A hybrid HFACS-BN model for analysis of mongolian aviation professionals' awareness of human factors related to aviation safety

Abstract

Safety is crucial to ensure the sustainability of aviation growth. To better clarify the influences of human factors on aviation accident risks, this study developed a hybrid HFACS-BN model (HFACS: Human Factors Analysis and Classification System; BN: Bayesian Network). The authors designed and implemented a questionnaire survey based on the four-level HFACS framework and collected valid data from 180 out of 649 aviation professionals working in the Ulaanbaatar International Airport, Mongolian in 2017. The model identified 35 major human factors out of 129 factors. The model validation was performed in terms of content validity and predictive validity. The results showed that even though a majority of respondents perceived that many human factors had a middle- or high-effect on aviation accident risks, the probability of the risks caused by human factors was estimated to be just 1.37%. The Unsafe Acts level is most influential to the risks among the four levels, while the Unsafe Supervision level contributes least. It is revealed that enhancing aviation professionals' awareness of human factors should make full use of causal chaining effects among human factors. Finally, this study contributes to the literature from the perspectives of both methodological development and important empirical analysis.