REASONS AND TRIGGERS USING RESEARCH RESULTS IN CORPORATE PRODUCT ENGINEERING
Bibliographic record
Abstract
In product engineering, the development of new innovations is a big challenge due to the high complexity of nowadays systems. Research results are a high-potential input for companies to still advance their technologies and solutions. However, using research as a source of new knowledge and technologies is a non-trivial process that can often be neglected by many companies. The goal of this investigation is to gain and provide an understanding of the specific triggers and reasons for corporate product engineers to use research results as input in their development process. Based on nine semi-structured interviews with experts from different engineering companies, we present a model that explains these reasons, triggers, and situations to use research results. We believe that our model will help to raise the awareness of corporate product engineers to the potential of using research results within their design activities. Furthermore, the outcome of this investigation provides a basis for further research to improve the usability and actual usage of research results in corporate product engineering. Therefore, in our further research, we will use these results for developing decision support for corporate engineers to decide when to look into research.
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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 | 0.174 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".