Measurement equivalence of a safety climate scale across multiple trucking companies

Safety Climate in the Utility Industry: Perceptual Discrepancies Across Organizational Hierarchy

OBJECTIVES

This study aims to extend safety climate research by considering perceptions across the following three hierarchical levels within a workplace: (1) senior leaders/executives, (2) field leaders/supervisors, and (3) front-line employees.

METHODS

We conducted a quantitative survey study at a US utility company where we collected data related to safety climate perceptions and employee-reported safety behaviors across the different levels of organizational hierarchy.

RESULTS

The findings revealed the highest safety climate scores among senior leaders/executives, followed by field leaders/supervisors, and then employees, suggesting potential discrepancies between espoused and enacted safety values in the workplace.

CONCLUSIONS

These results suggest that supervisors and top managers may have different mental models of workplace safety compared with employees. Consequently, assessing perceptions at different organizational levels provides a fuller picture of safety in the workplace.

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.

The traffic climate in China: The mediating effect of traffic safety climate between personality and dangerous driving behavior

Traffic safety climate is defined as road users' attitudes and perceptions of traffic in a specific context at a given point in time. The current study aimed to validate the Chinese version of the Traffic Climate Scale (TCS) and to explore its relation to drivers' personality and dangerous driving behavior. A sample of 413 drivers completed the Big Five Inventory (BFI), the Chinese version of the TCS, the Dula Dangerous Driving Index (DDDI) and a demographic questionnaire. Exploratory factor analysis and confirmatory factor analysis were performed to confirm a three-factor (external affective demands, internal requirements and functionality) solution of the TCS. The reliability and validity of the Chinese version of TCS were verified. More importantly, the results showed that the effect of personality on dangerous driving behavior was mediated by traffic climate. Specifically, the functionality of the TCS mediated the effect of neuroticism on negative cognitive/emotional driving and drunk driving, while openness had an indirect impact on aggressive driving, risky driving and drunk driving based on the internal requirements of the TCS. Additionally, agreeableness had a negative direct impact on four factors of the DDDI, while neuroticism had a positive direct impact on negative cognitive/emotional driving, drunk driving and risky driving. In conclusion, the Chinese version of the TCS will be useful to evaluate drivers' attitudes towards and perceptions of the requirements of traffic environment in which they participate and will also be valuable for comparing traffic cultures and environments in different countries.

Does company size matter? Validation of an integrative model of safety behavior across small and large construction companies

INTRODUCTION

Previous safety climate studies primarily focused on either large construction companies or the construction industry as a whole, while little is known about whether company size has significant effects on workers' understanding of safety climate measures and relationships between safety climate factors and safety behavior. Thus, this study aims to: (a) test the measurement equivalence (ME) of a safety climate measure across workers from small and large companies; (b) investigate if company size alters the causal structure of the integrative model developed by Guo, Yiu, and González (2016).

METHOD

Data were collected from 253 construction workers in New Zealand using a safety climate measure. This study used multi-group confirmatory factor analyses (MCFA) to test the measurement equivalence of the safety climate measure and structure invariance of the integrative model.

RESULTS

Results indicate that workers from small and large companies understood the safety climate measure in a similar manner. In addition, it was suggested that company size does not change the causal structure and mediational processes of the integrative model.

CONCLUSIONS

Both measurement equivalence of the safety climate measure and structural invariance of the integrative model were supported by this study. Practical applications: Findings of this study provided strong support for a meaningful use of the safety climate measure across construction companies in different sizes. Safety behavior promotion strategies designed based on the integrative model may be well suited for both large and small companies.