PurposeThis study aimed to analyse user experiences and perceptions of eRupee banking applications in India, focussing on understanding the key factors driving user satisfaction and dissatisfaction.Design/methodology/approachA comprehensive text-mining approach was employed to analyse 5,176 user reviews collected from the Google Play Store. Sentiment analysis and latent Dirichlet allocation (LDA) were used to classify reviews and uncover prevailing themes.FindingsThe analysis revealed that positive reviews highlighted the themes of usefulness, convenience, satisfaction, app attributes, and ease of use. Negative reviews emphasise issues related to lack of trust, faulty updates, unreliability, security concerns, and inadequate customer support. The Logistic Regression model demonstrated superior performance in predicting user sentiments, achieving an AUC of 0.7926 and an accuracy rate of 77.90%.Research limitations/implicationsThis study was limited to reviews from a single-platform source. Future research could incorporate data from multiple online sources and employ qualitative methods to gain deeper insight. Additionally, longitudinal studies and cross-cultural analyses are recommended to capture evolving user sentiments and global perspectives.Practical implicationsThe findings provide actionable insights for bank managers, app developers and policymakers to enhance eRupee applications by addressing identified issues and leveraging positive aspects to improve overall user experience and satisfaction.Originality/valueThis study makes a novel contribution to the literature on digital currency and advanced text-mining techniques using machine-learning models to analyse user feedback in the context of an emerging economy. The proposed conceptual model and practical recommendations serve as the foundation for future research and practical development in digital financial services.
