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

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

1. 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
2. Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes.
3. 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
4. Alghamdi, R., & Alfalqi, K. (2015). A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl.(IJACSA), 6(1).
5. 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
6. 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
7. 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
8. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.

9. 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.
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. Data.ai. (2024). State of Mobile 2024 The Industry’s Leading Report. Retrieved from https://sensortower.com/state-of-mobile-2024
18. 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
19. 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
20. 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
21. Google Colab. (2024). Welcome to Colab. Retrieved from https://colab.research.google.com/?hl=en
22. Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint. https://arxiv.org/abs/2203.05794
23. 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
24. Herzberg, F., Mausner, B., & Snyderman, B. (1959). The motivation to work. Herzberg, F. (1966). Work and the nature of man. World.
25. 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
26. 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
27. İnal, Y., & Cagiltay, N. E. (2019). E-NABIZ MOBİL SAĞLIK UYGULAMASINA YÖNELİK KULLANICI DEĞERLENDİRMESİ. Hacettepe Sağlık İdaresi Dergisi, 22(2), 375-388.
28. Keller, K.L., & Parameswaran, M. G. (2019). Strategic Brand Management: Building, Measuring, and Managing Brand Equity, Global Edition 5th Edition. Pearson
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. Mingyu, J. (2024). Google Play Scraper. GitHub repository. Retrieved from https://github.com/JoMingyu/google-play-scraper
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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.
46. 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.
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. Van Rossum, G., & Drake Jr, F. L. (1995). Python tutorial (Vol. 620). Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica.
57. 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
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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