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Record W4400473402 · doi:10.1111/1911-3838.12367

An Exploration of Technological Innovations in the Audit Industry: Disruption Theory Applied to a Regulated Industry*

2024· article· en· W4400473402 on OpenAlexaffvenue
Krista Fiolleau, Carolyn MacTavish, Errol Osecki, Linda Thorne

Bibliographic record

VenueAccounting Perspectives · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of OttawaWilfrid Laurier UniversityYork UniversityUniversity of Waterloo
Fundersnot available
KeywordsAuditDilemmaBusinessProfitability indexMainstreamIndustrial organizationPerformance auditInformation technology auditMarketingAccountingInternal auditJoint auditFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.273
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes2
Has abstractyes

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