Business Modeling to Improve Auditor Risk Assessment: An Investigation of Alternative Representations
Bibliographic record
Abstract
ABSTRACT This study investigates the effects of alternative methods for documenting business models on audit risk assessment behavior. We consider tabular versus diagrammatic representations of the relationship between business model components such as environmental factors, strategic goals, internal processes and resources, and financial statement accounts. Multiple scenarios based on a real company were constructed and 24 participants, including audit partners, managers, and novice auditors performed a risk assessment for each scenario, presented in either a diagrammatic or a tabular format. The participants' verbal discussions as they performed the risk assessments were tape recorded, transcribed, and coded. A content analysis of the participants' coded verbal behavior indicates that the tabular presentation appears to elicit more frequent mention of accounts by the participants, while the diagram format leads to more mentions of other business model components. There is also some evidence of expertise effects. This study indicates that a tabular presentation can possess many of the benefits often associated with a diagrammatic representation. However, in our study, obtaining such benefits involved the deliberate structuring of the tabular presentation to organize the components of the business model and the links between them and financial statement accounts.
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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 | 0.021 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".