Financial Technology and Its Impact on Digital Literacy in India: Using Poverty as a Moderating Variable
Bibliographic record
Abstract
Financial technology is a powerful tool in financial infrastructure, used to strengthen and smooth the delivery of financial services into the broader space. Financial technology involves software, applications, and other technologies designed to improve and automate traditional forms of financial services for businesses established in different areas. The authors aimed to explore the impact of financial technology on the digital literacy rate in India, by utilizing the poverty score as a moderating variable. The panel data analysis (PDA) has been employed in the current study. Data from 29 states and two union territories (UTs) of India were considered for three financial years, i.e., 2017–2018 to 2019–2020. The study’s findings reveal that Kisan Credit Cards (KCCs), both in terms of numbers and amount, are positively associated with the literacy rate. However, ATMs are negatively significant in association with literacy rate. Furthermore, the study’s empirical results show that KCCs and ATMs positively impact literacy when interacting with poverty scores. The study’s findings bring noteworthy implications for the government and other officials to understand the situation at the ground level of Indian states and UTs while forming new rules and policies for society’s betterment, particularly in finance and digital literacy. Additionally, the findings imply that ordinary people living in urban and rural areas of India should take advantage of financial technology and get motivated towards digital literacy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".