The Effect of Audit Firm Size on Independent Auditor’s Opinion: Conceptual Framework
Bibliographic record
Abstract
Independent auditor’s opinion enhances the confidence of investors in the reporting system and leads to an increased in capital markets efficiency. Thus, the effectiveness and quality of opinion developed by the auditor about the financial statements is significantly important, because financial statements should be reliable, useful and relevant for investors and creditors. A significant issue frequently raised in the accounting literature is whether judgments of auditors from large firms vary substantially from those of auditors employed by other firms. Then past researchers attempted to find the relationship between the size of company and auditor’s opinion and its quality. Review on the literature revealed that the size of firm can not affect auditor’s opinion but this survey found that some factors such as experience, education, skills and employee competence may have influence on quality of auditors and their opinion. This paper has categorized these views under special category of capital known as human capital. Thus, the study anticipates improvement in the relationship between audit firm’s size and independent auditor’s opinion by introducing human capital as a mediator variable.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".