This study assessed airport service quality by conducting importance-performance analysis (IPA) of user-generated content and examining the usefulness of a priori segmentation in the airport industry. The data were drawn from 35,138 Web reviews of airports worldwide shared online via the Skytrax website. Importance ratings were derived using the indirect method based on an artificial neural network. The results reveal that the most important attributes are staff and queuing time. The findings also include that service quality attributes’ importance and priority areas needing improvement vary according to traveler type, airport experience category, and region of origin. This study produced valuable insights into how airports can use IPA to leverage their passengers’ online reviews in order to enhance service quality and address customer heterogeneity.

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Service quality,Skytrax,Importance-performance analysis (IPA),Artificial neural network (ANN),Airport,Market segmentation

• Mathematics - Natural Sciences
• Economics and Business - Social Sciences
• Other Social Sciences - Social Sciences