Leadership is essential in the digital era for managing change and propelling business strategies through innovation, due to the speed of technological progress, the new demands of customers and increased competition. The IBM HR Analytics Employee Attrition & Performance dataset, which includes a comprehensive data pre-processing pipeline with data cleaning, feature selection, normalization, and class balancing, is used to examine the impact of digital leadership on innovation capability and performance using machine learning methods. Decision Tree (DT) and Light Gradient Boosting Machine (LGBM) were among the provided prediction models that achieved 98% accuracy (ACC). The DT model showed the precision (PRE) to be 98%, the recall (REC) to be 92%, and the F1 score (F1) to be 95%, while the LGBM model showed 98% precision, 91% recall, and 94% F1 score, respectively, demonstrating the capacity of the model to learn the complex patterns and make fairly good predictions. In summary, the results emphasize the potential of ML to improve the data-driven decision-making for digital leadership and capability for innovation.

Organization Performance, digital leadership, digital transformation

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