The effectiveness of electronic auditing on improving the financial performance: Evidence from the Jordanian banking industry
Bibliographic record
Abstract
The purpose of this research is to find out the effectiveness of E- auditing in improving financial performance at banks operating in Jordan by identifying the association between financial performance, (ROE, Profitability) and E- auditing. To attain the study objectives, A cross-sectional survey method was used to collect data from a sample of employees who work in banks, The preliminary data was collected using electronic structured Likert-Scale questionnaires. The population comprised 25 banks operating in Jordan with 160 employees in the accounting departments. A total of 113 questionnaires were completed and returned electronically from accountants who work in the banks. The SPSS statistical programs were used to analyze the data collected; the results of the analysis found that E- auditing significantly improves the financial performance at banks operating in Jordan. The study concludes that banks should adopt the effective and expanded use of E- audit as it gives independent, truthful, and trusted financial audit information. Banks in Jordan should embrace the effective and expanded use of E- auditing to assure detection of financial fraud, thus sealing routine financial loopholes.
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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 | 0.005 | 0.017 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".