Cited by 350Communication and COVID-19 ImpactCOVID-19 Prevention and ImpactBehavioral Health and Interventions

Don’t get it or don’t spread it: comparing self-interested versus prosocial motivations for COVID-19 prevention behaviors

Open access
Jordan, Jillian J., Yoeli, Erez, Rand, David G.·2021·Scientific Reports

Abstract

COVID-19 prevention behaviors may be seen as self-interested or prosocial. Using American samples from MTurk and Prolific (total n = 6850), we investigated which framing is more effective—and motivation is stronger—for fostering prevention behavior intentions. We evaluated messaging that emphasized personal, public, or personal and public benefits of prevention. In initial studies (conducted March 14–16, 2020), the Public treatment was more effective than the Personal treatment, and no less effective than the Personal + Public treatment. In additional studies (conducted April 17–30, 2020), all three treatments were similarly effective. Across all these studies, the perceived public threat of coronavirus was also more strongly associated with prevention intentions than the perceived personal threat. Furthermore, people who behaved prosocially in incentivized economic games years before the pandemic had greater prevention intentions. Finally, in a field experiment (conducted December 21–23, 2020), we used our three messaging strategies to motivate contact-tracing app signups (n = 152,556 newsletter subscribers). The design of this experiment prevents strong causal inference; however, the results provide suggestive evidence that the Personal + Public treatment may have been more effective than the Personal or Public treatment. Together, our results highlight the importance of prosocial motives for COVID-19 prevention.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-021-97617-5

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Jordan, J. J., Yoeli, E., & Rand, D. G.
Affiliations
  • Harvard University (US)
  • Massachusetts Institute of Technology (US)
Corresponding author
Jordan, Jillian J.
Venue
Scientific Reports
Published
2021-10-12
Volume
11
Issue
1
Pages
20222-20222
ISSN
2045-2322 (ISSN-L)
OpenAlex
W3097856151
Cited by
350
References
5
Citations by year
2026: 16 · 2025: 21 · 2024: 35 · 2023: 59 · 2022: 85 · 2021: 90 · 2020: 44
FWCI
88.89
Type
journal article
Language
en
Open access
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • OSF Preprints (OSF Preprints) (submittedVersion)
  • PsyArXiv (OSF Preprints) (submittedVersion)
Topics
  • Social Sciences › Communication › Communication and COVID-19 Impact
  • Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
  • Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
Keywords
COVID-19 prevention behavior, prosocial motivation, framing effects, public health communication, threat perception, economic games
MeSH terms
COVID-19, Adult, Female, Humans, Male, Motivation, Social Behavior, United States, Contact Tracing, Pandemics, Mobile Applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review