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
Contributor:
Phi-Hung Nguyen

Description

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

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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.

Institutions

FPT University

Categories

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

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