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Dergiler / Uluslararası Ekonomi İşletme ve Politika Dergisi / 2026 / Cilt: 10 Sayı: 2

Platform Tabanlı Tedarik Zinciri Bilgi Sistemlerinde Operasyonel Sorunlar: BERTopic Tabanlı Bir Analiz

Operational Issues in Platform-Based Supply Chain Information Systems: A BERTopic-Based Analysis

Oğuz Emre Balkar(ERZINCAN BINALI YILDIRIM UNIVERSITY, VOCATIONAL SCHOOL)0000-0001-5853-0926
Yunus Emre Özenoğlu(ATATURK UNIVERSITY, INSTITUTE OF SOCIAL SCIENCES, MANAGEMENT INFORMATION SYSTEMS (DR))0000-0001-5051-4622
Okan Alkan(ATATURK UNIVERSITY, INSTITUTE OF SOCIAL SCIENCES, MANAGEMENT INFORMATION SYSTEMS (DR))0000-0002-6526-1645
Veysel Şahin(ATATURK UNIVERSITY, INSTITUTE OF SOCIAL SCIENCES, MANAGEMENT INFORMATION SYSTEMS (DR))0000-0002-0827-9478
Sayfa
572–601
Yayın Tarihi
2026-10-01
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bulgular, en kritik sorun alanlarının iade, komisyon ve satıcı maliyeti (K1), sipariş ve müşteri bildirimlerinin gelmemesi (K2) ile giriş, şifre ve erişim sorunları (K3) olduğunu göstermektedir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Bu çalışma, platform tabanlı tedarik zinciri bilgi sistemlerinde iş ortaklarının karşılaştığı operasyonel sorunları mobil uygulama yorumları üzerinden belirlemeyi amaçlamaktadır.
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Temel Bulgular & Veri:Bu kapsamda Google Play Store’da yer alan beş iş ortağı/satıcı uygulamasına ait 5.612 ham yorum toplanmış, ön işleme sonrasında 5.541 yorum analiz edilebilir nitelikte kalmıştır.
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Sonuç & Katkı:Bulgular, en kritik sorun alanlarının iade, komisyon ve satıcı maliyeti (K1), sipariş ve müşteri bildirimlerinin gelmemesi (K2) ile giriş, şifre ve erişim sorunları (K3) olduğunu göstermektedir.
🔬Tür:Laboratuvar & Saha Deneyi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Tedarik Zinciri Bilgi Sistemleri · Operasyonel Sorunlar · Konu Modelleme

Özet

Bu çalışma, platform tabanlı tedarik zinciri bilgi sistemlerinde iş ortaklarının karşılaştığı operasyonel sorunları mobil uygulama yorumları üzerinden belirlemeyi amaçlamaktadır. Bu kapsamda Google Play Store’da yer alan beş iş ortağı/satıcı uygulamasına ait 5.612 ham yorum toplanmış, ön işleme sonrasında 5.541 yorum analiz edilebilir nitelikte kalmıştır. İlk BERTopic modelinde kısa ve genel memnuniyet ifadelerinin konu kümelerini baskılaması nedeniyle, 2.854 yorumdan oluşan problem odaklı veri seti oluşturulmuştur. Problem odaklı BERTopic analizi altı anlamlı konu kümesi ve bir aykırı/karma problem havuzu üretmiştir. Modelin sağlamlığı; konu tutarlılığı, konu çeşitliliği, kararlılık kontrolleri ve manuel konu etiketleme sürecinde kodlayıcılar arası güvenirlik ölçütü aracılığıyla incelenmiştir. Bulgular, en kritik sorun alanlarının iade, komisyon ve satıcı maliyeti (K1), sipariş ve müşteri bildirimlerinin gelmemesi (K2) ile giriş, şifre ve erişim sorunları (K3) olduğunu göstermektedir. Araştırma, iş ortağı uygulamalarını platform tabanlı tedarik zinciri bilgi sistemlerinin operasyonel bileşenleri olarak konumlandırmakta ve BERTopic çıktılarını süreç iyileştirme odaklı bir karar destek matrisine dönüştürerek yazına katkı sağlamaktadır.

Abstract

This study aims to identify the operational issues encountered by business partners in platform-based supply chain information systems through mobile application reviews. In this context, 5,612 raw reviews of five business partner/seller applications available on the Google Play Store were collected, and 5,541 reviews remained analyzable after preprocessing. Since the initial BERTopic model was dominated by short and general satisfaction expressions, a problem-focused dataset consisting of 2,854 reviews was constructed. The problem-focused BERTopic analysis produced six meaningful topic clusters and one outlier/mixed problem pool. Model robustness was examined through topic coherence, topic diversity, stability checks, and intercoder reliability in manual topic labeling. The findings showed that the most critical issue areas are return, commission, and seller cost problems (K1), missing order and customer notifications (K2), and login, password, and access problems (K3). The study positions partner applications as operational components of platform-based supply chain information systems and contributes to the literature by transforming BERTopic outputs into a process improvement-focused decision support matrix.

