Americans’ early behavioral responses to COVID-19

Abstract

Understanding human responses to pandemics can improve public health. A survey of US residents (n = 2004) February 28, 2020, very early in the coronavirus pandemic, tested predictors of five “protective” actions: washing hands, wearing masks, avoiding travel, avoiding large public gatherings, and avoiding Asians (given COVID-19’s first appearance in China). We added to the Protective Action Decision Model—positing threat, protective action, and stakeholder perceptions as immediate predictors of intentions—objective and subjective coronavirus knowledge as predictors of these perceptions, and psychological distance to predict threat perceptions. We presumed objective and subjective knowledge were affected by following US and China news about COVID-19. Structural equation modeling indicated adequate fit for this parsimonious model; variance explained in behavioral intentions ranged from .12 (handwashing) to .33 (Asians). Behavioral intentions rose with higher threat, action, and stakeholder (trust) perceptions, psychological distance reduced threat perceptions, objective knowledge reduced threat and action perceptions but increased trust, and subjective knowledge did the opposite. Coronavirus-news following increased both objective and subjective knowledge, but subjective knowledge exhibited stronger associations and US news dominated China news. Moderate model fit and variance explained might reflect model parsimony and/or data collection when US cases were in the low double digits.

Keywords:

• Coronavirus
• COVID-19
• protective behavior

Acknowledgments

This research was supported by the U.S. National Science Foundation under Grant No. 2022216.

Disclosure statement

The authors declare they have no financial interest or benefit that has arisen from this research.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research was supported by the U.S. National Science Foundation under Grant No. 2022216.