Role of Computerized AIS Applications in Preserving Organizational Financial Performance during COVID19: Moderating Role of Accountants' Experience
Bibliographic record
Abstract
Current study aimed at examining the role of AIS application in preserving financial performance of organizations during COVID19 pandemic. Depending on quantitative approach, study utilized a questionnaire built on likert scale which was distributed on (109) individuals within Jordanian organizations. Results of study indicated that accounting information systems contributed to the continuous follow-up and knowledge of the financial performance of the organizations during the pandemic period, which in turn supported the principle of correct and quick decision-making that is in the interest of shareholders, working individuals and customers by taking precautionary measures to ensure that the organization does not reach financial insolvency in view of financial data that together constitute the informational outputs of accounting information systems. This support was backed up with the moderating variable of accountants' experience, experience in this case managed to help accountants predict the coming situations and help the organization overcome the obstacles based on their previous experiences in similar situations. Study recommended the necessity of activating risk management strategies by organizations when facing crises and epidemics
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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 | 0.003 | 0.026 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".