Uncovering airline service quality dimensions from passengers' online reviews: A text mining approach to validate and extend Servqual

Abstract

This study investigates airline service quality dimensions by employing text-mining techniques to analyse passengers' emotional responses and experiences with airlines as reflected in their online reviews. The study aims to identify the relationship between these and SERVQUAL dimensions to confirm its reliability in the airline industry and reveal new and promising dimensions of airline service quality. The study contributes to academia by enriching literature on airline service quality and text mining applications, identifying areas for enhancement, and suggesting strategic directions for continuous improvement to practitioners based on passengers' quality perceptions of airline service experiences. With a large dataset from 100 airlines obtained from Skytrax, the study offers a deeper understanding of airline service quality. It encourages using big data and text-mining techniques to explore consumer preferences. The study's findings challenge traditional models and bring out new, context-specific service quality characteristics to provide a greater understanding of passenger experiences. These give the airlines insightful guidance on improving service offerings and fulfilling customer expectations, eventually improving customer satisfaction, loyalty, and competitive positioning.