Investigating the decision-making processes that contribute to impaired driving.

Duckworth, M. P.; Renqvist, J. G.; Dahir, V. B. · 2015 · ROSA P / Nevada. Dept. of Transportation

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Summary

This study addresses the persistent issue of alcohol-impaired (AI) driving among college students, a demographic that suffers a disproportionate number of fatalities. Motivated by the lack of optimal prevention programs, the research investigates the decision-making processes contributing to AI driving and tests the efficacy of theoretically based online video advertisements in reducing AI driving willingness and behavior. The study aims to develop an integrative model accounting for sociodemographic, dispositional, historical, and current risk factors, alongside rational and emotional decision-making pathways. The researchers conducted a longitudinal study involving 600 college students from the University of Nevada, Reno, with a high-risk subsample of 108 participants who had driven within two hours of drinking in the past month. These 108 participants were randomly assigned to one of three conditions: viewing a "rational" advertisement (presenting facts about legal limits and financial costs), viewing an "emotional" advertisement (highlighting negative peer opinions and social consequences), or a control group receiving no advertisement. Data were collected via online surveys at four time points: pre-advertisement, post-advertisement, one-month follow-up, and three-month follow-up. The advertisements were developed based on dual-process decision-making theory and refined through focus group testing. Statistical analyses, including logistic regression and mixed-model ANCOVA, were used to evaluate risk factors and the impact of the interventions. The results identified several key predictors of AI driving behavior. Logistic regression analyses of the larger sample revealed that older age, higher frequency of alcohol use, more positive attitudes toward AI driving, and a higher perceived ability to mitigate risks significantly predicted driving after "perhaps too much to drink." Additionally, sensation seeking and positive normative beliefs predicted driving shortly after consuming three or more drinks. Regarding the intervention, students who viewed either the rational or emotional advertisement reported significant decreases in their general willingness to drive after drinking from pre-advertisement to both post-advertisement and the three-month follow-up. In contrast, the control group reported increases in willingness over the same periods. However, the advertisements did not significantly reduce AI driving willingness in specific vignettes, nor did they reduce actual reported AI driving behavior compared to the control group. Notably, all participants, regardless of condition, reported a significant decrease in actual AI driving behavior over time, suggesting that study participation itself may have influenced behavior. The findings provide preliminary support for the efficacy of theoretically grounded online video advertisements in reducing the general willingness to drive after drinking among high-risk college students. The study highlights that both rational and emotional appeals can be effective in shifting general attitudes, though they may not immediately alter specific behavioral intentions or actions. The research contributes to the field by identifying specific decision-making and risk factors associated with AI driving and taking initial steps toward developing a comprehensive model of impaired driving that integrates sociodemographic, dispositional, and decision-making variables. The authors suggest that broader testing of such advertisements within larger young adult populations is warranted.

Key finding

College students who viewed rational or emotional advertisements reported decreased general willingness to drive after drinking, while the control group reported increased willingness, and all participants showed a significant decrease in self-reported impaired driving behavior over time.

Methodology

lab_experiment

Sample size: 108

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• public messaging
• polydrug
• risk taking
• decision making risk perception

Information type

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• Theoretical Contribution: computational model