Green Human Resource Management and Energy Transition Behavior: Evidence from Structural Equation Modeling and Artificial Neural Networks

This study examines how green human resource management (GHRM) practices influence employees’ energy transition behavior (ETB) through the theory of planned behavior (TPB), the ability–motivation–opportunity (AMO) framework, and organizational citizenship behavior (OCB) theory. Methodology: We surveyed 492 employees of a large electricity utility and analyzed the data using partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN). GHRM was validated as a multidimensional system that positively influenced employees’ attitudes, subjective norms, perceived behavioral control, and organizational citizenship behaviors; perceived behavioral control and OCB fully mediated GHRM’s effect on ETB. The ANN model confirmed these results, highlighting perceived behavioral control and OCB as the strongest predictors of ETB. Implications: These findings suggest that managers should implement comprehensive GHRM practices to foster employees’ ability, motivation, and opportunities to engage in energy-saving actions, thereby enhancing perceived control and OCB that support organizational energy transition goals.