External auditing costs of fair value model amongst Jordanian financial institutions: The moderating effect of ownership structure
Bibliographic record
Abstract
The paper presents a fresh empirical approach for clarifying the impact of Jordan's most prevalent forms of ownership on the link between the “fair value (FV)” model share of assets and auditing costs. Using information gathered from 105 Jordanian financial listed companies spanning 2005 to 2018, ordinary least squares regression is applied in this paper. While financial institution ownership variables cause the opposite to be observed, family ownership decreases the link among the share of assets at FV and audit expenses. Family ownership results in decreased auditing costs paid only for “Level 1” assets; conversely, the extremely uncertain FV assets “Level 2 & 3” show the opposite. Financial institutional ownership demonstrates that auditing FV Level 1 leads to higher auditing costs. When relating FV Levels 2 and 3, the moderating effect of financial institutional ownership was significantly negative. No significant moderating effect of government ownership is confirmed. The inconclusive and limited empirical explanation of audit costs resulting from the FV model from a Western setting motivates our investigation. This study is considered as a unique study as it takes into account the most prevalent types of ownership in the Jordanian context in the FV studies reviewing auditees in Jordan. New evidence is generated by documenting audit characteristics of Jordan, a developing country, and its institutional environment and compliance with the FV model. The results are useful to regulators and policymakers in regulating the auditing profession and resolving FV audit-related conflicts and issues.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".