Exploring How Employee Satisfaction Moderates the Relationship between Firm Performance and Innovative Behaviour Based on Machine Learning Models

This study explores how employee satisfaction moderates the relationship between corporate performance and innovative behavior using deep learning models: Autoencoder and restricted Boltzmann machines (RBM). The Autoencoder extracts key features for better analysis, while the RBM-based model analyzes the relationships among employee satisfaction, corporate performance, and innovative behavior. Results show a positive correlation between employee satisfaction and innovative behavior (0.460) and between innovative behavior and corporate performance (0.348). Regression analysis reveals that employee satisfaction indirectly impacts corporate performance through innovative behavior (impact: 0.10, t = 5.25). Differences in satisfaction, innovative behavior, and performance were observed across employee attributes. This study highlights the role of employee satisfaction in enhancing corporate performance and innovation, offering insights for human resource strategies.