Table 3.

Summary of key findings.

Author, year, country	Accident Causation method	Study aim	Study design	Population/industry	Key findings
Woolley, M. Goode, N. Read, G. & Salmon, P. 2018 Australia	AcciMap	To apply the AcciMap technique to the construction industry to analyse the contributing factors to prevent incident reoccurrence.	AcciMap technique was applied to accident investigation reports to analyse the contributing factors in the reports. 100 serious injury or high potential accident investigation reports from five Australian construction organisations.	Three medium-sized (100–500 employees) and two large organisations (501–2000 employees) in the Construction industry. Used Incident Causation and Accident Analysis method (ICAM)	AcciMap has not been applied in the construction context. Seven levels were identified: Government, regulatory bodies, client and external associations, company management, operational management, staff and work. Reports focused on human error and decision-making factors. Need to also focus on understanding accident and causation from a systems perspective.
Filho, A. Berlink, T & Vasconcelos, T. 2019 Brazil	HFACS	To analyse the causal factors of machine and equipment accidents from 2009 to 2014 in Brazil.	Four analysts coded each incident/accident case following a training using the HFACS framework. Cases were coded independently.	Accident reports from the Ministry of Labour classified as industrial activities involving machines and equipment.	A total of 96 accident casual factors were coded in active errors, preconditions for active errors and unsafe supervision. Unsafe acts were identified in 90.5 % of cases. Unsafe supervision was identified in 87.1 % of cases. Organisational influences were identified in only 56 % of cases.
Woolley, M. Goode, N. Read, G. & Salmon, P. 2019 Australia	Cross section of models	To determine if construction accident analysis literature has applied a systems thinking approach to understand accident causation.	A literature review of the three types of accident causation models, simple linear, complex linear and complex non-linear models.	Construction sector	Three types of accident causation models. Simple linear – culmination of predictable preventable series of events. Complex linear – interaction of underlaying latent conditions and unsafe human acts for example the Swiss Cheese Model. Complex non-linear – system-wide factors and complex interactions between individuals, organisations, technology, behaviours, performance and safety. Identified 266 contributing factors. There was a deficit in systems thinking. An absence of identification or examination of regulatory factors. Lack of focus on higher level factors.
Yousefi, A. Hernandez, M.R. & Pena, V.L. 2018 Spain	Comparison Between AcciMap, FRAM and STAMP	To compare and discuss the difference in using FRAM, STAMP and AcciMap to analyse a single accident.	Case study comparison of three accident analysis models used to analyse the Chevon Richmond refinery accident which was investigated by the U.S. Chemical Safety and Hazards Investigation board.	In 2012 the Chevon Richmond refinery experienced a catastrophic pipe rupture of light gas oil. Following the accident 15,000 people in the area sought medical attention.	AcciMap was used officially while FRAM and STAMP were applied to the Chevron accident retrospectively. The results indicated that the STAMP model was more instrumental and comprehensive in generation of recommendations because it captured failures, inadequate controls, unsafe decisions, control actions and process model flaws. STAMP provided a more complete understanding of the causes of the accident, enhancing the ability to identify system improvements.
Marquardt, N. 2019 Germany	Situation Awareness (SA)	To test the assumptions of the triple-loop learning model of human error and SA in a sociotechnical real-world manufacturing work environment.	Teams of 15–20 people rotated to simulated marked workstations in the production line and answered the situation awareness performance test and the human error questionnaire.	A total of 108 employees of a large automotive manufacturing company were tested using the situation awareness performance test (SAPT) and the human error questionnaire.	The human error questionnaire was based on the “Dirty Dozen Errors” originating from aviation maintenance and covers the 12 most common causes of human error. The study found that having an appropriate mindset of error causation is a prerequisite for organisational learning and maintaining adequate situation awareness. This simulated study was in a single industry and would need further testing to assess its application in other sectors.