Do Clients’ Enterprise Systems Affect Audit Quality and Efficiency?
Bibliographic record
Abstract
Abstract Enterprise systems ( ES s) are widely used to support business processes along the enterprise value chain. It has been shown that ES s, by integrating business functions and making information about day‐to‐day activities available, enhance operational transparency and improve the internal information environment. However, while ES ‐based business infrastructures can offer many benefits, their prevalence and increased complexity have also brought new challenges to external auditors. Motivated by the prominence of this issue for auditors and regulators and by the scarcity of research jointly examining ES s and auditors’ work, we investigate whether the presence and extent of client firms’ ES implementations are related to the quality and efficiency of auditors’ work. Using proprietary archival data on ES implementations and controlling for self‐selection, we find that ES implementation improves the quality and efficiency of current and future years’ audit work. Specifically, there are fewer restatements, a greater likelihood of auditors issuing going‐concern opinions to firms that do not survive, higher accruals‐based auditing quality, a lower likelihood of Form 10‐K filing delays, and generally lower audit fees. We further show that the benefits of ES s generally increase with the scope of implementation and are generally greater when the ES includes accounting and finance systems. Inconsistent with improvement in the quality of auditors’ work, we find no evidence that ES s help auditors identify material weaknesses in advance of restatement announcements and we find that, even in the presence of ES s, auditors issue an excessive number of going‐concern opinions to clients that survive.
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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 | 0.022 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".