Factors Influencing the Adoption of FinTech for the Enhancement of Financial Inclusion in Rural India Using a Mixed Methods Approach
Bibliographic record
Abstract
The swift expansion of financial technology (FinTech) can substantially improve financial inclusion, especially in the rural regions of emerging nations such as India. FinTech has the potential to drive inclusive growth, reduce inequalities, and foster sustainable economic development. This research examines the determinants affecting the adoption of FinTech services in rural India by synthesizing three theoretical frameworks: The Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Technology Readiness Index (TRI). A mixed methods approach that combines partial least squares structured equation modeling (PLS-SEM) and fuzzy set comparative qualitative analysis (fsQCA) was used to evaluate the suggested framework. The integrated PLS-SEM and fsQCA offer a comprehensive, elegant, and resilient method for data analysis. While fsQCA addresses more intricate patterns within the data, PLS-SEM effectively identifies the relationships among significant factors. This makes the mixed method approach more judicious and advantageous than the single method approach. The findings showed that attitude (β = 0.35), perceived behavioral control (β = 0.28) from the Theory of Planned Behavior (TPB), perceived ease of use (β = 0.31) from the Technology Acceptance Model (TAM), and perceived insecurity (β = −0.19) from the Technology Readiness Index (TRI) all have a big impact on how people use FinTech. The findings also indicate that the desire to adopt FinTech positively influences financial inclusion among rural residents. These research findings enhance the debate on sustainable development by demonstrating how specific FinTech interventions can close the financial inclusion gap, empower rural populations, and achieve various Sustainable Development Goals (SDGs). The study’s findings could help governments, banks, and FinTech firms aiming to enhance the accessibility and use of digital financial services in rural India.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".