Predictive Modeling of Innovation Failure Risk from Organizational Stress, Workload Distribution, and Team Conflict Using Machine Learning Classification - Thompson, Daniel; Rahman, Nur Aisyah; Deshmukh, Priyanka (International Journal of Innovation Management and Organizational Behavior (IJIMOB), 2026)
Objective: The objective of this study was to develop and validate a machine learning–based predictive model for estimating innovation failure risk using organizational stress, workload distribution, and team conflict as primary predictors. Methods and Materials: This quantitative cross-sectional study was conducted among 612 full-time employees from innovation-driven organizations in Malaysia. Data were collected using standardized survey instruments measuring organizational stress, workload distribution, team conflict, and perceived innovation failure risk. After psychometric validation, the dataset underwent preprocessing including normalization, outlier detection, and feature engineering. Innovation failure risk was converted into a binary classification outcome. Multiple machine learning classifiers were trained and compared, including logistic regression, support vector machines, random forest, gradient boosting, and extreme gradient boosting. Hyperparameter optimization and nested cross-validation were applied to ensure model stability and generalizability. Findings: The XGBoost classifier achieved the highest predictive performance with an accuracy of 94%, precision of 93%, recall of 92%, F1-score of 92%, and AUC of 0.97, significantly outperforming all baseline models. Feature importance analysis revealed that emotional exhaustion and task overload were the strongest predictors of innovation failure risk, followed by relationship conflict and resource imbalance. The final model demonstrated high sensitivity for detecting high-risk innovation cases, confirming the robustness and reliability of the proposed predictive framework. Conclusion: The findings demonstrate that innovation failure risk is strongly driven by human-centered organizational factors and can be accurately predicted using advanced machine learning models. The proposed framework provides organizations with a powerful early-warning system for preventing innovation breakdowns and strengthening innovation sustainability through proactive management of psychological and structural risk factors.