Akıllı Ev Mobil Uygulamaları Pazarını Keşfetmek: Mobil Uygulama Değerlendirmeleri Üzerinde Bir Konu Modelleme Yaklaşımı

Year 2025, Volume: 39 Issue: 4, 430 - 440

Fatih Pınarbaşı , Fatma Zeybek Pınarbaşı

https://doi.org/10.16951/trendbusecon.1574784

Abstract

Günümüz tüketicileri son yıllarda mobil cihazların ve teknolojinin yoğun kullanımıyla birlikte yeni teknolojilerle tanışmaktadır. Son on yılların teknolojilerinden biri olan akıllı ev teknolojileri, tüketicilerin ev yaşamlarını kolaylaştırmakta ve tüketici araştırmaları için farklı araştırma bağlamları oluşturmaktadır. Çeşitli cihaz ve sensörleri bünyesinde barındıran akıllı ev teknolojileri, mobil uygulamalar aracılığıyla kullanılabilmektedir ve tüketici deneyimi pazarı anlamak için büyük önem taşımaktadır. Akıllı ev mobil uygulamaları bağlamını inceleyen bu çalışma, akıllı ev mobil uygulamalarına ilişkin kullanıcı yorumlarında yer alan konuları keşfetmeyi amaçlamaktadır. Bu amaçla çalışmada BERTopic dönüştürücü tabanlı konu modelleme metodolojisi kullanılmış ve Google Play mağazasında 35 mobil uygulamayla ilgili yazılmış 15.157 kullanıcı yorumu çalışma örneklemi olarak kullanılmıştır. Konu modellemesinde tespit edilen tekli konular sekiz ana konu grubunda (marka / model bahsi, özellik, işlevsellik, ürünler / cihazlar, hizmet deneyimi, kullanıcı deneyimi, kullanıcı geri bildirimi ve kullanıcı arayüzü) toplanmıştır. Çalışmada tespit edilen konular, akıllı ev mobil uygulamaları pazarının anlaşılmasına yardımcı olmaktadır ve çalışma, elektronik kulaktan kulağa pazarlama kuramını akıllı ev mobil uygulamaları bağlamında genişletmektedir.

Keywords

Akıllı ev , Dijital pazarlama , Mobil uygulama , Kullanıcı değerlendirmeleri , Kulaktan kulağa pazarlama

Exploring Smart Home Mobile Apps Market: A Topic Modeling Approach to Mobile App Reviews

Abstract

Today’s consumers have been experiencing new technologies in recent years with the intensive use of mobile devices and technology. Smart home technologies, one of the recent decades’ technologies, facilitate consumers’ home lives and create different research contexts for consumer research. Smart home technologies containing various devices and sensors can be used through mobile applications and consumer experience is crucial for understanding the marketplace. The study examining the context of smart home mobile apps aims to identify the topics covered in user reviews of smart home mobile apps. For this purpose, topic modeling methodology through BERTopic transformer-based model is employed in the study and 15.157 user reviews regarding 35 mobile apps in Google Play store are used as study sample. Individual topics detected in topic modeling are grouped into eight main topics (brand/model mention, feature, functionality, products/devices, service experience, user experience, user feedback, user interface). The topics concluded in the study help understanding of smart home mobile apps market and the study extends the eWOM concept by smart home mobile applications context.

Keywords

Smart home , Digital marketing , Mobile application , User reviews , Word of mouth

