The Role of Technological Readiness in Enhancing the Quality of Audit Work: Evidence from an Emerging Market
Bibliographic record
Abstract
This study examines the impact of remote audit quality (RAQ) on the quality of audit work (QAW). Further, it explores the moderating effect of both client technological readiness (CLTR) and auditor technology readiness (ADTR) on the link between RAQ and QAW. Data were collected through a questionnaire survey distributed to all external auditors working in Egypt. The final sample consists of 280 auditors. The data were analyzed with smart partial least squares (Smart-PLS) software. The results showed that RAQ has a positive and significant impact on QAW. Moreover, the results revealed that CLTR and ADTR moderate the relationship between RAQ and QAW. CLTR was found to have a positive moderating role, as CLTR was found to strengthen the relationship between RAQ and QAW, while ADTR was found to have a negative moderating role, as ADTR was found to weaken the relationship between RAQ and QAW. The findings can provide a pivotal yardstick for guiding companies, auditing firms, auditing professional bodies, and regulators in the Egyptian context. Positioned as one of the early studies to concentrate on the moderating role of CLTR and ADTR in the relationship between RAQ and QAW, this research suggests insights within an emerging market context.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".