How workplace identities and team management practices affect distributed team auditors' willingness to speak up
Bibliographic record
Abstract
Abstract Distributed, rather than co‐located, teams increasingly perform audit work, raising regulator concerns that distributed team communication issues may affect audit quality. We investigate upward communication (i.e., raising issues to supervisors), a key communication dimension related to audit quality. In Study 1, we survey 69 senior auditors to establish that distributed team upward communication suffers. Furthermore, distributed team auditors identify less with their teams and struggle to know when and how to speak up. Because both are linked to inhibited upward communication, we next experimentally test firm practices with the potential to attenuate these problems. Study 2, an experiment with 128 staff auditors and interns, reveals that making auditors' professional identities salient improves upward communication on co‐located teams with strong team identities but not on distributed teams with weak team identities. Study 3, an experiment with 58 staff auditors, replicates a key Study 2 finding with a design that makes no reference to team identity strength. Specifically, in the presence of a salient professional identity, upward communication is significantly higher for co‐located relative to distributed team auditors. Study 4, an experiment with 69 staff auditors, focuses solely on distributed teams. It indicates that several distributed team management practices, including one‐on‐one and standing daily meetings, encourage upward communication. Finally, Study 5, informal interviews with eight audit seniors, corroborates key survey and experimental findings. Combined, our results provide insights into why distributed team communication suffers, refine audit voice theory, and provide regulators, practitioners, and researchers with multiple paths to improve audit quality.
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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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".