Research to Determine the Frequency and Cause of Injury Accidents in Underground Mining

1987 ◽  
Vol 31 (8) ◽  
pp. 926-930
Author(s):  
Brian E. Shaw ◽  
Mark S. Sanders
Keyword(s):  
Human Error ◽  
Systems Approach ◽  
Underground Mining ◽  
Causal Chain ◽  
Causal Factors ◽  
Relative Contribution ◽  
Accident Case

A systems approach was used to investigate 188 underground mining accidents. A team of raters assessed the relative contribution of 10 causal factors in each accident case. The results illustrate the importance of human error and management in the causal chain of accidents.

Research to Determine the Frequency and Cause of Injury Accidents in Underground Mining

1989 ◽  
Vol 33 (15) ◽  
pp. 1004-1008 ◽  
Author(s):  
Brian E. Shaw ◽  
Mark S. Sanders
Keyword(s):  
Annual Meeting ◽  
Human Error ◽  
Underground Mining ◽  
Comprehensive Analysis ◽  
Data Set ◽  
Causal Factors ◽  
Relative Contribution ◽  
Technical Report ◽  
Accident Causation ◽  
Final Data

The objective of this project was to perform a comprehensive analysis of underground mining injury accidents to determine the relative contribution of various causal factors, including human error. A paper presented at the Human Factors Society 31st Annual Meeting (Shaw and Sanders, 1985) describes the methodology for assessing the relative contribution of various factors to accident causation and presented preliminary findings. A Bureau of Mines Technical Report (Sanders and Shaw, 1988) completely documents the research project and presents final findings and recommendations. The present paper focuses on the final data set to explore the multiplicity of contributing causal factors, and the underlying reasons for involvement of these factors, in underground mining accidents.

scholarly journals Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling

2019 ◽  
Vol 7 (4) ◽  
pp. 96 ◽  
Author(s):  
Shenping Hu ◽  
Zhuang Li ◽  
Yongtao Xi ◽  
Xunyu Gu ◽  
Xinxin Zhang
Keyword(s):  
Human Factors ◽  
Structural Equation ◽  
Human Error ◽  
Structural Model ◽  
Causal Chain ◽  
Equation Modeling ◽  
Prevention Measures ◽  
Hypothetical Model ◽  
Causal Factors ◽  
Marine Traffic

Many causal factors to marine traffic accidents (MTAs) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to accident mechanisms, the complex structural chains on causes to MTA systems were analyzed by combining the human failure analysis and classification system (HFACS) with theoretical structural equation modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of a MTA, and the constituent elements of the causes of the accident were conducted. Second, a hypothetical model of human factors classification was proposed by applying the practice of the structural model. Third, with the data resources from ship accident cases, this hypothetical model was discussed and simulated, and as a result, the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behavior. Application examples show that relationships in the HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

scholarly journals Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling

2019 ◽  
Author(s):  
Shenping Hu ◽  
Zhuang Li ◽  
Yongtao Xi ◽  
Xunyu Gu ◽  
Xinxin Zhang
Keyword(s):  
Human Factors ◽  
Structural Equation ◽  
Human Error ◽  
Structural Model ◽  
Causal Chain ◽  
Equation Modeling ◽  
Prevention Measures ◽  
Hypothetical Model ◽  
Causal Factors ◽  
Marine Traffic

Many causal factors to marine traffic accidents (MTA) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to the accident mechanisms, the complex structural chains on causes to MTA systems were analyzed combining the Human Failure Analysis and Classification System (HFACS) with theoretical Structural Equation Modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of MTA, and the constituent elements of the causes of accident was conducted. Second, a hypothetical model of Human factors classification was proposed applying the practice of the structural model. Third, with the data resource from ship accident cases, this hypothetical model was discussed and simulated, and as a result the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behaviors. Application examples show that relationships in HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

scholarly journals Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling

2019 ◽  
Author(s):  
Shenping Hu ◽  
Zhuang Li ◽  
Yongtao Xi ◽  
Xunyu Gu ◽  
Xinxin Zhang
Keyword(s):  
Human Factors ◽  
Structural Equation ◽  
Human Error ◽  
Structural Model ◽  
Causal Chain ◽  
Equation Modeling ◽  
Prevention Measures ◽  
Hypothetical Model ◽  
Causal Factors ◽  
Marine Traffic

