PROBLEM: Injuries resulting from lifting are costly, and create significant pain and discomfort. While engineering controls are the most effective means of reducing risks, most organizations continue to rely on manual lifting techniques. The problem, however, is that the use of safe-lifting techniques is inconsistent and managers have a difficult time motivating use of these techniques. Consequently, it is important to understand the factors driving safe-lifting behaviors. METHODS: This study used a survey to apply the theory of planned behavior (Ajzen., I., 1991. The theory of planned behavior. Organization Behavior and Human Processes, 50, 179-211) to safe-lifting among 136 materials management employees at a heavy manufacturing organization. Structural equation modeling and factor analysis were employed to analyze relationships among constructs. RESULTS: Results revealed that perceived behavioral control and intention were the strongest predictors of safe-lifting behavior. Subjective norms, to a lesser degree, were also important influences on intention. Attitudes did not surface as effective direct predictors of safe-lifting behavior, but did affect behavior and intent via mediating factors (subjective norms and perceived behavioral control). Finally, the theory of planned behavior was supported as an effective model explaining safe-lifting behavior, and had potential application for many other safety-related behaviors. IMPACT ON INDUSTRY: Results from this study emphasize the importance of perceived behavioral control as a factor associated with safety-related behavior. 相似文献
Based on the second Fick’s law, the theoretical equation of gas desorption of particle coal under the non-uniform pressure condition was developed in this paper. The analytical solution of the theoretical equation and the method of gas desorption quantity of particle coal under non-uniform pressure condition were obtained. 相似文献
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