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Examining Mobile Consumption through Paid Apps Context: A Topic Modeling Approach to Türkiye Market

Yıl 2025, Cilt: 11 Sayı: 1, 21 - 28, 30.06.2025
https://doi.org/10.51803/yssr.1669339

Öz

Mobile consumption as a vital part of today’s consumers is one of the fundamental area in the marketing research. Several contexts and markets are examined in the mobile consumption literature, however, the literature on paid apps context and Türkiye market is limited. The study aims to fill this gap by examining the paid apps context in Türkiye mobile app market and discovering the topics included in the user conversation. For this purpose, 11.749 reviews of 25 mobile apps are used as study sample. The study employs topic modeling methodology by using BERTopic approach to extract the topics in the sample set. Following descriptive evaluation of the sample, 57 individual topics are extracted in topic modeling, and they are grouped into three main topic groups namely: app-related (27 topics), money-related (11 topics) and user evaluations (19 topics). Insights and implications regarding the topic groups can shed light for the developers and mobile marketing decision-makers.

Kaynakça

  • REFERENCES
  • Allsop, D. T., Bassett, B. R., & Hoskins, J. A. (2007). Word-of-mouth research: Principles and applications. Journal of Advertising Research, 47(4), 398–411. [CrossRef]
  • Belk, R. W. (2013). Extended self in a digital world. Journal of Consumer Research, 40(3), 477–500. [CrossRef]
  • Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84. [CrossRef]
  • Chen, Q., Chen, C., Hassan, S., Xing, Z., Xia, X., & Hassan, A. E. (2021). How should I improve the UI of my app? A study of user reviews of popular apps in the Google Play. ACM Transactions on Software Engineering and Methodology (TOSEM), 30(3), 1–38. [CrossRef]
  • Chopra, I. P., Lim, W. M., & Jain, T. (2024). Electronic word-of-mouth on social networking sites: What inspires travelers to engage in opinion seeking, opinion passing, and opinion giving? Tourism Recreation Research, 49(4), 726–739. [CrossRef]
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. [CrossRef]
  • Google. (2024). How Google Play Works. https://play.google/howplayworks/
  • Google. (2025a). Google Play. https://play.google.com/
  • Google. (2025b). Google Colab. https://colab.research.google.com/
  • Grewal, L., & Stephen, A. T. (2019). In mobile we trust: The effects of mobile versus nonmobile reviews on consumer purchase intentions. Journal of Marketing Research, 56(5), 791–808. [CrossRef]
  • Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv. https://arxiv.org/abs/2203.05794
  • Hashimi, H., Hafez, A., & Mathkour, H. (2015). Selection criteria for text mining approaches. Computers in Human Behavior, 51, 729–733. [CrossRef]
  • Hussain, A., Hannan, A., & Shafiq, M. (2023). Exploring mobile banking service quality dimensions in Pakistan: A text mining approach. International Journal of Bank Marketing, 41(3), 601–618. [CrossRef]
  • Ismagilova, E., Slade, E. L., Rana, N. P., & Dwivedi, Y. K. (2020). The effect of electronic word of mouth communications on intention to buy: A meta-analysis. Information Systems Frontiers, 22, 1203–1226. [CrossRef] Jo, M. (2019). Google-Play-Scraper. https://github.com/JoMingyu/google-play-scraper
  • Kim, E., Lin, J. S., & Sung, Y. (2013). To app or not to app: Engaging consumers via branded mobile apps. Journal of Interactive Advertising, 13(1), 53–65. [CrossRef]
  • Khalid, H., Shihab, E., Nagappan, M., & Hassan, A. E. (2014). What do mobile app users complain about? IEEE Software, 32(3), 70–77. [CrossRef]
  • Kumar, A., Chakraborty, S., & Bala, P. K. (2023). Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews. Journal of Retailing and Consumer Services, 73, 103363. [CrossRef]
  • Liang, T. P., Li, X., Yang, C. T., & Wang, M. (2015). What in consumer reviews affects the sales of mobile apps: A multifacet sentiment analysis approach. International Journal of Electronic Commerce, 20(2), 236–260. [CrossRef]
  • 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. [CrossRef]
  • Mladenović, D., Bruni, R., Filieri, R., Ismagilova, E., Kalia, P., & Jirásek, M. (2024). The power of electronic word of mouth in inducing adoption of emerging technologies. Technology in Society, 79, 102724. [CrossRef]
  • Noei, E., Zhang, F., & Zou, Y. (2019). Too many user-reviews! What should app developers look at first? IEEE Transactions on Software Engineering, 47(2), 367–378. [CrossRef]
  • Ransbotham, S., Lurie, N. H., & Liu, H. (2019). Creation and consumption of mobile word of mouth: How are mobile reviews different? Marketing Science, 38(5), 773–792. [CrossRef]
  • Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. http://arxiv.org/abs/1908.10084 [CrossRef]
  • Sally, M. S. (2023). Why are consumers dissatisfied? A text mining approach on Sri Lankan mobile banking apps. International Journal of Intelligent Computing and Cybernetics, 16(4), 727–744. [CrossRef]
  • Sensor Tower. (2025). Top Charts - Market Analysis. https://app.sensortower.com/top-charts? country=TR&category=all&date=2025-02-19&device=iphone&os=android
  • 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. [CrossRef]
  • Shankar, A., Jebarajakirthy, C., & Ashaduzzaman, M. (2020). How do electronic word of mouth practices contribute to mobile banking adoption? Journal of Retailing and Consumer Services, 52, 101920. [CrossRef]
  • Srisopha, K., Phonsom, C., Lin, K., & Boehm, B. (2019, September). Same app, different countries: A preliminary user reviews study on most downloaded iOS apps. In 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) (pp. 76–80). IEEE. [CrossRef]
  • Tang, A. K. (2016). Mobile app monetization: App business models in the digital era. International Journal of Innovation, Management and Technology, 7(5), 224. [CrossRef]
  • Ullah, R., Amblee, N., Kim, W., & Lee, H. (2016). From valence to emotions: Exploring the distribution of emotions in online product reviews. Decision Support Systems, 81, 41–53.
  • Van Rossum, G., & Drake, F. L. (1995). Python reference manual. Amsterdam: Open Documents Library.
  • We Are Social & Meltwater. (2024). Digital 2024. https://wearesocial.com/uk/blog/2024/01/digital-2024/
  • Wohllebe, A., & Stoyke, T. (2022, February). What are app store reviews on mobile apps in retail about? Insights from the German market. In International Conference on Remote Engineering and Virtual Instrumentation (pp. 463–472). Cham: Springer International Publishing. [CrossRef]

