On the notion of soft‐goals in business process modeling
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The paper aims at providing a conceptual framework based on clearly defined concepts and notions, which integrates goals into process modeling and specifically distinguishes goals from soft‐goals or business measures. The application of this framework facilitates a systematic use of soft‐goals in process design. Design/methodology/approach The framework is developed on the basis of Bunge's well‐established ontology. It is applied to processes taken from the SCOR supply chain reference model for demonstration and evaluation. Findings Applying the framework to the SCOR processes resulted in a set of focused relations between soft‐goals and processes, as opposed to the ones suggested originally in the SCOR model. This demonstrates the usefulness of the framework in process design. Research limitations/implications The approach presented in the paper is still rather a theoretical framework than a fully validated procedure. It should be tested on larger‐scale cases in more practical settings and evaluated accordingly. Practical implications Applying the clearly defined concepts of the framework and the suggested analysis procedure is expected to lead to focused and applicable measures tied to business process during process design, and provide a basis for process measurement requirements to be supported by an information system. Originality/value The contribution of the paper is both theoretical and practical. It provides clear‐cut ontology‐based definitions to concepts which so far have been assigned fuzzy and ambiguous meaning and uses these definitions for systematically tying business measures to business processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it