVE) equal to 0.73 and Composite Reliability (CR) equal to 0.89. Each Observed Variable has a Standard Component Weight value between 0.78–0.90, suitable for further input into the structural equation. The reliability coefficient of all Observed Variables values measured by R2, indicate the shared variance of the Observed. Variables and Organizational Performance innovation is at a medium to high level (R2 is between 0.61 and 0.82). 3.2. Results of Analysis of Relationship between Latent Variables From Table 11, it is an analysis of the relationship between Latent Variables for all 5 variables found that the Bartlett's Test of Sphericity statistic has a Chi-Square statistic value of 4349 (P<.01) with a df of 120. Index analysis results Kaiser-Meyer-Olkin of Sampling Adequacy has a value of 0.94 with a value greater than 0.50 (Kanlaya Wanichbancha, 2008) showing that Latent Variables All five variables are related to each other in an appropriate size to be used in further analysis. As for the results of the analysis of the relationship between Latent A total of 10 pairs of Variables using Pearson correlation found that the relationship between Latent All 10 pairs of variables have values greater than zero with statistical significance at the .01 level, showing that the correlation coefficients between the variables have a positive relationship or go in the same direction and range from 0.61 to 0.77, showing that Latent All Variables are appropriate to use. in further structural equation analysis. Table 11. Latent relationship values Variables in the structural equation model of innovation and organizational management factors that affect the performance of local government organizations, types of municipalities. Latent Variables AVE CR Observed Variables Standard Component Weight R2 Organizational Performance 0.73 0.89 Finance (OP1) 0.78 0.61 Clients (OP2) 0.88 0.77 Internal Process (OP3) 0.90 0.82 Latent Variables IO IC SI PM OP Innovative Organization (IO) 1 Innovative Capability (IC) 0.61** 1 Service Innovation (T) 0.66** 0.64** 1 Performance Management (PM) 0.67** 0.67** 0.66** 1 Organizational Performance (OP) 0.78** 0.76** 0.77** 0.76** 1 KMO : Measure of Sampling Adequacy = 0.938 Bartlett's Test of Sphericity : Chi-Square = 7436.234, df = 120, p = .000 Remarks: **Sig. < .01 4204 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate 3.3. Results of the Development of the Causal Relationship Model Analysis of Observed Variables Confirmatory Factors Analysis of Latent Variables and the relationship between Latent Variables were found to be suitable to be introduced into the structural equation. Researcher has developed and adjusted the model until it meets the standards. In the final model, it was found that the results of the valid analysis of the structural equation model of innovation and organizational management factors affected the performance of local administrative organizations in the municipality category and the influence values between the variables in the model by means of influence analysis with Latent Variables and using the Goodness of Fit Statistics criteria (Nonglak Wiratchai, 1995 and Supamas Angsuchot et al., 2011) as follows: c2 value, c2 /df value < 2.00 The p- value > 0.05, the RMSEA value < 0.08 and the GFI value > 0.90 are standardized according to the statistical criteria for measuring harmony with statistical significance. Figure 7. Structural equation model of innovation and organizational management factors that affect the performance of municipal local government organizations before adjusting the model. From the results of examining the structural equation model of innovation and organizational management factors that affect the performance of local government organizations in the municipality category, as shown in Figure 7, it was found that the structural equation model of innovation and organizational management factors that Affecting the performance of local government organizations in the municipal category is not consistent with empirical data. The researcher therefore adjusted the model 6 times, as shown in details in Figure 8 and Table 12-13. 4205 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate Table 12. Results of the analysis of validity and importance weight of the factor structural equation model of Innovation and organizational management that affect the performance of local government organizations in the municipality category after adjusting the model Note: <--> = mandatory parameter, SE and t values are not reported. From Table 12, it was found that the operational and organizational management equation model Performance of local government in Thailand that has developed is valid Because the model is good fit with empirical data Considering the Chi-Square = 411.995, df = 226 RMSEA = 0.06, the GIF index = 0.96 is close to 1 and R2 / df = 1.82, which is less than 2 Component Weight of Observed. Variables of Latent All variables have positive values. and significantly different from zero at the .01 level by Observed Variables of Latent Internal Variables have the highest element weight: The goal setting variable (PM1) has a Standard Component Weight equal to 0.91. Observed Variables of Latent External Variable with the highest component weight is the New Service Concept (SI2) variable with a Standard