No abstract

The objective of this research was to develop guidelines on the prevention of offenses under Thailand’s computer-related crime act for industrial business. This research was conducted both qualitatively and quantitatively. Quantitative data were obtained from a questionnaire survey with 500 IT executives in the industrial sector. The data analysis employed descriptive, inferential, and descriptive statistics. The results found that guidelines on the prevention of offenses under Thailand's computer-related crime act for industrial business can be prioritized in all four components as follows: 1) morality (X̅ = 4.21), 2) workforce (X̅ = 4.18), 3) internal control (X̅ = 4.16), and 4) punishment (X̅ = 4.14). The hypothesis testing revealed that large, small, and medium-sized industrial businesses gave a significant difference to guidelines for the prevention of offenses under Thailand's computer-related crime act at a statistically significant level of 0.05. The results of the developed structural equation modeling showed that all values were above the evaluation criteria and consistent with the empirical data. The chi-square probability level value was 0.092. The relative chi-square was 1.134. The conformity index was 0.960. The root index of the squared mean of the error estimation was 0.016.

This research aimed to analyze the confirmatory factors and validate the compliance between the confirmatory factor structure of industrial inventory management optimization and empirical data. The sample were 500 industrial executives in Thailand. The research tool was a questionnaire, with a reliability value of 0.95. The data was analyzed with confirmatory factor analysis and second order confirmatory factor analysis. The results found that the factors of inventory management optimization comprised four factors based on the Deming Cycle, including Planning (Plan), Implementation (Do), Assessment (Check), and Improvement (Act). The findings of first order confirmatory factor analysis showed that all index values were over the criteria with a composition weight of 0.72-0.87 at a statistical significance level of 0.01. The second order confirmatory factor analysis showed that all index values were over the criteria with a composition weight of 0.86-0.91 at a statistical significance level of 0.01. The model was congruent with the empirical data. The results of model validation indicated the p = 0.46, CMIN/DF = 0.99, GFI = 0.99 and RMSEA = 0.00. The results of this research could be applied for further improvement of the efficiency of the organization's inventory management.