Role of Artificial Intelligence in Enhancing Efficiency of Accounting Information System and Non-Financial Performance of the Manufacturing Companies
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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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".