In the face of vaccine hesitancy, public health officials are seeking more effective risk communication approaches to increase vaccination rates. We test the influence of visual policy narratives on COVID-19 vaccination behavior through a panel survey experiment conducted in early 2021 (n=3900) and then eight weeks later (n=2268). We examine the effects of three visual policy narrative messages that test the narrative mechanism of character selection (yourself, your circle, and your community) and a non-narrative control on COVID-19 vaccine behavior. Visual risk messages that use narratives positively influence COVID-19 vaccination through serial mediation of affective response to the messages and motivation to get the COVID-19 vaccination. Additionally, character selection matters, as messages focusing on protecting others (i.e., your circle and your community) perform stronger than that of yourself. Political ideology moderated some of the effects, with conservative respondents in the non-narrative control condition having a higher probability of vaccination in comparison to the protect yourself condition. Taken together, these results suggest that public health officials should use narrative-based visual communication messages that emphasize communal benefits of vaccinations.

Our research team conducted a three-wave online panel survey through Qualtrics^XM of individuals in 50 U.S. states and Washington D.C. in 2021. We contracted with Qualtrics^XM to administer the survey using their proprietary panels of online survey respondents. These panel participants are recruited from a variety of sources, including website intercept recruitment, targeted email lists, gaming sites, customer loyalty web portals, and social media. Here, we present data from T₁ (n = 3,900; launched between January 11 and February 3, 2021) and T₂ (n = 2,268; launched between March 22 and April 9, 2021). In these survey waves, we conducted an experiment wherein respondents were randomly assigned to one of four experimental conditions in T₁, comprised of three visual policy narrative treatment conditions (“protect yourself,” n = 986, 25%; “protect your circle,” n = 974, 25%; “protect your community,” n = 955, 25%) and a control condition (“get the vaccine”, n = 969, 25%). 

We conduct two main analyses. Analysis 1 assessed the overall effect of narrative risk messages (X_i) on vaccination (Y₁), moderated by political ideology (W), controlling for covariates risk perception, COVID-19 experience, flu vaccine behavior, and demographics. Analysis 2, we test (i) the mediation effects of message conditions (X_i) through affective response (M₁) and motivation to vaccinate (M₂) on vaccine behavior at T₂ (Y₁), both as individual mediators and as serial mediators, and (ii) the moderation effects of political beliefs (W) for these mediation effects, as well as for the direct effect of message conditions on affective response (M₁), motivation to vaccinate (M₂), and on vaccine behavior (Y₁), all controlling for covariates risk perception, COVID-19 experience, flu vaccine behavior, and demographics.

For the analysis, we use Hayes’ PROCESS models, a regression-based moderated mediation model, the regression coefficients of which are estimated using OLS regression (when M₁ or M₂ are the outcome variables) and logit regression analysis (when Y₁ is the dichotomous outcome variable). For all models, we use 95% confidence intervals, and 5000 bootstrap samples to generate bias-corrected confidence intervals. Given that the X_i variable is a multicategorical variable, both Analysis 1 used two kinds of coding to represent the experimental conditions. The first is indicator coding, which compares each treatment condition to the control.

• X₁ = protect yourself compared to control
• X₂ = protect the circle compared to control
• X₃ = protect the community compared to control

The second is Helmert contrast coding, which is a successive comparison of conditions.

• X₄ = the average of all three narrative message conditions compared to control
• X₅ = “protect the circle” plus “protect the community” vs. “protect yourself”
• X₆ = “protect the community” compared to “protect the circle”

Thus, in the full statistical model, each path from X_i to other variables includes three different paths, each representing the path from X_i to other variables. Similarly, each moderating effect of W includes interaction terms with X_i.

The data file here is a CSV file. Hayes PROCESS may be conducted in SPSS, R, and SAS.