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

Abstract

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.