The moderation of trust on the relationship between TOE factors and generalized audit software usage and financial performance
Bibliographic record
Abstract
The importance of Generalized Audit Software (GAS) is particularly important for nations' development. Picking 'Over Conduct Theorized Results and Consequences' Poly GAS as a test subject, results have been inconsistent in previous studies on predictor variables and consequences of using GAS. This study aims to investigate the predictors and consequences of using GAS. From the perspective of Resource-oriented technology (Approach (TOE) fitness - View (RBV Environment), It is intended that technology's relative advantage, compatibility, and complexity, as well as organizational readiness top management support IS committee) Villa have an important influence on GAS, which in turn is expected to affect financial performance. Trust will serve as a moderating variable between technological and organizational factors, and GAS. Profession MB. This counts all audit firms in Jordan. The research questionnaire was distributed by purposive sampling for subsequent investigation. As many as 210 valid questionnaires out of all completed questionnaires were gathered from this study by using Smart PLS as the data analysis software. Technological relative advantage, compatibility, and complexity as well as organizational readiness (top management and organizational readiness) have a significant effect on GAS which in turn affects financial performance. Accepted Southern Trustor did not affect the impact of technology and organization factors in GAS. So, the results can provide some ideas to policymakers in Jordan about how to foster the use of GAS to improve financial performance and bring down the new technology adoption costs.
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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 | 0.004 | 0.020 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".