Purpose: This study empirically verifies the relationship between the quality of education in on-site employee job training institutions and the job competencies of organizational members, focus on a comparative analysis between supervisors and employees, and machine learning clustering. First, the direction of the components within education quality is identified and the model for the causal relationship between education quality and job competency is compared and analyzed. Next, the interaction effect of the cognitive level of the education quality components on job competency is confirmed and the points of improvement in education quality are derived through comparison between members and superiors.Methods: This study conducted a survey to identify the causal relationship between education quality and job competency among military unit quality education graduates and verified through the structural equation model analysis, Machine learning clustering and response surface method using the R statistical program. The questionnaire was created under a theoretical background, and the structural equation model analysis method was used to determine the significance of the paths appearing in the research model. Machine learning clustering and response surface method were used to derive the characteristics of clusters and ideas for customized strategies.Results: This study sought to explore educational intervention strategies that can enhance job competency by providing sustainable education quality from a managerial perspective of job training institutions. The quality of educational services experienced in the field was found to have a positive effect on job competency. Furthermore, the job suitability of education influenced field usefulness, which in turn led to improved job competency—demonstrated through empirical evidence. The interaction effect between job suitability and field usefulness on job competency showed differences between supervisors and members. Additionally, the study confirmed the need for differentiated strategies for tailored education and competency development based on the characteristics of each cluster identified through machine learning-based clustering.Conclusion: By confirming the direction of education quality improvement that meets the needs and demands of field work, it can help increase the job competency of field workers by improving the differentiated education quality of related educational institutions
Hong et al. (Tue,) studied this question.