The Role of Board Independence in Enhancing External Auditor Independence
Bibliographic record
Abstract
Legislative regulations have recognized the significance of board independence in enhancing the board’s role and strengthening its autonomy, which are among the key features that mitigate conflicts of interest between management and shareholders. External auditing serves as a pivotal element of corporate governance, acting as a monitoring mechanism to reduce information asymmetry and safeguard principal interests by ensuring the accuracy and fairness of financial statements. This, in turn, reassures data users and stakeholders. The study aimed to examine the effect of board independence on enhancing external auditor independence among 72 Jordanian service companies listed on the Amman Stock Exchange from 2017 to 2021, with a study sample of 62 companies. The findings revealed a negative impact of board member independence on external auditor independence, as measured by audit firm size. However, company size positively influenced external auditor independence, while no effect was found for financial leverage or company age. The findings highlight the need for companies to strengthen internal controls and governance practices to enhance external auditor independence. Additionally, they suggest that company size plays a crucial role, while other factors like financial leverage and company age may have limited impact, indicating areas for further exploration in future research.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".