Sağlık & Fitness Mobil Uygulama Kategorisi Kullanıcı Değerlendirmeleri Konularının Ortaya Çıkarılması: Bir Konu Modelleme Yaklaşımı

Yıl 2025, Cilt: 32 Sayı: 2, 247 - 263, 23.06.2025

Fatih Pınarbaşı

https://doi.org/10.18657/yonveek.1541485

Öz

Günümüz pazarlamasının vazgeçilmez bir parçası olan mobil cihazlar ve platformlar, pazarlama karar vericileri için önemli fırsatlar oluşturmaktadır. Tüketicinin sosyal etkileşimler, e-ticaret ve günlük aktiviteler hakkındaki çevrimiçi değerlendirmeleri üzerinden tüketiciyi anlamak dijital pazarlamada temel bir konu haline gelmiştir. Mobil uygulama pazarı, farklı bağlamlarda incelenmesi gereken farklı özelliklere sahip birkaç kategoriden oluşmaktadır. Çalışmada sağlık & fitness kategorisine Türkiye pazarı ve mobil uygulamalar bağlamında odaklanılmıştır. Araştırmanın amacı, Google Play Store kullanıcı yorumları üzerinden sağlık ve fitness mobil uygulama pazarını değerlendirmektir ve araştırma soruları, kullanıcı yorumlarındaki konuların ve alt konuların keşfedilmesiyle ilgilidir. Araştırma amaçlarına uygun olarak, çalışmada 20 mobil uygulama için 17.921 çevrimiçi yorum üzerinde BERTopic modelini kullanan bir konu modelleme yaklaşımı benimsenmiştir. Çalışmada 80 ayrı konu başlığı altında 10 konu grubu (aktiviteler & fitness, aşırı reklam yüklemesi / reklam varlığı, memnuniyetsizlik, deneyim paylaşımı, özellikler, geri bildirim & soru, işlevsellik, gizlilik, önerme & önermeme, memnuniyet) oluşturulmuştur. Çalışmanın dört teori üzerinden teorik katkıları ve pratik çıkarımları gelecekteki araştırmalara ve endüstriyel uygulamalara ışık tutmaktadır.
Anahtar Kelimeler: Sağlık, Fitness, Mobil Uygulama, Kullanıcı Değerlendirmeleri, Kulaktan Kulağa Pazarlama, Ağızdan Ağıza Pazarlama
JEL Sınıflandırması: M31

Anahtar Kelimeler

Sağlık , Fitness , Mobil Uygulama , Kullanıcı Değerlendirmeleri , Kulaktan Kulağa Pazarlama

Revealing Topics in Health & Fitness Mobile App Category User Reviews: A Topic Modeling Approach

Öz

Mobile devices and platforms, as an essential part of today’s marketing, lead to significant opportunities for marketing decision-makers. Understanding the consumer through online reviews regarding social interactions, e-commerce, and daily activities becomes fundamental in digital marketing. The mobile app market consists of several categories with different characteristics, which require examination in different contexts. The study focuses on health & fitness category in the context of Türkiye market and mobile apps. The research aims to evaluate health & fitness mobile application market through Google Play Store user reviews and research questions are related to discovering topics and sub-topics in the user reviews. Consistent to research aims, the study adopts a topic modeling approach utilizing BERTopic model on 17.921 online reviews for 20 mobile apps. The study concludes 80 individual topics grouped into 10 topic groups namely: activities & fitness, advertisement overload & presence, dissatisfaction, experience sharing, features, feedback & question, functionality, privacy, recommended & not recommended, satisfaction. Theoretical contributions through four theories and practical implications of the study can shed light on future researches and industrial applications.
Key Words: Health, Fitness, Mobile Application, User Reviews, Word Of Mouth
JEL Classification: M31

