Research on mobile banking (m-banking) adoption has expanded rapidly, yet the field still lacks a clear framework that organizes the many user-level factors influencing adoption. This study addresses this gap by distinguishing between intrinsic and extrinsic factors and developing a data-driven categorization of intrinsic factors. We conducted a systematic literature review of 143 peer-reviewed studies published between 2018 and 2024. This study follows an inductive, data-driven approach using text-mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling, alongside statistical tests such as ANOVA, to identify patterns in the theories, methods, and factors used across the literature. We then grouped intrinsic factors based on how users internally evaluate m-banking–through perceptions, personal traits, social influences, and psychological processes. Results show substantial variation in motivators and barriers across contexts, theories, methods, and sample sizes. Perceived usefulness, ease of use, and trust dominate prior research, while many other intrinsic factors remain underexplored. We also observe an over-reliance on a small set of theoretical models (TAM, UTAUT/UTAUT2) and limited methodological diversity. The proposed categorization clarifies the structure of intrinsic influences and highlights overlooked themes, including emotional and social-psychological factors. This framework supports more consistent definitions, guides theory selection, and identifies research gaps. It also offers practical insights for designing more engaging, transparent, and supportive m-banking experiences.
Using the context of urban working women, the purpose of the study is to investigate (1) the influence of financial literacy (FL) and financial inclusion (FI) in building financial capability (FC); (2) the positive linkage between FC and financial satisfaction (FS); 3) the mediating role of FC between FL and FS; and (4) the moderating effect of digital financial services (DFS) between FC and FS. Purposive and snowball sampling was employed to collect data from 212 full-time urban working women from the nine most prominent metropolitan cities in India with the condition that these women must have been using any kind of DFS for the last one year. We performed covariance-based structural modeling using AMOS 22, including confirmatory factor analysis, path analysis, and moderation analysis to test the proposed hypothesis. The results support significant association between FL, FI and FS, mediated by FC and DFS positively moderating between FC and FS, thus implying reduction in gender gaps. This would help further improve their human development index, thus aligning with SDG 5 (gender equality), SDG 10 (reducing inequalities), and SDG 11 (promoting sustainable cities and communities).
Drawing on Human Capital Theory and Social Cognitive Theory, we investigate how basic financial literacy and financial market literacy translate into tangible investment performance. We collected data from 664 retail investors in Bangladesh and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that basic financial literacy, distinct from financial market literacy, is a predictor of both financial self-efficacy and financial interest. While self-efficacy strongly predicts risk tolerance, neither of these factors exerts a direct influence on investment performance. Additionally, financial interest fosters investment priority and positively influences performance. Investment priority also serves as a driver for seeking financial advice, which subsequently emerges as the most substantial determinant of investment performance. Moreover, although risk tolerance demonstrates no direct impact on performance, interaction analyses confirm its role as a moderator that enhances the positive relationship between financial interest and investment performance. Ultimately, these findings may help individual investors recognize the potential confidence-competence gap, assist financial advisors in highlighting the dual value propositions that clients pursue, and guide educators and policymakers in refining targeted financial literacy programs.
The growing use of online banking after the COVID-19 pandemic has increased the importance of understanding how customers evaluate digital banking services. This study examines the effects of service quality and price fairness on perceived value and, subsequently, customer satisfaction with online banking across the six Gulf Cooperation Council (GCC) countries. Data were collected from 938 active online banking users and analysed using exploratory factor analysis and structural equation modelling. The results show that service quality and price fairness have positive and statistically significant effects on perceived online banking value, while perceived value has a positive and significant effect on customer satisfaction. These findings indicate that customers are more satisfied when online banking services provide reliable service and reasonable charges that strengthen the value they receive. For bank managers, the results highlight the importance of improving digital service quality while maintaining transparent and fair pricing to strengthen perceived value and customer satisfaction.
Mobile trading apps are a key component of the global FinTech landscape, enabling retail investors to access financial markets. Despite their rapid growth, these platforms operate in high-stakes environments where system reliability, transaction transparency, fraud risk, and ethical considerations shape users’ post-adoption evaluations. Prior research has primarily examined pre-adoption intentions using survey-based approaches, providing limited insight into users’ actual post-use experiences. This study develops a theory-informed, data-driven model of post-adoption satisfaction and continuance intention using an initial dataset of 5,500 Arabic-language app store reviews from three mobile trading platforms; after spam filtering and preprocessing, 3,381 high-quality reviews were retained for the final analysis. A sequential mixed-methods approach combined topic modeling and expert-guided construct development in the exploratory phase with Elastic Net regression and Random Forest in the confirmatory phase. The results indicate that Platform System Quality, Transaction Quality, Service Quality, Investment Utility, and Sharia Compliance are positively associated with linguistically expressed user satisfaction, whereas Fraud Concerns are negatively associated with satisfaction. User satisfaction is positively associated with continuance-oriented expressions, although the relatively modest explanatory power of the continuance model suggests that satisfaction alone does not fully explain continuance-related language in user reviews. Accordingly, the reported findings should be interpreted as associations among linguistically inferred constructs rather than independently validated causal relationships. The study contributes to FinTech and information systems research by contextualizing post-adoption theory for high-stakes mobile trading environments and by demonstrating a scalable, transparent, and theory-informed framework that integrates user-generated content analytics with machine learning.
