A Hybrid PCA–Random Forest Model for Predicting Employee Performance from Work Ethics and Work Values

This study examined the predictive relationship of work ethics and work values with employee task performance using a hybrid PCA–Random Forest model. Data were obtained from 231 PSU employee respondents and 81 questionnaire variables covering task performance, work ethics, and work values. A quantitative, cross-sectional predictive design was used; therefore, the findings are interpreted as predictive associations rather than causal effects. Descriptive statistics, reliability analysis, correlation analysis, Principal Component Analysis (PCA), baseline regression models, Random Forest regression, and a hybrid PCA–Random Forest model were applied. PCA reduced the 74 predictor items into 19 components, explaining 80.9% of the predictor variance. On the held-out test set, the hybrid PCA–Random Forest model achieved MAE = 0.272, RMSE = 0.383, and R² = 0.524. Standard Random Forest produced a similar test performance (MAE = 0.283, RMSE = 0.383, R² = 0.526), indicating that PCA did not substantially improve accuracy but produced a more compact and less redundant feature representation. Professionalism, commitment to public interest, and nationalism and patriotism emerged as important predictors of task performance. The study demonstrates the usefulness of interpretable machine-learning models for evidence-based human resource development in public higher education, while noting limitations related to ceiling effects, self-report data, single-institution sampling, and the need for external validation.