Determinants of Customer Satisfaction at the San Francisco International Airport

Abstract

This study attempts to determine the overall satisfaction factors from airline passengers at the San Francisco International Airport (SFO), using the classification method of random forest. The analysis is based on the 2014 annual survey conducted by SFO that collects data on passenger demographics and satisfaction with airport facilities and services. Results of this study indicate that some service attributes are more important than others for passengers’ overall satisfaction at SFO. Study results are expected to provide practical insights to the airport industry. This study, in addition, introduces the machine learning method of random forest to tourism research.