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Contributory factors interactions model: A new systems‐based accident model
Author(s) -
Jing Linlin,
Bai Qingguo,
Guo Weiqun,
Feng Yan,
Liu Lin,
Zhang Yingyu
Publication year - 2019
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/sres.2618
Subject(s) - sociotechnical system , accident (philosophy) , risk analysis (engineering) , systems thinking , affect (linguistics) , accident analysis , coal mining , computer science , computer security , engineering , psychology , knowledge management , transport engineering , business , artificial intelligence , coal , philosophy , communication , epistemology , waste management
Understanding the interactions between contributory factors associated with accidents is important. However, the widely applied Swiss cheese model (SCM) and the human factors analysis and classification system (HFACS) oversimplify the causes of accidents and ignore the interactions among the various components of an entire system, which is the essence of systems thinking. A new systems‐based accident model that reflects the nature of the accidents and can be explained by the underlying accident model must therefore be developed. This study aims to create a new accident model that represents the interactions between the contributory factors, which can offer a systems approach to conducting a comprehensive examination of accidents. We use data obtained from the official accident reports on 94 extraordinary major coal mining catastrophes that took place from 1997 to 2011 in China. The results reveal that all latent conditions, including external factors, organizational influences, unsafe supervision, and preconditions for unsafe behaviours can influence active failures, that is, unsafe behaviours. However, only the preconditions for unsafe behaviours can influence unsafe behaviours within the SCM and HFACS frameworks. Furthermore, latent conditions can affect each other within the contributory factors interactions model. This study provides a new systems‐based accident model that allows us to examine and explain the relationships and linkages within and between complex sociotechnical systems and identify the common factors and interactions that cross human, external, and organizational factors.