Tam Hizmet Sunan ve Düşük Maliyetli Havayollarında Müşteri Tatmin Boyutlarının Dinamik Konu Modelleme Yaklaşımı ile Değerlendirilmesi

Year 2025, Volume: 16 Issue: 46, 559 - 584, 31.05.2025

Bilgehan Özkan , Özlem Atalık

https://doi.org/10.21076/vizyoner.1553607

Abstract

Bu araştırma tam hizmet sunan ve düşük maliyetli havayollarına yönelik müşteri tatmin boyutlarını ortaya çıkararak bu boyutların 2015-2022 yılları arasındaki değişimlerini incelemeyi hedeflemektedir. TripAdvisor’dan elde edilen 32,000 çevrimiçi müşteri değerlendirmesi bağlamsal bilgilerin elde edilmesine olanak tanıyan bir konu modelleme yöntemi olan BERTopic ile analiz edilmiştir. Analiz sonucunda, tam hizmet sunan havayollarında öne çıkan müşteri tatmin boyutları arasında bagaj, uçuş gecikmeleri, kabin ekibi ve kabin içi hizmetler yer alırken, düşük maliyetli havayollarında fiyat, COVID-19 ile ilgili konular ve yardımcı hizmetler öne çıkmıştır. Dinamik konu modelleme sonuçlarına göre, tam hizmet sunan havayollarında kabin ekibi ile ilgili olumsuz değerlendirmeler artarken, kabin içi eğlenceye ilişkin değerlendirmelerin azaldığı belirlenmiştir. Düşük maliyetli havayollarında ise COVID-19 süreciyle birlikte uçuş iptalleri ve müşteri hizmetlerine yönelik değerlendirmelerde artış görülmüştür. Bu bulgular, hem müşteri tatminine yönelik önemli hizmet alanlarını belirleyerek sektöre stratejik öneriler sunmakta hem de müşteri geri bildirimlerinin zaman içinde nasıl evrildiğini göstermektedir. Çalışma, havayolu sektöründe uzun vadeli müşteri tatmini yönetimi için değerli sonuçlar ve çıkarımlar sağlamaktadır.

Keywords

Müşteri Tatmini , Havayolu Hizmet Özellikleri , Metin Madenciliği , Dinamik Konu Modelleme

Evaluation of Customer Satisfaction Dimensions in Full Service and Low-Cost Airlines Via Dynamic Topic Modeling Approach

Abstract

The study aims to uncover the dimensions of customer satisfaction for full-service and low-cost airlines and examine the changes in these dimensions between 2015 and 2022. A total of 32,000 online customer reviews obtained from TripAdvisor are analyzed using BERTopic, a topic modelling method that facilitates the extraction of contextual information. The analysis reveals that the prominent dimensions of customer satisfaction for full-service airlines include baggage, flight delays, cabin crew, and in-flight services, while for low-cost airlines, price, COVID-19-related issues, and auxiliary services are emphasized. According to the results of dynamic topic modelling, negative reviews regarding cabin crew have increased for full-service airlines, whereas reviews related to in-flight entertainment have decreased. For low-cost airlines, reviews concerning flight cancellations and customer service have surged during the COVID-19 pandemic. These findings provide strategic recommendations to the industry by identifying key service areas affecting customer satisfaction and illustrating how customer feedback has evolved over time. The study offers valuable insights for managing long-term customer satisfaction in the airline industry.

Keywords

Customer Satisfaction , Airline Service Attributes , Text Mining , Dynamic Topic Modeling

