One Team or Two? Investigating Relationship Quality between Auditors and IT Specialists: Implications for Audit Team Identity and the Audit Process
Bibliographic record
Abstract
ABSTRACT While prior research focuses on the audit team made up of auditors, we focus on the collective audit team made up of auditors and specialists—in our context, information technology (IT) specialists. Complex systems in today's audits and researcher and regulator concerns regarding ineffective coordination and communication between the two specializations motivate better understanding of this collective audit team. We investigate how auditors and IT specialists perceive their relationship and how the audit process unfolds when these relationships are good and when they are difficult. Results of interviews conducted with Big 4 audit and IT practitioners provide evidence that they perceive their relationship quality to depend on the level of mutual value and respect. Auditors assert a one‐team view of the collective audit team that includes IT specialists, but IT specialists feel auditors see them as a separate team and a “necessary evil.” The audit process vastly differs between relationships perceived as difficult and good. In difficult relationships, the two specializations often struggle for status, with limited communication or effort to understand how their work fits together. Our findings imply difficult relationships are at risk for poor integration and unsupported reliance on IT functions, shedding light on recurring threats to audit quality identified by PCAOB inspections. In good relationships, auditors and IT specialists appear motivated to engage in frequent and open communication to help understand, coordinate, and complete the audit. Inferences gleaned from good relationships let us highlight prescriptions for audit firms to improve effectiveness of collective audit teams.
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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 | 0.062 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".