Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88193
Title: | Nudging to reduce the perceived threat of coronavirus | Authors: | Kim, J Giroux, M GonzalezJimenez, H Jang, S Kim, S Park, J Kim, JE Lee, JC Choi, YK |
Issue Date: | 2020 | Source: | Journal of advertising, Published online: 29 Aug 2020, p. 1-15, https://doi.org/10.1080/00913367.2020.1806154 | Abstract: | Prior research in behavioral economics has examined the effects of nudging and the diverse aspects of choice on individuals’ decisions and behaviors. Based on this premise, the current research offers a novel and timely view by examining how communication messages in public service advertisements (PSAs) can alter the perception of threat under uncertain situations such as the SARS-CoV-2 coronavirus pandemic. This article investigates the role of additional relative statistical information on the perception of threat and stockpiling intention. First, we examine whether there is a reduction in the perceived threat of the coronavirus if information about the potential severity of an alternative threat (car accidents) is activated, when compared to offering only statistics on the disease caused by SARS-CoV-2, which is known as COVID-19. Furthermore, we established the mediating role of a perceived threat in consumers’ decisions and behavior in times of severe crisis. This suggests that organizations and policymakers can influence individuals by increasing or decreasing their perceived level of threat depending on the desired outcomes (e.g., respecting authorities’ recommendations or avoiding stockpiling). This research offers a deeper understanding of how consumers can be “nudged” toward desired behavior in the context of public health and safety. | Publisher: | Routledge, Taylor & Francis Group | Journal: | Journal of advertising | ISSN: | 0091-3367 | EISSN: | 1557-7805 | DOI: | 10.1080/00913367.2020.1806154 |
Appears in Collections: | Journal/Magazine Article |
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88193_link.htm | Interim Free E-Resources | 125 B | HTML | View/Open |
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