The Determinants of Audit Expectation Gap: An Empirical Study from Kingdom of Bahrain
Bibliographic record
Abstract
The research aims at identifying the determinants of audit expectation gap between the auditors and the users of financial statements in the Kingdom of Bahrain. This issue is noticed in many frauds or errors or illegal matters by the general public after every scam whether Enron and WorldCom from United States or Satyam and Punjab National Bank from India or Tesco and BHS from United Kingdom or Mobily from Kingdom of Saudi Arabia. As per International Standards on Auditing (ISAs), auditors are not responsible to detect each and every fraud or error or illegal act as it is the responsibility of management. However, auditors are expected to assess the possibility of an error or fraud to occur and assess risks of material misstatement due to error or fraud and they are supposed to express their independent and objective opinion on financial statements whether financial statements are prepared in accordance to suitable criteria (International Financial Reporting Standards in the case of Bahrain).This quantitative research and its descriptive design aims empirically to analyze determinants that may impact the audit expectation gap in the Kingdom of Bahrain. The study used a detailed questionnaire as a measuring instrument across the sample group to measure 4 determinants that are expected to have a significant impact on the level of the audit expectation gap. Those determinants are the efforts of auditors, the skills of auditors, the knowledge of the public about the audit profession and the users’ needs from auditors. The research inferred that identified factors found to have a significant impact on the level of audit expectation gap. It is recommended that audit firms should provide training to the audit staff that how to utilize the required efforts in conducting an audit engagement and go extra miles to fill the gap. Furthermore, the auditors should keep themselves updated about the latest frauds and the best audit practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".