Review of Evidence between Corporate Governance and Mandatory IFRS Adoption from the Perspective of Agency Theory and Information Asymmetry
Bibliographic record
Abstract
Prior studies illustrate the issues of agency theory stemmed from the separation between ownership and management. As such, information asymmetry between the agent and principal is the major reason why agent can take advantages from adverse selection and moral hazard, which is the obvious problem in recent accounting scandals. Boards of directors therefore have fiduciary duties to exercise effective corporate governance mechanism to control information asymmetry. We have reviewed the extant literature on whether corporate governance is positively related to more and better disclosure as an attempt to reduce information asymmetry. Also, when IFRS requires more disclosure and IFRS adoption becomes mandatory for many jurisdictions, we examined recent studies on whether firms adopting IFRS with corporate governance regimes can reduce information asymmetry by making themselves more transparent. In general, empirical findings are mixed due to the complex and inter-related nature of corporate governance systems including single-country or cross-country studies, self-constructed or comprehensive corporate governance metrics and whether self-selection and endogeneity can be controlled in modeling.
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 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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".