Wirote Phanayingphaisal, Sakrapee Worawattanaparinya
Aim: This research investigates the effective management guidelines for Industrial Estate Authority to support sustainable growth of the country.
Methodology: The mixed method of qualitative and quantitative study was employed to collect data. Questionnaires were administered with 500 supervisors working in the factories located in the Authority of Industrial Estate. The data were used to develop simulation equation model congruent with the empirical data of light and heavy industry.
Finding: The results revealed that: 1) the effective management guidelines for Industrial Estate Authority to support sustainable growth of the country consisted of 4 main components, i.e. plan and policy defining, environment management, infrastructure and transportation management, and knowledge management. The informants rated the importance and the efficiency of the management model at high level with the average of 4.01 and 4.05. 2) The evaluation of the management model showed the congruence with the empirical data with the index of objective congruence (IOC) at 0.969 and the root mean square error of approximation at 0.021. 3) The hypothesis result showed that the plan and policy-defining factor has direct influence on the environment management factor with the factor loading value of 0.29. Moreover, this factor has direct influence on the infrastructure and transportation factor at the factor loading value of 0.52 with 0.001 statistically significant values. However, the plan and policy-defining factor did not show direct influence on the knowledge management factor. The infrastructure and logistics structure show direct influence on the environment management factor with the factor loading value of 0.61. It influences the knowledge management factor at 0.57 with 0.001 statistically significant values.
Conclusion: It can be concluded that the effective management guidelines for Industrial Estate Authority to support sustainable growth of the country consists of 4 main components, i.e. plan and policy defining, environment management, infrastructure and logistics management, and knowledge management with the highest value on the knowledge management, environment management, infrastructure and logistics management, and plan and policy defining respectively. The supervisors from both light and heavy industry also gave the highest importance on the knowledge management factor. According to the evaluation of the model, it was found that the developed guidelines were congruent with the empirical data at the qui square probability of 0.056, relative qui square of 1.219, congruent index of 0.969, and the root mean square error of approximation of 0.021.