The Applicability of AIS Practices within Modern Business Environment – Case Study of Amman Stock Exchange
Bibliographic record
Abstract
Current study aimed at examining the applicability of Accounting Information Systems within modern business environment in Amman stock exchange between the variables of Software (Industrial application structure, Business application structure, Cloud computing structure) and Hardware (Stand-alone structure design, Multi-user structure design, Network structure design, Cloud computing structure design). In order to be able to highlight extent of applicability researcher has chosen quantitative approach through applying the study tool (questionnaire) on accounting managers within (102) companies in Amman Stock Exchange. After application process total of (65) accounting managers responded to questionnaire with a response rate of 63.72% which was statistically acceptable. Results of study indicated a high level of applicability of AIS within Amman Stock Exchange companies attributed to high awareness of accounting managers regarding AIS. In addition to that, it appeared through the analysis that the applicability degree is more influenced by software equipment more than the hardware which explains that high awareness of individuals. In light of such results; study recommends companies in Amman stock exchange should continue embracing AISs in their business practices to increase their speed of processing tasks, use data entered into the system to compile reports, and make correction easily where necessary.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".