The Effect of the Reliability of Accounting Information Systems on Electronic Disclosures on the Stock Prices: Applied Study on Industrial Companies Listed on Amman Stock Exchange
Bibliographic record
Abstract
The study aims at identifying the impact of the reliability of the accounting information systems (security, confidentiality, privacy, the integrity of processes and readiness) on the stock prices of Jordanian industrial companies in Amman Stock Exchange and determining the effect of electronic disclosures (the appropriate timing, predictability and ability to reorganizing) in improving that impact; depending on the descriptive analytical approach, the questionnaire was designed and distributed to the sample of the study composed of directors of financial departments and divisions related to financial issues, monitoring and internal audit and information technology at industrial companies listed on Amman Stock Exchange, with the average of two questionnaires in each company. The number of questionnaires retrieved and statistically analyzed (118), (90.8%) of distributed questionnaires. The results of the multiple regression analysis showed a statistically significant effect on the reliability of accounting information systems in their dimensions (security, confidentiality, privacy, integrity of processes, and readiness) on the stock prices of Jordanian industrial companies in Amman Stock Exchange, where the moral effect appeared in all dimensions, except (security, integrity of processes). The results of the regression analysis showed that electronic disclosures were represented by (the appropriate timing, predictability and ability to reorganizing) which lead to improve the effect of accounting information systems on the stock prices of Jordanian industrial companies in Amman Stock Exchange. The study recommended to increase the level of interest in the reliability of accounting information systems, and to increase the level of its application and activation in the Jordanian industrial companies, because of its role in improving the performance of the company, and maximize their market value through its accounting information to shareholders of accurate, convenience, timely, reliable, consequently ensuring their survival and continuity, and to increase the awareness of the management of Jordanian industrial companies and financial statements preparers about the importance of electronic disclosure and the benefits it achieves in addition to the resulting risks, and adopt effective procedures to verify the security and reliability of accounting information that electronically disclosed and to transmit the information securely by users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".