酒後駕車風險知覺之實驗研究－－資訊整合理論之應用

Experiment Study on Risk Perception of Drunk Driving --An Application of The Information Integration Theory

最近五年國人因酒後駕車失控肇事平均每年約有334 件，奪走343 人性命、造成248 人受傷，並衍生諸多社會成本，而所犯之公共危險罪更是高居所有刑案之首位。政府雖修法大幅提高罰則並以刑法伺候，惟其所造成的慘劇仍一再上演。根據研究顯示，藉由風險溝通傳達風險訊息以改變民眾的風險知覺，可進而促成行為的改變。本研究以資訊整合理論(Information Integration Theory)的實驗方式探討執法人員、酒後駕車風險自願與受迫群體對酒後駕車不同風險之「機率資訊」及「結果資訊」其認知代數模式(cognitive algebra model)及其風險知覺。研究結果顯示，在認知代數模式方面，對酒後駕車被取締之高機率低結果風險類型，執法人員整體使用相乘模式(multiplying model)；酒後駕車群體與受迫群體使用等權重平均模式(equal-weight averaging model)整合資訊，而發生交通事故之低機率高結果風險類型，執法人員、酒後駕車群體與受迫群體均使用相加模式(adding model)，但較重視結果資訊。在風險知覺方面，對酒後駕車被取締之高機率低結果風險類型的風險知覺以酒後駕車群體的風險知覺最高，不喝酒群體風險知覺次之，執法人員最低；對於發生交通事故之低機率高結果風險類型的風險知覺以不喝酒群體風險知覺最高，酒後駕車群體次之，執法人員最低，而兩個類型的風險知覺均以執法人員最低。本研究並據此提出政策意義上的討論，以及風險溝通策略與實務上的建議。

Over the past five years, an average of about 334 traffic accidents that were caused by drunk driving occurred in Taiwan every year, resulting in 343 deaths and injuring 248 people and costing Taiwan society a lot. Concerned government agencies have recognized this harsh problem and taken measures as a counteract, for example, imposing heavy fines and punishing the driver with penal code, in order to discourage this poor habit of drunk driving. However, the tragedies caused by drunk driving have not abated. Government agencies usually execute public powers from the subjective perspective of statutory law, and similarly the research and investigation of related problems conducted in Taiwan usually emphasize the enforcement aspect. However, the results of foreign research indicate that risk message can be conveyed through risk communication so as to change the public’s risk perception, resulting in their changes in behavior. This study began with the cognitive perspective of the risk of drunk driving perceived by the law enforcers, voluntary group of drunk driving risk, and pressured group of drunk driving risk, and used the experimental method of Information Integration Theory to investigate the integrated algebra model of each concerned group’s “probability information” and “outcome information” of drunk driving risk and its level of risk perception. The findings are as follows: I. Cognitive algebra model (I) On drunk driving cases with high-probability, low-outcome risk of being stopped, the law enforcers as a whole used the multiplying model, while the drunk driving group and the ordinary driving group used the equal-weight averaging model to integrate the information. (II) On drunk driving cases with low-probability, high-outcome risk of causing traffic accidents, the law enforcers, the drunk driving group, and the ordinary group all used the adding model, with more emphasis on the outcome information. (III) On drunk driving cases with high-probability, low-outcome risk of being stopped, most people used the equal-weight averaging model, and they used the adding model on the cases of low-probability, high-outcome risk of causing traffic accidents. II. Risk perception (I) The drunk driving group had the highest risk perception on the type of high-probability, low-outcome risk of being stopped drunk driving, the ordinary group had the second highest risk perception, and the law enforcers had the lowest risk perception. (II) The ordinary group had the highest risk perception on the type of low-probability, high-outcome risk of drunk driving causing traffic accidents, the drunk driving group had the second highest risk perception, and the law enforcers had the lowest risk perception.