Component Weight of 0.96. In contrast, the Observed Variables of Latent The internal variables with the least component weight are the service user variables (OP2) with a Standard Component Weight equal to 0.78, while the Observed Variables of Latent The external variable with the least component weight is the organizational culture variable (IO1) with a Standard Component Weight of 0.73. In addition, the reliability coefficient of the Observed Variables (R2) describes the covariance of the Observed Variables. External Variables ranged from 0.54 to 0.92 and Internal Observed Variables ranged from 0.61 to 0.89. Latent Variables Observed Variables Component Weight bsc SE t (R2) IO IO1 0.73** 0.05 15.98 0.54 IO2 0.89** 0.04 21.78 0.80 IO3 0.91** <--> <--> 0.84 IC IC1 0.90** 0.03 28.51 0.81 IC2 0.92** 0.03 29.56 0.85 IC3 0.93** <--> <--> 0.86 SI SI1 0.93** 0.03 35.94 0.87 SI2 0.96** 0.03 39.55 0.92 SI3 0.95** <--> <--> 0.91 PM PM1 0.91** <--> <--> 0.83 PM2 0.89** 0.04 30.82 0.89 PM3 0.80** 0.04 26.45 0.80 PM4 0.90** 0.04 26.36 0.81 OP OP1 0.80** <--> <--> 0.64 OP2 0.78** 0.05 18.29 0.61 OP3 0.90** <--> <--> 0.81 Chi-Square = 411.995, df = 226, c2/ df = 1.82, p = 0.163, RMSER = 0.06, GIF = 0.96 Remarks: **p<.01, bsc =Standard Component Weight 4206 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate Table 13. Standardized coefficients of influence in the structural equation model of innovation and organizational management factors affecting the performance of local government organizations of the type municipality. Note: Remarks: **Sig. < .01 * Sig. < .05 DE = Direct effect, IE = Indirect effect, TE = Total effect, – = No parameter according to Hypotheses From Table 13, all causal variables in the model have a positive influence on the Organizational variables, Performance of local government in Thailand by being able to jointly explain the variation in factors influencing Organizational Performance of local government in Thailand (OP) (R2) is 73% when considering the total influence on Organizational variables, Performance of local government in Thailand found that the Innovative Capability (IC) variable had the greatest total influence size of 0.80, followed by the Performance Management (PM) variable with a total influence size of 0.45, followed by the Service Innovation (SI) variable with a total influence size of 0.80. The total is equal to 0.23 and the Innovative Organization (IO) variable has a total influence size of 0.19. Each cause variable has a causal relationship and influence on the Organizational variable. Performance of local government in Thailand is arranged from highest to lowest as follows: 1) Innovative Capability (IC) variables are Latent Variables that have a positive influence on Organizational Performance of local government in Thailand and has the highest overall influence. It is a direct influence statistically significant at the .05 level, with an influence value of 0.24, indirect influence statistically significant at the .05 level, with an influence value of 0.56 and a total influence statistically significant at the .05 level, with an influence value of 0.80. 2) Performance Management (PM) variables are Latent Variables that have a positive influence on Organizational Performance of local government in Thailand and has the second largest overall influence, especially direct influence statistically significant at the .01 level, with an influence value of 0.45 and a total influence statistically significant at the .01 level, with an influence value of 0.45. 3) Service Innovation (SI) variables are Latent Variables that have a positive influence on Organizational Performance of local government in Thailand and has the third largest overall influence, especially direct influence statistically significant at the .05 level, with an influence value of 0.23 and a total influence statistically significant at the .05 level, with an influence value of 0.23. 4) Innovative Organization (IO) variables are Latent Variables that have a positive influence on Organizational Performance of local government in Thailand and has the 4th largest overall influence, with only indirect influence statistically significant at the .01 level, with an influence value of 0.19 and a total influence statistically significant at the .01 level, with an influence value of 0.19. In addition to the direct influence and indirect influence that affects Organizational Performance of local government in Thailand (OP) also has other variables that are directly influenced, including the Service Innovation (SI) variable that is directly influenced by the Innovative Capability (IC) variable with statistical significance at the .01 level. The influence size is 0.89 and the Performance Management Dependent Variable R2 Effect Independent Variable IO IC SI PM SI 0.80 DE - 0.89** - - IE - - - - TE - 0.89** - - PM 0.81 DE 0.42** 0.80** - - IE - - - - TE 0.42** 0.80** - - OP 0.73 DE - 0.24* 0.23* 0.45** IE 0.19** 0.56* - - TE 0.19** 0.80* 0.23* 0.45** 4207 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate (PM) variable is directly influenced by the variable. The variables Innovative Organization (IO) and Innovative Capability (IC) are statistically significant at the .01 level, with influence sizes equal to 0.42 and 0.80, respectively. From the development of a structural equation model for factors in innovation and organizational management that affect the performance of local government organizations in the type of municipality, it is found that the variable Innovative Capability (IC) is a factor that affects Organizational. Performance of local government in Thailand is the highest, followed by Performance Management (PM), Service Innovation (SI) and Innovative Organization (IO) variables, respectively, as shown in Figure 8. Figure 8. Structural equation model of innovation and organizational management factors that affect the performance of local government organizations in the municipality category after the sixth model revision. 