Impact of Digital Financial Services on Customers’ Choice of Financial Institutions: A Modified UTAUT Study in Bangladesh

Abstract

This study investigates the factors influencing customers' choice of financial institutions in Bangladesh, focusing specifically on the role of Digital Financial Services (DFS). The study explores the relationship between trust, risks, benefits, social influences and intention to choose financial institutions through DFS. The research adopts a quantitative methodology and employs Structural Equation Modeling (SEM) to analyze the data collected through a survey that use digital financial services in Bangladesh. The study examines by the modified UTAUT model to explore the direct and indirect relationships that influence the intention to choose financial institutions through using DFS. The results reveal that 'Benefits' and 'Social Influences' have a significant positive impact on the choice of financial institutions through DFS in Bangladesh, while 'Trust' and 'Perceived Risks' demonstrate an insignificant relationship with 'Users' Intention' to choose financial institutions through DFS. This study contributes to the literature on digital financial services (DFS) adoption by examining the factors influencing customers' choice of financial institutions in Bangladesh. Also, the research adds value by incorporating a modified theory of UTAUT model for finding the appropriate relationship among the variables. The findings highlight the importance of understanding the complex factors affecting customers' preferences for financial institutions and emphasize the need for financial institutions in Bangladesh to prioritize not only building trust and managing risks but also leveraging social influences and the benefits of using DFS to attract and retain customers. Finally, it also suggests important insights for financial institutions in Bangladesh to better attract and retain digitally-savvy customers.

Secure Image Retrieval and Sharing Technologies for Digital Inclusive Finance: Methods and Applications

“…On the other hand, Sample 2's embedding rate, albeit the nadir among the datasets, registered a commendable 1.92%, bolstering the assertion of the algorithm's expansive adaptability across a plethora of financial image categories. 2) Threshold of (3,3) 2, it was discerned that the embedding rate across all samples exhibited an escalating trend as the threshold progressed from (3,3) to (3,4), and subsequently to (4,4). This trend suggests an augmentation in the volume of embedded information with the elevation of the shared threshold.…”

“…In the intertwined realms of global financial markets and technological evolution, digital inclusive finance has emerged as a pivotal catalyst in the innovation and widespread adoption of financial services [1,2]. This novel financial paradigm not only transcends the confines of traditional financial offerings but also facilitates unparalleled accessibility for individuals across diverse economic strata [3][4][5][6]. However, an exponential surge in data, especially image data pertinent to customer identity verification, electronic signatures, and transaction evidences, has been observed in financial institutions, primarily driven by advancements in big data and cloud computing technologies [7][8][9].…”

“…Figure 8. Comparison of embedding rate using different image sharing methods with different thresholds Upon examination of Table2, it was discerned that the embedding rate across all samples exhibited an escalating trend as the threshold progressed from (3,3) to(3,4), and subsequently to(4,4). This trend suggests an augmentation in the volume of embedded information with the elevation of the shared threshold.…”

Decision Making with the Use of Digital Inclusive Financial Systems by New Agricultural Management Entities in Guangdong Province, China: A Unified Theory of Acceptance and Use of Technology-Based Structural Equation Modeling Analysis

“…Effort expectations reflect the effort required for farmers to use these applications to access credit services, and there are differences in the effects of community levels of digital finance on household income growth among heterogeneous farmers, with the "digital divide" and "knowledge divide" leading to the ineffectiveness of digital finance in increasing the incomes of poor farmers' households. The "digital divide" and the "knowledge divide" lead to the ineffectiveness of digital finance on poor farmers' household income growth [35]. If an application has a complex design, requires high digital literacy from farmers, or has a cumbersome application and operation process, farmers may perceive that using the application requires greater effort, thus reducing their willingness to use it [36].…”