Why do older drivers self-regulate: Psychological factors influencing self-regulation in a Chinese sample. (February 2022) - Chen, Bingshuo; Zhao, Xiaohua; Ding, Zhen; Li, Yang; Wan, Meina; He, Qing; Liu, Xiaoming
Highlights: Self-regulation was negatively correlated with the amount of driving experience (days per week and mileages per month) in a significant way and so was the number of family-owned motor vehicles. The proposed Structural Equation Model (SEM) explained 73.673% of the variances in older drivers' self-regulation intentions. Attitude had the strongest influence among all variables on older drivers' self-regulation intentions. Subjective norms, perceived behavior control, and alternative traffic quality significantly influenced older drivers' self-regulation intention. Intention and physical condition imposed a significant effect on older drivers' self-regulation behavior. Abstract: Introduction: As China has recently lifted the age limit for getting access to driving licenses, older drivers' safety issues have received wide attention. Driving self-regulation can be regarded as an adjustment strategy for older adults to reduce risks and extend driving lives. Studies abroad mainly stress the three levels and influencing factors of driving self-regulation. China has a long history with a unique cultural background and social reality and relevant research are still in the initial stage. Method: This study applied the extended Theory of Planned Behavior (TPB) to explore the psychological factors that affected self-regulation of older drivers. 317 participants mainly from Beijing urban area completed the questionnaires including demographic information and extend TPB items. Results: Bivariate correlation analysis showed that self-regulation was negatively correlated with the amount of driving experience (days per week and mileages per month) in a significant way. And so was the number of family-owned motor vehicles. Exploratory factor analysis indicated that the extended TPB questionnaire was reliable and effective for measuring self-regulation. The proposed Structural Equation Model (SEM) explained 73.673% of the variance in self-regulation intention. Attitude (0.50) had the strongest influence among all variables on intention. Subjective norms (0.28), perceived behavior control (0.27), and alternative traffic quality (0.20) significantly influenced intention. Intention (0.34) and physical condition (0.22) imposed significant effect on self-regulation behavior. Practical applications: Feasible suggestions were put forward that contribute to self-regulate reasonably. This study helps to better understand the nature of self-regulation behaviors and provides a theoretical basis for formulating scientific intervention measures. On the transportation side, people from all communities of society should care for and support older drivers.