Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.
Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.
Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.
Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance. 相似文献
We conduct a meta‐analytic review that yields important insights about the existing research on transformational leadership and creativity. Additionally, we propose and test an integrated model using meta‐analytic structural equation modeling (MASEM) and full information MASEM (FIMASEM) techniques to better understand the intervening mechanism through which transformational leadership acts on creativity. The results of the meta‐analysis of 127 studies show that most of the bivariate relationships among transformational leadership, employee creativity, and pre‐identified mediators are significant; further, geographic base of studies significantly moderates some of the relationships. The MASEM results indicate that several mediators intervene in the relationship between transformational leadership and creativity. Although the total effect of transformational leadership on creativity is positive, its direct effect is negative when mediators are included. Additionally, there are significant relationships among the mediators that can be theoretically supported, but have not been investigated in prior transformational leadership and creativity studies. On the basis of these findings, we provide conclusions and directions for future studies. 相似文献
