Novita Lizza Anggraini, Anis Rohmana Malik, Putri Gesan Prabawa Anwar, Irfan Andri Fadana, Dinar Padang Mutiara Jingga, Tyas Ayu Ramadhani

Analysis of the Relationship Between Big Five Personality Traits of Inter-Provincial Truck Drivers and Their Accident History

Introduction

Analysis of the relationship between big five personality traits of inter-provincial truck drivers and their accident history. Explore the link between Big Five personality traits, working conditions, and accident history in inter-provincial truck drivers. Discover high vibration levels in trucks, informing transportation safety.

Abstract

Based on data from the National Transportation Safety Committee, traffic accidents involving trucks in East Kalimantan resulted in four fatalities, one serious injury, and 29 minor injuries. This study aims to determine the relationship between working hours, rest periods, and personality dimensions through the Big Five Personality theory with the accident history of inter-provincial truck drivers. The study uses an analytical design with a cross-sectional approach. The research subjects are truck drivers selected using purposive sampling techniques, with a sample size of 30 for the Big Five Personality measurements and 15 truck units for vibration measurements. This is a quantitative study. The results show that there is no significant relationship between respondent characteristics, such as age, education, work duration, and length of service, and the history of work accidents. Vibration measurements indicate that 12 out of 15 truck units operated by drivers have vibration levels above the threshold limit value (TLV), which is 0.8660 during an 8-hour exposure period.

Review

This study addresses a highly relevant and critical issue concerning road safety: the relationship between truck driver characteristics, specifically Big Five personality traits, and accident history. The problem is well-situated within the context of significant road fatalities and injuries involving trucks in East Kalimantan, as highlighted by the National Transportation Safety Committee data. The intention to explore the influence of personality dimensions using the established Big Five theory, alongside working hours and rest periods, offers a promising multi-faceted approach to understanding accident causation in this high-risk profession. Methodologically, the study employs an analytical design with a cross-sectional approach, which is appropriate for investigating associations. However, a significant concern arises regarding the sample sizes: 30 subjects for Big Five personality measurements and a separate 15 truck units for vibration. This substantial disparity and the small absolute numbers, particularly for personality assessment, raise questions about the statistical power and the generalizability of any findings. Furthermore, while the abstract reports no significant relationship between demographic characteristics (age, education, work duration, length of service) and accident history, it conspicuously omits any results pertaining to the *primary stated aim* – the relationship between Big Five personality traits and accident history. The inclusion of vibration measurements, while potentially relevant, is presented somewhat independently, noting that a majority of trucks exceed the TLV, but without clearly articulating its integrated relationship with personality or accident history in the context of the reported results. The most critical feedback for this study is the disconnect between its stated primary aim and the presented results. For a paper titled "Analysis of the Relationship Between Big Five Personality Traits... and Their Accident History," the absence of findings on this core relationship within the abstract is a major oversight. Future iterations or a full manuscript must explicitly detail these results, whether positive or null, and discuss their implications. Additionally, the limited sample sizes severely constrain the validity and generalizability, particularly for personality dimensions where individual variations can be subtle. The authors are strongly encouraged to pursue larger, more representative sample sizes and potentially longitudinal designs to strengthen causality inferences. Integrating the vibration data more explicitly into the overall analysis, perhaps as a moderating or mediating factor for accident risk, would also enhance the study's coherence and impact. The research question is important, but its execution requires greater methodological rigor and comprehensive reporting of all primary findings.

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