A Hybrid MRA-BN-NN Approach for Analyzing Airport Service Based on User-Generated Contents

Abstract

As the world transitions from the COVID-19 pandemic to a new normal, the Airports Council International (ACI) has disclosed that the Asia-Pacific region is lagging other regions in terms of air traffic recovery. This research comprehensively examines passenger satisfaction at leading airports in Southeast Asia. A multimethod approach incorporating multiple regression analysis, Bayesian networks, and neural network analysis was utilized to scrutinize user-generated content from Skytrax. The study contemplates eight distinct attributes of airport customer ratings: queuing time, cleanliness, seating areas, signage, food services, retail options, Wi-Fi availability, and staff courtesy. The findings reveal that queuing time and staff courtesy are the most important factors influencing the overall airport service rating. These results provide empirical evidence supporting the enhancement of airport services in the region and contribute significantly to the theoretical understanding and managerial implications for airport management and authorities. This research thus offers a valuable resource for improving service quality and operational efficiency in the airport industry, which could lead to a recovery and increase in the number of air passengers in this region.