References

• Abdullah, E. F., Alasadi, S. A., & Al-Joda, A. A. (2021). Text mining based sentiment analysis using a novel deep learning approach. International Journal of Nonlinear Analysis and Applications, 12(Special Issue), 595–604. https://doi.org/10.22075/ijnaa.2021.5378
• Abuzayed, A., & Al-Khalifa, H. (2021). BERT for Arabic Topic Modeling: An Experimental Study on BERTopic Technique. Procedia Computer Science, 189, 191–194. https://doi.org/10.1016/j.procs.2021.05.096
• Alhaj, F., Al-Haj, A., Sharieh, A., & Jabri, R. (2022). Improving Arabic Cognitive Distortion Classification in Twitter using BERTopic. International Journal of Advanced Computer Science and Applications, 13(1). https://doi.org/10.14569/IJACSA.2022.0130199
• Ali, F., Kim, W. G., & Ryu, K. (2016). The effect of physical environment on passenger delight and satisfaction: Moderating effect of national identity. Tourism Management, 57, 213–224. https://doi.org/10.1016/j.tourman.2016.06.004
• Atalik, Ö. (2007). Customer complaints about airline service: a preliminary study of Turkish frequent flyers. Management Research News, 30(6), 409–419. https://doi.org/10.1108/01409170710751908
• Baker, D. Mc. A. (2013). Service Quality and Customer Satisfaction in the Airline Industry: A Comparison between Legacy Airlines and Low-Cost Airlines. American Journal of Tourism Research, 2(1). https://doi.org/10.11634/216837861302317
• Ban, H.-J., Kim, H.-S., & Joung, H.-W. (2019). The Text Mining Approach to Understand Seat Comfort Experience of Airline Passengers through Online Review. Culinary Science & Hospitality Research, 25(9), 38–46. https://doi.org/10.20878/cshr.2019.25.9.005
• Blei, D. M., & Lafferty, J. D. (2006). Dynamic topic models. Proceedings of the 23rd International Conference on Machine Learning - ICML ’06, 113–120. https://doi.org/10.1145/1143844.1143859
• Blei, D. M., Ng, A. Y., & Michael I. Jordan. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
• Bogicevic, V., Yang, W., Bujisic, M., & Bilgihan, A. (2017). Visual Data Mining: Analysis of Airline Service Quality Attributes. Journal of Quality Assurance in Hospitality & Tourism, 18(4), 509–530. https://doi.org/10.1080/1528008X.2017.1314799
• Boyd-Graber, J., Hu, Y., & Mimno, D. (2017). Applications of Topic Models. Foundations and Trends® in Information Retrieval, 11(2–3), 143–296. https://doi.org/10.1561/1500000030
• Brochado, A., Rita, P., Oliveira, C., & Oliveira, F. (2019). Airline passengers’ perceptions of service quality: themes in online reviews. International Journal of Contemporary Hospitality Management, 31(2), 855–873. https://doi.org/10.1108/IJCHM-09-2017-0572
• Calisir, N., Basak, E., & Calisir, F. (2016). Key drivers of passenger loyalty: A case of Frankfurt–Istanbul flights. Journal of Air Transport Management, 53, 211–217. https://doi.org/10.1016/j.jairtraman.2016.03.002
• Çallı, L., & Çallı, F. (2023). Understanding airline passengers during Covid-19 outbreak to improve service quality: Topic modeling approach to complaints with latent dirichlet allocation algorithm. Transportation Research Record: Journal of the Transportation Research Board, 2677(4), 656–673. https://doi.org/10.1177/03611981221112096
• Cardozo, R. N. (1965). An experimental study of customer effort, expectation, and satisfaction. Journal of Marketing Research, 2(3), 244. https://doi.org/10.2307/3150182
• Chang, J., Boyd-Graber, J., Gerrish, S., Wang, C., & Blei, D. (2009). Reading Tea leaves: How humans ınterpret topic models. Neural Information Processing Systems, 32, 288–296.
• Chau, M. & Xu, J. (2012). Business intelligence in blogs: Understanding Consumer interactions and communities. MIS Quarterly, 36(4), 1189. https://doi.org/10.2307/41703504
