The Impact of the Use of Accounting Information Systems on the Quality of Financial Data
Bibliographic record
Abstract
This study aimed at identifying the effect of the use of accounting information systems on the quality of financial data, applied over service companies of Amman Stock Market. The study sample consisted of (70) individuals who work in different service sectors, where a questionnaire was designed and distributed. (56)Valid questionnaires were retrieved for statistical analysis purposes (80%).The study results indicated that there is a statistically significant positive effect of the nature and security of accounting information systems on the quality of financial data. However, the inputs of the accounting information systems did not affect the quality of the financial data. The level of quality of the financial data that the Jordanian service companies depend on has been found to be high.The results of this study showed significant differences at (α ≤ 0.05) among the Jordanian service companies in terms of the nature, inputs and security of accounting information systems and the quality of financial data attributed to the sector to which the company belongs.Based on these results, the study came out with several recommendations, the most important of which is that Jordanian service companies should be keen to update the accounting information systems used in accordance with the technological developments, and the necessity of Jordanian service companies to continue to pay attention to the quality of financial data provided to their beneficiaries, which are used to evaluate the company’s performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".