THE IMPACT OF THREE BOARD CHARACTERISTICS, MODERATED BY CEO ATTRIBUTES, ON EARNINGS MANAGEMENT
Bibliographic record
Abstract
Earnings management has had consequence in financial disasters, such as Enron, WorldCom and Nortel. More recently, it is alleged in the Lehman bankruptcy, which ushered in a global financial meltdown. Yet despite increased regulation and focus on governance and auditing, researchers find that earnings management remains a common practice. Accounting academics have responded to the earnings management problem by conducting studies using secondary data for governance variables and financial models to measure earnings management indirectly. Meanwhile, governance variables measured with secondary data now show little variability because of improved best practice and regulation, and there is strong evidence that the agency causal model and the earnings management measures are seriously flawed. This study uses a mixed-mode research model based on agency and stewardship theory to explain earnings management, and uses a more direct measure of its occurrence, namely the level of board information asymmetries and board monitoring and control actions, as a proxy for earnings management. Primary data is used to provide direct measures of important governance variables, which produce mixed results relative to earnings management using secondary data. In a survey of 245 Canadian public company directors, this study finds that an independent chair, less busy directors, and a smaller board does reduce earnings management, but that this impact is strongly moderated by the CEO's attributes. A CEO with stewardship attributes reduces earnings management, and a CEO with agency attributes increases earnings management. There also is evidence in the study that agency conflict variables improve governance outcomes, in this case, reducing the level of earnings management, and that board processes around monitoring and control actions could be a problem.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".