Understanding How Big Data Technologies Reconfigure the Nature and Organization of Financial Statement Audits: A Sociomaterial Analysis
Bibliographic record
Abstract
This study focuses on the recent development in audit technologies, i.e., the rise of Big Data and Analytics (BDA) tools, and how auditors make use of them in audits. While prior audit studies have acknowledged that audit technologies shape and re-construct the market for audit services, they have not devoted much attention to the performative nature of such technologies and how their properties may shape the dynamics of technological change. Drawing on sociomateriality literature as well as observations, documentary materials and 25 semi-structured interviews with individuals directly engaging with BDA, this study explores how BDA users interact with particular properties of the technology in the course of an audit. We then consider how these interactions reconfigure aspects of the audit process and change the relational dynamics within audit firms. In particular, our findings suggest that properties of BDA such as scripts have afforded large-scale automation of audit routines, generating opportunities for expanding the evidential scope and depth of audit work. Further, we also show how the visualization dashboards have contributed to auditors’ ability to communicate and justify their claims and judgements. Finally, we demonstrate that BDA has reshaped the nature of work relationships and flows between audit firms’ different functions and service lines.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".