Predicting airline passengers’ loyalty using artificial neural network theory |
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| Institution: | 1. Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden;2. School of Behavioural and Health Sciences, Australian Catholic University, Strathfield 2135, Australia;1. Associate Professor of Marketing Griffith Business School Griffith University, 170 Kessels Road, Nathan QLD 4111, Australia;2. Professor of Marketing Business Research Unit (BRU/UNIDE), Instituto Universitário de Lisboa (ISCTE-IUL), Portugal;1. The Hull University Business School, United Kingdom;2. Tanta University, Egypt;3. DHL, Germany |
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| 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. |
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| Keywords: | Loyalty Service quality Perceived value Brand image Artificial neural network Aviation |
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