Causal Attribution, Benefits Sharing, and Earnings Management*
Bibliographic record
Abstract
ABSTRACT We conduct two experiments to investigate the joint effect of two justification factors of earnings management—namely, attribution for the firm's underperformance and benefits accruing to other employees from inflating reported earnings. This investigation is important because prior research examines the effects of individual justification factors, whereas real‐world settings entail more complexity involving multiple justification factors. In Experiment 1, we predict and find that managers are more likely to manage earnings when the firm's underperformance is caused by an external event and misreported earnings benefit other employees besides the reporting manager. Furthermore, we show that the extent to which participants use moral justifications mediates the effect of benefits sharing on earnings management, but only when causal attribution is external, and that it mediates the effect of causal attribution on earnings management, but only when benefits are shared. In Experiment 2, we use a neutral control condition that makes no mention of inconsistent incentives to demonstrate that it is the combination of causal attribution and benefits sharing that triggers earnings management. We contribute to the accounting and psychology literature by proposing and testing a theory that explains how multiple justification factors interact to cause opportunistic behavior. Our results suggest that policy‐makers and governing parties should consider developing a holistic view of possible justification factors, focusing on situational opportunities created by combinations of factors rather than individual factors alone.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".