Impact of Digital Financial Services on Customers’ Choice of Financial Institutions: A Modified UTAUT Study in Bangladesh
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".