Does Digital Business Reporting, XBRL, Regulate Financial Reporting Quality in Emerging Market? The Institutional View
Bibliographic record
Abstract
This study investigates how real earnings management (REM) and accruals-based earnings management (AEM) as proxies for financial reporting quality are affected by eXtensible business reporting language (XBRL). It seeks to integrate Institutional Theory within the theoretical framework, instead of Agency Theory, the principal theory of management opportunism. It is possible to think of XBRL as the external regulations that influence management's practice toward financial reporting. This study tests the set of suggested hypotheses using regression analysis on a special dataset from Thailand. The results demonstrate a considerable decrease in the practice of earnings management following the deployment of XBRL. Support is given to the notion of Institutional Theory, which holds that an individual's behavior is influenced by their surroundings. Furthermore, this study clarifies the continuing discussion about whether XBRL enhances the quality of financial reporting. Regulators should find this information useful. In particular, the findings complement prior literature in the way that XBRL is beneficial for financial reporting. It sheds lights on the ongoing debate whether XBRL is beneficial for improving the quality of financial reports.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| 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".