The Factors Influencing Safety Compliance Behavior Among New-Generation Construction Workers in China: A Safety Compliance Behavior–Artificial Neural Network Model Approach

Abstract

Amid an aging workforce and labor shortages, this study investigates the key factors influencing construction workers’ safety compliance behavior (SCB). SCB is categorized into three distinct types: non-compliance behavior, general behavior, and compliance behavior. The study compares and analyzes the differences in influencing factors between the new generation and older generation of construction workers. By integrating the SCB framework with a multi-layer perceptron (MLP) model, this research develops a safety compliance behavior–artificial neural network (SCB-ANN) model. An enhanced method for optimizing connection weight (CW) is applied to identify the key determinants of SCB. The findings reveal that the SCB-ANN model offers superior predictive accuracy compared to a standard MLP model. Additionally, the refined CW method significantly improves the neural network’s interpretability. The analysis shows that organizational factors have a stronger influence on the new generation of construction workers (NGCWs), while individual factors play a more crucial role for the older generation (OGCWs). As a result, the study proposes tailored safety management measures for different worker groups to mitigate non-compliance behaviors, providing a robust foundation for future research and the development of safety management strategies.