Measuring the Quality of the Interim Financial Reports Using the Qualitative Characteristics of the Accounting Information and its Effect on the Investment Decisions According to the “IAS 34”
Bibliographic record
Abstract
This study aimed at measuring the quality of the interim financial reports using the quality characteristics of the accounting information and its effect on the investment decisions according to IAS 34 from the point of view of a category of the financial information users working in the brokerage companies. To achieve the objectives of the study, a questionnaire was designed that contains three constructs related to the primary qualitative characteristics, the enhancing qualitative characteristics, and quality of the interim financial reports. The questionnaire was distributed to a sample consisting of 72 individuals. Descriptive statistics were used to describe the study sample such as the frequencies, arithmetic mean, and standard deviation. In addition, the one-sample t-test and simple linear regression analysis were employed to test the study hypotheses at the 0.05 level of significance. Of the main results which the study reached to are that (i) there is effect of the qualitative characteristics of both the primary and enhancing aspects on the quality of the interim financial reports, and (ii) there is effect of the interim financial reports on the investment decision taking. The study provided a number of recommendations, most important of which is directing the prepares of the interim financial reports to pay attention to providing the qualitative characteristics for the accounting information in the interim financial reports for their positive effect on the quality of these reports to help the decision takers in predicting the economical events and building the future plans.
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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 | 0.014 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".