This study aimed to examine the relationship between demographic characteristics and job satisfaction among workers with disabilities and to explore the mediating role of self-esteem in this relationship. Data were drawn from the 2023 Panel Survey of ...
This study aimed to examine the relationship between demographic characteristics and job satisfaction among workers with disabilities and to explore the mediating role of self-esteem in this relationship. Data were drawn from the 2023 Panel Survey of Employment for the Disabled, 2nd Wave, 8th Survey, conducted by the Korea Employment Agency for Persons with Disabilities. Among the registered persons with disabilities included in the survey, 1,514 workers with disabilities who provided complete responses to the variables used in this study were selected as the final sample.
The independent variables were demographic characteristics, including gender, age, education level, marital status, residential area, and possession of a professional certificate. Self-esteem was set as the mediating variable, and job satisfaction was set as the dependent variable. Frequency analysis, descriptive statistics, regression-based path analysis, and hierarchical regression analysis were conducted to identify the relationships among the major variables.
The results of the study are as follows. First, demographic characteristics were found to be significantly related to job satisfaction among workers with disabilities. Education level and marital status showed positive relationships with job satisfaction, indicating that workers with higher education levels and those who were married tended to report higher levels of job satisfaction. In contrast, gender, age, and residential area showed negative relationships with job satisfaction. Specifically, being male, being older, and residing in the Seoul metropolitan area or a major city were associated with lower job satisfaction. However, possession of a professional certificate was not significantly related to job satisfaction.
Second, self-esteem showed a significant positive relationship with job satisfaction. When self-esteem was added to the model, the explanatory power increased from 7.5% to 11.1%. This result indicates that self-esteem contributes to explaining job satisfaction among workers with disabilities beyond demographic characteristics. In other words, workers with disabilities who perceive themselves as valuable and competent are more likely to experience higher levels of satisfaction in their work.
Third, the changes in standardized coefficients after adding self-esteem suggest that self-esteem may play an explanatory role in the relationship between some demographic characteristics and job satisfaction. In particular, the effect of education level on job satisfaction decreased but remained statistically significant after self-esteem was included in the model. This suggests the possibility of a partial mediating role of self-esteem. In the case of marital status, its significant relationship with job satisfaction disappeared after self-esteem was added, suggesting that self-esteem may partially explain the relationship between marital status and job satisfaction. However, because the indirect effects were not directly tested through bootstrapping, these findings should be interpreted as indicating the possibility of mediation rather than confirming a definitive mediating effect.
Based on these findings, this study suggests that improving job satisfaction among workers with disabilities requires not only support for education and vocational competency development but also psychological and emotional interventions aimed at enhancing self-esteem. In addition, family support, workplace adjustment support, age-sensitive employment services, and region-based employment policies should be strengthened. This study is meaningful in that it highlights the importance of self-esteem as a psychological factor related to job satisfaction among workers with disabilities. However, future studies should use longitudinal data and more advanced structural equation modeling with item-level variables to verify causal relationships and indirect effects more precisely.