Predicting Intention to Take a COVID-19 Vaccine in the United States: Application and Extension of Theory of Planned Behavior.

PURPOSE:This study aims to apply and extend the theory of planned behavior (TPB) to predict intention to take a COVID-19 vaccine. DESIGN:Cross-sectional. SETTING:Online. SAMPLE:Adult US residents recruited from Amazon Mechanical Turk (n = 172). MEASURES:Intention to take a COVID-19 vaccine (outcome variable), demographic variables (predictors), standard TPB variables (perceived behavioral control, attitude, and subjective norm; predictors), and non-TPB variables (anticipated regret, health locus of control, and perceived community benefit; predictors). ANALYSIS:Hierarchical linear regression predicting intention to take a COVID-19 vaccine, with demographic, standard TPB, and non-TPB variables entered in regression models 1, 2, and 3, respectively. RESULTS:The extended TPB model accounted for 72.5% of the variance in vaccination intention (p < .001), with perceived behavioral control (β = .29, p < .001), attitude (β = .23, p = .043), and perceived community benefit (β = .23, p = .020) being significant unique predictors. CONCLUSION:Despite the relatively small and non-representative sample, this study, conducted after COVID-19 vaccines were widely available in the USA, demonstrated that perceived behavioral control was the most robust predictor of intention to take a COVID-19 vaccine, suggesting that the TPB is a useful theoretical framework that can inform effective strategies to promote vaccine acceptance.