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Bu Makaleye Atıf Yap

Balkar, O. E., Özenoğlu, Y. E., Alkan, O., Şahin, V. (2026). Platform Tabanlı Tedarik Zinciri Bilgi Sistemlerinde Operasyonel Sorunlar: BERTopic Tabanlı Bir Analiz. Uluslararası Ekonomi İşletme ve Politika Dergisi, Cilt: 10 Sayı: 2, 572–601. https://doi.org/10.29216/ueip.1942368

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Kaynakça (39)

  1. Balkar, O. E. (2025). E-Ticaret Lojistiği Hizmet Kalitesi ile Müşteri Memnuniyeti Arasındaki İlişkide Dijital Okuryazarlığın Aracılık Etkisi. In 14. Ulusal Lojistik ve Tedarik Zinciri Kongresi (pp. 77–78). Ekin Yayınevi.
  2. Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
  3. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
  4. Chen, N., Lin, J., Hoi, S. C. H., Xiao, X., & Zhang, B. (2014). AR-miner: mining informative reviews for developers from mobile app marketplace. Proceedings of the 36th International Conference on Software Engineering, 1, 767–778. https://doi.org/10.1145/2568225.2568263
  5. Collier, J. E., & Bienstock, C. C. (2006). Measuring Service Quality in E-Retailing. Journal of Service Research, 8(3), 260–275. https://doi.org/10.1177/1094670505278867
  6. Culotta, C., Blome, C., & Henke, M. (2024). Theories of digital platforms for supply chain management: a systematic literature review. International Journal of Physical Distribution & Logistics Management, 54(5), 449–475. https://doi.org/10.1108/IJPDLM-01-2023-0016
  7. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  8. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
  9. Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. https://arxiv.org/abs/2203.05794
  10. Gunasekaran, A., & Ngai, E. W. (2004). Information systems in supply chain integration and management. European Journal of Operational Research, 159(2), 269–295. https://doi.org/10.1016/j.ejor.2003.08.016
  11. Guzman, E., & Maalej, W. (2014). How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews. 2014 IEEE 22nd International Requirements Engineering Conference (RE), 153–162. https://doi.org/10.1109/RE.2014.6912257
  12. Halim, D., Budi, I., Mubina, B. F., Budi Santoso, A., & Kresna Putra, P. (2026). Comparative Topic Modelling of Mobile Banking User Reviews Using LDA and BERTopic: A Case Study of wondr by BNI. The Indonesian Journal of Computer Science, 15(2), 2236–2246. https://doi.org/10.33022/ijcs.v15i2.5118
  13. Hanifah, A., Saputra, M., & Fa’rifah, R. Y. (2025). Exploring User Sentiments and Adoption Barriers in E-Wallet Services in Indonesia using BERTopic on Play Store Reviews. 2025 4th International Conference on Electronics Representation and Algorithm (ICERA), 286–291. https://doi.org/10.1109/ICERA66156.2025.11086632
  14. Ivanov, D., Dolgui, A., & Sokolov, B. (2022). Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service.” Transportation Research Part E: Logistics and Transportation Review, 160, 102676. https://doi.org/10.1016/j.tre.2022.102676
  15. Jain, N. K., Gajjar, H., & Shah, B. J. (2021). Electronic logistics service quality and repurchase intention in e-tailing: Catalytic role of shopping satisfaction, payment options, gender and returning experience. Journal of Retailing and Consumer Services, 59, 102360. https://doi.org/10.1016/j.jretconser.2020.102360
  16. Johnson, M. E., & Whang, S. (2002). E-Business and Supply Chain Management: An Overview and Framework. Production and Operations Management, 11(4), 413–423. https://doi.org/10.1111/j.1937-5956.2002.tb00469.x
  17. Kumar, A., Sikdar, P., & Saha, R. (2021). Seller experience assessment in online marketplace: a scale development study. Benchmarking: An International Journal, 28(7), 2315–2342. https://doi.org/10.1108/BIJ-06-2020-0305
  18. Li, L., Xu, S. X., Ning, Y., Liu, Y., & Yang, S. (2023). How should companies deploy their digital supply chain platforms to gain competitive advantages? An asset orchestration perspective. Information & Management, 60(6), 103842. https://doi.org/10.1016/j.im.2023.103842
  19. Li, S., & Lin, B. (2006). Accessing information sharing and information quality in supply chain management. Decision Support Systems, 42(3), 1641–1656. https://doi.org/10.1016/j.dss.2006.02.011
