Digital Financial Literacy and Its Impact on Financial Decision-Making of Women: Evidence from India
Bibliographic record
Abstract
Despite the increasing accessibility of digital financial instruments globally, a number of women encounter obstacles in properly using these platforms due to insufficient digital financial literacy, which profoundly affects their financial decision-making and economic empowerment. This study aims to promote digital financial literacy and Fintech adoption for women in India by examining the effects of digital financial literacy on financial decision-making while considering the mediating effect of government support and digital financial literacy. Furthermore, in this study, we analyzed the relationship between independent variables such as financial attitude (FAtt), subjective norms (SNs), perceived behavior control (PBC), digital financial literacy (DFL), and financial accessibility (FA) on the dependent variable, i.e., financial decision-making (FDM). We also explored how financial decision-making impacts women’ intention towards investment (INT). By analyzing 385 Indian women respondents using Structural Equation Modeling (SEM), this study revealed that financial attitude (FAtt) leads to higher financial decision-making (FDM), exerting moderate effects. Similarly, subjective norms (SNs), perceived behavioral control (PBC), digital financial literacy (DFL), and financial accessibility (FA) significantly lead to financial decision-making. Overall, the five predictors of financial decision-making explained around 71% of the variance. Financial decision-making exerted a significant and robust effect on women’s intention towards investment. Financial resilience significantly moderated the effects of financial decision-making on women’s intention towards investment. These findings emphasize the necessity of implementing a distinct government strategy and programs to enhance the adoption of Fintech among women living in urban and rural regions across India. This study is aligned with UN Sustainable Development Goals, especially Sustainable Development Goal (SDG) 1: No Poverty, SDG 5: Gender Equality, and SDG 8: Decent Work and Economic Growth.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".