STRUCTURAL EQUATION MODELING OF FACTOR AFFECTING SMALL THAI FABRIC PRODUCT ENTERPRISES IN THAI SOUTHERN BORDER PROVINCE - Rattanapong, Nopparat; Thonglim, Thongtang; Inthawongse, Choat; Thurapaeng, Chertchai (Life Sciences and Environment Journal, 2024)
The objectives of this study were 1) to investigate the factors affecting the operating efficiency of small enterprises producing Thai fabric products in the southern border provinces of Thailand and 2) to analyze the structural equation modeling of the factors affecting operating efficiency. Small enterprises work on Thai fabric products in the southern border provinces of Thailand. A combination of quantitative and qualitative research was used for the study. For the quantitative research, 340 business owners, managers or directors of small businesses working on Thai fabric in the southern border provinces of Thailand were interviewed. The instruments used in the study included interviews, questionnaires, and observation questionnaires. Reliability and validity were assessed and scored 0.912. The statistics used for data analysis are mean and standard deviation, structural equation modeling, causal influence analysis, and confirmatory component analysis of latent variables. The statistics used in the data analysis are mean and standard deviation. Structural equation modeling, causal influence analysis and confirmatory component analysis of the latent variables. The results of the study show that the structural equation modeling of factors affecting the operating efficiency of small firms producing Thai fabric products in the southern border provinces. It consists of three latent variables that are independent variables, namely internal factors (INF), external factors (EXF), and entrepreneurship (ENT), and one latent variable that is the dependent variable, namely labor efficiency (EFF). The structural equation modeling (SEM) analysis yielded the following index values: Chi-square = 7.554, Chi-square/df = 1.511, df = 5, P = 0.183, CFI = 0.995, RMR = 0.028, GFI =0. 978, RMSEA = .034, and each observed variable is statistically significant at the 0.05 level, indicating that the indicator or observed variable with a value between 0.45-0.69 reliably represents the group of latent variables.