Exploring Platform Trust, Borrowing Intention, and Actual Use of PayLater Services in Indonesia and Malaysia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores how system-based and cognitive-based factors affect platform trust and its role in the actual use of PayLater services (buy now, pay later or BNPL) in Indonesia and Malaysia. PayLater, a fintech innovation, provides fast and convenient payment options through online platforms. By incorporating platform trust into the technology acceptance model (TAM), the research investigates whether borrowing intention acts as a mediator between platform trust and actual usage. Utilizing a quantitative approach with purposive sampling, data were gathered from 106 respondents in Indonesia and 169 in Malaysia, with 62 and 85 respondents meeting the criteria, respectively. Partial least squares (PLS) analysis indicates notable differences in how Indonesian and Malaysian users perceive platform trust, while the effect of platform trust on borrowing intention remains consistent across both nations. Borrowing intention emerges as a crucial factor influencing the actual use of PayLater services. The results offer important insights into the adoption of fintech services in emerging markets, highlighting the significance of platform trust in shaping user behavior. This research provides practical suggestions for fintech providers to improve platform trust and user engagement in cross-country scenarios.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it