Many causal factors to marine traffic accidents (MTA) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to the accident mechanisms, the complex structural chains on causes to MTA systems were analyzed combining the Human Failure Analysis and Classification System (HFACS) with theoretical Structural Equation Modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of MTA, and the constituent elements of the causes of accident was conducted. Second, a hypothetical model of Human factors classification was proposed applying the practice of the structural model. Third, with the data resource from ship accident cases, this hypothetical model was discussed and simulated, and as a result the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behaviors. Application examples show that relationships in HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

scholarly journals Human Failure: An Analysis of 2000 Incident Reports

1993 ◽  
Vol 21 (5) ◽  
pp. 678-683 ◽  
Author(s):  
J. A. Williamson ◽  
R. K. Webb ◽  
A. Sellen ◽  
W. B. Runciman ◽  
J. H. Van Der Walt
Keyword(s):  
Crisis Management ◽  
Human Error ◽  
Best Practice ◽  
Contributing Factors ◽  
Rule Based ◽  
Relative Contribution ◽  
Technical Errors ◽  
Human Failure ◽  
Traditional Patterns ◽  
The Individual

Information of relevance to human failure was extracted from the first 2,000 incidents reported to the Australian Incident Monitoring Study (AIMS). All reports were searched for human factors amongst the “factors contributing”, “factors minimising”, and “suggested corrective strategies” categories, and these were classified according to the type of human error with which they were associated. In 83% of the reports elements of human error were scored by reporters. “Knowledge-based errors” contributed directly to about one-quarter of incidents; the outcome of one third of incidents was thought to have been minimised by prior experience or awareness of the potential problems, and in one fifth some strategy to improve knowledge was suggested. Correction of “rule-based errors” or provision of protocols or algorithms were thought, together, to have a potential impact on nearly half of all incidents. Failure to check equipment or the patient contributed to nearly one-quarter of all incidents, and inadequate crisis management contributed to a further I in 8. “Skill-based errors” (slips and lapses) were directly responsible for I in 10 of all incidents, and were thought to make an indirect contribution in up to one quarter. “Technical errors” were responsible for about 1 in 8 incidents. Analysing the relative contribution of each type of error for each type of problem allows the development of rational preventative strategies. Continued efforts must be made to improve the knowledge-base of anaesthetists, but AIMS has shown that there may also be much to gain from directing attention towards eliminating rule-based errors, for promoting the use of protocols, check-lists and crisis management algorithms, and improving anaesthetists’ insight into the factors contributing and circumstances in which slips and lapses may occur. Traditional patterns of behaviour in doctors may also make them more liable to certain types of human error; removing the onus for adhering to standards and approved work practices from the individual to the “system” may lead to more consistent application of the “best practice”.

scholarly journals Towards a neo-configurational theory of intrinsic motivation

2021 ◽  
Author(s):  
Martyna Daria Swiatczak
Keyword(s):  
Intrinsic Motivation ◽  
Chain Structure ◽  
Causal Chain ◽  
Public Utility ◽  
Empowerment Theory ◽  
Causal Factors ◽  
Theoretical Perspectives ◽  
Configurational Theory ◽  
Perceived Impact ◽  
Perceived Challenge

AbstractThis research seeks to improve our understanding of how intrinsic motivation is instantiated. Three motivation theories, flow theory, self-determination theory, and empowerment theory, have informed our understanding of the foundations of intrinsic motivation at work. Taken jointly, they suggest six causal factors for intrinsic motivation: (1) perceived competence, (2) perceived challenge, (3) perceived autonomy, (4) perceived impact, (5) perceived social relatedness, and (6) perceived meaningfulness. Integrating different theoretical perspectives, I employ a case-based configurational approach and conduct coincidence analyses on survey data from a German public utility to analyse the nuanced interplay of these six causal factors for intrinsic motivation. My data show that high perceived meaningfulness or high perceived autonomy is sufficient for high perceived intrinsic motivation and at least one of the two conditions must be present. Further, my findings reveal a common cause structure in which perceived impact is not a causal factor for intrinsic motivation but an additional outcome factor. Subsequent analyses shed light on possible roles of the remaining proposed causal factors by drawing a tentative causal chain structure. The results of this study enhance our understanding of the causal complexity underlying the formation of intrinsic motivation.

