The moderation of trust on the relationship between TOE factors and generalized audit software usage and financial performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it