Learning the “Craft” of Auditing: A Dynamic View of Auditors' On‐the‐Job Learning
Bibliographic record
Abstract
Abstract We investigate how auditors learn the technical aspects of their professional role while performing client engagements, and how that learning process has been shaped by changes in societal, economic, and regulatory forces. Prior studies explicitly recognize that auditors need social skills and demeanor consistent with professional norms as well as requisite knowledge, but those studies generally focus on the processes through which new auditors are molded toward consistency with social norms. In contrast, we focus on forces affecting the transfer of technical knowledge from supervisor (guide) to subordinate (learner) in the everyday work setting. Our evidence derives from semi‐structured interviews with 30 relatively new and more experienced audit partners at one Big 4 firm, thus spanning multiple “generations” of experience. Results confirm that auditors primarily acquire technical knowledge on the job, through the interactions among individual engagement team members. However, partners express concern about changes in the practice environment that may limit effectiveness of on‐the‐job learning, including characteristics of personnel, the approach to formal training at induction, supervisors' reluctance to provide candid feedback, regulatory and economic pressures, and the increased distraction, and reduced interpersonal contact associated with the use of information technology. At the end of the day, our findings raise implications for practice regarding the difficulty of developing effective learning conditions for auditors in the face of these challenges.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".