Objective: Road accidents are an important public health concern, and speeding is a major contributor. Although flow theory (FLT) is a valid model for understanding behavior, currently the nature of the roles and interplay of FLT constructs within the theory of planned behavior (TPB) framework when attempting to explain the determinants of motivations for intention to speed and speeding behavior of car drivers is not yet known. The study aims to synthesize TPB and FLT in explaining drivers of advanced vehicles intentions to speed and speed violation behaviors and evaluate factors that are critical for explaining intention and behavior.
Method: The hypothesized model was validated using a sample collected from 354 fully licensed drivers of advanced vehicles, involving 278 males and 76 females on 2 occasions separated by a 3-month interval. During the first of the 2 occasions, participants completed questionnaire measures of TPB and FLT variables. Three months later, participants' speed violation behaviors were assessed.
Results: The study observed a significant positive relationship between the constructs. The proposed model accounted for 51 and 45% of the variance in intention to speed and speed violation behavior, respectively. The independent predictors of intention were enjoyment, attitude, and subjective norm. The independent predictors of speed violation behavior were enjoyment, concentration, intention, and perceived behavioral control.
Conclusions: The findings suggest that safety interventions for preventing speed violation behaviors should be aimed at underlying beliefs influencing the speeding behaviors of drivers of advanced vehicles. Furthermore, perceived enjoyment is of equal importance to driver's intention, influencing speed violation behavior. 相似文献
Objective: Lane departure, caused by inattention, distraction, drowsiness, or any unusual driver behavior, is a typical risk threatening the driver as well as other road users. Accurate perception of such situations through effective warnings would help drivers to avoid serious consequences. With regard to critical functions of warning symbols for risk communication, the present study focused on providing effective and easily perceivable symbols, compatible with human cognitive capabilities. Thus, the main purpose of the present study was to design and cognitively appraise 6 newly designed dynamic symbols, candidates for a new type of lane departure warning system.
Methods: Simplicity, familiarity, concreteness, meaningfulness, and semantic closeness were the major assessment criteria, defining cognitive features by the earlier researchers in the field. A total number of 187 driving license applicants, with a mean age of 20.58 years (SD = 3.20), participated in the present survey. The participants rated cognitive features of the 6 dynamic symbols along a 0–100 scale.
Results: Significant main effect of the element factor type of the designed symbols on rating cognitive features revealed that the existence of car element was the best predictor for illustrating lane departure. The interaction of both element factor and location of element factor significantly affected the ratings. However, the location of element factor did not solely have any strong effect on the ratings. The results also demonstrated that semantic closeness received the highest overall mean score across symbols (M = 61.80), especially within the symbols that include the car element (M = 75.67). Moreover, a significant difference was observed between the average ratings of the cognitive features, despite the fact that a significant correlation was found between cognitive features.
Conclusion: The most considerable result of the current study was the match between the symbol with the highest ratings and the International Organization for Standardization (ISO)-related icon in appearance. Because previous studies demonstrated a strong correlation between comprehension scores of the symbol and both semantic closeness and meaningfulness, high-level comprehensibility of the best ranked symbol is expected. 相似文献
Understanding complex systems is essential to ensure their conservation and effective management. Models commonly support understanding of complex ecological systems and, by extension, their conservation. Modeling, however, is largely a social process constrained by individuals’ mental models (i.e., a small-scale internal model of how a part of the world works based on knowledge, experience, values, beliefs, and assumptions) and system complexity. To account for both system complexity and the diversity of knowledge of complex systems, we devised a novel way to develop a shared qualitative complex system model. We disaggregated a system (carbonate coral reefs) into smaller subsystem modules that each represented a functioning unit, about which an individual is likely to have more comprehensive knowledge. This modular approach allowed us to elicit an individual mental model of a defined subsystem for which the individuals had a higher level of confidence in their knowledge of the relationships between variables. The challenge then was to bring these subsystem models together to form a complete, shared model of the entire system, which we attempted through 4 phases: develop the system framework and subsystem modules; develop the individual mental model elicitation methods; elicit the mental models; and identify and isolate differences for exploration and identify similarities to cocreate a shared qualitative model. The shared qualitative model provides opportunities to develop a quantitative model to understand and predict complex system change. 相似文献
A visual-visual dual computer task was designed to test the effect of the thermal environment on dual task performance. The temperatures selected for testing were 20 and 35 °C Wet Bulb Globe Temperature (WBGT). 34 volunteers were randomly assigned to 1 of the 2 temperature conditions. Individual differences in single task performance were controlled by equating the baselines of single task performance. Once individual differences in single task capacity were controlled, statistically significant differences in performance were demonstrated. Mean accuracy was computed over a 1-hr testing period in each temperature condition. Participants’ mean accuracy in the 35° condition (38.18%) was substantially lower than in the 20° condition (50.88%). 相似文献
Introduction. The majority of industrial accidents occur because of human errors. Human error has different causes, however, in all cases cognitive abilities and limitations of human play an important role. Occupational cognitive failures are cognitively-based human errors that occur at work. The aim of this study was to examine the relationship between occupational cognitive failures and safety consequences. Method. Personnel of a large industrial company in Iran filled out an occupational cognitive failure questionnaire (OCFQ) and answered questions on accidents. Univariate and multiple logistic regression analysis were used to determine the relationship between cognitive failures and safety consequences. Results. According to developed regression models, personnel with a high rate of cognitive failure, in comparison to low rate, have a high risk of minor injury involvement (OR 5.1, 95% CI [2.62, 10.3]); similar results were for major injury and near miss. Discussion. The results of this study revealed usefulness of the OCFQ as a tool of predicting safety-related consequences and planning preventive actions. 相似文献