A Hybrid Three-staged SF-AHP, PLS-SEM and ANN Model to Predict Vaccination Intention against COVID-19 Pandemic

Published: 18 October 2021| Version 2 | DOI: 10.17632/v8bw5fsrkk.2
Phi-Hung Nguyen


Spherical Fuzzy Analytic Hierarchy Process (SF-AHP), Structural Equation Model (SEM), and Artificial Neural Network (ANN) approaches were applied to test the proposed hypotheses and predict the vaccination intention


Steps to reproduce

The proposed research framework consists of 3 phases. In Phase 1, assigning fuzzy weights to criteria based on pairwise comparisons is done using the SF-AHP model. In Phase 2, the PLS-SEM approach is used to validate the hypotheses as indirect/direct effects. In Phase 3, significant predictors from PLS-SEM analysis were taken as the ANN model’s input neurons. According to the normalized importance obtained from the multilayer perceptrons of the feed-forward-back-propagation ANN algorithm, we can find significant effects of vaccination intention and determine the accurate prediction rates.


FPT University


Mathematics, Vaccine, Fuzzy Logic, Analytical Hierarchy Process, Intention