Abstract
The prevention of industrial accidents is not completely practical by implementing safety programs unless focusing on protecting vulnerable workers. The unsafe behavior cognitive factors (UBCFs) are essential determinants contributing to human failure. This study aimed at eliciting the most important UBCFs, along with investigating hierarchical cause-effect interactions among them. A qualitative approach using metasynthesis was utilized to extract all essential UBCFs among industrial workplaces. Afterward, the relationships between UBCFs were recognized using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method and rated by an expert panel. Also, a hierarchical model was developed based on the final matrix of DEMATEL by employing the interpretive structural modeling (ISM) method. Ten criteria were extracted as UBCFs through the metasynthesis method. The threshold value was set as 0.175 in DEMATEL following experts’ ideas. Inadequacy of persons’ resilience and habitual rule ignorance were recognized as the most important predictive UBCFs. The developed model was tested through a case study in a petrochemical company. The results of the study can be used to help industrial managers and HSE practitioners to consider workers’ capabilities either cognitively or physically when designing the required tasks to reduce unsafe behaviors. Also, the findings of the study are applicable for other researchers to prioritize the most important factors affecting unsafe behavior in different workplaces.
Highlights
Human resource is considered as one of the most important properties of any society and as the central pillar required for continuous development
In the research literature on unsafe behaviors and in the studies focused on industrial front-line safety management of workers and prediction of human error, there is a gap in specific research about cognitive factors affecting workers to participate in different types of unsafe behaviors [20]
Is study aimed at (1) extending a structure for existing UBCF representation to help preventing similar future incidents by analyzing accidents, and (2) incorporating quantitative cause-effect relationships between UBCFs as well as dependency assessment as an important point in an industrial system. erefore, regarding the essential role of unsafe behavior cognitive factors in human-centered process tasks and to meet the initial goals considered for this study, a hybrid fuzzy decision-making trial and evaluation laboratory (DEMATEL)-interpretive structural modeling (ISM) approach was introduced. e developed model is helpful for safety practitioners in the development of accident prevention strategies for recruiters and industrial managers in the establishment of eligibilitybased task designs and for novel research studies with the purpose of designing and development of cognitivebased human behavior monitoring devices in industrial safety applications
Summary
Human resource is considered as one of the most important properties of any society and as the central pillar required for continuous development. The root cause analysis as a means for making the black swan to a white swan has revealed human unsafe behavior as the most repeated important factor in industrial accident occurrence [6, 7]. E investigation of contributing factors of unsafe behavior seems to be a preferable replacement for other methods such as those exclusively focusing on accidents/incidents indices for monitoring the safety of workplaces, especially the performance of front-line workers with a glance on finding a permanent proactive approach; the approaches in safety which are according to the behavior were lately attractive for many researchers [19]. In the research literature on unsafe behaviors and in the studies focused on industrial front-line safety management of workers and prediction of human error, there is a gap in specific research about cognitive factors affecting workers to participate in different types of unsafe behaviors [20]. Is study aimed at (1) extending a structure for existing UBCF representation to help preventing similar future incidents by analyzing accidents, and (2) incorporating quantitative cause-effect relationships between UBCFs as well as dependency assessment as an important point in an industrial system. erefore, regarding the essential role of unsafe behavior cognitive factors in human-centered process tasks and to meet the initial goals considered for this study, a hybrid fuzzy DEMATEL-ISM approach was introduced. e developed model is helpful for safety practitioners in the development of accident prevention strategies for recruiters and industrial managers in the establishment of eligibilitybased task designs and for novel research studies with the purpose of designing and development of cognitivebased human behavior monitoring devices in industrial safety applications
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