Exploring User Acceptance of Digital Payments in India: An Empirical Study Using an Extended Technology Acceptance Model in the Fintech Landscape
Bibliographic record
Abstract
India has experienced a significant digital payments transformation, driven by technological advancements, smartphone penetration, and government initiatives.This research explores the role of digital payments and mobile payments in India's Fintech revolution.Using an extended technology acceptance model, the study investigates user acceptance and perceived usability.The research identifies factors influencing perceived usefulness, behavioral intention, and actual usage of digital payments.Four constructs-Financial Literacy, Trust and Privacy, Service Quality, and Perceived Ease of Use-are analyzed using structural equation modeling.The results show the growing adoption of digital and mobile payment platforms like Paytm, Google Pay, and PhonePe, even among financially excluded segments.However, financial literacy does not directly impact digital payment acceptance.The study concludes that digital and mobile payments have disrupted the payment landscape in India, bringing efficiency and simplicity.Enhancing financial literacy is crucial for wider adoption.The findings contribute insights for businesses, policymakers, and users in leveraging the benefits of digital and mobile payments during India's Fintech revolution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.001 | 0.000 |
| 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".