A Machine Learning Model to Evaluate Digital Financial Services Adoption and Sustainable Women Empowerment
Bibliographic record
Abstract
Purpose: Financial services enabled by digital technology can help address the challenges faced by women by overcoming the barriers of proximity and cost. Despite notable advancements in digital financial inclusion in India, women still face obstacles in accessing and utilizing digital financial services. Design/methodology/approach: A machine learning-based self-efficacy-value adoption model (SVAM) is applied to study the influence of self-efficacy and perceived value on the intention to adopt digital financial services (DFS). Likewise, a machine learning-based threshold decision theory was applied to examine the relationship between digital financial services access and the dimension of sustainable women empowerment in rural India. Findings: The results suggest that enhancing user experience and highlighting the benefits of DFS can increase adoption rates among women, thus promoting their economic and social empowerment. Originality/value: In this study, the authors examine an integrated framework based on supervised machine learning to access digital financial services for rural women. They are among the first to apply a self-efficacy-based value adoption model through machine learning to explore this topic. The adoption of digital financial services significantly enhances women's economic, social, and psychological empowerment in rural areas. This evidence-based study will inform policy discussions on developing a gender-sensitive strategy to promote the adoption of digital financial services among women.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".