Internet Financial Reporting Disclosure in the Saudi Listed Manufacturing Companies
Bibliographic record
Abstract
The broad aim of this study was to measure the extent of Internet Financial Reporting Disclosure (IFRD) in the Saudi Listed Manufacturing Companies (SLMCs). It extends the current literature on IFRD by providing empirical data on Saudi Arabia, a developing country which has been scarcely researched in this field of study. Fifty-three SLMCs were investigated based on an unweighted checklist of 75 items (20 for presentation and 55 for content). The study also employed multi regression analysis to examine the status of IFRD. The analysis revealed an overall level of IFRD of 45 per cent. The study also provided empirical evidence of significant positive associations between IFRD and both company size and profitability. However, no significant positive associations were found between IFRD and company leverage or listed age. The study provides empirical evidence of a moderate IFRD rate. This is likely to motivate Saudi regulatory bodies and administrators of the SLMCs to increase the amount of information they disclose on their websites in order to enhance the transparency of their reports and meet all stakeholder expectations. The findings of this study are exclusive to the SLMCs and do not provide a complete picture about online disclosure by all Saudi listed companies. Therefore, future studies could investigate the status of online disclosure across all Saudi Listed Companies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".