Mobil Tüketimin Ücretli Uygulamalar Bağlamı Üzerinden İncelenmesi: Türkiye Pazarına Konu Modelleme Yaklaşımı

Yıl 2025, Cilt: 11 Sayı: 1, 21 - 28, 30.06.2025
https://doi.org/10.51803/yssr.1669339

Öz

Günümüz tüketicilerinin hayati bir parçası olan mobil tüketim, pazarlama araştırmalarının temel alanlarından biridir. Mobil tüketim literatüründe çeşitli bağlamlar ve pazarlar incelenmiştir, ancak ücretli uygulamalar bağlamı ve Türkiye pazarına ilişkin literatür sınırlıdır. Bu çalışma, Türkiye mobil uygulama pazarındaki ücretli uygulamalar bağlamını inceleyerek ve kullanıcı konuşmalarında yer alan konuları keşfederek bu boşluğu doldurmayı amaçlamaktadır. Bu amaçla, 25 mobil uygulamaya ait 11.749 yorum çalışma örneği olarak kullanılmıştır. Çalışma, örneklem kümesindeki konuları çıkarmak için BERTopic yaklaşımını kullanarak konu modelleme metodolojisini kullanmıştır. Örneklemin betimlemeyici değerlendirilmesinin ardından, konu modellemesinde 57 ayrı konu elde edilmiş ve bu konular üç ana konu grubunda toplanmıştır: uygulama ile ilgili konular (27 konu), para ile ilgili konular (11 konu) ve kullanıcı değerlendirmeleri (19 konu). Konu gruplarına ilişkin içgörüler ve çıkarımlar, geliştiricilere ve mobil pazarlama karar vericilerine ışık tutabilir.

