Assessment of effects in advances of accounting technologies on quality financial reports in Jordanian public sector
Bibliographic record
Abstract
The study aimed to examine the effects of accounting technology improvements on the generation of accurate and reliable financial reports in the public sector of Jordan. In order to carry out this inquiry, the researchers set research goals and formulated null hypotheses that were derived from these objectives and afterwards used in the study. The study used an ex-post facto survey methodology as its research technique. The study sample included 250 persons employed at the Ministry of Finance in Jordan. The research included a sample size including 152 people. A questionnaire was used as the primary tool for data collection in this study. The validity of the instrument was established by an evaluation conducted by experts specialising in the field of testing and measurement. The evaluation of the instrument's dependability was performed using the Cronbach Alpha reliability approach, yielding a reliability coefficient ranging from 0.73 to 0.85. The findings of this research demonstrate that the instrument has a significant level of dependability. The data obtained from the surveys underwent analysis using the Pearson Product-Moment Correlation (PPMC) and regression analysis approaches. The present study offers empirical data and affirms the growing significance of financial reporting in the global economic landscape. Ensuring unwavering trust in the financial information pertaining to the public sector has considerable significance for investors. The article proposes that the establishment of a comprehensive framework of guidelines for enterprises' information technology infrastructure would be advantageous for regulatory bodies, such as the Jordan Central Bank. The aim of this method is to reduce the potential danger of the public sector being overwhelmed by outdated technology.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".