Investigation and Modeling of the Variables of the Decision to Vaccinate as the Foundation of an Algorithm for Reducing Vaccination Reluctance

Abstract

The purpose of this study is to examine the factors that influence vaccination options, including vaccination against COVID-19, in order to develop a management algorithm for decision-makers to reduce vaccination reluctance. This paper’s primary objective is to empirically determine the relationships between different variables that correlate to non-vaccination behavior of the target population, as well as the implications for public health and situational management strategies for future vaccination intentions. We created a questionnaire to investigate the personal approach to disease prevention measures in general and vaccination in particular. Using SmartPLS, load factors for developing an algorithm to manage vaccination reluctance were calculated. The results shows that the vaccination status of an individual is determined by their vaccine knowledge. The evaluation of the vaccine itself influences the choice not to vaccinate. There is a connection between external factors influencing the decision not to vaccinate and the clients’ motives. This plays a substantial part in the decision of individuals not to protect themselves by vaccination. External variables on the decision not to vaccinate correlate with agreement/disagreement on COVID-19 immunization, but there is no correlation between online activity and outside influences on vaccination refusal or on vaccine opinion in general.

Keywords

• COVID-19 vaccine
• vaccine hesitancy
• model reducing vaccine reluctance
• public health