The Data Analytics Journey: Interactions Among Auditors, Managers, Regulation, and Technology*
Bibliographic record
Abstract
ABSTRACT Data analytics is transforming our global markets and significantly impacting the financial reporting environment. We investigate how auditors, company managers, and regulation interact with data analytics and one another to affect the diffusion (i.e., development and spread) of data analytics throughout the financial reporting environment. We interview company managers and their audit partners, as well as additional stakeholders, including regulators. We interpret findings from our interviews using theory that highlights the importance of dynamic interactions between people and their environments, which include the prevailing rules (e.g., regulatory guidance). Our findings contribute to the accounting literature and practice by revealing three areas of conflict emerging from stakeholders' disparate preferences for data analytics. First, we uncover growing tensions between managers and audit partners regarding audit fees. Second, we find that managers and auditors believe the lack of accounting regulation specific to data analytics causes confusion and frustration. Finally, auditors report that they strategically leverage data analytics to provide clients with business‐related insights. However, regulators voice concerns that this practice might impair auditor independence and reduce audit quality. These areas of conflict suggest a need to revisit key tensions surrounding the audit function in a contemporary context characterized with significant technological shift.
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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 | 0.054 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.032 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".