Transformation of the hexagon fraud model (S.C.C.O.R.E.L.L Model): moderated by social media as a whistleblowing system information channe
Bibliographic record
Abstract
This research investigates the impact of stimulus, capability, collusion, opportunity, rationalization, ego, lack of empathy, and spirituality on financial reporting fraud. This chapter also explores how social media can moderate these impacts as a channel for whistleblowing information. Based on balanced panel data with a sample of 441 research data, the research focuses on commercial banks registered with the Financial Services Authority, Indonesia, from 2016 to 2022. The research results reveal that opportunity has a negative influence on fraudulent financial reporting; lack of empathy also has a negative influence on fraudulent financial reporting; lack of spirituality has a positive influence on fraudulent financial reporting; social media as an information channel for the whistleblowing system weakens the influence of lack of spirituality on fraudulent financial reporting, and social media strengthens the influence of opportunity and lack of empathy on fraudulent financial reporting. This research not only aims to bridge the research gap regarding financial reporting that contains fraud (fraud theory) but also provides practical insights and recommendations for regulators, especially in monitoring financial reporting that contains fraud in the banking sector. Hence, these findings are very relevant and can be applied.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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 source (direct Gemma or distilled Codex), 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".