Understanding employee digital learning engagement and innovative work behavior in hospitality sectors: A machine learning based multistage approach

Based on learning engagement theory, we develop and test a theoretical framework that examines key employee digital learning engagement directly affecting drivers and other indirect drivers of innovative work behavior in hospitality. We adopted the partial least squares structural equation model-small batch training-based neural network approach for the linear and nonlinear analyses. According to the sample of hospitality sectors employees’ responses, the linear results demonstrate that a) employees’ digital learning engagement has a direct impact on innovative work behavior, b) commitment to innovation plays a mediating role between employee digital learning engagement directly affecting drivers and innovative working behavior, and c) organizational climate support moderates the relationship between commitment to innovation and innovative work behavior. The nonlinear results show that employees’ digital emotion learning engagement and commitment to innovation have strong nonlinear effects owing to the importance of nonlinear normalization. The contributions to theory and their practical implications are discussed accordingly.