A new approach to analysing human-related accidents by combined use of HFACS and activity theory-based method
Autor: | Dong-Han Ham, Wan Chul Yoon, YS Yoon |
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Rok vydání: | 2017 |
Předmět: |
050210 logistics & transportation
Engineering business.industry 05 social sciences Human error Accident analysis Root cause computer.software_genre Causality Computer Science Applications Human-Computer Interaction Philosophy Risk analysis (engineering) Taxonomy (general) 0502 economics and business 0501 psychology and cognitive sciences Human Factors Analysis and Classification System Data mining Set (psychology) business computer 050107 human factors Reliability (statistics) |
Zdroj: | Cognition, Technology & Work. 19:759-783 |
ISSN: | 1435-5566 1435-5558 |
DOI: | 10.1007/s10111-017-0433-3 |
Popis: | This study proposes a new method for modelling and analysing human-related accidents. It integrates HFACS (Human Factors Analysis and Classification System), which addresses most of the socio-technical system levels and offers a comprehensive failure taxonomy for analysing human errors, and AT (Activity Theory)-based approach, which provides an effective way for considering various contextual factors systematically in accident investigation. By combining them, the proposed method makes it more efficient to use the concepts and principles of AT. Additionally, it can help analysts use HFACS taxonomy more coherently to identify meaningful causal factors with a sound theoretical basis of human activities. Therefore, the proposed method can be effectively used to mitigate the limitations of traditional approaches to accident analysis, such as over-relying on a causality model and sticking to a root-cause, by making analysts look at an accident from a range of perspectives. To demonstrate the usefulness of the proposed method, we conducted a case study in nuclear power plants. Through the case study, we could confirm that it would be a useful method for modelling and analysing human-related accidents, enabling analysts to identify a plausible set of causal factors efficiently in a methodical consideration of contextual backgrounds surrounding human activities. |
Databáze: | OpenAIRE |
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