Unveiling the drivers of satisfaction in mobile trading: Contextual mining of retail investor experience through BERTopic and generative AI

The proliferation of mobile stock trading has introduced various apps with distinct features, emphasizing the need to understand users' evaluations after adopting the service. This study explores the determinants of retail investors’ satisfaction with mobile stock trading services by employing an advanced textual analysis of customer reviews for four leading trading applications. We utilized Bidirectional Encoder Representations from Transformers (BERT) based Topic modeling (BERTopic modeling) to identify key topics within customer reviews and used the results as input for generative AI to discern the theme and sentiment of each topic. Based on Service Quality (SERVQUAL) theory, topics are categorized into key quality dimensions: functionality, usability, information quality, customer service, and system quality. Regression models were employed to assess the impact of the quality dimensions on investor satisfaction, revealing positive feedback on usability, information quality, and service quality as primary enhancers of satisfaction. In contrast, negative feedback on service quality, system quality, and functionality was identified as the primary inhibitor of satisfaction. This study explores how the influence of each quality dimension varies among different types of brokers (full-service vs. online-only brokerages). Finally, we propose a visualization tool called Topic Rating Impact and Frequency Analysis (TRIFA), which is designed to categorize topics based on their frequency of occurrence and impact on satisfaction. This tool aids in identifying the strengths and areas for improvement in services by effectively visualizing the results of text review analysis. This research not only deepens our understanding of the quality dimensions of mobile financial services but also offers valuable insights for service providers by suggesting predictive models that could help increase customer retention.