Accounting Conservatism and Earnings Quality
Bibliographic record
Abstract
Purpose—The study on the relationship between accounting conservatism and earnings quality is not new. However, the results are inconsistent and mixed, and to some degree, even contradictory, which represents a gap in the literature. The purpose of this study is to provide some explanations for these mixed results in the literature by investigating the effect of corporate governance mechanisms, as a moderator variable (which has not been considered in the literature before), on the relationship between accounting conservatism and earnings quality based on the Dechow and Dichev model and the modified Jones model. Design/methodology/approach—The statistical model used in this study is a multivariate regression model; furthermore, the statistical technique used to test the hypotheses is panel data. Findings—The findings reveal that the adopted models (Dechow and Dichev) and the corporate governance mechanisms (such as board independence, large shareholders, and institutional ownership) can have a moderating effect on the relationship between accounting conservatism and earnings quality. These findings are exciting, contribute to the current literature, and explain some of the reasons for mixed results. Practical implications—The findings of the current study provide an important guideline for firms to consider the impact of adopted models (Dechow and Dichev), as well as the corporate governance mechanisms (such as board independence, large shareholders, and institutional ownership) on the relationship between accounting conservatism and earnings quality. Originality/value—Examining the impact of Dechow and Dichev models as well as the corporate governance mechanisms on the relationship between accounting conservatism and earnings quality is new in this paper. It can explain part of the reasons for the mixed and inconsistent results in the literature.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".