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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".