Predicting airline passengers’ loyalty using artificial neural network theory

Balgopal Singh

Journal of Air Transport Management, 2021, vol. 94, issue C

Abstract: The study explores a model for predicting airline loyalty using the antecedents indicated in previous studies. Data was collected using a questionnaire distributed to 614 domestic air passengers using the snowball sampling method. The measurement tool had 16 scale items constructed on the recommendations of previous studies. Passenger satisfaction, airline service quality, passenger perceived value, and airline image are identified as determinants for airline loyalty. The predictive analytical approach of Artificial Neural Network theory and covariance-based Structural Equation Modelling for determining causality is employed in the study. The artificial neural network model predicts airline loyalty with 89% accuracy. Sensitivity analysis suggests passenger satisfaction as the most significant predictor of airline loyalty. The causal study supports that passenger satisfaction mediates the relationship between airline service quality and airline loyalty.

Keywords: Loyalty; Service quality; Perceived value; Brand image; Artificial neural network; Aviation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:94:y:2021:i:c:s0969699721000636

DOI: 10.1016/j.jairtraman.2021.102080

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