Acceso usuarios
[1]
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S K , Manivannan
Purpose: Covid 19 pandemic has taken the world by shock for last few years, and it has greatly impacted the livelihood of people across all walks of life and even the economies of many nations got greatly affected. Governments across the globe revived from the impact of covid-19 pandemic using many strategies and policies which were formulated under the guidance of the world health organization. One of the Prime weapons which helped the governments and public against covid -19 is vaccination. This research which was conducted August 2021 was done to understand the perception of the public towards the covid 19 vaccination and to predict the public intention to take up covid -19 vaccination using the health belief model constructs.
Theoretical framework:The Study has used the variables of the health belief model namely the perceived severity, perceived susceptibility, Perceived Benefits, Cues to action and other socio-demographic variables to predict the intent of the respondents towards taking Covid-19 vaccination. Design/methodology/approach: Data was collected using a self-administered online questionnaire distributed to the respondents from Tamil Nadu, India who are above 18 years of age. Machine Learning Algorithms like Logistic Regression, Artificial Neural Networks were used to predict the public intent to take up covid 19 vaccination.
Findings: From the Analysis of Logistic Regression and Artificial Neural Network, it was found that Health Belief Model Constructs Perceived Barriers, Perceived Benefits and Cues to action, were significant factors that affect the public intention to vaccinate.
Research, Practical & Social implications:Findings of the research will help the government, stake holders to understand the factors impacting the respondent’s intent to covid-19 vaccination which will guide them to plan better strategies for future vaccination drives Originality/value:The Study has used to two different machine learning algorithms to compare and corroborate the research findings and in turn identifying the significant predictors of covid-19 vaccination intent
