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Predicting airline passengers’ loyalty using artificial neural network theory

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  • Singh, Balgopal

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.

Suggested Citation

  • Singh, Balgopal, 2021. "Predicting airline passengers’ loyalty using artificial neural network theory," Journal of Air Transport Management, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:jaitra:v:94:y:2021:i:c:s0969699721000636
    DOI: 10.1016/j.jairtraman.2021.102080
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    References listed on IDEAS

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    Cited by:

    1. Leon, Steven & Dixon, Sonoma, 2023. "Airline satisfaction and loyalty: Assessing the influence of personality, trust and service quality," Journal of Air Transport Management, Elsevier, vol. 113(C).
    2. Ayat Zaki Ahmed & Manuel Rodríguez Díaz, 2022. "A Methodology for Machine-Learning Content Analysis to Define the Key Labels in the Titles of Online Customer Reviews with the Rating Evaluation," Sustainability, MDPI, vol. 14(15), pages 1-31, July.

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