Predicting COVID-19 vaccine uptake: Comparing the health belief model and theory of planned behavior.

ABSTRACT

The COVID-19 outbreak has had a significant impact on the global health landscape, underscoring the crucial role that vaccinations play in achieving herd immunity and reducing the effects of pandemics. Given the importance of this issue, it is imperative to gain a deeper understanding of the various factors that influence individuals' decisions to seek vaccination. This study aimed to compare the prediction level of the Health Belief Model (HBM), the Theory of Planned Behavior (TPB), and a combined model in explaining the intention of adults to receive COVID-19 immunization. A cross-sectional online survey was conducted among adults (n = 505) in Saudi Arabia. The survey contained variables related to the HBM and TPB. The prediction level of the two models as well as a combined model were evaluated utilizing Structural Equation Modeling (SEM). Among the recruited 505 participants, 88% fell within the 18 to 30 age range, and 54.5% were male. The proposed HBM model accounted for 68% of the variation in intention, whereas the TPB model explained 78.2% of the variation in COVID-19 vaccination intention. The combined model showed greater explanatory power (82%). The variables of susceptibility (ß = 0.20, p < .001), severity (ß = 0.49, p < .001), advantages (ß = 0.63, p < .001), and obstacles (ß = - 0.24, p < .001), perceptions of behavioral control (ß = 1.58, p < .001) and attitudes (ß = 0.44, p < .001) were found to significantly predict increased vaccination intentions in the combined model. However, the subjective norm construct did not significantly predict vaccination intentions (ß = 0.06, p = .34). The TPB has greater explanatory power than the HBM in predicting the intention to obtain COVID-19 vaccination. However, the combined model showed a greater prediction level. Understanding and identifying people's perceived health beliefs and practices is critical for developing successful COVID-19 intervention methods.