Socioeconomic influences on drunk driving recidivism: an integrated Prototype Willingness Model approach

Drunk driving offenders represent a high-risk group for repeated violations. This study employs an integrated Prototype Willingness Model (PWM) framework to examine whether socioeconomic characteristics significantly influence various psychological constructs, and provides policy recommendations to inform more effective prevention strategies.

Multivariate Analysis of Variance (MANOVA) was used to analyze the effects of socioeconomic variables on multiple psychological constructs, including attitude, subjective norms, perceived behavioral control, prototype, drunk driving behavioral intention, and behavioral willingness. Post hoc tests were conducted to explore group differences across socioeconomic variables. The study sample comprised individuals who were apprehended for drunk driving and attended road safety courses at regulatory agencies.

The results indicate no significant overall differences in the integrated PWM dimensions between first-time and repeat drunk driving offenders, except for significant differences in attitude and recidivism behavior. Socioeconomic variables-including gender, age, marital status, education level, monthly income, and age at first alcohol consumption-showed significant effects on the integrated PWM dimensions, reflecting group differences in psychological constructs. These findings highlight the associations between socioeconomic factors and psychological mechanisms, providing an empirical basis for developing targeted prevention and intervention strategies.

This study reveals that drunk driving recidivism is influenced by the interplay of psychological constructs and socioeconomic characteristics, with attitude, subjective norms, and perceived behavioral control playing key roles in decision-making. Applying MANOVA to the PWM framework facilitates identification of group differences and supports multi-level, targeted interventions. It is recommended that government implement integrated measures combining legal enforcement, education, and social counseling to reduce recidivism and enhance traffic safety. Given the study's cross-sectional design, reliance on self-reported data, and regional limitations, future research should adopt longitudinal designs and larger samples to improve generalizability and policy applicability.