ArticleBMC psychology2023
Moral, self-interested, and social motivation each predict compliance with social distancing rules: utilitarianism is an indirect positive predictor.
Article in BMC psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSocial distancing rules have proven to be essential in reducing the spread of COVID-19. However, we can optimise these rules if we identify factors which predict compliance. Thus, in this study we investigated whether compliance with distancing rules is predicted by whether an individual is motivated by moral, self-interested, or social reasons. We also investigated the impact of an individual's utilitarian orientation both on compliance itself and on reasons for compliance.
methodsOur sample consisted of 301 participants recruited from four US states - California, Oregon, Mississippi, and Alabama - who completed an anonymous online survey. Six vignettes describing hypothetical social distancing rules were developed for the study. Participants indicated (i) how likely they were to violate each hypothetical distancing rule, (ii) how morally wrong violating each rule would be, (iii) how much risk of contracting COVID-19 they would tolerate in order to violate each rule, and (iv) how much social condemnation they would tolerate in order to violate each rule. Based on these responses, we gauged each participant's overall degree of compliance with social distancing rules as well as the extent to which each participant's compliance is motivated by moral, self-interested, and social reasons. We also measured other variables that could affect compliance including personality, level of religiosity, and inclination to engage in utilitarian reasoning. Multiple regression and exploratory structural equation modelling were used to determine predictors of compliance with social distancing rules.
resultsWe found that moral, self-interested, and social motivation each positively predicted compliance, with self-interested motivation being the strongest predictor. Furthermore, utilitarian orientation indirectly predicted compliance, with moral, self-interested, and social motivation as positive mediating factors. No controlled covariates (personality factors, religiosity, political orientation, or other background variables) predicted compliance.
conclusionThese findings have implications not only for the design of social distancing rules but also for efforts to ensure vaccine uptake. Governments need to consider how to harness moral, self-interested, and social motivation to promote compliance, perhaps by co-opting utilitarian reasoning, which positively influences these motivational forces.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.