Modeling Business Process Knowledge Using a Combined DEMATEL Approach and Fuzzy Network Analysis Process
Bibliographic record
Abstract
One of the most valuable assets of an organization in today's competitive world is knowledge resources because they can create value. Knowledge management aims to identify and manage the organization; part of the valuable organizational knowledge is related to business processes. In this study, a framework for identifying and modeling processes is presented. Different dimensions of knowledge affect business processes and ultimately affect the value of the organization. To investigate and confirm the dimensions of knowledge, a process of fuzzy network analysis and integration of Demeter multi-criteria decision-making methods has been used to find cause-and-effect relationships and prioritize the dimensions and examples of knowledge. First, the key and valuable processes of the organization is identified during the map and then a model including the dimensions of knowledge such as knowledge input to the process, knowledge of the environment outside the process, knowledge during the process, Knowledge of the process output and Knowledge about the process has been developed. Next, dimensions, knowledge related to each dimension were presented. After analyzing the matrix, it was observed that Knowledge from the outside environment, knowledge about the process, and Knowledge during the process have the most significant impact on the organization's value, respectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".