An interpretable clustering approach to safety climate analysis: Examining driver group distinctions.

The transportation industry, particularly the trucking sector, is prone to workplace accidents and fatalities. Accidents involving large trucks accounted for a considerable percentage of overall traffic fatalities. Recognizing the crucial role of safety climate in accident prevention, researchers have sought to understand its factors and measure its impact within organizations. While existing data-driven safety climate studies have made remarkable progress, clustering employees based on their safety climate perception is innovative and has not been extensively utilized in research. Identifying clusters of drivers based on their safety climate perception allows the organization to profile its workforce and devise more impactful interventions. The lack of utilizing the clustering approach could be due to difficulties interpreting or explaining the factors influencing employees' cluster membership. Moreover, existing safety-related studies did not compare multiple clustering algorithms, resulting in potential bias. To address these problems, this study introduces an interpretable clustering approach for safety climate analysis. This study compares five algorithms for clustering truck drivers based on their safety climate perceptions. It also proposes a novel method for quantitatively evaluating partial dependence plots (QPDP). Then, to better interpret the clustering results, this study introduces different interpretable machine learning measures (Shapley additive explanations, permutation feature importance, and QPDP). The Python code used in this study is available at https://github.com/NUS-DBE/truck-driver-safety-climate. This study explains the clusters based on the importance of different safety climate factors. Drawing on data collected from more than 7,000 American truck drivers, this study significantly contributes to the scientific literature. It highlights the critical role of supervisory care promotion in distinguishing various driver groups. Moreover, it showcases the advantages of employing machine learning techniques, such as cluster analysis, to enrich the scientific knowledge in this field. Future studies could involve experimental methods to assess strategies for enhancing supervisory care promotion, as well as integrating deep learning clustering techniques with safety climate evaluation.

A mixed-methods approach to examining safety climate among truck drivers.

The purpose of the current study was to use a mixed-methods approach to understanding safety climate and the strategies to improve safety climate among truck drivers. Using both survey (N = 7246) and interview (N = 18) responses provided by truck drivers regarding key safety climate items, the current study identified a number of positive and negative policies, procedures and practices that truck drivers perceived as the determinants of whether their organizations are committed to the promotion of safety at work. Item response theory (IRT) analyses were conducted to identify discrimination parameters indicating which safety climate items were most sensitive to the safety climate level. Discriminative items were identified at both the organization and group levels which can be used to evaluate safety climate and differentiate a high versus low safety climate across groups and organizations in the trucking industry. Based on our results, we also offer safety researchers and practitioners some recommendations on what and/or how to intervene with and promote organizational safety climate in the trucking industry.