Trends and Insights in Arab Audit Research: A Bibliometric Exploration
Bibliographic record
Abstract
ABSTRACT This study employs bibliometric analysis to investigate audit research trends across five Arab countries—Jordan, Saudi Arabia, Egypt, Tunisia and the United Arab Emirates—each reflecting distinct sociopolitical, economic and legal contexts. The analysis focuses on audit research published between 2018 and 2023, uncovering dominant themes, methodological approaches and country‐specific nuances. Audit quality emerges as a central theme across all five countries, driven by regional aspirations for enhanced governance and transparency, though often shaped by data limitations and a prevailing culture of secrecy. Subthemes vary, reflecting local priorities: auditor legal liability in Jordan, sociopolitical dimensions of auditing in Egypt, gender dynamics in Saudi Arabia, ethical issues in Tunisia and global standards integration in the UAE. Quantitative research dominates, highlighting methodological constraints while underscoring the need for qualitative approaches to address unexplored dimensions of auditing practices. This study provides a comprehensive view of the Arab audit research landscape, offering insights into the interplay of global trends and local contexts while identifying critical gaps for future exploration.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.112 | 0.168 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".