Predicting Employee Turnover in South Korea: Transformer-Based NLP with Cultural Context

This study introduces a BERT-based framework integrating cultural variables (e.g., hierarchical titles, collectivist norms) to predict employee turnover through semantic analysis of South Korean job postings. We collected 10,000 job ads from major platforms, culturally annotated them, and utilized 2,932 enterprise employee records. After fine-tuning KoBERT, we developed an NLP-survival hybrid model achieving an F1-score of 0.89 (95% CI [0.86-0.92]), significantly outperforming CNN (F1=0.78) and Logistic Regression (F1=0.72) baselines. Cultural variables critically influence turnover: emphasizing "loyalty" reduced risk by 18%, while hierarchical terms increased it by 30%. Enterprises can optimize job ads (e.g., reducing hierarchical language by 30%) to mitigate turnover. Theoretically, we validate Transformers for non-Western cultural text analysis and propose a "Cultural Sensitivity Index" (CSI) for model optimization. Practically, HR teams can apply CSI to refine job postings and deploy the hybrid model for real-time risk monitoring.