Managing Perceptions of Technical Competence: How Well Do Auditors Know How Others View Them?*
Bibliographic record
Abstract
Abstract We investigate factors that influence an auditor's accuracy in knowing how subordinates, peers, and superiors view his or her own technical competence (metaperception). Extant literature on reputation management in auditing contexts depicts preparers of audit workpapers as strategic agents (subordinates) who stylize workpapers and engage in behaviors that enhance their reputations with reviewers (superiors). These superiors, in turn, are represented as strategically engaging in coping behaviors in response to such stylization attempts. One of the necessary conditions for auditors to enhance their reputations on a sustainable basis is accurate metaperception. We report the results of an experiment that investigates determinants of auditors' metaperception accuracy. Our participants comprise teams of audit partners, managers, and seniors who work together in the field. Each auditor performs two tasks of varying complexity and then predicts whether other team members can accurately perform the task and how other team members assess his or her performance on the tasks. Results show that accuracy in knowing what others think of one's technical proficiency (metaperception) is generally high, particularly when the predictor auditors are partners and managers; however, metaperception accuracy is asymmetric and varies depending on the predictor auditor, the target auditor being predicted, and task complexity. Implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".