Organizational Risk Management and Performance from the Perspective of Fraud: A Comparative Study in Iraq, Iran, and Saudi Arabia
Bibliographic record
Abstract
This study aimed to examine the impact of enterprise risk management (ERM) on the firm performance of capital markets in developing nations such as Iran, Saudi Arabia, and Iraq. In order to achieve the study’s primary purpose, the economic environments of Iran, Iraq, and Saudi Arabia, three neighboring and developing nations, were examined from 2012 to 2019. The hypotheses were tested using panel regression analysis. According to the data, ERM might boost the return on assets and lower the total assets of Iranian enterprises while raising the total assets of Iraqi firms. In addition, the data demonstrated that ERM decreased sales growth and boosted net profit margins in Saudi Arabian companies. ERM enhanced the return on assets in Iranian enterprises and sales growth in Saudi Arabian firms while lowering sales growth in Iraqi firms. In addition, it was shown that total asset turnover increased in non-fraudulent Iranian companies but fell in their Iraqi counterparts. The outcomes of this study revealed substantial evidence regarding the financial conditions and performance of companies operating in emerging nations. As a result, it can be inferred that ERM efficiency and firm performance can be influenced by the firm’s nature and structure, as the findings in these three economic environments were fundamentally distinct. This research contributed to the literature on ERM as one of the essential elements influencing business performance in emerging economies with varying capital market laws. In addition, the literature and acquired data demonstrate the scope of fraud and its influence on the performance of businesses in developing nations.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".