The Effect of Applying Analytical Procedures on Understanding Business Environment in Light of Using Accounting Information Systems in Auditing
Bibliographic record
Abstract
Since the entire world is moving with a steady acceleration to invest the technology in various fields and professions, exploring the effectiveness of these technologies in achieving the desired goals became an imperative need. Accordingly, the strengths and weaknesses that surround these systems could be identified. In this vein, the current study aimed to explore the effect of using client's accounting information systems by auditors on the effectiveness of analytical procedures in understanding business environment of entities, assessing the going concern of entities, and estimating potential misstatements in financial reports. To carry out the current study quantitative approach was adopted. Thus, a survey questionnaire was designed and distributed to 164 qualified and unqualified person who work in auditing profession in Jordan. In order to test hypotheses, a multivariate regression analysis tests were performed. The results showed that using client's accounting information systems by auditors has a significant positive effect on the relationship between analytical procedures and understanding business environment of entities, and estimating potential misstatements in financial reports. Conversely, the results showed that using client's accounting information systems by auditors has a significant negative effect on the relationship between analytical procedures and assessing the going concern of entities. The results imply that, auditors should benefit from using clients' accounting information systems in enhancing their understanding of business environment and estimating potential misstatements in financial reports. On the other hand, they should not depend extensively on these systems in assessing the going concern of these entities.
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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 | 0.032 | 0.131 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".