An analysis of the determinants of corporate governance disclosure policies in multinational enterprises: A multi- medium study
Bibliographic record
Abstract
This research aims to identify the factors underlying the corporate governance disclosure policies of the world’s largest multinational companies (MNCs) based on the following: (1) national factors related to the MNCs’ home countries (2) governance factors related to their governance systems and (3) operational factors arising from the operational characteristics of the MNCs. Methodology – Our sample includes 159 MNCs from 24 countries representing three geographic regions. The corporate governance disclosure policy is examined in terms of level and quality of disclosed information in two different mediums (traditional i.e .paper vs. websites). Results – Multiple linear regressions indicate that national factors, especially cultural ones, are important determinants of MNCs corporate governance disclosure policy in the traditional print mediums. National factors, however, seem to play no part in governance disclosures on the internet but can rather be explained by the international MNCs listing status. Practical implications – This study could guide the harmonization efforts of international standard setters in identifying factors leading to different governance disclosure behaviors and the disclosure medium most influenced by these factors.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".