Journals / Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Cilt: 28 Sayı: Özel Sayı Özel Sayı

Evaluating Complaint-Based Service-Failure Profiles in Guided Package Tours by Integrating Guided LDA, MEREC, and MARCOS Methods

Rehberli Paket Turlarda Şikâyet Temelli Hizmet Başarısızlığı Profillerinin Guided LDA, MEREC ve MARCOS Yöntemlerinin Bütünleştirilmesiyle Değerlendirilmesi

Akın Özkan(Muş Alparslan Üniversitesi)0000-0003-2862-2496
Pages
442–457
Publication Date
2026-10-21
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal that issues related to the extra tour pressure and pricing are the strongest differentiating criterion across tour types.

📌 3 Key Takeaways

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Objective & Hypothesis:This study proposes an integrated decision-making framework based on complaint data to evaluate the service failure profiles of guided package tour types.
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Key Findings & Data:From 3,076 complaints obtained from a publicly available online customer complaint platform the dimensions of recurring service failure were identified using the Guided Latent Dirichlet Allocation (Guided LDA) method and transformed into decision criteria within the scope of multi-criteria decision-making analysis.
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Conclusion & Impact:The findings reveal that issues related to the extra tour pressure and pricing are the strongest differentiating criterion across tour types.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Çok Kriterli Karar Verme (ÇKKV) · Guided LDA · Müşteri şikâyet yönetimi

Abstract

This study proposes an integrated decision-making framework based on complaint data to evaluate the service failure profiles of guided package tour types. From 3,076 complaints obtained from a publicly available online customer complaint platform the dimensions of recurring service failure were identified using the Guided Latent Dirichlet Allocation (Guided LDA) method and transformed into decision criteria within the scope of multi-criteria decision-making analysis. The Method based on the Removal Effects of Criteria (MEREC) was used to calculate the objective criterion weights, while eight package tour types, treated as alternatives, were ranked according to their complaint-based profiles using the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. Additionally, in the Guided LDA-supported analysis, topic probabilities were included in the decision matrix, and service failures in complaint narratives were evaluated together in terms of both their frequency of occurrence and thematic strength. The findings reveal that issues related to the extra tour pressure and pricing are the strongest differentiating criterion across tour types. This is followed by shortcomings in safety/health-related support and guiding services. As for the additional analysis, which also included thematic strength, it resulted in limited changes in the ranking of tour types.

Özet

Bu çalışma, rehberli paket tur türlerinin hizmet başarısızlığı profillerini değerlendirmek amacıyla şikâyet verilerine dayalı bütünleşik bir karar verme çerçevesi önermektedir. Herkese açık bir çevrimiçi müşteri şikâyet platformundan elde edilen 3.076 şikâyetten, tekrarlayan hizmet başarısızlığı boyutları Güdümlü Gizli Dirichlet Tahsisi (Guided LDA) yöntemi kullanılarak belirlenmiş ve çok kriterli karar verme analizi kapsamında karar kriterlerine dönüştürülmüştür. Objektif kriter ağırlıklarını hesaplamak için Kriterlerin Kaldırılma Etkilerine Dayalı Yöntem (MEREC) kullanılmış, sekiz paket tur türü ise alternatif olarak ele alınarak şikâyet temelli profillerine göre Uzlaşık Çözüme Göre Alternatiflerin Ölçülmesi ve Sıralanması (MARCOS) yöntemiyle sıralanmıştır. Ayrıca, Guided LDA destekli analizde konu olasılıkları karar matrisine dahil edilmiş; şikâyet metinlerindeki hizmet başarısızlıkları hem görülme sıklığı hem de tematik güç açısından birlikte değerlendirilmiştir. Bulgular, ekstra tur baskısı ve fiyatlandırmaya ilişkin sorunların tur türleri arasında en güçlü ayırt edici kriter olduğunu ortaya koymaktadır. Bunu, güvenlik/sağlıkla ilgili destekteki ve rehberlik hizmetlerindeki eksiklikler izlemektedir. Tematik gücü de içeren ek analiz ise tur türlerinin sıralamasında sınırlı değişikliklere yol açmıştır.