Anahtar Kelimeler

Health , Fitness , Mobile Application , User Reviews , Word Of Mouth

Kaynakça

• Acikgoz, F., Filieri, R., & Yan, M. (2023). Psychological predictors of intention to use fitness apps: The role of subjective knowledge and innovativeness. International Journal of Human–Computer Interaction, 39(10), 2142-2154. https://doi.org/10.1080/10447318.2022.2074668
• Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes.
• Akbolat, M., Yıldırım, Y., & Amarat, M. (2019). HASTANE MOBİL UYGULAMALARINDA KULLANICI YORUMLARININ İNCELENMESİ. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 10(25), 511-522. https://doi.org/10.21076/vizyoner.567454
• Alghamdi, R., & Alfalqi, K. (2015). A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl.(IJACSA), 6(1).
• Bach, R. L., & Wenz, A. (2020). Studying health-related internet and mobile device use using web logs and smartphone records. PloS one, 15(6), e0234663. https://doi.org/10.1371/journal.pone.0234663
• Barde, B. V., & Bainwad, A. M. (2017, June). An overview of topic modeling methods and tools. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 745-750). IEEE. https://doi.org/10.1109/ICCONS.2017.8250563
• Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of interactive marketing, 15(3), 31-40. https://doi.org/10.1002/dir.1014
• Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
• Canbolat, Z. N., & Pinarbasi, F. (2020). Using Sentiment Analysis for Evaluating e-WOM: A Data Mining Approach for Marketing Decision Making. In Exploring the Power of Electronic Word-of-Mouth in the Services Industry (pp. 101-123). IGI Global.
• Chembakottu, B., Li, H., & Khomh, F. (2023). A large-scale exploratory study of android sports apps in the google play store. Information and Software Technology, 164, 107321. https://doi.org/10.1016/j.infsof.2023.107321
• 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. https://doi.org/10.1145/3447808
• Chiu, W., Cho, H., & Chi, C. G. (2020). Consumers’ continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model. Information Technology & People, 34(3), 978-998. https://doi.org/10.1108/ITP-09-2019-0463
• Cho, H., Chi, C., & Chiu, W. (2020). Understanding sustained usage of health and fitness apps: Incorporating the technology acceptance model with the investment model. Technology in Society, 63, 101429. https://doi.org/10.1016/j.techsoc.2020.101429
• Christensen, K., Scholderer, J., Hersleth, S. A., Næs, T., Kvaal, K., Mollestad, T., ... & Risvik, E. (2018). How good are ideas identified by an automatic idea detection system?. Creativity and Innovation Management, 27(1), 23-31. https://doi.org/10.1111/caim.12260
• Çallı, L. (2023). Exploring mobile banking adoption and service quality features through user-generated content: The application of a topic modeling approach to Google Play Store reviews. International Journal of Bank Marketing, 41(2), 428-454. https://doi.org/10.1108/IJBM-08-2022-0351
• Dam, L., Roy, D., Atkin, D. J., & Rogers, D. (2018). Applying an integrative technology adoption paradigm to health app adoption and use. Journal of Broadcasting & Electronic Media, 62(4), 654-672. https://doi.org/10.1080/08838151.2018.1519568
• Data.ai. (2024). State of Mobile 2024 The Industry’s Leading Report. Retrieved from https://sensortower.com/state-of-mobile-2024
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). Technology acceptance model. J Manag Sci, 35(8), 982-1003. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv. https://arxiv.org/abs/1810.04805
• Easton, S., Morton, K., Tappy, Z., Francis, D., & Dennison, L. (2018). Young people’s experiences of viewing the fitspiration social media trend: Qualitative study. Journal of medical Internet research, 20(6), e219. https://doi.org/10.2196/jmir.9156
• Fan, W., Wallace, L., Rich, S., & Zhang, Z. (2006). Tapping the power of text mining. Communications of the ACM, 49(9), 76-82. https://doi.org/10.1145/1151030.1151032
• Google Colab. (2024). Welcome to Colab. Retrieved from https://colab.research.google.com/?hl=en
• Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint. https://arxiv.org/abs/2203.05794
