Do Shareholders Assess Managers' Use of Accruals to Manage Earnings as a Negative Signal of Trustworthiness Even When Its Outcome Serves Shareholders' Interests?*
Bibliographic record
Abstract
ABSTRACT We examine how shareholders' trust in managers is affected by (i) the outcome of earnings management (inconsistent vs. consistent with shareholders' interests) and (ii) the method of earnings management (accruals vs. real methods). Using a controlled experiment, we predict and find that trust is impaired when the outcome of earnings management suggests that managers have put their interests above shareholders' interests and/or when the method of earnings management suggests that managers misreported the firm's economic performance. We argue that shareholders assess managers putting their interests above shareholders' interests as a signal of untrustworthiness because it involves a transfer of the firm's resources away from shareholders to managers. We argue that shareholders also assess managers' use of accruals to manage earnings as a signal of untrustworthiness because, in this instance, managers misreport the firm's economic performance. Finally, we show that trust mediates the combined effects of the outcome of earnings management and the method of earnings management on investment decisions. Our study incrementally contributes to the literature by highlighting the adverse implications of managers' use of accruals to manage earnings even when its outcome serves shareholders' interests.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".