Machine Learning Identification of Innovation Bottlenecks: A Behavioral Analytics Approach Using Gradient Boosting Models
Keywords:
Innovation bottlenecks, behavioral analytics, organizational behavior, leadership, psychological safety, innovation managementAbstract
Objective: The objective of this study was to develop and validate a machine learning–based behavioral analytics framework for identifying organizational innovation bottlenecks through the interaction of leadership, psychological, and behavioral factors.
Methods and Materials: This study employed a cross-sectional explanatory design involving 547 employees and middle-level managers from diverse organizations in Georgia. Data were collected using validated behavioral, psychological, and organizational measures capturing resistance to change, psychological safety, leadership support, communication friction, knowledge sharing, learning orientation, and innovation outcomes. Organizational performance indicators were integrated with survey data to enhance behavioral signal extraction. Gradient boosting algorithms (XGBoost, LightGBM, CatBoost) and an optimized ensemble model were implemented using five-fold cross-validation and Bayesian hyperparameter tuning. Feature engineering and explainable artificial intelligence techniques (SHAP values) were applied to uncover the relative importance and interaction effects of predictors.
Findings: The ensemble model demonstrated strong predictive performance (R² = 0.846, RMSE = 0.387), explaining nearly 85% of the variance in innovation bottleneck intensity. Resistance to change was the strongest positive predictor, while leadership support for innovation, psychological safety, knowledge sharing quality, and proactive behavior significantly reduced bottleneck severity. Communication friction and excessive process formalization amplified innovation constraints. Behavioral segmentation revealed four distinct innovation profiles, with “Resistant Traditionalists” exhibiting the highest bottleneck levels and “Adaptive Innovators” the lowest.
Conclusion: The findings confirm that innovation bottlenecks are systemic behavioral–organizational phenomena emerging from complex non-linear interactions among leadership dynamics, employee psychology, communication structures, and organizational culture.
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