Bibliographic record
Abstract
Purpose Previous work has explored the ability of auditors to expand successfully into seemingly unrelated fields, referred to as new audit spaces. The present paper focuses on how auditors respond to challenges when entering a new audit field and devising strategies to sensemake and sensegive about those challenges. Design/methodology/approach This study builds on findings from 32 interviews with auditors and participant observation of interactions between auditors and blockchainers to understand how auditors approached a new audit space. Findings We find that when auditors enter a new audit space, they endeavour to impose a logic of auditability. First, they determine an acceptable knowledge basis for this target audit space by developing a codified set of rules to organize knowledge, then develop a codified set of practices to verify conformity to the auditor’s set of rules. Next, auditors engage in three strategic tactics to influence members of the target audit space: appealing to the financial benefits of adopting a logic of auditability; appealing to their credentials from established audit markets; and appealing to bona fides in the target audit space to establish credibility. We posit that these sensemaking and sensegiving strategies do not take hold in the blockchain space because auditors are approaching these activities from a different mental model than blockchain natives. Because auditors are unable to adopt the mental model of the blockchain space, they are unable to devise strategies to compellingly influence blockchain natives and secure a stronghold in this new audit space. We developed a model for sensemaking and sensegiving when auditors enter new audit spaces. Originality/value This paper challenges and contrasts prior accounts of the seemingly unending expansion of audit firms into new spaces. The study demonstrates that there are limitations to auditors’ abilities to transplant their verification skills in the blockchain field.
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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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.012 |
| 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".