Bibliographic record
Abstract
Purpose This study provides significant empirical data and analysis on the international standard‐setting process as conducted by the forerunner of the International Accounting Standards Board (IASB). It reveals the influences from four key stakeholder groups (users, preparers, accountants, and regulators) in order to ascertain why International Accounting Standards (IAS) turn out the way they do. Design/methodology/approach In‐depth interviews with board representatives and content analysis of documents were used to provide triangulating perspectives. The concept of power from the sociological and political science literature provides the theoretical lens. The standard setting projects on segment reporting and intangible assets were studied in detail. Findings The results show that the process can be best characterized as a mixed power system where no party is accorded the absolute power potential to dictate IAS. Nonetheless, while the user group is the target beneficiaries of IAS, the preparer group has significant influence, as inferred from the changes made to the IAS in line with the preparers' preferences. Research limitations/implications There is always the possibility of researchers missing out on “secret” exercise of power, given that the focus of this study was on “public” paths of influence. After this study, the IASB's meetings became open to public, providing new opportunities for future research. Originality/value This paper contributes to understanding accounting standard setting for international harmonization.
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 | 0.029 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".