Evaluation of Audit Expectation Gap in Sudan: Existence, Causes, and Subsequent Effects
Bibliographic record
Abstract
Purpose: This study mainly focus on evaluating the existence, causes and subsequent effects of audit expectation gap in Sudan.Proposed Design/Methodology/Approach: A cross sectional research design in which data to be collected through structured questionnaires, and semi-structured interviews. Partial Least Square Path Modeling, using Smart PLS Software to analyze the data.Findings: A detailed literature review reveals that, there is a shortage of research concerning the impact of multi-responsibility auditor, standard external audit process, accounting and auditing regulations in a developing country, auditor independence in fact and in appearance, external audit rotation, and rendering of non-audit services on audit expectation gap.Moreover, there is dearth of studies investigating the moderating role of professional code of ethics, and the mediating effect of audit report .Therefore, the study proposes a framework to incorporate these factors into future research.Practical Implications: The results of this study will assist policy makers in finding ways that can be instituted to tackle expectation gap in Sudan, to improve the quality of the auditing profession in the country.Originality/value: This study contributes to the existing literature by adding evidence to the important debate about audit expectation gap, from a region that had little coverage on the studied matter. Specifically, it proposes a framework to extend the research on the factors that cause such a gap in Sudan. Moreover, a significant feature of this study is in introducing accounting and auditing regulations in a developing country as a new variable to investigate its effects on audit expectation gap.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".