Impact analysis of behavior of front-line managers on employee safety behavior by integrating interpretive structural modeling and Bayesian network

Employee safety behavior is a basic element of enterprise work safety. The results of accident investigations and risk assessments in enterprises indicate that management factors are some of the most important factors that affect employee safety behavior. The purpose of this study is to explore the relationship between the behavior of front-line managers (FLMs) and employee safety behavior by integrating a qualitative method, i.e., the interpretive structural model (ISM), and a quantitative method, i.e., the Bayesian network (BN). The results of the BN analysis showed that safety incentives and safety communication were the best predictors of safety participation, while safety supervision and safety control were the best predictors of safety compliance. Moreover, the results revealed that an instantaneous improvement of safety communication, safety incentives, safety supervision and safety guidance was the most effective joint measure to reach a high-level of safety behavior of employees in the workplace.

Automated summary

Employee safety behavior is crucial for work safety in enterprises. This study investigated the relationship between the behavior of front-line managers and employee safety behavior using a combination of qualitative (interpretive structural model) and quantitative (Bayesian network) methods. The results revealed that safety incentives and safety communication were the main predictors of safety participation, while safety supervision and safety control were the main predictors of safety compliance. Furthermore, the study found that instant improvements in safety communication, safety incentives, safety supervision, and safety guidance were the most effective measures to achieve a high level of employee safety behavior in the workplace.