An Exploration of Technological Innovations in the Audit Industry: Disruption Theory Applied to a Regulated Industry*
Bibliographic record
Abstract
ABSTRACT Technological innovation is increasing throughout the audit industry. Although prior research has explored how specific technological innovations have influenced the audit product and the profitability of the audit, the strategic implications of technological innovation for the audit industry have yet to be examined. To address this issue, we adopt Christensen's seminal theory of technological innovation (introduced in his 1997 book The Innovator's Dilemma) to gain insight into the results of 27 semistructured interviews with auditors and audit technical specialists. Consistent with Christensen's sustaining and efficiency strategic responses, our findings suggest that, at this time, technology is primarily being used by the audit industry to strengthen the audit industry's ability to serve mainstream clients by providing a “higher‐quality” and lower‐cost audit to replace menial tasks that historically have been done by junior auditors. We find that industry‐disruptive new market entry is currently prevented by regulatory and professional barriers; however, strategic disruption to the audit industry appears inevitable as technology is already being used in audits of nonregulated markets by new entrants. Strategically, the audit industry will survive in its current recognizable form only if self‐disruption occurs before the regulatory barriers are dropped, which requires significant upskilling in the industry to ensure that firms have the skills to be first movers whenever technological innovations are introduced.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".