KMAN Publication Inc. (KMANPUB) Journal of Psychosociological Research in Family and Culture 3041-8550 4 Serial Number 13 2026 01 01 Machine Learning Identification of Cultural Value Systems Using Collectivism, Power Distance, and Uncertainty Avoidance Machine Learning Identification of Cultural Value Systems Using Collectivism, Power Distance, and Uncertainty Avoidance 1 10 10.61838/kman.jprfc.5346 EN Journal Article 2026 07 06 Objective: The present study aimed to identify and model latent cultural value system profiles using machine learning techniques based on collectivism, power distance, and uncertainty avoidance. Methods and Materials: This study employed a cross-sectional, descriptive–analytical design with a predictive modeling approach. The sample consisted of 512 adult participants from Canada selected through stratified random sampling to ensure demographic and cultural diversity. Data were collected using standardized instruments measuring collectivism, power distance, and uncertainty avoidance, all of which demonstrated established validity and reliability in prior research. After data preprocessing, including normalization and handling of missing values, both supervised and unsupervised machine learning techniques were applied. Classification models included support vector machines, random forest, gradient boosting, and logistic regression, while k-means clustering was used to identify latent cultural profiles. Model evaluation was conducted using stratified k-fold cross-validation, with performance metrics including accuracy, precision, recall, F1-score, and AUC-ROC. Feature importance and interpretability were assessed using SHAP analysis. Findings: The results indicated significant positive associations among collectivism, power distance, and uncertainty avoidance (p < 0.01). Among classification models, gradient boosting demonstrated the highest predictive performance (AUC-ROC = 0.927), followed by random forest (AUC-ROC = 0.912), indicating strong model discrimination. Logistic regression showed comparatively lower performance, suggesting the presence of nonlinear relationships among variables. Clustering analysis identified three distinct cultural profiles characterized by low, moderate, and high levels of the examined dimensions. Feature importance analysis revealed that power distance was the strongest predictor of cultural profile classification, followed by uncertainty avoidance and collectivism. Conclusion: The study underscores the importance of power distance and uncertainty avoidance in shaping cultural profiles and supports the utility of advanced computational methods in cultural research.

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