Don’t get it or don’t spread it: comparing self-interested versus prosocial motivations for COVID-19 prevention behaviors
Open accessAbstract
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.
Integrity signals
Registry
Cites
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.
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
Altruistic punishment in humans
Fehr, E., & Gächter, S. (2002) · Nature · doi:10.1038/415137a · Cited by 5,616 · see citing works
Using social and behavioural science to support COVID-19 pandemic response
Bavel, J. J. V., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M., Crockett, M. J., Crum, A. J., Douglas, K. M., Druckman, J. N., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0884-z · Cited by 4,964 · see citing works
Self-citationWebb, T. L., & Sheeran, P. (2006) · Psychological Bulletin · doi:10.1037/0033-2909.132.2.249 · Cited by 3,732 · see citing works
Fehr, E., & Fischbacher, U. (2003) · Nature · doi:10.1038/nature02043 · Cited by 3,475 · see citing works
Evolution of indirect reciprocity
Nowak, M. A., & Sigmund, K. (2005) · Nature · doi:10.1038/nature04131 · Cited by 2,762 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Impact of community masking on COVID-19: a cluster-randomized trial in Bangladesh
Abaluck, J., Kwong, L. H., Styczynski, A., Haque, A., Kabir, M. A., Bates-Jefferys, E., Crawford, E., Benjamin-Chung, J., Raihan, S., Rahman, S., et al. (2022) · Science · doi:10.1126/science.abi9069 · Cited by 333 · see citing works
Prosociality predicts health behaviors during the COVID-19 pandemic
Campos-Mercade, P., Meier, A. N., Schneider, F. H., & Wengström, E. (2021) · Journal of Public Economics · doi:10.1016/j.jpubeco.2021.104367 · Cited by 242 · see citing works
The role of social connection on the experience of COVID-19 related post-traumatic growth and stress
Matos, M., McEwan, K., Kanovský, M., Halamová, J., Steindl, S. R., Ferreira, N., Linharelhos, M., Rijo, D., Asano, K., Vilas, S. P., et al. (2021) · PLOS ONE · doi:10.1371/journal.pone.0261384 · Cited by 73 · see citing works
COVID-19 protective behaviors are forms of prosocial and unselfish behaviors
Dinić, B. M., & Bodroža, B. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.647710 · Cited by 61 · see citing works
Kwon, S., & Park, A. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107087 · Cited by 33 · see citing works
Durand, H., Bacon, S. L., Byrne, M., Farrell, K., Kenny, E., Lavoie, K. L., McGuire, B. E., McSharry, J., Meade, O., Mooney, R., et al. (2021) · HRB Open Research · doi:10.12688/hrbopenres.13237.1 · Cited by 9 · see citing works
- 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)
- PMC
- PMC8511002
- 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
- gold
- doi.org
- 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