Exploring mobile banking adoption and service quality features through user-generated content: the application of a topic modeling approach to Google Play Store reviews

Purpose The primary purpose of this research is to analyze the online user reviews, where real customer experiences can be observed, with text mining and machine learning approaches, which are seen as a gap in the related literature. This study aims to compare the latent themes uncovered by the topic modeling approach with studies focused on both mobile banking (m-banking) adaptation and service quality features, suggest new aspects and examine the effect of latent topics on customer satisfaction. Design/methodology/approach This study analyzed 21,526 reviews posted by customers of private and state banks operating in Türkiye. An unsupervised machine learning method, Latent Dirichlet algorithm (LDA), was conducted to reveal topics, and the distribution of all reviews was visualized with the t-SNE algorithm. Random Forest, logistic regression, k-nearest neighbors (kNN) and Naive Bayes algorithms were utilized to predict user satisfaction through the given score. Findings In total, 11 topics were revealed by considering user reviews based on their experience. Among these topics, perceived usefulness and convenience and time-saving are much more important in the scoring given to m-banking apps. Furthermore, in more detail, seven topics have been identified related to technical and security problems related to m-banking apps. Originality/value This paper is a pioneer study regarding the method used and sample size reached in the m-banking literature. The findings also provide fresh insight into the post-Covid-19 era, both academically and practically, by providing new features for mobile bank adoption.