References

• Aldrich, F. K. (2003). Smart homes: past, present and future. In Inside the smart home. London, Springer London, 17-39.
• Alghamdi, R., & Alfalqi, K. (2015). A survey of topic modeling in text mining. International Journal of Advanced Computer Science and Applications (IJACSA), 6(1), 147-153.
• 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.
• Anderson, R. E., & Srinivasan, S. S. (2003). E‐satisfaction and e‐loyalty: A contingency framework. Psychology & Marketing, 20(2), 123-138.
• Balta-Ozkan, N., Davidson, R., Bicket, M., & Whitmarsh, L. (2013). The development of smart homes market in the UK. Energy, 60, 361-372.
• Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84.
• Canbolat, Z. N., & Pinarbasi, F. (2022). Using sentiment analysis for evaluating e-WOM: A data mining approach for marketing decision making. In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, IGI Global, 1360-1383.
• Coşkun, E., & Atesgoz, K. (2020). Skeuomorfik tasarımın kullanıcı deneyimi bağlamında marka algısının tüketiciler tarafından değerlendirilmesi. Dördüncü Kuvvet Uluslararası Hakemli Dergi, 3(1), 113-126.
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
• De Silva, L. C., Morikawa, C., & Petra, I. M. (2012). State of the art of smart homes. Engineering Applications of Artificial Intelligence, 25(7), 1313-1321.
• Devlin, J. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. Arxiv Preprint arXiv:1810.04805.
• Eriksson, K., & Nilsson, D. (2007). Determinants of the continued use of self-service technology: The case of Internet banking. Technovation, 27(4), 159-167.
• Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10 (2), 130-132.
• Gefen, D. (2000). E-commerce: The role of familiarity and trust. Omega, 28(6), 725-737.
• Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236.
• Google. (2024a). Android Apps on Google Play. https://play.google.com/
• Google. (2024b). Welcome to Colab. https://colab.google/
• Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. Arxiv Preprint, Arxiv:2203.05794. https://arxiv.org/abs/2203.05794
• Hong, A., Nam, C., & Kim, S. (2020). What will be the possible barriers to consumers’ adoption of smart home services?. Telecommunications Policy, 44(2), 1-15
• Hubert, M., Blut, M., Brock, C., Zhang, R. W., Koch, V., & Riedl, R. (2019). The influence of acceptance and adoption drivers on smart home usage. European Journal of Marketing, 53(6), 1073-1098.
• Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564.
• Lee, B., Kwon, O., Lee, I., & Kim, J. (2017). Companionship with smart home devices: The impact of social connectedness and interaction types on perceived social support and companionship in smart homes. Computers in Human Behavior, 75, 922-934.
• Li, W., Yigitcanlar, T., Erol, I., & Liu, A. (2021). Motivations, barriers and risks of smart home adoption: From systematic literature review to conceptual framework. Energy Research & Social Science, 80, 102211.
• Marikyan, D., Papagiannidis, S., & Alamanos, E. (2019). A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change, 138, 139-154.
• Mingyu, J. (2024). Google Play Scraper. GitHub repository. https://github.com/JoMingyu/google-play-scraper
• Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS Quarterly, 34(1), 185-200.
• Nascimento, D. R., Tortorella, G. L., & Fettermann, D. (2023). Association between the benefits and barriers perceived by the users in smart home services implementation. Kybernetes, 52(12), 6179-6202.
• Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.
• Nilashi, M., Abumalloh, R. A., Samad, S., Alrizq, M., Alyami, S., Abosaq, H., Alghamdi, A., & Akib, N. A. M. (2022). Factors impacting customer purchase intention of smart home security systems: Social data analysis using machine learning techniques. Technology in Society, 71, 102118.
• Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for measuring consumer perc. Journal of Retailing, 64(12).
• Rogers, E. (2003). Diffusion of Innovations, 5th Edition. Simon and Schuster Santos, J. (2003). E‐service quality: a model of virtual service quality dimensions. Managing Service Quality: An International Journal, 13(3), 233-246.
• Shuhaiber, A., & Mashal, I. (2019). Understanding users’ acceptance of smart homes. Technology in Society, 58, 1-9.
• Van Rossum, G., & Drake Jr, F. L. (1995). Python tutorial, 620. Amsterdam, The Netherlands, Centrum voor Wiskunde en Informatica.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
• Vrain, E., & Wilson, C. (2021). Social networks and communication behaviour underlying smart home adoption in the UK. Environmental Innovation and Societal Transitions, 38, 82-97.
• Yang, H., Lee, H., & Zo, H. (2017). User acceptance of smart home services: an extension of the theory of planned behavior. Industrial Management & Data Systems, 117(1), 68-89.
• Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2000). E-service quality: definition, dimensions and conceptual model. Marketing Science Institute, Cambridge, MA, Working Paper, 22.
• Zhao, S. (2021). Thumb up or down? A text‐mining approach of understanding consumers through reviews. Decision Sciences, 52(3), 699-719.