Key drivers of trucking safety climate from the perspective of leader-member exchange: Bayesian network predictive modeling approach

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.

Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network

The causes of crashes on urban expressways are mostly related to the unsafe behaviors of drivers before the crash. This study focuses on sideswipe collisions on urban expressways. Through real and visual crash data, 17 unsafe behaviors were identified for the analysis of sideswipe collisions on an urban expressway. The chains of high-risk and unsafe behaviors were then revealed to investigate the relationship between drivers’ unsafe behaviors and sideswipe collisions. A Bayesian network diagram of unsafe behaviors was used to obtain the correlation between unsafe behaviors and their influence. A topology diagram of unsafe behaviors was then constructed, and relational reasoning of typical behavioral chains was conducted. Finally, the unsafe behaviors and behavior chains that were likely to cause sideswipe collisions on the urban expressway were determined. The possibility of each behavior chain was quantified through the reasoning of variable structures constructed by the Bayesian network. The result shows that the significant influential single unsafe behavior leading to sideswipe collision on urban expressways was lane change without checking the rearview mirror or not scanning the road around and queue-jumping; moreover, based on unsafe behavior chains analysis, the most influential chains leading to sideswipe collision were: improper driving behavior in an emergency—failure to turn on signal when changing lanes—distracted and inattentive driving. Some safety precautions and countermeasures aimed at unsafe behaviors could be taken before the crash. The results of the study can be used to reduce the number of sideswipe collisions, thereby improving traffic safety on urban expressways.

Divergent effects of transformational leadership on safety compliance: A dual-path moderated mediation model

Research on the relationship between transformational leadership and safety compliance has yielded equivocal results. This study investigates how and when transformational leadership produces divergent effects on safety compliance. Using a time-lagged research design, we collect data from a sample of 309 employees in the Chinese construction industry to examine the hypothesized relationship. We find that transformational leadership positively affects safety compliance through employees’ felt obligation toward their leader. However, transformational leadership also negatively impacts safety compliance through safety risk tolerance. We further show that employees’ perception of the safety climate plays a contingent role in the above processes. Specifically, a high-level perceived safety climate strengthens the positive indirect effect of transformational leadership on safety compliance through felt obligation, while a low-level perceived safety climate strengthens the negative indirect effect of transformational leadership on safety compliance through safety risk tolerance. The theoretical and practical implications of the findings are also discussed.

Extending the Association Between Leader-Member Exchange Differentiation and Safety Performance: A Moderated Mediation Model

PURPOSE

This study examines and analyses the impact of leader-member exchange differentiation (LMXD) on employee safety performance.

METHODS

A quantitative study was conducted on a sample of 357 Chinese construction industry employees through a structured questionnaire. The research hypothesis was tested by using the structural equation modelling (SEM) technique.

RESULTS

The results showed that LMXD could directly and positively affect the negative emotions and indirectly affect the safety performance of employees through the mediating effect of negative emotions and work engagement. Interpersonal trust has a moderating impact on the relationship between LMXD and negative emotions.

CONCLUSION

This study contributes to the literature on organizational behavior. Employee workplace safety is a great challenge in the construction industry. Enterprises should pay attention to the negative impact of LMXD. A fair working environment has significant importance to the employee's safety.

The effects of safety-specific transformational leadership and active transactional leadership on mindful safety practice adoption in the Chinese petroleum industry

Previous studies have concentrated on the value of managerial leadership for safety behavior conduct by employees in the workplace. However, safety leadership styles concerning mindful safety practices have rarely been examined. The goal of this study is therefore to investigate the relationships between safety-specific leaderships (transformational and active transactional leadership) and the adoption of mindful safety practices among first-line workers mediated by safety climate in the Chinese petroleum industry. Data were obtained from first-line workers in two Chinese petroleum companies. PLS-SEM results show that transformational and active transactional leadership are positively related to safety climate, which in turn affects mindful safety practice adoption in the Chinese petroleum industry. The results of the current study indicate that transformational and active transactional leadership in a safety-specific view offer a comprehensive leadership model for mindful safety practices conduct in the Chinese petroleum industry.