The effect of data analytics quality on fintech P2P lending sustainability through operational performance as an intervening variabl
Bibliographic record
Abstract
This study aims to explore the effect of data analytics quality on company sustainability through operational performance as an intervening variable of Fintech Peer to Peer Lending (P2P) companies registered and licensed at the Financial Services Authority (known as OJK) Indonesia. This study is quantitative research using primary data collected through questionnaires and interviews. The data came from 104 company leaders and involved 91 Fintech P2P Lending companies registered and licensed at OJK until December 2023. Data were processed using statistical tools Structural Equation Modeling (SEM)-Lisrel. The result of processed data indicates that data analytics quality has a positive and significant effect on company sustainability through operational performance as an intervening variable. Data analytics quality with AI-based automation makes repetitive work operations easy, efficient and effective which has implications for increasing the sustainability opportunities of fintech companies.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.002 | 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".