Exploring the Success Factors Digital Lending: An Approach from Consumer's Perspective
Bibliographic record
Abstract
The rapid growth of fintech has given rise to many fintech companies and new banking services. Thanks to technological advances, digital lending is a service that is widely used by the public. However, the number of inabilities to pay is a problem with digital lending, which is why this research was conducted. By conducting this research, we can determine what factors influence money borrowers to be aware of paying their digital loan debts. This research was conducted quantitatively by distributing questionnaires to borrowers of money in digital lending at banks. Data was collected using purposive sampling using Google Forms, and SMART PLS, a statistical tool used to calculate 100 data responses from Indonesia. The result from eight hypotheses found only the five hypotheses significant. It found that perceived risk and financial inclusion are the factors most influencing the intention to pay for digital lending. It can be a concern for banking actors to pay more attention to matters that affect their customers' perceived risk and financial inclusion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".