• Gu, D., Yang, X., Li, X., Jain, H. K., & Liang, C. (2018). Understanding the role of mobile internet-based health services on patient satisfaction and word-of-mouth. International journal of environmental research and public health, 15(9), 1972. https://doi.org/10.3390/ijerph15091972
• Herzberg, F., Mausner, B., & Snyderman, B. (1959). The motivation to work. Herzberg, F. (1966). Work and the nature of man. World.
• Hong, J. W., & Park, S. B. (2019). The identification of marketing performance using text mining of airline review data. Mobile Information Systems, 2019(1), 1790429. https://doi.org/10.1155/2019/1790429
• Hussain, S., Ahmed, W., Jafar, R. M. S., Rabnawaz, A., & Jianzhou, Y. (2017). eWOM source credibility, perceived risk and food product customer’s information adoption. Computers in human behavior, 66, 96-102. https://doi.org/10.1016/j.chb.2016.09.034
• İnal, Y., & Cagiltay, N. E. (2019). E-NABIZ MOBİL SAĞLIK UYGULAMASINA YÖNELİK KULLANICI DEĞERLENDİRMESİ. Hacettepe Sağlık İdaresi Dergisi, 22(2), 375-388.
• Keller, K.L., & Parameswaran, M. G. (2019). Strategic Brand Management: Building, Measuring, and Managing Brand Equity, Global Edition 5th Edition. Pearson
• Kim, K., Yoon, S., & Choi, Y. K. (2019). The effects of eWOM volume and valence on product sales–an empirical examination of the movie industry. International Journal of Advertising, 38(3), 471-488. https://doi.org/10.1080/02650487.2018.1535225
• Kraemer, T., Weiger, W. H., Trang, S., & Trenz, M. (2023). Deflected by the tin foil hat? Word‐of‐mouth, conspiracy beliefs, and the adoption of innovative public health apps. Journal of Product Innovation Management, 40(2), 154-174. https://doi.org/10.1111/jpim.12646
• Laestadius, L. I., & Wahl, M. M. (2017). Mobilizing social media users to become advertisers: Corporate hashtag campaigns as a public health concern. Digital health, 3, 2055207617710802. https://doi.org/10.1177/2055207617710802
• Lee, H. M., Zhang, P., & Mehta, M. R. (2022). Effect of competitors’ ewom in the mobile game market. Journal of Computer Information Systems, 62(1), 196-204. https://doi.org/10.1080/08874417.2020.1768176
• Liang, B., & Scammon, D. L. (2011). E‐Word‐of‐Mouth on health social networking sites: An opportunity for tailored health communication. Journal of Consumer Behaviour, 10(6), 322-331. https://doi.org/10.1002/cb.378
• Liu, Y., & Wan, F. (2024). Unveiling Temporal and Spatial Research Trends in Precision Agriculture: A BERTopic Text Mining Approach. Heliyon. https://doi.org/10.1016/j.heliyon.2024.e36808
• Ma, B., Wong, Y. D., Teo, C. C., & Wang, Z. (2024). Enhance understandings of Online Food Delivery’s service quality with online reviews. Journal of retailing and consumer services, 76, 103588. https://doi.org/10.1016/j.jretconser.2023.103588
• Mingyu, J. (2024). Google Play Scraper. GitHub repository. Retrieved from https://github.com/JoMingyu/google-play-scraper
• Mondal, A. S., Zhu, Y., Bhagat, K. K., & Giacaman, N. (2024). Analysing user reviews of interactive educational apps: a sentiment analysis approach. Interactive learning environments, 32(1), 355-372. https://doi.org/10.1080/10494820.2022.2086578
• Naraine, M. L., Pegoraro, A., & Wear, H. (2021). # WeTheNorth: Examining an online brand community through a professional sport organization’s hashtag marketing campaign. Communication & Sport, 9(4), 625-645. https://doi.org/10.1177/2167479519878676
• 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. https://doi.org/10.1109/TSE.2019.2893171
• Noei, E., & Lyons, K. (2022). A study of gender in user reviews on the Google Play Store. Empirical Software Engineering, 27(2), 34. https://doi.org/10.1007/s10664-021-10080-8
• Oh, Y. K., Yi, J., & Kim, J. (2023). What enhances or worsens the user-generated metaverse experience? An application of BERTopic to Roblox user eWOM. Internet Research. https://doi.org/10.1108/INTR-03-2022-0178
• Oliveira, N., & Marques dos Santos, J. P. (2022). Online Brand Community Characterization with Engagement and Social Network Analysis (SNA) for Marketing Communication: The Subreddit r/intel. In Marketing and Smart Technologies: Proceedings of ICMarkTech 2021, Volume 2 (pp. 577-591). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-9272-7_47
• Pal, S., Biswas, B., Gupta, R., Kumar, A., & Gupta, S. (2023). Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach. Journal of Business Research, 156, 113484. https://doi.org/10.1016/j.jbusres.2022.113484
