Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia
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 — deci...
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| Format: | Article |
| Language: | English |
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Sciendo
2025-06-01
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| Series: | Engineering Management in Production and Services |
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| Online Access: | https://doi.org/10.2478/emj-2025-0011 |
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| author | Pholsook Thitinan Ramjan Sarawut Wipulanusat Warit |
| author_facet | Pholsook Thitinan Ramjan Sarawut Wipulanusat Warit |
| author_sort | Pholsook Thitinan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-542399e6a33c44ee8ab7e79bb36266db |
| institution | DOAJ |
| issn | 2543-912X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Engineering Management in Production and Services |
| spelling | doaj-art-542399e6a33c44ee8ab7e79bb36266db2025-08-20T03:17:19ZengSciendoEngineering Management in Production and Services2543-912X2025-06-01172376210.2478/emj-2025-0011Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast AsiaPholsook Thitinan0Ramjan Sarawut1Wipulanusat Warit21Thammasat University, Bangkok, 10200, Thailand2Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand3Thammasat University, Pathumthani, 12120, ThailandAirports 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.https://doi.org/10.2478/emj-2025-0011airport quality servicemachine learningdata classificationhyperparameter tuning |
| spellingShingle | Pholsook Thitinan Ramjan Sarawut Wipulanusat Warit Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia Engineering Management in Production and Services airport quality service machine learning data classification hyperparameter tuning |
| title | Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia |
| title_full | Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia |
| title_fullStr | Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia |
| title_full_unstemmed | Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia |
| title_short | Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia |
| title_sort | enhancing airport services data driven analysis of passenger satisfaction and service quality in southeast asia |
| topic | airport quality service machine learning data classification hyperparameter tuning |
| url | https://doi.org/10.2478/emj-2025-0011 |
| work_keys_str_mv | AT pholsookthitinan enhancingairportservicesdatadrivenanalysisofpassengersatisfactionandservicequalityinsoutheastasia AT ramjansarawut enhancingairportservicesdatadrivenanalysisofpassengersatisfactionandservicequalityinsoutheastasia AT wipulanusatwarit enhancingairportservicesdatadrivenanalysisofpassengersatisfactionandservicequalityinsoutheastasia |