• Chen, C.-F. (2008). Investigating structural relationships between service quality, perceived value, satisfaction, and behavioral intentions for air passengers: Evidence from Taiwan. Transportation Research Part A: Policy and Practice, 42(4), 709–717. https://doi.org/10.1016/j.tra.2008.01.007
• Churchill, G. A., & Surprenant, C. (1982). An investigation into the determinants of customer satisfaction. Journal of Marketing Research, 19(4), 491. https://doi.org/10.2307/3151722
• Dennis, N. (2007). End of the free lunch? The responses of traditional European airlines to the low-cost carrier threat. Journal of Air Transport Management, 13(5), 311–321. https://doi.org/10.1016/j.jairtraman.2007.04.005
• Egger, R., & Yu, J. (2022). a topic modeling comparison between LDA, NMF, Top2Vec, and BERTopic to demystify twitter posts. Frontiers in Sociology, 7. https://doi.org/10.3389/fsoc.2022.886498
• Forgas, S., Moliner, M. A., Sánchez, J., & Palau, R. (2010). Antecedents of airline passenger loyalty: Low-cost versus traditional airlines. Journal of Air Transport Management, 16(4), 229–233. https://doi.org/10.1016/j.jairtraman.2010.01.001
• Gilbert, D., & Wong, R. K. C. (2003). Passenger expectations and airline services: a Hong Kong based study. Tourism Management, 24(5), 519–532. https://doi.org/10.1016/S0261-5177(03)00002-5
• Goodrich, J. N. (2002). September 11, 2001 attack on America: a record of the immediate impacts and reactions in the USA travel and tourism industry. Tourism Management, 23(6), 573–580. https://doi.org/10.1016/S0261-5177(02)00029-8
• Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure.
• Grootendorst, M. (2023, August 23). BERTopic - The Algorithm. https://maartengr.github.io/BERTopic/algorithm/algorithm.html
• Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467–483. https://doi.org/10.1016/j.tourman.2016.09.009
• Han, H., & Hwang, J. (2017). In-flight physical surroundings: quality, satisfaction, and traveller loyalty in the emerging low-cost flight market. Current Issues in Tourism, 20(13), 1336–1354. https://doi.org/10.1080/13683500.2014.960371
• Hong, J.-W., & Park, S.-B. (2019). The Identification of Marketing Performance Using Text Mining of Airline Review Data. Mobile Information Systems, 2019, 1–8. https://doi.org/10.1155/2019/1790429 Hotho, A., Nürnberger, A., & Paaß, G. (2005). A Brief Survey of Text Mining. Journal for Language Technology and Computational Linguistics, 20(1), 19–62. https://doi.org/10.21248/jlcl.20.2005.68
• Hussain, R., Al Nasser, A., & Hussain, Y. K. (2015). Service quality and customer satisfaction of a UAE-based airline: An empirical investigation. Journal of Air Transport Management, 42, 167–175. https://doi.org/10.1016/j.jairtraman.2014.10.001
• IATA. (2021). World Air Transport Statistics 2021.
• ICAO. (2022, December 12). List of Low-Cost-Carriers (LCCs). https://www.icao.int/sustainability/Documents/LCC-List.pdf
• Jacobi, C., van Atteveldt, W., & Welbers, K. (2016). Quantitative analysis of large amounts of journalistic texts using topic modelling. Digital Journalism, 4(1), 89–106. https://doi.org/10.1080/21670811.2015.1093271
• Josephat, P., & Ismail, A. (2012). A Logistic Regression Model of Customer Satisfaction of Airline. International Journal of Human Resource Studies, 2(4), 255. https://doi.org/10.5296/ijhrs.v2i4.2868
• Kim, Y. K., & Lee, H. R. (2011). Customer satisfaction using low cost carriers. Tourism Management, 32(2), 235–243. https://doi.org/10.1016/j.tourman.2009.12.008
• Kim, Y.-J., Ban, H.-J., Kim, D.-H., & Kim, H.-S. (2020). Understanding Customer Experience of Airline Lounge Using Text Mining of Online Review. Culinary Science & Hospitality Research, 26(2), 36–44. https://doi.org/10.20878/cshr.2020.26.2.005
