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

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English
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  • Creators:
  • Corporate Creators:
    University of Nevada, Reno
  • Subject/TRT Terms:
  • Publication/ Report Number:
    514-13-803
  • Resource Type:
    Tech Report
  • Geographical Coverage:
    Nevada
  • Corporate Publisher:
    Nevada. Dept. of Transportation
  • Abstract:
    Alcohol-impaired (AI) driving continues to cause a disproportionate number of fatalities within the college and

    young adult populations, indicating optimal prevention programs for AI driving have yet to be developed. The

    current study tested the efficacy of two theoretically- and empirically-based online video advertisements at

    reducing AI driving willingness and behavior in a sample of 108 college students at high risk for future AI

    driving. The relations of AI driving risk factors to AI driving willingness and behavior in a larger sample of 600

    college students was also tested. Findings revealed that students who viewed either advertisement reported

    decreases in general willingness to drive after drinking, while participants who did not view an advertisement

    reported increases in general willingness to drive after drinking. While viewing an advertisement was not

    associated with decreased AI driving, study participants reported a significant decrease in AI driving overall.

    Findings revealed AI driving risk and decision-making factors to have both shared and unique relations to driving

    after “perhaps too much to drink” and driving “shortly after three or more drinks.” Findings from the current

    study provide preliminary support for the efficacy of both advertisements in reducing general willingness to drive

    after drinking within the subgroup of the college population most at risk for AI driving. Additionally, this study

    takes one of the first steps in developing an AI driving model that accounts for the influence of sociodemographic,

    dispositional, historical, and current AI driving risk factors as well as rational and emotional decision-making

    factors.

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