The Effect of Information Content of Integrated Business Reports on the Credit Decision Making at Jordanian Commercial Banks
Bibliographic record
Abstract
The current paper tested the effect of the information content of integrated business reports in enabling credit officers at Jordanian commercial banks to assess the company’s capability to create value, and the effect of the so mentioned on credit decision making in the Jordanian environment. To achieve this objective, the researcher presented one of Jordanian companies’ financial reports through a proper comprehensive questionnaire, distributed over a sample of credit officers at Jordanian commercial banks, through three alternatives of accounting disclosure which include: traditional financial reports that express financial disclosure only, traditional financial reports in addition to non-financial information in the form of separated reports, and then integrated business reports (consolidated financial and non-financial information). The number of the analyzed questionnaires was 95. Arithmetic means, standard deviations, Simple Liner Regression analysis and One Way ANOVA analysis have been applied to test the research hypotheses and achieve its objectives. The results showed that there is an effect for the disclosure of (only financial information through traditional financial reports, non-financial information through separated reports, and consolidated financial and non-financial information through integrated business reports) in order to take the decision of credit at Jordanian commercial banks. The research also showed that there are significant differences between the variables of the decision of granting loans according to the three disclosure alternatives. It is noted that the information content of the integrated business reports had the greatest effect than the other two alternatives on the decision of granting credit.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".