4. Conclusion Hypothesis 1 Innovative Organization has a direct influence on Performance Management. The study results found that Innovative Organization has a direct influence on Performance Management (p<.01), which is consistent with the previous study by Ibrahim Rashed AlTaweel and Sulieman Ibraheem Al-Hawary (2021 ) and the study of Mohammed Saleh, et al. (2019), which is consistent with the study of Thi Thuc Anh Phan (2019) or even the results of the study of Abdul karim Suhag et al. (2015) that Innovative Organization is a factor that Conducive to Performance Management. In addition, the results of this study can also confirm the results of the study by Wattanachai Siriyan, Wittaya Charoensiri and Sanya Kenaphum (2017) that organizational culture Working atmosphere and employee behavior It is a component of Innovative Organization. Hypothesis 2: Innovative Capability has a direct influence on Performance Management. The results show that Innovative Capability has a direct influence on Performance Management (p<.01), which is consistent with the results of a previous study by Antonio Franco-Crespo and Juan Ibujes- Villacis. (2022) which has results in the same direction as the study of R.P. Jayani Rajapathirana and Yan Hui (2018) that innovation ability and the company's performance are significantly related. In addition, this study was able to confirm the Innovative elements. Capability consists of learning abilities. 4208 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate Innovative Organization ability and strategic ability This corresponds to the studies of Sasinipa Srikanlayaniwart (2022) and Silva&Cirani(2020). Hypothesis 3: Innovative Capability has a direct influence on Service Innovation. The results of the study found that Innovative Capability has a direct influence on Service Innovation (p<.01), which is consistent with the studies of Kong YuSheng and Masud Ibrahim (2020) and Malkah Noor Kiani, et. al. (2019) that Innovative Capability is a factor that facilitates Service Innovation. Therefore, this study can confirm the relationship between Innovative Capability that directly influences Service Innovation has gotten even more. Hypothesis 4: Performance Management has a direct influence on Organizational Performance. The results of the study found that Performance Management has a direct influence on Organizational Performance (p<.01), which is consistent with the study of Wen Zhang, et al. (2023) that Performance Management and Organizational Performance have a significant relationship, indicating that Performance Management is a factor contributing to Organizational Performance, which is consistent with studies by Julio C. Acosta-Prado, et al. (2020) and Janes O. Samwel (2018). This study can confirm the relationship between Performance. Management that has direct influence Organizational Performance gets more Hypothesis 5: Innovative Capability has a direct influence on Organizational Performance. The results of the study found that Innovative Capability has a direct influence on Organizational Performance (p<.05), which is consistent with the results of the study by Augustina Asih Rumanti, et al. (2022). That said, Innovative Capability and Organizational Performance are significantly related, showing that Innovative Capability is a factor that contributes to Organizational Performance and is also consistent with the study of Jameel Al-kalouti, et al. (2020). This study can confirm the relationship of Innovative. Capability that directly influences Organizational Performance gets more Hypothesis 6: Service innovation has a direct influence on Organizational Performance. The study results found that Service innovation has a direct influence on Organizational Performance (p<.05), which is consistent with previous studies. For example, a study by Tadhg Blommerde (2022) found that service innovation and Organizational Performance is significantly related. It shows that service innovation is a factor contributing to Organizational Performance, which is consistent with studies by Masud Ibrahim and Kong Yusheng (2020) and Angelica Nataya1, J. E. Sutanto (2018). This study can confirm the relationship between innovation. Services that have direct influence Organizational Performance gets more. 4209 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 4197-4211, 2024 DOI: 10.55214/25768484.v8i6.2912 © 2024 by the authors; licensee Learning Gate 4.1. New Academic Discoveries from Research Figure 9. Findings from the Organizational structural equation model Performance of local government in Thailand. Copyright: © 2024 by the authors. 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