Eco-innovation and knowledge management: issues and organizational challenges to small and medium enterprises
Bibliographic record
Abstract
Abstract The proposed methodology is based on a (global and multi-criteria) simplified environmental but thorough assessment. In this stage we do not directly give the solution to designers. It will therefore translate the results of evaluation design axes, but in general, the lines proposed are inconsistent or contradictory. Therefore, what we find is a compromise given to the solution. The challenge we are facing in an industrial reality is that one should not go for a compromise solution. TRIZ (Teorija Reshenija Izobretateliskih Zadatch) or the theory of solving inventive problems, in the field, will be reformulated and go through the contradiction matrix and then intervene with the principles of interpretation resolutions to give possible solutions. To assist small and medium enterprises (SMEs) in their product development, the objective of this paper is to propose a methodological approach named Ecatriz, that will allow us to achieve our eco-innovative goal. The applicability of this method is justified by the many contradictions in the choices in a study of the life cycle. As a starting point, a qualitative multi-criteria matrix will allow the prioritization of all impacts on the environment. A customized implementation of the inventive TRIZ (Teorija Reshenija Izobretateliskih Zadatch, Russian acronym for theory of solving inventive problems) principles will help us choose eco-innovative solutions. To that end, we have created a new approach named Ecatriz (ecological approach TRIZ), based on a new contradiction matrix. It was tested in various contexts, such as the “24 h of Innovation” competition and eco-innovative patents.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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".