Technological innovation is driving the intelligent transformation of China’s coal mining industry, leading to significant changes in miners’ working methods and risk structures. To explore the predictors of miners’ safety citizenship behaviors in an intelligent mining environment, this study introduces regulatory focus based on the JD-R model of miners and proposes safety climate and self-efficacy as additional predictors. Using multiple methods including machine learning, response surface methodology (RSM), and latent profile analysis (LPA), data from a sample of 1168 miners were analyzed. The results indicate that the random forest model performed best, with the lowest prediction error and strongest explanatory power. In the variable importance analysis, safety climate (SAC), promotion focus (PRF), prevention focus (PF), and self-efficacy (SE) were identified as key factors influencing miners’ safety citizenship behaviors. Additionally, four distinct miner work characteristic groups were identified, showing significant differences; the more aligned the job demands and resources, the higher the safety citizenship behavior. This study aims to provide a basis for segmented and classified management in coal mine safety management from the perspective of multi-method evidence and heterogeneity.
Lei et al. (Thu,) studied this question.