The Effect of Business Process Representation Type on Assessment of Business and Control Risks: Diagrams versus Narratives
Bibliographic record
Abstract
ABSTRACT The Sarbanes-Oxley Act (SOX) (U.S. House of Representatives 2002) mandates the assessment of internal controls over financial reporting, and many organizations are using diagrams to document their internal control processes. While educators regularly stress the effectiveness of diagrammatic representation of process information over textual representation, no prior study has offered convincing evidence that diagrammatic representation leads to improved performance. In an experiment, we examine students' performance on a business process risk and control assessment task using two informationally equivalent methods that are commonly taught in the classroom to document business processes: descriptive narrative (hereafter, textual) and diagrammatic. We also examine whether students' academic achievement and perceptions of their ability (self-efficacy) affect performance by type of representation. First, we find that while the method of representation has no effect on students' accuracy, those receiving the textual representation were more efficient and had a greater weighted-average performance than those receiving the diagrammatic representation. Second, we find academic achievement increases students' accuracy, decreases their efficiency, and has no effect on their weighted-average performance. Third, we find self-efficacy has no effect on students' accuracy, has no effect on their efficiency, and decreases their weighted-average performance. Finally, we find that both self-efficacy and academic achievement interact with the type of representation to affect students' performance. Implications for education and practice are discussed.
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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 | 0.011 | 0.199 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".