Kaynakça

  • REFERENCES
  • Allsop, D. T., Bassett, B. R., & Hoskins, J. A. (2007). Word-of-mouth research: Principles and applications. Journal of Advertising Research, 47(4), 398–411. [CrossRef]
  • Belk, R. W. (2013). Extended self in a digital world. Journal of Consumer Research, 40(3), 477–500. [CrossRef]
  • Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84. [CrossRef]
  • Chen, Q., Chen, C., Hassan, S., Xing, Z., Xia, X., & Hassan, A. E. (2021). How should I improve the UI of my app? A study of user reviews of popular apps in the Google Play. ACM Transactions on Software Engineering and Methodology (TOSEM), 30(3), 1–38. [CrossRef]
  • Chopra, I. P., Lim, W. M., & Jain, T. (2024). Electronic word-of-mouth on social networking sites: What inspires travelers to engage in opinion seeking, opinion passing, and opinion giving? Tourism Recreation Research, 49(4), 726–739. [CrossRef]
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. [CrossRef]
  • Google. (2024). How Google Play Works. https://play.google/howplayworks/
  • Google. (2025a). Google Play. https://play.google.com/
  • Google. (2025b). Google Colab. https://colab.research.google.com/
  • Grewal, L., & Stephen, A. T. (2019). In mobile we trust: The effects of mobile versus nonmobile reviews on consumer purchase intentions. Journal of Marketing Research, 56(5), 791–808. [CrossRef]
  • Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv. https://arxiv.org/abs/2203.05794
  • Hashimi, H., Hafez, A., & Mathkour, H. (2015). Selection criteria for text mining approaches. Computers in Human Behavior, 51, 729–733. [CrossRef]
  • Hussain, A., Hannan, A., & Shafiq, M. (2023). Exploring mobile banking service quality dimensions in Pakistan: A text mining approach. International Journal of Bank Marketing, 41(3), 601–618. [CrossRef]
  • Ismagilova, E., Slade, E. L., Rana, N. P., & Dwivedi, Y. K. (2020). The effect of electronic word of mouth communications on intention to buy: A meta-analysis. Information Systems Frontiers, 22, 1203–1226. [CrossRef] Jo, M. (2019). Google-Play-Scraper. https://github.com/JoMingyu/google-play-scraper
  • Kim, E., Lin, J. S., & Sung, Y. (2013). To app or not to app: Engaging consumers via branded mobile apps. Journal of Interactive Advertising, 13(1), 53–65. [CrossRef]
  • Khalid, H., Shihab, E., Nagappan, M., & Hassan, A. E. (2014). What do mobile app users complain about? IEEE Software, 32(3), 70–77. [CrossRef]
  • Kumar, A., Chakraborty, S., & Bala, P. K. (2023). Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews. Journal of Retailing and Consumer Services, 73, 103363. [CrossRef]
  • Liang, T. P., Li, X., Yang, C. T., & Wang, M. (2015). What in consumer reviews affects the sales of mobile apps: A multifacet sentiment analysis approach. International Journal of Electronic Commerce, 20(2), 236–260. [CrossRef]
  • 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. [CrossRef]
  • Mladenović, D., Bruni, R., Filieri, R., Ismagilova, E., Kalia, P., & Jirásek, M. (2024). The power of electronic word of mouth in inducing adoption of emerging technologies. Technology in Society, 79, 102724. [CrossRef]
  • Noei, E., Zhang, F., & Zou, Y. (2019). Too many user-reviews! What should app developers look at first? IEEE Transactions on Software Engineering, 47(2), 367–378. [CrossRef]
  • Ransbotham, S., Lurie, N. H., & Liu, H. (2019). Creation and consumption of mobile word of mouth: How are mobile reviews different? Marketing Science, 38(5), 773–792. [CrossRef]
  • Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. http://arxiv.org/abs/1908.10084 [CrossRef]
  • Sally, M. S. (2023). Why are consumers dissatisfied? A text mining approach on Sri Lankan mobile banking apps. International Journal of Intelligent Computing and Cybernetics, 16(4), 727–744. [CrossRef]
  • Sensor Tower. (2025). Top Charts - Market Analysis. https://app.sensortower.com/top-charts? country=TR&category=all&date=2025-02-19&device=iphone&os=android
  • 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. [CrossRef]
  • Shankar, A., Jebarajakirthy, C., & Ashaduzzaman, M. (2020). How do electronic word of mouth practices contribute to mobile banking adoption? Journal of Retailing and Consumer Services, 52, 101920. [CrossRef]
  • Srisopha, K., Phonsom, C., Lin, K., & Boehm, B. (2019, September). Same app, different countries: A preliminary user reviews study on most downloaded iOS apps. In 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) (pp. 76–80). IEEE. [CrossRef]
  • Tang, A. K. (2016). Mobile app monetization: App business models in the digital era. International Journal of Innovation, Management and Technology, 7(5), 224. [CrossRef]
  • Ullah, R., Amblee, N., Kim, W., & Lee, H. (2016). From valence to emotions: Exploring the distribution of emotions in online product reviews. Decision Support Systems, 81, 41–53.
  • Van Rossum, G., & Drake, F. L. (1995). Python reference manual. Amsterdam: Open Documents Library.
  • We Are Social & Meltwater. (2024). Digital 2024. https://wearesocial.com/uk/blog/2024/01/digital-2024/
  • Wohllebe, A., & Stoyke, T. (2022, February). What are app store reviews on mobile apps in retail about? Insights from the German market. In International Conference on Remote Engineering and Virtual Instrumentation (pp. 463–472). Cham: Springer International Publishing. [CrossRef]
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonometri (Diğer)
Bölüm Makaleler
Yazarlar

Fatih Pınarbaşı 0000-0001-9005-0324

Erken Görünüm Tarihi 30 Haziran 2025
Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 2 Nisan 2025
Kabul Tarihi 4 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 11 Sayı: 1

Kaynak Göster

APA Pınarbaşı, F. (2025). Examining Mobile Consumption through Paid Apps Context: A Topic Modeling Approach to Türkiye Market. Yildiz Social Science Review, 11(1), 21-28. https://doi.org/10.51803/yssr.1669339