The novel coronavirus (COVID-19) outbreak has impacted the aviation industry worldwide. Several restrictions and regulations have been implemented to prevent the virus’s spread and maintain airport operations. To recover the trustworthiness of air travelers in the new normality, improving airport service quality (ASQ) is necessary, ultimately increasing passenger satisfaction in airports. This research focuses on the relationship between passenger satisfaction and the ASQ dimensions of airports in Thailand. A three-stage analysis model was conducted by integrating structural equation modeling, Bayesian networks, and artificial neural networks to identify critical ASQ dimensions that highly impact overall satisfaction. The findings reveal that airport facilities, wayfinding, and security are three dominant dimensions influencing overall passenger satisfaction. This insight could help airport managers and operators recover passenger satisfaction, increase trustworthiness, and maintain the efficiency of the airports in not only this severe crisis but also in the new normality.

Airports encompass a range of service touchpoints that directly impact passenger satisfaction and, consequently, the likelihood of service recommendation. This study investigates the service quality of Southeast Asian airports by applying five supervised machine learning classification models — decision trees, random forests, support vector machines, neural networks, and gradient boosting machines — on passenger satisfaction data extracted from the Skytrax website. The dataset includes evaluations of various service dimensions, such as staff behaviour, queuing time, and overall experience. This study incorporates cross-validation and hyperparameter tuning to identify the most suitable model for classifying passenger satisfaction. Among the models tested, the random forest classifier achieved the highest accuracy (0.91), demonstrating strong robustness and interpretability. Model performance was assessed using confusion matrices, balanced accuracy, the Matthews correlation coefficient (MCC), and ROC curves. Furthermore, SHAP values were used to identify the most influential service touchpoints, highlighting airport staff performance and queue management as key factors. These findings align with existing literature emphasising the pivotal role of well-trained airport employees and efficient queuing systems in shaping positive passenger experiences. Studies have shown that courteous staff interactions, efficient conflict resolution, and reduced waiting times significantly contribute to customer satisfaction and loyalty. Additionally, the integration of smart technologies such as self-service kiosks, automated security systems, and touchless check-in and baggage solutions enhances operational efficiency and aligns with sustainability initiatives. This study offers a data-driven approach for airport managers to optimise service delivery, increase passenger experiences, and tailor improvements to specific airport environments.

Objective: The primary objective of this research is to develop a model that can identify the significance of variables impacting the expectations and perceptions of service users at Thai airports. This investigation specifically focuses on the indicators of service quality. Methodology: The Classification and Regression Trees (CART) method. Two distinct models were constructed: (1) a model analyzing airport user expectations, and (2) a model assessing the perceived service quality by airport users. The target variable for both models was airport passenger satisfaction. Findings: The analysis of the airport service user expectation model unveiled that the most crucial factor impacting customer satisfaction is the adequacy of parking spaces. Conversely, the perception model demonstrated that customer satisfaction is primarily influenced by the politeness and assistance provided by airport inspectors. Conclusion: The results derived from this study can provide valuable insights to airport regulators, aiding their comprehension of user requirements in terms of both expectations and perceptions. By understanding these needs, airport authorities can work towards satisfying them effectively. Implications: The findings of this research have several implications for airport authorities. They can utilize the guidelines and recommendations proposed in this study to enhance airport facilities and processes, ultimately improving passenger satisfaction.