Can't drive, I'm drunk – Exploring factors influencing the intention to drink and drive among Indian drivers

计划行为理论 感觉寻求 心理学 背景(考古学) 毒物控制 人为因素与人体工程学 一致性 伤害预防 社会心理学 宿命论 心理干预 自杀预防 社会心理的 控制(管理) 环境卫生 医学 精神科 管理 人格 经济 古生物学 生物 哲学 神学
作者
Ankit Kumar Yadav,Sajid Shabir Choudhary,Nishant Mukund Pawar,Nagendra R. Velaga
出处
期刊:Iatss Research [Elsevier BV]
卷期号:46 (4): 467-478 被引量:8
标识
DOI:10.1016/j.iatssr.2022.07.004
摘要

The crash risk of drivers increases significantly while driving under the influence of alcohol. However, the drivers' intention to drink and drive has not been explored yet in the context of a developing country like India. The present study applied an extended version of the theory of planned behaviour (TPB) to investigate the psychosocial predictors of drunk driving intentions of Indian drivers. 252 drivers participated in a self-reported survey designed for the study. Apart from the standard TPB components (attitudes, subjective norms, and perceived behavioural control), the survey also captured the extension measures such as risk perceptions, moral norms, traffic fatalism, sensation-seeking, conformity tendency, past engagement in drunk driving, crash history and driver demographics. The standard TPB model was successful in explaining 68.6% of the variance in the intention to drink and drive. The attitudes displayed the strongest influence on the intention (β = 0.696, p < 0.001), followed by perceived behavioural control (β = 0.180, p < 0.001); whereas subjective norms showed a moderate significance in influencing the intention (β = 0.062, p < 0.1). Among the extension variables, past behaviour showed the strongest influence on the intention to drink and drive (β = 0.123, p < 0.05), followed by sensation-seeking (β = 0.080, p < 0.05) and traffic fatalism (β = 0.072, p < 0.1). The extended TPB model explained 72% of the variance in the intention to drink and drive. The study findings can assist in the development of strategies/interventions to reduce drunk driving incidences. An earlier version of the paper was presented at the TRB 101st Annual Meeting, Washington, D. C., 2022.

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