Impact of Corporate Governance on the Effectiveness of Accounting Information System in Jordanian Industrial Companies
Bibliographic record
Abstract
In this study, the impact of corporate governance factors on the effectiveness of accounting information system has been investigated considering Jordanian Industrial Companies. Authors considered the influence of corporate governance factors such as organisational vision, and goal translation, data-driven decision making, expertise and experience of governing committee, KPI based performance evaluation and effective collaboration between leaders and departments on the effectiveness of accounting information system. The effectiveness of accounting information system has been measured by the ease of use, security, storage, it's usability for delivery and decision making. The secondary data was collected using a questionnaire from 30 accounting firms in Jordan. Specialised software called Statistical Package for the Social Sciences (SPSS) has been used to analyse the data gathered and draw conclusions. Authors found that AIS is an effective tool for decision making and performance evaluation when management adopts the data-driven approach. However, for an effective AIS, it must be governed by subject matter experts who have expertise and experience is financial and accounting methodologies. Authors emphasised on the role of the leadership team in creating a clear vision and ASMART goals so that the AIS system can be aligned and help the departments to deliver on targets. It was observed that effective communication, collaboration among the leaders and departments significantly influence the effectiveness of AIS structure and it's ability to deliver results.
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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 | 0.022 |
| 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 | 0.003 | 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".