A Study of Expert Judgment on Human Error Probability

1988 ◽  
Vol 32 (15) ◽  
pp. 954-957
Author(s):  
Bernhard Zimolong ◽  
Barbara Stolte
Keyword(s):  
Flexible Manufacturing ◽  
Human Error ◽  
Calibration Equation ◽  
Performance Shaping Factors ◽  
Relative Contribution ◽  
Missing Data Problem ◽  
Subjective Estimation ◽  
Error Probabilities ◽  
Reliability Measurement ◽  
Human Error Probability

An experiment was conducted to derive empirically human error probabilities from a task performed under 12 different conditions. The task was to control a simulated flexible manufacturing scenario (FMS) under three Performance Shaping Factors (PSF): Incentive, workload and event frequency of breakdowns. Six experts with background in human factors assess the relative contribution of each PSF in affecting the likelihood of failure with the multi attribute decomposition technique. The conversion of the assessment values to probabilities was achieved by the use of an empirically derived calibration equation. Results indicate a poor match of empirical HEPs and their estimates and increase the doubts that subjective estimation is a solution to the missing data problem in reliability measurement.

The Systematic Consideration of Human Factors in Vessel Collision Investigations

2012 ◽  
Vol 46 (6) ◽  
pp. 142-159 ◽  
Author(s):  
Bryan R. Emond
Keyword(s):  
Human Factors ◽  
Human Error ◽  
Human Factor ◽  
Causal Chain ◽  
Historical Events ◽  
Vast Number ◽  
Available Information

AbstractHistorically, human factors have caused or contributed to the cause of nearly every vessel collision. However, given the vast number and type of human factors that can possibly be involved, the typical marine investigator risks either considering these factors only superficially or becoming bogged down in an academic exercise. Beyond just saying the collision was caused by “human error,” the marine investigator should understand the role of human factors in the causal chain of events. Some human factor issues can be difficult to parse from the available information. This is particularly the case for historical events but is also true even where witnesses are available. Nonetheless, there are a number of key areas where hard facts can reveal human factor issues that directly caused or contributed to the collision or somehow exacerbate the results. This paper divides that consideration into three parts, (1) human factors that affect the risk of the collision occurring, (2) human factors that affect the response once risk of collision is perceived, and (3) human factors that affect witness perception and recollection after the accident. The construct described in this paper can be used by the investigator to ensure a systematic consideration of key human factors relevant to a collision.

Identifying pan-industry common contributors to major accident events

2020 ◽  
Vol 60 (1) ◽  
pp. 41
Author(s):  
Joelle Mitchell ◽  
Alice Turnbull
Keyword(s):  
Human Error ◽  
Multidisciplinary Approach ◽  
Error Management ◽  
Causal Factors ◽  
Incident Investigation ◽  
Safety Research ◽  
Single Lens ◽  
Major Accident ◽  
Proactive Measures ◽  
Culture And Climate

Analysis of incident investigation findings as a means of identifying common precursors or causal factors is a common topic of safety research. Historically this type of research has been conducted through a single lens, depending on the researcher’s discipline, with incidents analysed in accordance with a favoured theory, or grouped according to industry or region. This has led to the development of numerous frameworks and taxonomies that attempt to predict or analyse events at various levels of granularity. Such theories and disciplines include safety culture and climate, human factors, human error, management systems, systems theory, engineering and design, chemistry and maintenance. The intent of such research is ostensibly to assist organisations in understanding the degree to which their operations are vulnerable to known precursors or causal factors to major accident events and to take proactive measures to improve the safety of their operations. However, the discipline-specific nature of much of this research may limit its application in practice. Specific frameworks and taxonomies may be of assistance when organisations have identified a relevant area of vulnerability within their operations, but are unlikely to assist organisations in identifying those vulnerabilities in the first place. This paper seeks to fill that gap. A multidisciplinary approach was taken to identify common causal factors. Investigation reports published by independent investigation agencies across various industries were analysed to determine common causal factors regardless of discipline or industry.

Root-Cause Analysis

2020 ◽  
pp. 101-107
Author(s):  
Greg Fisher ◽  
John E. Wisneski ◽  
Rene M. Bakker
Keyword(s):  
Problem Solving ◽  
Root Cause Analysis ◽  
Causal Chain ◽  
Causal Factors ◽  
Cause Analysis ◽  
Root Cause ◽  
Underlying Theory ◽  
Core Idea ◽  
Undesirable Outcome

A root-cause analysis is used to identify the initiating, or root, of a causal chain that leads to an observed undesirable outcome. It is useful in helping managers to focus their problem-solving efforts on providing remedies to issues that actually prevent the undesirable outcome from recurring. Failure to identify the root cause of a problem often leads to time spent on removing causal factors, which can alleviate the symptoms of a problem yet may not prevent recurrence with full certainty. This chapter discusses the underlying theory, core idea, depiction, process, insight or value created, and risks and limitations of root-cause analysis. The chapter also discusses the illustration of the DISH Network and applies the steps of root-cause analysis to this case.

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