• Korfiatis, N., Stamolampros, P., Kourouthanassis, P., & Sagiadinos, V. (2019). Measuring service quality from unstructured data: A topic modeling application on airline passengers’ online reviews. Expert Systems with Applications, 116, 472–486. https://doi.org/10.1016/j.eswa.2018.09.037
• Koruyan, K. (2022). BERTopic Konu Modelleme Tekniği Kullanılarak Müşteri Şikayetlerinin Sınıflandırılması. İzmir Sosyal Bilimler Dergisi, 4(2), 66–79. https://doi.org/10.47899/ijss.1167719
• Kos Koklic, M., Kukar-Kinney, M., & Vegelj, S. (2017). An investigation of customer satisfaction with low-cost and full-service airline companies. Journal of Business Research, 80, 188–196. https://doi.org/10.1016/j.jbusres.2017.05.015
• Kua, J., & Baum, T. (2004). Perspectives on the Development of Low-cost Airlines in South-east Asia. Current Issues in Tourism, 7(3), 262–276. https://doi.org/10.1080/13683500408667982
• Kumar, S., & Zymbler, M. (2019). A machine learning approach to analyze customer satisfaction from airline tweets. Journal of Big Data, 6(1), 62. https://doi.org/10.1186/s40537-019-0224-1
• Kwon, H.-J., Ban, H.-J., Jun, J.-K., & Kim, H.-S. (2021). Topic Modeling and Sentiment Analysis of Online Review for Airlines. Information, 12(2), 78. https://doi.org/10.3390/info12020078
• Lee, C. K. M., Ng, K. K. H., Chan, H. K., Choy, K. L., Tai, W. C., & Choi, L. S. (2018). A multi-group analysis of social media engagement and loyalty constructs between full-service and low-cost carriers in Hong Kong. Journal of Air Transport Management, 73, 46–57. https://doi.org/10.1016/j.jairtraman.2018.08.009
• Li, W., Yu, S., Pei, H., Zhao, C., & Tian, B. (2017). A hybrid approach based on fuzzy AHP and 2-tuple fuzzy linguistic method for evaluation in-flight service quality. Journal of Air Transport Management, 60, 49–64. https://doi.org/10.1016/j.jairtraman.2017.01.006 Lim, J., & Lee, H. C. (2020). Comparisons of service quality perceptions between full service carriers and low cost carriers in airline travel. Current Issues in Tourism, 23(10), 1261–1276. https://doi.org/10.1080/13683500.2019.1604638
• Liou, J. J. H., Hsu, C.-C., Yeh, W.-C., & Lin, R.-H. (2011). Using a modified grey relation method for improving airline service quality. Tourism Management, 32(6), 1381–1388. https://doi.org/10.1016/j.tourman.2011.01.013
• Lucini, F. R., Tonetto, L. M., Fogliatto, F. S., & Anzanello, M. J. (2020). Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews. Journal of Air Transport Management, 83, 101760. https://doi.org/10.1016/j.jairtraman.2019.101760
• Martinez-Garcia, E., & Royo-Vela, M. (2010). Segmentation of low-cost flights users at secondary airports. Journal of Air Transport Management, 16(4), 234–237. https://doi.org/10.1016/j.jairtraman.2010.01.003
• McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. The Journal of Open Source Software, 2(11), 205. https://doi.org/10.21105/joss.00205
• McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.
• Misopoulos, F., Mitic, M., Kapoulas, A., & Karapiperis, C. (2014). Uncovering customer service experiences with Twitter: the case of airline industry. Management Decision, 52(4), 705–723. https://doi.org/10.1108/MD-03-2012-0235
• Nadiri, H., Hussain, K., Haktan Ekiz, E., & Erdoğan, Ş. (2008). An investigation on the factors influencing passengers’ loyalty in the North Cyprus national airline. The TQM Journal, 20(3), 265–280. https://doi.org/10.1108/17542730810867272
• Nam, S., & Lee, H. C. (2019). A Text Analytics-Based Importance Performance Analysis and Its Application to Airline Service. Sustainability, 11(21), 6153. https://doi.org/10.3390/su11216153
