The Impact of Cyber Governance in Reducing the Risk of Cloud Accounting in Jordanian Commercial Banks - from the Perspective of Jordanian Auditing Firms
Bibliographic record
Abstract
The objective of this study is identifying the impact of cyber governance on reducing the risk of cloud accounting in the Jordanian commercial banks. To achieve the objectives of this study, the descriptive and analytical approach was used; the study community is composed of external legal accountants who practices auditing in Jordan, (477) of them are practicing external auditing at the end of (2018) according to the statistics of the Jordanian association of certified public accountants (JACPA). Due to the difficulty and cost of the comprehensive survey, a simple random sample was taken. The sample included (213) auditors. The questionnaire was distributed to the sample of the study by the researchers personally and through e-mails, (182) questionnaires were recovered, after excluding (7) for the incompetence, of which (175) were valid for the statistical analysis. Thus, the percentage of retrieved and analyzed questionnaires was (82.2%), which is statistically acceptable. and In order to analyze the study data and test hypotheses, the statistical package for social sciences (SPSS) was used in the various statistical analyses, which are the descriptive statistics and coefficient of internal consistency (Cronbach's alpha). also, The multiple correlation test was used, using the Pearson correlation coefficient, Multiple linear regression and stepwise regression analysis. The study came to find several results , the most important was The presence of a statistically significant impact of cyber security governance (cybersecurity security governance requirements, cybersecurity program, cyber security policy, cyber information management, evaluating and managing cyber risks) in reducing cloud accounting risks in Jordanian commercial banks, The most important recommendations are the need for Jordanian commercial banks to adopt the cyber governance as a basic reference to their banking policy to address the risks associated with the use of cloud accounting, As well as the need to establish a special department for human resources management within the bank which would have a pioneering intellectual orientation to cope with modern trends in cyber governance.
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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 | 0.008 |
| 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 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".