Digital Transformation, Blockchain, Impact on the Quality of Financial Reporting and Corporate Governance
Bibliographic record
Abstract
The biggest challenges facing accountants in the modern business world are digital transformation and the ambiguous data environment. When we combine these issues with the significance of corporate governance and its function in improving the caliber of financial reporting, the effect is immediately felt at the heart of accounting work. This study aims to investigate how blockchain technology and digital transformation can enhance the quality of financial reports and the effectiveness of corporate governance in the global business environment, particularly in Iraq. The researchers examined a variety of hypotheses in the research community, which is represented by Iraq's private banking industry, to meet the study's goal. A survey was created by the researchers and given to 136 bank workers who were chosen at random. This study used the partial least squares method for structural equation modeling and the SmartPLS3 application for descriptive statistical analysis. The findings showed that the quality of financial reports and the efficiency of corporate governance in Iraqi banks are significantly enhanced by new financial technologies like blockchain technology. These banks need to do more research and analysis on this technology, which is still showing promise.
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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 | 0.005 | 0.023 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".