• Nejati, M., Nejati, M., & Shafaei, A. (2009). Ranking airlines’ service quality factors using a fuzzy approach: study of the Iranian society. International Journal of Quality & Reliability Management, 26(3), 247–260. https://doi.org/10.1108/02656710910936726
• O’Connell, J. F., & Williams, G. (2005). Passengers’ perceptions of low cost airlines and full service carriers: A case study involving Ryanair, Aer Lingus, Air Asia and Malaysia Airlines. Journal of Air Transport Management, 11(4), 259–272. https://doi.org/10.1016/j.jairtraman.2005.01.007
• Oliver, R. L. (2010). Satisfaction: A behavioral perspective on the consumer (2nd ed.). Taylor & Francis.
• Pakdil, F., & Aydın, Ö. (2007). Expectations and perceptions in airline services: An analysis using weighted SERVQUAL scores. Journal of Air Transport Management, 13(4), 229–237. https://doi.org/10.1016/j.jairtraman.2007.04.001
• Park, E., Jang, Y., Kim, J., Jeong, N. J., Bae, K., & del Pobil, A. P. (2019). Determinants of customer satisfaction with airline services: An analysis of customer feedback big data. Journal of Retailing and Consumer Services, 51, 186–190. https://doi.org/10.1016/j.jretconser.2019.06.009
• Park, J. (2023). Combined Text-Mining/DEA method for measuring level of customer satisfaction from online reviews. Expert Systems with Applications, 232, 120767. https://doi.org/10.1016/j.eswa.2023.120767
• Park, J.-W., Robertson, R., & Wu, C.-L. (2004). The effect of airline service quality on passengers’ behavioural intentions: a Korean case study. Journal of Air Transport Management, 10(6), 435–439. https://doi.org/10.1016/j.jairtraman.2004.06.001
• Park, S., Lee, J.-S., & Nicolau, J. L. (2020). Understanding the dynamics of the quality of airline service attributes: Satisfiers and dissatisfiers. Tourism Management, 81, 104163. https://doi.org/10.1016/j.tourman.2020.104163
• Punel, A., Al Hajj Hassan, L., & Ermagun, A. (2019). Variations in airline passenger expectation of service quality across the globe. Tourism Management, 75, 491–508. https://doi.org/10.1016/j.tourman.2019.06.004
• Punel, A., & Ermagun, A. (2018). Using Twitter network to detect market segments in the airline industry. Journal of Air Transport Management, 73, 67–76. https://doi.org/10.1016/j.jairtraman.2018.08.004
• Rajaguru, R. (2016). Role of value for money and service quality on behavioural intention: A study of full service and low cost airlines. Journal of Air Transport Management, 53, 114–122. https://doi.org/10.1016/j.jairtraman.2016.02.008
• Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3980–3990. https://doi.org/10.18653/v1/D19-1410
• Roberts, M. E., Stewart, B. M., & Airoldi, E. M. (2016). A model of text for experimentation in the social Sciences. Journal of the American Statistical Association, 111(515), 988–1003. https://doi.org/10.1080/01621459.2016.1141684
• Rüdiger, M., Antons, D., Joshi, A. M., & Salge, T.-O. (2022). Topic modeling revisited: New evidence on algorithm performance and quality metrics. PLOS ONE, 17(4), e0266325. https://doi.org/10.1371/journal.pone.0266325
• Rust, R. T., & Oliver, R. L. (1994). Service Quality: insights and managerial implications from the frontier. in service quality: New Directions in Theory and Practice (p. 1–20). SAGE Publications, Inc. https://doi.org/10.4135/9781452229102.n1
• Ryu, K., & Han, H. (2010). Influence of the quality of food, service, and physical environment on customer Satisfaction and Behavioral Intention in Quick-Casual Restaurants: Moderating Role of Perceived Price. Journal of Hospitality & Tourism Research, 34(3), 310–329. https://doi.org/10.1177/1096348009350624
