Individual cognitive factors affecting unsafe acts among Iranian industrial workers: An integrative meta-synthesis interpretive structural modeling (ISM) approach

Abstract

Abstract Most of the incidents including near misses are attributed to human unsafe acts. An available means for preventing incidents is to recognize “accident prone” workers in a system as the last layer of working system. This study was conducted to recognize individual cognitive factors affecting unsafe acts among Iranian industrial workers as well as finding hierarchical relationships among the factors. This study was mainly performed in two phases including a seven stage meta-synthesis as a basic qualitative approach to extract the effective factors on unsafe acts of workers in industries and Interpretive Structural Modeling (ISM) as an appropriate approach to modeling the factors hierarchically with a glance on their relationships. Twenty experts partook in this study from different universities and industrial companies in Iran to complete the matrices used in ISM model. Fifty-one papers were included out of which 10 dimensions were extracted as the main dimensions affecting unsafe acts individually. Kappa indicator was 0.69 indicating valid status of agreement for the extracted items. Lack of alertness due to mental overload and lack of individual’s resilience were identified as the driving factors. The factor of difficulties in remembering work-related information had the characteristics of both independent and linkage clusters. The study provided a hierarchy of factors influencing unsafe acts; hence, the hierarchy could help all employers, industrial managers and even workers themselves to make a better decision when predicting workers’ possible unsafe acts.