The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Predictors of COVID-19 voluntary compliance behaviors: An international investigation
Open accessAbstract
With a large international sample (n = 8317), the present study examined which beliefs and attitudes about COVID-19 predict 1) following government recommendations, 2) taking health precautions (including mask wearing, social distancing, handwashing, and staying at home), and 3) encouraging others to take health precautions. The results demonstrate the importance of believing that taking health precautions will be effective for avoiding COVID-19 and generally prioritizing one's health. These beliefs continued to be important predictors of health behaviors after controlling for demographic and personality variables. In contrast, we found that perceiving oneself as vulnerable to COVID-19, the perceived severity of catching COVID-19, and trust in government were of relatively little importance. We also found that women were somewhat more likely to engage in these health behaviors than men, but that age was generally unrelated to voluntary compliance behaviors. These findings may suggest avenues and dead ends for behavioral interventions during COVID-19 and beyond.
Overview
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
Verification
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.
Citations
Cites
Statistical Power Analysis for the Behavioral Sciences
Muller, K. (1989) · Technometrics · doi:10.1080/00401706.1989.10488618 · Cited by 99,167
Statistical power analysis for the behavioral sciences
(1990) · Computers, Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,269
The Health Belief Model: a decade later.
Janz, N. K., & Becker, M. H. (1984) · Health Education Quarterly · doi:10.1177/109019818401100101 · Cited by 8,437
(1999) · Cited by 7,995
Health Promotion by Social Cognitive Means
Bandura, A. (2004) · Health Education & Behavior · doi:10.1177/1090198104263660 · Cited by 7,238
Historical Origins of the Health Belief Model
Rosenstock, I. M. (1974) · Health Education Monographs · doi:10.1177/109019817400200403 · Cited by 6,116
Estimates of the severity of coronavirus disease 2019: a model-based analysis
Verity, R., Okell, L. C., Dorigatti, I., Winskill, P., Whittaker, C., Imai, N., Cuomo-Dannenburg, G., Thompson, H., Walker, P. G. T., Fu, H., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30243-7 · Cited by 3,660
How will country-based mitigation measures influence the course of the COVID-19 epidemic?
Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30567-5 · Cited by 3,648
Henrich, J., Heine, S. J., & Norenzayan, A. (2010) · Nature · doi:10.1038/466029a · Cited by 3,441
Prince, S. A., Adamo, K. B., Hamel, M., Hardt, J., Connor Gorber, S., & Tremblay, M. (2008) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/1479-5868-5-56 · Cited by 3,162
Cited by
Chater, N., & Loewenstein, G. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x22002023 · Cited by 710
Nivette, A., Ribeaud, D., Murray, A., Steinhoff, A., Bechtiger, L., Hepp, U., Shanahan, L., & Eisner, M. (2021) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113370 · Cited by 575
The role of business model innovation in the hospitality industry during the COVID-19 crisis
Breier, M., Kallmuenzer, A., Clauss, T., Gast, J., Kraus, S., & Tiberius, V. (2021) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2020.102723 · Cited by 451
Self-citationBuecker, S., & Horstmann, K. T. (2021) · European Psychologist · doi:10.1027/1016-9040/a000453 · Cited by 225
COVID-19 risk perception and coping mechanisms: Does gender make a difference?
Rana, I. A., Bhatti, S. S., Aslam, A. B., Jamshed, A., Ahmad, J., & Shah, A. A. (2021) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2021.102096 · Cited by 177
Metadata
- Authors
- Clark, C., Davila, A., Regis, M., & Kraus, S.
- Venue
- Global Transitions
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 2
- Pages
- 76-82
- ISSN
- 2589-7918 (ISSN-L)
- OpenAlex
- W3037536037
- Cited by
- 649
- References
- 37
- FWCI
- 176.82
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Predictors of COVID-19 voluntary compliance behaviors: An international investigation. https://app.cochat.ai/papers/10.1016/j.glt.2020.06.003
- The paper
- Clark, C., Davila, A., Regis, M., & Kraus, S. (2020). Predictors of COVID-19 voluntary compliance behaviors: An international investigation. Global Transitions. https://doi.org/10.1016/j.glt.2020.06.003
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
