A Bayesian network model integrating organizational, individual and psychological factors for strengthening construction worker safety behavior

Abstract

Objectives. Construction worker safety behavior (CWSB) may be affected by a confluence of multilevel and interrelated factors. Cultivating and maintaining CWSB is vital for improving construction safety. Current studies focus on organization-level or individual-level CWSB antecedents. However, few studies have examined the influence of psychological factors on CWSB, thereby reducing the joint effects of multilevel factors on CWSB. Methods. To determine effective strategies for strengthening CWSB, this study adopted the Bayesian network technique to explore the interrelationships between CWSB and its antecedent factors. A Bayesian belief network model was developed and trained with data collected from Chinese construction workers, which connected organizational, individual and psychological factors with CWSB. Results. According to the sensitivity analysis, safety knowledge, safety climate and psychological capital are the three most significant influencing factors for CWSB. A combined strategy that enhances safety knowledge, safety climate and communication competence simultaneously is the most effective option for strengthening CWSB. The validation and robustness of the network showed good accuracy for safety behavior judgment. Conclusion. This study proposes an alternative way to improve safety behavior by identifying its interactive causes and illustrates the importance of initiating systematic safety measures, which may help to mitigate the problem of safety plateau.