PDOSPERT: A New Scale to Predict Domain-Specific Risk-Taking Behaviors in Times of a Pandemic

IF 1.8 3区 心理学 Q3 PSYCHOLOGY, APPLIED
Benno Guenther, Matteo M. Galizzi, Jet G. Sanders
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引用次数: 0

Abstract

Understanding risk tolerance is crucial for predicting and changing behavior across various domains, including health and safety, finance, and ethics. This remains true during a crisis, such as the COVID-19 pandemic, and leads to a key question: Do current risk measures reliably predict risk-taking in the drastically different context of a pandemic? The Domain Specific Risk-Taking (DOSPERT) scale, one of the most widely used risk-taking measures, assesses self-reported risk-taking in response to 30 risky situations across five domains. With the hypothetical risks of the DOSPERT being based on prepandemic circumstances, we estimate that three out of four of its risk-taking situations were not possible due to preventive measures or did not reflect risk-taking in times of COVID-19. In addition, COVID-19 brought forth new behaviors deemed risky. With an aim to better predict risk-taking in times of a pandemic, we introduce the Pandemic DOSPERT (PDOSPERT). We summarize three preregistered online studies with 1254 UK participants to validate the scale against the original DOSPERT and three other common risk-taking measures. We also test its ability to predict pandemic risk-related behaviors at three points in time over 2 years. Overall, we find that the PDOSPERT scale significantly improves predictions for pandemic-related risk behavior as compared to the original DOSPERT. In particular, the health/safety subscale is significantly and strongly associated with pandemic-related risk behavior. We not only validate a pandemic-specific risk task but also introduce a template for developing context- and domain-sensitive measures for risk-taking in the future.

PDOSPERT:预测大流行时期特定领域冒险行为的新量表
了解风险承受能力对于预测和改变健康与安全、金融和道德等各个领域的行为至关重要。在 COVID-19 大流行病等危机期间,这一点依然适用,并引出了一个关键问题:目前的风险测量方法是否能可靠地预测在大流行病这种截然不同的情况下的风险承担?特定领域风险承担(DOSPERT)量表是最广泛使用的风险承担测量方法之一,它针对五个领域的 30 种风险情况对自我报告的风险承担进行评估。由于 DOSPERT 的假设风险是基于疫情爆发前的情况,因此我们估计,由于采取了预防措施,其中四分之三的风险承担情况不可能发生,或者不能反映 COVID-19 期间的风险承担情况。此外,COVID-19 还带来了新的风险行为。为了更好地预测大流行时的冒险行为,我们引入了大流行 DOSPERT(PDOSPERT)。我们总结了三项预先登记的在线研究,共有 1254 名英国参与者参加,通过与最初的 DOSPERT 和其他三种常见的冒险行为测量方法进行对比,验证了该量表的有效性。我们还测试了该量表在两年内的三个时间点预测大流行风险相关行为的能力。总体而言,我们发现与原始的 DOSPERT 相比,PDOSPERT 量表大大提高了对大流行相关风险行为的预测能力。特别是,健康/安全子量表与大流行相关风险行为有明显且密切的关联。我们不仅验证了针对大流行病的风险任务,而且还为将来开发对情境和领域敏感的风险承担测量方法提供了模板。
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来源期刊
CiteScore
4.40
自引率
5.00%
发文量
40
期刊介绍: The Journal of Behavioral Decision Making is a multidisciplinary journal with a broad base of content and style. It publishes original empirical reports, critical review papers, theoretical analyses and methodological contributions. The Journal also features book, software and decision aiding technique reviews, abstracts of important articles published elsewhere and teaching suggestions. The objective of the Journal is to present and stimulate behavioral research on decision making and to provide a forum for the evaluation of complementary, contrasting and conflicting perspectives. These perspectives include psychology, management science, sociology, political science and economics. Studies of behavioral decision making in naturalistic and applied settings are encouraged.
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