User Innovation and European Manufacturing Industries: Scenarios, Roadmaps and Policy Recommendations
Bibliographic record
Abstract
Recently the emergence of more open innovation models which draw on a greater diversity of distributed knowledge sources often including users and customers has received growing attention not only in academia and industry but also in policy circles. Many governments have launched initiatives to explore how to benefit from these developments and how to support companies in thier adoption (e.g. the EU lead market initiative, Danish User Innovation Lab duci.dk/). The rationales for these activities are manifold. First of all, policy makers are regconising the growing relevance of open innovation models which is being driven by changing sicio-economic framework conditions on the one hand and availability of enabling technologies such as innovation interfaces, connecting platforms and rapid manufacturing technologies on the other (v. Hippel 2005). Accordingly, policy makers strive to enable companies to unlock the potential benefits by adopting concepts of user involvement such as the lead user strategy (Luthje and Herstatt 2004). At the same time it has been shown that the adoption of democratized innovation models is likely to yield substantial benefits for welfare (Henkel and v. Hippel 2005). Additionally, the empowerment of innovating users is responding to a regconised societal demand with a high potential to increase quality of life in many domains where the uptake of users centered innovation models will better match the high diversity of user needs and the growing demand for creative experience. Finally, for industrial policy there is a very concrete motivation behind the interest in such innovation models. In the face of increasing relocation of manufacturing activities to low wage production sites, concepts of production and consumption patterns that the place large part of the value chain close to the customer such as distributed production in mini factories are becoming increasingly attractive to keep jobs and access to high quality products within the country. In many high wage locations where whole sectors have been disappearing, policy initiatives towards personalized production and customer integration are motivated by this goal (e.g. for US and Canadian furniture industry cf. Lihra et al. MCPC 2007). To sum up, there are many good reasons for policy makers to support the transition towards democratized innovation models within economy and society. However, to achieve this goal tailored and efficient policy actions aligning research and innovation policy with measures from other realms such as IPR and regulation are needed (Chesbrough 2006, v. Hippel 2006).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".