• Sánchez‐Franco, M. J., & Rey‐Moreno, M. (2022). Do travelers’ reviews depend on the destination? An analysis in coastal and urban peer‐to‐peer lodgings. Psychology & Marketing, 39(2), 441–459. https://doi.org/10.1002/mar.21608
• Sánchez-Rebull, M.-V., Rudchenko, V., & Martín, J.-C. (2018). The antecedents and consequences of customer satisfaction in tourism: a systematic literature review. Tourism and Hospitality Management, 24(1), 151–183. https://doi.org/10.20867/thm.24.1.3
• SBERT. (2022). Sentence-Transformers - Pretrained Models. https://www.sbert.net/docs/pretrained_models.html
• Sekar, S., & Santhanam, N. (2022). Effect of COVID-19: Understanding Customer’s evaluation on hotel and airline sector—a text mining approach. Global Business Review, 097215092211068. https://doi.org/10.1177/09721509221106836
• Seo, G.-H., & Itoh, M. (2020). Perceptions of Customers as Sustained Competitive Advantages of Global Marketing Airline Alliances: A Hybrid Text Mining Approach. Sustainability, 12(15), 6258. https://doi.org/10.3390/su12156258
• Sezgen, E., Mason, K. J., & Mayer, R. (2019). Voice of airline passenger: A text mining approach to understand customer satisfaction. Journal of Air Transport Management, 77, 65–74. https://doi.org/10.1016/j.jairtraman.2019.04.001
• Shadiyar, A., Ban, H.-J., & Kim, H.-S. (2020). Extracting key drivers of air passenger’s experience and satisfaction through online review analysis. Sustainability, 12(21), 9188. https://doi.org/10.3390/su12219188
• Shiwakoti, N., Jiang, H., & Nguyen, A. D. (2022). Passengers’ perception of safety and its relationship with demographics, service quality, satisfaction and loyalty in airlines sector - A case study of Vietnam to Australia route. Transport Policy, 124, 194–202. https://doi.org/10.1016/j.tranpol.2021.04.029
• Stamolampros, P., Korfiatis, N., Kourouthanassis, P., & Symitsi, E. (2019). Flying to quality: cultural influences on Online Reviews. Journal of Travel Research, 58(3), 496–511. https://doi.org/10.1177/0047287518764345
• Sulu, D., Arasli, H., & Saydam, M. B. (2021). Air-Travelers’ Perceptions of Service Quality during the COVID-19 Pandemic: Evidence from Tripadvisor Sites. Sustainability, 14(1), 435. https://doi.org/10.3390/su14010435
• Tam, C., Barroso, M., & Cruz-Jesus, F. (2022). Understanding the determinants of users’ continuance intention to buy low-cost airline flights online. Journal of Hospitality and Tourism Technology, 13(2), 264–280. https://doi.org/10.1108/JHTT-12-2020-0316
• Tian, X., He, W., Tang, C., Li, L., Xu, H., & Selover, D. (2019). A new approach of social media analytics to predict service quality: evidence from the airline industry. Journal of Enterprise Information Management, 33(1), 51–70. https://doi.org/10.1108/JEIM-03-2019-0086
• Tripadvisor Media Center. (2022, December 11). About Tripadvisor. Https://Tripadvisor.Mediaroom.Com/US-about-Us. https://tripadvisor.mediaroom.com/US-about-us
• Wan, Y., & Gao, Q. (2015). An Ensemble Sentiment Classification System of Twitter Data for Airline Services Analysis. 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 1318–1325. https://doi.org/10.1109/ICDMW.2015.7
• Xu, X., & Li, Y. (2016). Examining Key Drivers of Traveler Dissatisfaction with Airline Service Failures: A Text Mining Approach. Journal of Supply Chain and Operations Management, 14(1), 30–50.
• Xu, X., Wang, X., Li, Y., & Haghighi, M. (2017). Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors. International Journal of Information Management, 37(6), 673–683. https://doi.org/10.1016/j.ijinfomgt.2017.06.004
• Yee Liau, B., & Pei Tan, P. (2014). Gaining customer knowledge in low cost airlines through text mining. Industrial Management & Data Systems, 114(9), 1344–1359. https://doi.org/10.1108/IMDS-07-2014-0225