• Park, K., Weber, I., Cha, M., & Lee, C. (2016, February). Persistent sharing of fitness app status on twitter. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 184-194). https://doi.org/10.1145/2818048.2819921
• Pinarbasi, F. (2020). Understanding e-WOM evolution in social media with network analysis. In Exploring the power of electronic word-of-mouth in the services industry (pp. 69-87). IGI Global.
• Pınarbaşı, F., & Canbolat, Z. N. (2018). Evaluation of augmented reality mobile applications in türkiye market: A data mining approach to consumer reviews. Changing Organizations: From the Psychological and Technological Perspectives içinde, 187-197.
• Pınarbaşı, F. (2023). Zero waste and consumers: a thematic analysis of reddit r/zerowaste community posts. PressAcademia Procedia, 17(1), 191-191. https://doi.org/10.17261/Pressacademia.2023.1779
• Sensor Tower. (2024). Top Charts. Retrieved from https://app.sensortower.com/top-charts?category=health_and_fitness&country=TR&date=2024-08-18&device=iphone&os=android
• Shahhosseini, M., & Khalili Nasr, A. (2024). What attributes affect customer satisfaction in green restaurants? An aspect-based sentiment analysis approach. Journal of Travel & Tourism Marketing, 41(4), 472-490. https://doi.org/10.1080/10548408.2024.2306358
• Soni, M., Jain, K., & Jajodia, I. (2021). Mobile health (mHealth) application loyalty in young consumers. Young Consumers, 22(3), 429-455. https://doi.org/10.1108/YC-10-2020-1236
• 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. https://doi.org/10.1109/ICSME.2019.00017
• Srivastava, M., & Sivaramakrishnan, S. (2021). The impact of eWOM on consumer brand engagement. Marketing Intelligence & Planning, 39(3), 469-484. https://doi.org/10.1108/MIP-06-2020-0263
• Stancu, V., Frank, D. A., Lähteenmäki, L., & Grunert, K. G. (2022). Motivating consumers for health and fitness: The role of app features. Journal of Consumer Behaviour, 21(6), 1506-1521. https://doi.org/10.1002/cb.2108
• Tang, C., Mehl, M. R., Eastlick, M. A., He, W., & Card, N. A. (2016). A longitudinal exploration of the relations between electronic word-of-mouth indicators and firms’ profitability: Findings from the banking industry. International Journal of Information Management, 36(6), 1124-1132. https://doi.org/10.1016/j.ijinfomgt.2016.03.015
• Uncovska, M., Freitag, B., Meister, S., & Fehring, L. (2023). Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany. NPJ Digital Medicine, 6(1), 115. https://doi.org/10.1038/s41746-023-00862-3
• Van Rossum, G., & Drake Jr, F. L. (1995). Python tutorial (Vol. 620). Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica.
• Wang, W., Feng, Y., & Dai, W. (2018). Topic analysis of online reviews for two competitive products using latent Dirichlet allocation. Electronic Commerce Research and Applications, 29, 142-156. https://doi.org/10.1016/j.elerap.2018.04.003
• Wang, J., Lai, J. Y., & Lin, Y. H. (2023). Social media analytics for mining customer complaints to explore product opportunities. Computers & Industrial Engineering, 178, 109104. https://doi.org/10.1016/j.cie.2023.109104
• Westbrook, R. A. (1987). Product/consumption-based affective responses and postpurchase processes. Journal of marketing research, 24(3), 258-270. https://doi.org/10.1177/002224378702400302
• Yin, M., Tayyab, S. M. U., Xu, X. Y., Jia, S. W., & Wu, C. L. (2021). The investigation of mobile health stickiness: The role of social support in a sustainable health approach. Sustainability, 13(4), 1693. https://doi.org/10.3390/su13041693
• Yoo, J. W., Park, J., & Park, H. (2024). The Impact of AI-enabled CRM Systems on Organizational Competitive Advantage: A mixed-method approach using BERTopic and PLS-SEM. Heliyon. https://doi.org/10.1016/j.heliyon.2024.e36392
• Zhang, M., Fan, B., Zhang, N., Wang, W., & Fan, W. (2021). Mining product innovation ideas from online reviews. Information Processing & Management, 58(1), 102389. https://doi.org/10.1016/j.ipm.2020.102389
• Zhao, S. (2021). Thumb up or down? A text‐mining approach of understanding consumers through reviews. Decision Sciences, 52(3), 699-719. https://doi.org/10.1111/deci.12349