Accounting-based earnings management: motivations, players, implementation, and detection from the perspective of certified accountants
Bibliographic record
Abstract
In the scope of Behavioral Decision Theory, Accounting-based Earnings Management (AEM) may compromise the success of decision making of a firm’s stakeholders. Given that AEM constitutes a barrier to the decision-making process, we aim to identify the main motivations of the players of AEM. Besides, in this study we also intend to analyze the implementing and detecting of AEM practices in financial statements and to evaluate whether individual characteristics influence the ability to implement and detect creative accounting practices. To achieve the proposed objectives, a quantitative methodology approach was used. A survey was applied to Portuguese’s certified accountants. In the data analyses, we applied the univariate and multiple analysis. Based on 159 observations, we find that most certified accountants indicate the main motivations are related to the reduction of the cost of capital and tax burden, the strength of the “code law system”, and that the managers are the main players. Our evidence also shows that the AEM practices are easily implemented and detected in the financial statements. In addition, we find that age, professional experience, and academic qualifications of the certified accountant tend to have an impact on the ability to implement AEM in the financial statements, contrary to gender and training area. Furthermore, gender and academic financial statements. This research is important for the development of the literature, entities that operate in accounting standardization and for the users of accounting and financial information. This study contributes to a better understanding of AEM practice, and it originally combines individual characteristics of accounting professionals with AEM practice.
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 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".