Feature Importance Analysis of Organizational Citizenship Behaviors in Driving Product Innovation Performance via XGBoost Models
Keywords:
Organizational Citizenship Behavior, Product Innovation Performance, Machine Learning; XGBoost, Feature Importance, SHAP AnalysisAbstract
Objective: The objective of this study was to identify and quantify the relative importance of organizational citizenship behavior dimensions in predicting product innovation performance using an explainable machine learning framework.
Methods and Materials: This study employed a quantitative cross-sectional design with data collected from 412 employees working in medium and large organizations. Organizational citizenship behaviors were measured across five dimensions including conscientiousness, civic virtue, courtesy, altruism, and sportsmanship, while product innovation performance was assessed using multidimensional performance indicators. Data were analyzed using the XGBoost algorithm with hyperparameter optimization and five-fold cross-validation. Feature importance was examined using both gain-based metrics and SHAP (SHapley Additive exPlanations) values to ensure transparent and robust interpretation of predictor contributions. Model robustness was evaluated through comparison with Random Forest and Elastic Net models.
Findings: The XGBoost model demonstrated strong predictive performance, explaining 68.4% of the variance in product innovation performance. Feature importance analysis revealed that conscientiousness was the most influential predictor, followed by civic virtue, courtesy, altruism, and sportsmanship. Conscientiousness exhibited the highest SHAP contribution, indicating its dominant role in driving innovation outcomes. Comparative analysis showed that XGBoost significantly outperformed Random Forest and Elastic Net across all evaluation metrics, confirming its superior capacity to capture nonlinear behavioral effects on innovation performance.
Conclusion: The findings confirm that organizational citizenship behaviors constitute critical behavioral drivers of product innovation performance and that advanced machine learning models provide superior analytical precision for understanding their complex influence. The study offers strong empirical evidence supporting the strategic integration of citizenship behavior development into organizational innovation policies.
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