Predicting the safety climate in construction sites of Saudi Arabia: A bootstrapped multiple ordinal logistic regression modeling approach

Abstract

Construction site accidents can be reduced through proactive steps using prediction models developed based on factors that influence the safety climate. In this study, a prediction model of the safety climate observed by construction site personnel in Saudi Arabia was developed, identifying a set of significant safety climate predictors. The model was built with data collected from 401 active construction site personnel using a bootstrapped multiple ordinal logistic regression model. The model revealed five significant predictors: supervision, guidance, and inspection; social security and health insurance; management’s commitment to safety; management’s safety justice; and coworker influence. The model can correctly predict 67% of the safety evaluations. The identified predictors present proof of the importance of safety support, commitment, and interaction in construction sites and their influence on the perceived evaluations of the safety climate by personnel. Moreover, the prediction model can help construction industry decision makers, safety policy designers, government agencies, and stakeholders to estimate the safety climate and assess the current situation. Furthermore, the model can help form a better understanding and determine areas of improvement, which can translate into higher safety performance levels.