  20. Maalej, W., Nayebi, M., Johann, T., & Ruhe, G. (2016). Toward Data-Driven Requirements Engineering. IEEE Software, 33(1), 48–54. https://doi.org/10.1109/MS.2015.153
  21. Martin, W., Sarro, F., Jia, Y., Zhang, Y., & Harman, M. (2017). A Survey of App Store Analysis for Software Engineering. IEEE Transactions on Software Engineering, 43(9), 817–847. https://doi.org/10.1109/TSE.2016.2630689
  22. 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
  23. Murfield, M., Boone, C. A., Rutner, P., & Thomas, R. (2017). Investigating logistics service quality in omni-channel retailing. International Journal of Physical Distribution & Logistics Management, 47(4), 263–296. https://doi.org/10.1108/IJPDLM-06-2016-0161
  24. Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-QUAL. Journal of Service Research, 7(3), 213–233. https://doi.org/10.1177/1094670504271156
  25. Putri, N. A., Pravitasari, A. A., & Winarni, S. (2026). Topic-based sentiment analysis of Jamsostek mobile application reviews using the BERTopic method. Commun. Math. Biol. Neurosci., 2026, Article ID 2. https://doi.org/10.28919/cmbn/9682
  26. Rabinovich, E., & Bailey, J. P. (2004). Physical distribution service quality in Internet retailing: service pricing, transaction attributes, and firm attributes. Journal of Operations Management, 21(6), 651–672. https://doi.org/10.1016/j.jom.2003.11.004
  27. Rao, S., Goldsby, T. J., Griffis, S. E., & Iyengar, D. (2011). Electronic Logistics Service Quality (e-LSQ): Its Impact on the Customer’s Purchase Satisfaction and Retention. Journal of Business Logistics, 32(2), 167–179. https://doi.org/10.1111/j.2158-1592.2011.01014.x
  28. 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
  29. Sagama, Y., Alamsyah, A., & Tricahyono, D. (2024). Generating Mobile App Improvement Recommendations Through User Review Analysis Using BERTopic and GPT. 2024 International Conference on Digital Business and Technology Management (ICONDBTM), 1–6. https://doi.org/10.1109/ICONDBTM64135.2024.11122739
  30. Sorkun, M. F. (2019). Factors That Impact the Logistics Service Performance in Online Marketplaces. Journal of Yasar University, 14(54), 152–167.
  31. Urrea, N. T., Vishkaei, B. M., & De Giovanni, P. (2024). Operational Risk Management in E-Commerce: A Platform Perspective. IEEE Transactions on Engineering Management, 71, 3807–3819. https://doi.org/10.1109/TEM.2024.3358717
  32. Vakulenko, Y., Shams, P., Hellström, D., & Hjort, K. (2019). Service innovation in e-commerce last mile delivery: Mapping the e-customer journey. Journal of Business Research, 101, 461–468. https://doi.org/10.1016/j.jbusres.2019.01.016
  33. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward A Unified View1. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  34. Wixom, B. H., & Todd, P. A. (2005). A Theoretical Integration of User Satisfaction and Technology Acceptance. Information Systems Research, 16(1), 85–102. https://doi.org/10.1287/isre.1050.0042
  35. Wolfinbarger, M., & Gilly, M. C. (2003). eTailQ: dimensionalizing, measuring and predicting etail quality. Journal of Retailing, 79(3), 183–198. https://doi.org/10.1016/S0022-4359(03)00034-4
  36. Yi, J., Oh, Y. K., & Kim, J.-M. (2025). Unveiling the drivers of satisfaction in mobile trading: Contextual mining of retail investor experience through BERTopic and generative AI. Journal of Retailing and Consumer Services, 82, 104066. https://doi.org/10.1016/j.jretconser.2024.104066
  37. Zhao, J., Zhang, Z., Feng, J., & Li, A. (2026). Prioritizing App User Reviews Based on Multidimensional Factors: An Integrated BERTopic–SnowNLP Framework for Supporting Developer Decisions. IEEE Transactions on Computational Social Systems, 13(1), 1100–1109. https://doi.org/10.1109/TCSS.2025.3627971
  38. Zhao, Y. (2025). Platform Economy and Global Supply Chain Integration: A Technology-Driven Approach to Business Model Innovation. In Proceedings of 2024 6th International Conference on Economic Management and Cultural Industry (ICEMCI 2024) (pp. 612–620). Atlantis Press. https://doi.org/10.2991/978-94-6463-642-0_65
  39. Gunasekaran, A., & Ngai, E. W. (2004). Information systems in supply chain integration and management. European Journal of Operational Research, 159(2), 269–295. https://doi.org/10.1016/j.ejor.2003.08.016