The protection motivation theory for predict intention of COVID-19 vaccination in Iran: a structural equation modeling approach

Background Many efforts are being made around the world to discover the vaccine against COVID-19. After discovering the vaccine, its acceptance by individuals is a fundamental issue for disease control. This study aimed to examine COVID-19 vaccination intention determinants based on the protection motivation theory (PMT). Methods We conducted a cross-sectional study in the Iranian adult population and surveyed 256 study participants from the first to the 30th of June 2020 with a web-based self-administered questionnaire. We used Structural Equation Modeling (SEM) to investigate the interrelationship between COVID-19 vaccination intention and perceived susceptibility, perceived severity, perceived self-efficacy, and perceived response efficacy. Results SEM showed that perceived severity to COVID-19 ([beta] = .17, p < .001), perceived self-efficacy about receiving the COVID-19 vaccine ([beta] = .26, p < .001), and the perceived response efficacy of the COVID-19 vaccine ([beta] = .70, p < .001) were significant predictors of vaccination intention. PMT accounted for 61.5% of the variance in intention to COVID-19 vaccination, and perceived response efficacy was the strongest predictor of COVID-19 vaccination intention. Conclusions This study found the PMT constructs are useful in predicting COVID-19 vaccination intention. Programs designed to increase the vaccination rate after discovering the COVID-19 vaccine can include interventions on the severity of the COVID-19, the self-efficacy of individuals receiving the vaccine, and the effectiveness of the vaccine in preventing infection. Keywords: COVID-19, Vaccination, Intention, Structural equation modeling, Iran