NLP-Driven Psychological Contract Risk Detection in Cross-Cultural Teams: An XGBoost Approach with Cultural Adaptation

Abstract

Cross-cultural workforce management in multinational manufacturing operations faces unprecedented challenges in maintaining employee retention during the U.S. manufacturing resurgence. This research introduces an intelligent psychological contract breach detection mechanism combining natural language processing with XGBoost ensemble learning, integrated with Hofstede's six cultural dimensions. Text mining techniques extract sentiment indicators from multi-source employee communications including emails, meeting transcripts, and feedback sessions across six Chinese-invested U.S. manufacturing projects. The proposed framework digitizes the Psychological Safety Scale into continuous micro-surveys, triggering automated alerts when metrics deviate beyond one standard deviation. Validation across 847 employees demonstrated 91.3% prediction accuracy with 0.89 AUC-ROC, achieving 11% first-year turnover compared to industry baseline of 28%. SHAP interpretability analysis reveals power distance and sentiment polarity as dominant predictive features, enabling proactive intervention recommendations for HR practitioners in culturally diverse teams.