Analyzing the influence of TOE factors on e-auditing adoption in audit firms: The moderating effect of trust
Bibliographic record
Abstract
The rising usage of E-Auditing and its effect on businesses through new technology developments and rules demonstrates how audit systems can maximize operational efficiency and business decision quality. Researchers are undergoing a study to determine E-Auditing acceptance rates. The TOE model represents “Technological, Organizational, and Environmental” variables that function as key examination areas in organizational analysis and management practices when researchers study technological implementation and adoption patterns in industrial environments. The research proposes that these three aspects (technical aspects with both Relative advantage (RA) and Technology Compatibility (TC) and organizational aspects including top management support (TMS) and readiness (R)) along with environmental aspects such as competitive pressure (CP) contribute to e-auditing adoption. Auditor trust appeared in this study as the suggested moderating factor. A total of 235 participants provided info outside random sampling while the analysis used SPSS software. The experimental results proved that factors associated with TOE provide legitimate grounds for E-auditing acceptance. Evidence demonstrates that TOE variables provide justification for why organizations would accept E-auditing technology. Data show that trust functions as a supportive variable for the relationship between TOE and e-auditing but provides minimal strength. E-Auditing adoption research needs further investigation within emerging economies to understand better how users adopt this tool. The objective for decision-makers should focus on expanding user understanding of E-Auditing adoption along with educating decision-makers about the benefits of implementing this system.
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How this classification was reachedexpand
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".