Conceptualizing Routing Decisions in Business Processes: Theoretical Analysis and Empirical Testing
Bibliographic record
Abstract
Business process models are widely used for purposes such as analyzing information systems, improving operational efficiency, modeling supply chains, and re-engineering business processes. A critical aspect of process representation involves a choice among alternative or parallel routes. Such choices are usually represented in process models by routing structures that appear as “split” and “merge” nodes. However, evidence indicates that modelers face difficulties representing routing options correctly. Clearly, errors in representing routing options might negatively affect the effective use of business process models. We suggest that this difficulty can be mitigated by providing process modelers with a catalog of routing possibilities described in terms that are meaningful to analysts. Based on theoretical considerations, we develop such a catalog and demonstrate that its entries have business meaning and that it is complete with respect to a defined scope of process behaviors that do not depend on resources or on software features. The catalog includes some routing cases not previously recognized. We tested experimentally the catalog in helping subjects understand process behavior. The findings demonstrate that the catalog helps modelers understand and conceptualize process behavior and that the likely reasons are its completeness and the practical terms used to describe its entries.
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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 | 0.053 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.029 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".