The role of financial distress and fraudulent financial reporting: A mediation effect testing
Bibliographic record
Abstract
Research aims: This study examines the determinants of fraudulent financial reporting with financial distress as an intervening agent.Design/Methodology/Approach: The banking companies listed on the Indonesia Stock Exchange (IDX) between 2017 and 2020 comprised the study's population. One hundred-four companies comprised the entire sample, which was chosen using purposive sampling. The approach employed in this study was partial least squares (PLS)-SEM.Research findings: The results of this study found that financial targets and audit quality significantly affected financial distress. Financial distress had a significant effect on fraudulent financial reporting. Financial targets and audit quality had no significant effect on fraudulent financial reporting. Furthermore, audit quality significantly affected fraudulent financial reporting through financial distress. Financial targets did not significantly influence fraudulent financial reporting through financial distress.Theoretical contribution/Originality: This study provides literature on the role of financial conditions and good corporate governance in preventing fraudulent financial reporting in banking companies. This study can be an insight for practitioners and academics in Indonesia and internationally. Apart from that, this study contributes to the literature on the occurrence of fraudulent financial statements mediated by financial distress, which is not widely discussed, specifically in the context of the banking industry in developing countries.Practitioner/Policy implication: The practical implication in this research is the importance for investors and creditors to be more vigilant and pay attention to corporate governance and financial conditions to reduce errors in decisions based on financial reports. In addition, the strength of good corporate governance indicates that the supervision carried out by management will take the information conveyed to stakeholders free from material misstatement so that the implementation of good corporate governance can prevent fraud. Research limitation/Implication: This study exclusively includes companies in the banking sector listed on the Indonesia Stock Exchange (BEI) between 2017 and 2020. Out of 46 companies, only 26 may be used as research objects according to the purposive sampling method.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".