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Cite This Article

Özkan, A. (2026). Evaluating Complaint-Based Service-Failure Profiles in Guided Package Tours by Integrating Guided LDA, MEREC, and MARCOS Methods. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 28 Sayı: Özel Sayı Özel Sayı, 442–457. https://doi.org/10.33707/akuiibfd.1971828

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References (23)

  1. Akgün, D. Y., Ay, E., & Çalık, A. (2022). Customer complaints in package tours and complaint management of tour guides. Journal of Gastronomy, Hospitality and Travel, 5(4), 1482-1494.
  2. Anđelković, Ž., Kovačić, S., Bratić, M., Vujičić, M. D., Stankov, U., Pavluković, V., Dragin, A., Pivac, T., Ivkov Džigurski, A., & Ivanović Bibić, L. (2022). Museum tour guide performance: A visitor perspective. Sustainability, 14(16), 10269.
  3. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
  4. Bowie, D., & Chang, J. C. (2005). Tourist satisfaction: A view from a mixed international guided package tour. Journal of Vacation Marketing, 11(4), 303–322.
  5. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.
  6. Erkiliç, E., Karakuş, Y., & Angin, M. (2025). Examining e-complaints about tour guides: Service failures and their implications. GeoJournal of Tourism and Geosites, 63(4 supplement), 2613–2622.
  7. Fu, X., Liu, X., Hua, C., Li, Z., & Du, Q. (2021). Understanding tour guides’ service failure: Integrating a two-tier triadic business model with attribution theory. Journal of Hospitality and Tourism Management, 47, 506-516.
  8. 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.
  9. Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing & Management, 54(6), 1292–1307.
  10. Hu, N., Zhang, T., Gao, B., & Bose, I. (2019). What do hotel customers complain about? Text analysis using structural topic model. Tourism Management, 72, 417-426.
  11. Jagarlamudi, J., Daumé III, H., & Udupa, R. (2012). Incorporating lexical priors into topic models. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (pp. 204–213). Association for Computational Linguistics.
  12. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525.
  13. 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.
  14. Mijajlović, M., Puška, A., Stević, Ž., Marinković, D., Doljanica, D., Jovanović, S. V., Stojanović, I., & Beširović, J. (2020). Determining the competitiveness of spa centers in order to achieve sustainability using a fuzzy multi-criteria decision-making model. Sustainability, 12(20), 8584.
  15. Song, W., Rong, W., & Tang, Y. (2024). Quantifying risk of service failure in customer complaints: A textual analysis-based approach. Advanced Engineering Informatics, 60, 102377.
  16. Srinivas, S., & Ramachandiran, S. (2020). Discovering airline-specific business intelligence from online passenger reviews: An unsupervised text analytics approach. arXiv preprint arXiv:2012.08000.
  17. Srinivas, S., & Ramachandiran, S. (2024). Passenger intelligence as a competitive opportunity: Unsupervised text analytics for discovering airline-specific insights from online reviews. Annals of Operations Research, 333(2), 1045-1075.
  18. Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231.
  19. Štilić, A., Puška, A., Božanić, D., & Đurić, A. (2024). Ranking European countries using hybrid MEREC–MARCOS MCDA based on the Travel and Tourism Development Index. Tourism: An International Interdisciplinary Journal, 72(4), 592–608.
  20. Şikayetvar. (2026). Dünyanın ilk ve en büyük şikayet platformu. Retrieved May 7, 2026, from https://www.sikayetvar.com/hakkimizda
  21. Vargas-Calderón, V., Moros Ochoa, A., Castro Nieto, G. Y., & Camargo, J. E. (2021). Machine learning for assessing quality of service in the hospitality sector based on customer reviews. Information Technology & Tourism, 23(3), 351-379.
  22. Wang, K.-C., Hsieh, A.-T., & Chen, W.-Y. (2002). Is the tour leader an effective endorser for group package tour brochures? Tourism Management, 23(5), 489-498.
  23. Wang, K.-C., Hsieh, A.-T., & Huan, T.-C. (2000). Critical service features in group package tour: An exploratory research. Tourism Management, 21(2), 177-189.