A Deep Learning Framework for Modeling the Impact of Transformational Leadership and Organizational Climate on Continuous Innovation Capability - Mokoena, Thabo; Richter, Johannes (International Journal of Innovation Management and Organizational Behavior (IJIMOB), 2026)
Objective: The objective of this study was to develop and validate a deep learning model capable of explaining and predicting the combined effects of transformational leadership and organizational climate on continuous innovation capability within organizational settings. Methods and Materials: The study employed a quantitative cross-sectional design using data collected from 684 full-time employees and managers working in medium-to-large organizations in Germany. Transformational leadership was measured using the Multifactor Leadership Questionnaire, organizational climate was assessed through a standardized organizational climate scale, and continuous innovation capability was captured using a multidimensional innovation capability instrument. After establishing construct validity and reliability through confirmatory factor analysis, the dataset was analyzed using a deep neural network implemented with TensorFlow and Keras. Data were split into training, validation, and test sets (70–15–15). Model optimization employed Bayesian hyperparameter tuning, and performance was evaluated using mean squared error, root mean squared error, coefficient of determination, and prediction accuracy. Model interpretability was enhanced using SHAP analysis and permutation feature importance techniques. Findings: Inferential results demonstrated strong predictive performance of the deep learning model (R² = .89 training, .87 validation, .86 test; prediction accuracy = 90.4% on test set). Transformational leadership and organizational climate jointly explained a substantial proportion of variance in continuous innovation capability. Feature importance analysis revealed inspirational motivation and innovation-oriented climate as the most influential predictors, followed by intellectual stimulation and supportive climate. The deep neural network significantly outperformed random forest, gradient boosting, and multiple regression models. Conclusion: The findings confirm that continuous innovation capability is governed by complex, non-linear interactions between transformational leadership and organizational climate and that deep learning provides a superior analytical framework for modeling these dynamics, offering substantial theoretical and practical implications for innovation management.