DİJİTAL PERAKENDECİLİKTE MOBİL DENEYİMİN ÖNEMİ: LC WAIKIKI ÖRNEĞİ TEMELİNDE MÜŞTERİ GERİ BİLDİRİMLERİNİN ÇOK BOYUTLU ANALİZİ

Year 2026, Volume: 19 Issue: 1, 24 - 47, 27.01.2026

Sezai Tunca , Yavuz Selim Balcıoğlu

Abstract

Bu çalışma, LC Waikiki'nin mobil uygulaması bağlamında mobil deneyimin dijital müşteri memnuniyetini şekillendirmedeki rolünü incelemektedir. Google Play Store’dan elde edilen 35.500’den fazla kullanıcı yorumuna dayanarak yapılan araştırma, üç temel alana odaklanmaktadır: müşteri yolculuğundaki etkili temas noktaları, teknik performansın marka algısı üzerindeki etkisi ve müşteri hizmetleri yanıtlarının etkinliği. Duygu analizi, zamansal eğilim takibi ve özgün bir kuramsal yaklaşım olan Çok Katmanlı Dijital Sürtünme Teorisi (Multi-Layered Digital Friction Theory - MDFT) yoluyla yapılan analizler, ürün kalitesinin olumlu algılandığını ancak uygulama performansı, ödeme süreci ve kullanıcı arayüzü tasarımının memnuniyetsizliğin başlıca kaynakları olduğunu ortaya koymaktadır. Zamansal analizler, kullanıcı memnuniyetinde son dönemde bir düşüş yaşandığını ve bunun kümülatif dijital sürtünmeden kaynaklandığını göstermektedir. Ayrıca, LC Waikiki çoğu yoruma yanıt verse de, yanıtların kişiselleştirilmemiş yapısı memnuniyetin yeniden tesisinde yeterince etkili olamamaktadır. Bu araştırma, mobil perakende deneyimlerini temas noktaları üzerinden analiz etmeye yönelik bir çerçeve sunmakta ve dijital altyapının iyileştirilmesi, arayüz kullanılabilirliğinin artırılması ve hizmetlerin kişiselleştirilmesi konularında pratik stratejiler önermektedir. Bulgular, mobil etkileşimi ve müşteri sadakatini veri temelli içgörülerle geliştirmek isteyen perakende markaları için daha geniş çıkarımlar da sunmaktadır.

Keywords

Dijital müşteri deneyimi , duygu analizi , mobil ticaret , müşteri yolculuğu , temas noktaları , dijital sürtünme , müşteri hizmetleri , kullanıcı arayüzü

THE IMPORTANCE OF MOBILE EXPERIENCE IN DIGITAL RETAILING: A MULTI-DIMENSIONAL ANALYSIS OF CUSTOMER FEEDBACK BASED ON THE LC WAIKIKI CASE

Abstract

This study explores the role of mobile experience in shaping digital customer satisfaction within the context of LC Waikiki’s mobile application. Utilizing over 35,500 user reviews from the Google Play Store, the research investigates three core areas: influential touchpoints in the customer journey, the impact of technical performance on brand perception, and the effectiveness of customer service responses. Through sentiment analysis, temporal trend tracking, and a novel Multi-Layered Digital Friction Theory (MDFT), the study reveals that while product quality is positively received, app performance, checkout processes, and UI design are primary sources of dissatisfaction. Temporal analysis highlights a recent decline in user sentiment, pointing to cumulative digital friction as a key driver of dissatisfaction. Additionally, although LC Waikiki responds to a majority of reviews, the impersonal nature of its replies limits their effectiveness in restoring satisfaction. This research contributes a touchpoint-based framework for analyzing mobile retail experiences and offers practical strategies for enhancing digital infrastructure, interface usability, and service personalization. It holds broader implications for retail brands aiming to improve mobile engagement and customer retention through data-driven insights.

Keywords

Digital customer experience , sentiment analysis , mobile commerce , customer journey , touchpoints , digital friction , customer service , user interface

References

• Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485–1509.
• Bower, G. H. (1981). Mood and memory. American Psychologist, 36(2), 129–148.
• Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017). Sentiment analysis is a big suitcase. IEEE Intelligent Systems.
• Chaffey, D., & Ellis-Chadwick, F. (2019). Digital Marketing (7th ed.). Pearson.
• Dannenberg, P., Fuchs, M., Riedler, T., & Wiedemann, C. (2020). Digital transition by COVID‐19 pandemic? The German food online retail. Tijdschrift voor Economische en Sociale Geografie, 111(3), 543-560.
• Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171–4186).
• Gensler, S., Völckner, F., Liu-Thompkins, Y., & Wiertz, C. (2013). Managing brands in the social media environment. Journal of Interactive Marketing, 27(4), 242–256.
• Grewal, D., Roggeveen, A. L., & Nordfält, J. (2020). The future of retailing. Journal of Retailing, 96(1), 139–144. https://doi.org/10.1016/j.jretai.2019.12.008
• Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
• Karri, V., Devi, T. U., Sanapala, G. K., Mathew, D., & V, T. K. (2025). Retail 5.0: Leveraging Sustainability for Holistic Consumer Engagement and Competitive Expansion — Responsible Consumption and Production. Journal of Lifestyle and SDGs Review, 5(3), e05166. https://doi.org/10.47172/2965-730X.SDGsReview.v5.n03.pe05166
• Kim, H., Park, E., & Boo, S. (2022). Investigating the customer satisfaction through online reviews: Role of big data analytics and text mining. Journal of Big Data, 9(1), 1-18.
• Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
• McLean, G., Osei-Frimpong, K., & Barhorst, J. (2021). Alexa, do voice assistants influence consumer brand engagement? Examining the role of AI powered voice assistants in influencing consumer brand engagement. Journal of Business Research, 124, 312-328.
• Naeem, M., Ozuem, W., Howell, K., & Ranfagni, S. (2023). A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International Journal of Qualitative Methods, 22, 1–18.
• Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.
• Pantano, E., & Gandini, A. (2017). Exploring the forms of sociality mediated by innovative technologies in retail settings. Computers in Human Behavior, 77, 367–373.
• Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.
• Tax, S. S., Brown, S. W., & Chandrashekaran, M. (1998). Customer evaluations of service complaint experiences. Journal of Marketing, 62(2), 60–76. https://doi.org/10.2307/1252161
• Thelwall, M. (2018). Gender bias in sentiment analysis. Online Information Review, 42(1), 45–57.
• Van Noort, G., & Willemsen, L. M. (2012). Online damage control: The effects of proactive versus reactive webcare interventions in consumer-generated and brand-generated platforms. Journal of Interactive Marketing, 26(3), 131–140. https://doi.org/10.1016/j.intmar.2011.07.001
• Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing. Journal of Retailing, 91(2), 174–181.
• Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901.
• Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031
• Wirtz, J., Zeithaml, V. A., & Gistri, G. (2013). Technology-mediated service encounters. Journal of Service Management, 24(4), 339–360. https://doi.org/10.1108/JOSM-04-2013-0096
• You, Q., Luo, J., Jin, H., & Yang, J. (2015). Robust sentiment detection on Twitter from biased and noisy data. Proceedings of the 24th International Conference on World Wide Web, 519–524.
• Zhang, M., & Wang, Y. (2023). Predicting customer churn in mobile commerce: A machine learning approach integrating textual and numerical data. Electronic Commerce Research and Applications, 58, 101248.
• Zhang, Y., Zhao, K., & Kumar, A. (2021). A comprehensive study of mobile customer feedback and loyalty in fashion e-commerce. Electronic Commerce Research and Applications, 45, 101030.