Predicting the U.S. Public’s Prosocial Responses during the COVID-19 Pandemic

Abstract

This research examines three distinct processes that influence Americans’ prosocial responses during the COVID-19 pandemic. Employing a nationally representative sample, participants read either a mock-up news article on COVID-19 with a clear responsibility attribution (n = 496) or one without (n = 513) in a survey embedded experiment. Participants exposed to the responsibility attribution condition engaged in less systematic processing; systematic processing and all emotions mediated the relationship between responsibility attribution and support for government response measures. For donation intention, only systematic processing and the two socially oriented emotions were significant mediators. In essence, responsibility attribution in media coverage can exert powerful influence on public perception in an ongoing crisis.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ Ipsos employs a patented methodology based on address-based probability sampling. Participants were recruited from KnowledgPanel©, a large panel that is representative of the U.S. population. Participants received an e-mail invitation to complete the survey online. Households without Internet access are provided with the necessary devices.

² In February 2020, we conducted a pilot test of the survey instrument. Data were collected from a U.S.-based adult sample (N = 1303) recruited via Amazon’s Mechanical Turk (MTurk). All participants who completed the survey received $1 as compensation.

³ In general, participants were more likely to process the news article systematically than heuristically, which probably indicated a general interest in news stories about the COVID-19 pandemic given its high issue salience.

⁴ These variables were arguably more relevant at the time of data collection before the U.S. became the epicenter of the COVID-19 pandemic.

⁵ Only significant models are depicted in Figures 1 through 5.

Additional information

Funding

This work was supported by the National Science Foundation (#2020597).