Collaboration challenges in multidisciplinary audit teams for public sector assurance
Bibliographic record
Abstract
Purpose This study examines how multidisciplinary audit teams (MATs) collaborate in sustainability assurance engagements, focusing on an Australian government organisation responsible for natural resource management assurance. Specifically, it explores how MATs are resourced, how diverse expertise is integrated and managed, and the collaboration challenges arising from this diversity, as well as their perceived impact on assurance quality. Design/methodology/approach A qualitative case study approach was undertaken, drawing primarily on semi-structured interviews with multidisciplinary audit team members, including auditors, managers and contractors. Resource diversity theory was applied as a lens to examine how MATs were composed, managed and the collaboration challenges they encountered in assurance practice. Findings The study highlights that diverse expertise within MATs can enhance assurance quality, but these benefits are contingent on strategic planning, effective communication, and strong leadership. Where engagement was limited and communication unclear, tensions arose that undermined trust, impeded knowledge transfer and restricted collaboration, ultimately diminishing the effectiveness of MATs. Research limitations/implications The study highlights the importance of structured engagement strategies, robust communication protocols, and leadership development in enabling MATs to realise the benefits of multidisciplinarity. By strengthening these areas, organisations (private and public) can improve collaboration, build trust and integrate diverse knowledge more effectively to enhance the quality of sustainability assurance. Originality/value This study provides rare in-field insights into the collaborative dynamics of MATs in sustainability assurance in the public sector. It identifies the organisational conditions necessary for leveraging multidisciplinary expertise and addresses a significant gap in the literature on the effectiveness of audit team diversity.
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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 | 0.077 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".