Influence of FinTech Paylater, Financial Well Being, Behavioral Finance, and Digital Financial Literacy on MSME Sustainability in South Sumatera
Bibliographic record
Abstract
This study examines the influence of FinTech Paylater, Financial Well Being (FW), Behavioral Finance (BF), and Digital Financial Literacy (DFL) on the sustainability of Micro, Small, and Medium Enterprises (MSMEs) in South Sumatera, Indonesia. Using a quantitative explanatory design, data from 563 MSME owners were collected through a structured questionnaire and analyzed using Structural Equation Modeling–Partial Least Squares (SEM–PLS). The results show that FinTech Paylater, FW, BF, and DFL have positive and significant effects on MSME sustainability, with DFL emerging as the strongest predictor. Paylater services support sustainability by improving liquidity and access to short-term financing, while FW enhances financial stability and resilience. BF shapes financial decision-making through behavioral control and risk awareness. The integrated model explains 61% of the variance in MSME sustainability and demonstrates that digital capability and psychological factors jointly determine whether FinTech is used productively or consumptively. The findings provide theoretical contributions to the literature on FinTech and MSME sustainability and offer practical implications for policymakers and FinTech providers in designing targeted Digital Financial Literacy programs and responsible Paylater schemes for MSMEs in emerging economies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".