Stimulating Influenza Vaccination via Prosocial Motives

Abstract

Objective: Americans do not vaccinate nearly enough against Influenza (flu) infection, despite severe health and economic burden of influenza. Younger people are disproportionately responsible for transmission, but do not suffer severely from the flu. Thus, to achieve herd immunity, prosocial motivation needs to be a partial driver of vaccination decisions. Past research has not established the causal role of prosociality in flu vaccination, and the current research evaluates such causal relationship by experimentally eliciting prosociality through messages about flu victims.

Methods: In an experimental study, we described potential flu victims who would suffer from the decision of others to not vaccinate to 3952 Internet participants across eight countries. We measured sympathy, general prosociality, and vaccination intentions. The study included two identifiable victim conditions (one with an elderly victim and another with a young victim), an unidentified victim condition, and a no message condition.

Results: We found that any of the three messages increased flu vaccination intentions. Moreover, this effect was mediated by enhanced prosocial motives, and was stronger among people who were historical non-vaccinators. In addition, younger victim elicited greater sympathy, and describing identifiable victims increased general sympathy and prosocial motives.

Conclusions: These findings provide direct experimental evidence on the causal role of prosocial motives in flu vaccination, by showing that people can be prompted to vaccinate for the sake of benefiting others.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1. Mean Prosocial Motives as Measured in Rated Likelihood to Donate to an Unrelated Cause.

Error Bars: ± 2SEs (95% CI).

Fig 2. Mediational Model for the effect of identifiable victim on prosocial motives, mediated by sympathy.

This mediation was based on the bootstrapping method for calculating indirect effect. Analysis was performed in SPSS using the PROCESS macro developed by Hayes [32], with 5000 resampling to compute the Bias Corrected boot strapped 95% Confidence Intervals (BCa CI). Total effect of identifiable victim on prosocial motive was significant, B = 0.039, BCa 95% CI [0.017, 0.061], p = .007.

Fig 3. Mean Vaccination Intentions across Condition and Past Year Vaccination Status.

Error Bars: ± 2SEs (95% CI).

Fig 4. Mediational Model for the effect of message on vaccination intention among all participants, mediated by prosocial motives.

This mediation was based on the bootstrapping method for calculating indirect effect developed by Hayes[32]. Participants included anyone with a valid response on vaccination intention (n = 3581). Analysis was performed in SPSS using the PROCESS macro developed by Hayes [32], with 5000 resampling to compute the Bias Corrected boot strapped 95% Confidence Intervals (BCa CI). Total effect of message on vaccination intention was significant, B = 0.056, BCa 95% CI [0.029, 0.083], p = .0001.