Modelling the Impact of Driver Work Environment on Driving Performance among Oil and Gas Heavy Vehicles: SEM-PLS

Abstract

Driving heavy vehicles with dangerous cargo involves various work environments that can significantly impact road safety. This research aims to study the impact of oil and gas tanker drivers’ work environment on driving performance to identify and address any issues that may affect their ability to carry out their jobs effectively. To achieve this, a quantitative approach was employed using a questionnaire survey adapted from the literature review. The data collected from a sample of drivers of oil- and gas-heavy vehicles were analyzed using structural equation modelling. The study’s findings reveal a significant association between the drivers’ work environment and driving performance, represented by a path coefficient of β = 0.237. These results highlight the substantial contribution of the work environment to driving performance, with an effect of 63%. Consequently, the study emphasizes the importance of considering the work environment as a potential factor when assessing and enhancing tanker drivers’ driving abilities during oil and gas transportation.

Personal Competencies for Work–Family Integration and Its Relationship with Productivity and Comprehensive Health in Salaried Professionals

“…Relational analysis through Structural Equation Modeling (SEM) originates from a non-experimental study technique with processes previously unknown for hypothesis testing. The modeling approach of this technique represents the association between multiple relationships and unobservable constructs [21,22], aligning with the characteristics of this study. The stages proposed by [23] were followed in this research, consisting of two main steps: the factorial analysis (exploratory and confirmatory) of measurement models and the second step, the causal analysis and modeling inherent to the structural model (Figure 2).…”

The Association between Corporate Social Responsibility, Employee Performance, and Turnover Intention Moderated by Organizational Identification and Commitment

“…Unlike the Fornell-Larcker criteria and (partial) cross-loadings, the HTMT test of correlations is often considered to provide a higher-quality evaluation of discriminant validity. To determine discriminant validity, we applied a threshold of 0.9 as this investigation's criteria, as prior research suggested [113][114][115][116]. Specifically, we examined the HTMT ratios to assess whether the correlations between different constructs were below this threshold, indicating that they were sufficiently distinct from each other.…”

“…Specifically, we examined the HTMT ratios to assess whether the correlations between different constructs were below this threshold, indicating that they were sufficiently distinct from each other. We also considered the Fornell-Larcker criteria, which state that the average variance extracted (AVE) square root for a specific construct should be greater than its correlation with all other constructs [115,117]. This comparison, found in Table 4, provides an additional layer of validation for the distinctiveness of the constructs under study.…”