Leveraging AI to Improve Service Quality for Better Passenger Satisfaction at Airport

The aim of this research is to explore the impact of airport service quality on passenger satisfaction level in Dubai International Airport (DXB) and the study employed machine learning approach to establish the services quality dimensions relationship with passenger satisfaction. However, the initial objective is to analyze patterns and make future forecasts that DXB could use to enhance service delivery. The research focuses on key service quality dimensions: The study investigates the causal relationship between five constructs: reliability, service quality tangibility, assurance, responsiveness, and empathy using machine learning models such as regression and classification. The information data applies to the years 2021, 2022, and 2023 and embrace customer’s opinions and satisfaction ratings and demographics. Key findings reveal that the service attributes such as inflight entertainment, the convenience of online booking, and services offered on board should be a priority for boosting customer satisfaction. Moreover, demographic factors have an influential impact throughout the travelling course including age, purpose of travelling and gender. The research shows that there is an opportunity in machine learning to identify patterns of passenger satisfaction for corresponding recommendations for DXB to change its service delivery.