Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia

• Autorzy: Pholsook Thitinan , Ramjan Sarawut , Wipulanusat Warit

Identyfikatory

DOI

10.2478/emj-2025-0011

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

airport quality service; machine learning; data classification; hyperparameter tuning

PL

jakość usług lotniskowych; uczenie maszynowe; klasyfikacja danych; dostrajanie hiperparametrów

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Engineering Management in Production and Services
• Rocznik: 2025
• Tom: Vol. 17, no. 2
• Strony: 37--62
• Opis fizyczny: Bibliogr. 62 poz., tab., wykr.

Twórcy

autor

Pholsook Thitinan

• Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand

• https://orcid.org/0009-0005-8484-3156

autor

Ramjan Sarawut

• Thammasat University, Bangkok, 10200, Thailand

• https://orcid.org/0000-0002-6531-0419

autor

Wipulanusat Warit

• Thammasat University, Pathumthani, 12120, Thailand

• https://orcid.org/0000-0003-1006-6540

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