Role of Artificial Intelligence in Enhancing Efficiency of Accounting Information System and Non-Financial Performance of the Manufacturing Companies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current study launched from the main objective of examining the impact of artificial intelligence (AI) and its role in supporting and improving the efficiency of AIS on one hand, and non-financial performance standards on the other. In order to achieve this goal and indicate the extent of its conformity with reality; quantitative approach was used and a questionnaire were adopted as a study tool, the questionnaire was distributed electronically to a sample of (409) managers, heads of departments and accountants in industrial establishments operating in Jordan during the fiscal year 2020/2021. By analyzing the primary data based on SPSS, the study came to the conclusion that AI techniques played a significant role in enhancing efficiency of AIS outcomes through focusing on outcomes' understandability, reliability, credibility and comparability, on another level, AI techniques also proved its ability to influence non-financial performance through focusing on feeding organization with the needed information that locates weak points and develop them, and strength points to exploit them. Study recommended the need to link the operations of intelligent systems to the goals of the organization as a whole and ensure the complete interdependence between the AIS systems and the accounting information in the systems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it