Multi-Group Analysis of Workers in the Steel Production Industry Using Partial Least Squares Approach (PLS-MGA in Occupational Accidents)

Abstract

Introduction: Workers' accident-proneness is considered as an inherent and coherent characteristic in incidence of job accidents; therefore, its effective factors should be determined to prevent job accidents. This study recommends use of PLS-MGA in the field of occupational accidents. The present study was carried out to conduct Multi-group analysis of workers in the steel production industry using partial least squares approach.

Methods: This cross-sectional analytical study was conducted in 2018. The sample size of the study was calculated as 450 people using the sample size sampling method in structural equation modeling. Data analysis was performed using SPSS and SMARTPLS3.

Results: The findings showed the mediating role of stress between accident-proneness and individual and social factors. Moreover, the results of PLS-MGA showed no significant difference between the two groups considering all hypotheses. In other words, all research hypotheses were confirmed in the two groups except the hypothesis over the relationship between individual factors and accident- proneness. 

Conclusion: The present study indicated that workers of steel industry turned into accident-prone individuals in case of facing intense stress, effort-reward imbalance, and work-family conflict. Therefore, the odds of accidents and unsafe factors increase among them. In this regard, individuals' general health status and work locus of control should be investigated at the time of recruitment, so that only people with high internal locus of control are employed for stressful jobs.