Data quality analytics, business ethics, and cyber risk management on operational performance and fintech sustainability
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 conducted a test to see the influence of data quality analytics, business ethics, and cyber risk management on operational performance and its implication on corporate sustainability of Fintech P2P Lending companies registered and licensed in Indonesian Financial Services Authority (OJK). This study used descriptive analysis and statistical method Structural Equation Modeling (SEM)-Lisrel. The data was collected by using questionnaires given to 104 managers from 91 Fintech P2P Lending companies registered and licensed at OJK until the end of December 2021. The results show that data quality analytics and cyber risk management had a positive and significant influence on operational performance. The results also show that analytical data quality, business ethics and cyber risk management had a positive and significant influence on operational performance. The findings of this study added to the limitations of the research literature on the elaboration of variables that determine performance and business sustainability in Fintech P2P lending.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.007 |
| 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 it