Bibliographic record
Abstract
This study is motivated by the increasing incidences of corporate failures caused by fraudulent accounting and breaches in corporate governance and ethical conducts. For example, it is expected that managers will resort to activities that are not consistent with appropriate governance and ethics just to keep up with the market expectations to safeguard their position. This study investigates the impact of five managers’ opportunistic incentives on the reliability of firms’ reported earnings (namely free cash flow, abnormal capital expenditure, bonus plan, distress sign and leverage), controlled by six other widely implemented standard structural governance mechanisms. The earnings reliability tests are conducted using the balanced panel data analysis specification covering 323 sample firms with continuous accounting and corporate governance data of 15 years beginning 1993. Consistent with literature, the results reveal that firms’ earnings reliability is significantly related to all managerial opportunism attributes, implying the adverse effect of these incentives on the usefulness of accounting information in assessing management’s stewardship. These findings suggest the need for a better management mechanism to integrate governance and effective enforcement of control requirements to mitigate managerial opportunism and hence uphold the quality of published accounting information.
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.
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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".