Open innovation practices adopted by private stakeholders: perspectives for living labs
Bibliographic record
Abstract
Purpose – This paper aims to explore the role of private stakeholders in the living lab (LL) ecosystem and the relationship of private stakeholders to open innovation (OI) practices. There is extensive literature on private stakeholders and OI, but seldom mention is made on the specific question of how private stakeholders integrate OI in the context of a LL. Design/methodology/approach – The authors will analyze qualitatively how private businesses that have participated in ain situopen innovation evaluate and perceived their open innovation practices. Therefore, how they relate to open innovation. Then, the authors will identify a typology of the businesses in relation to OI. Findings – The research focused on the relationship of private stakeholders to OI in the context ofin situOI activities. The results obtained are consistent with literature on OI (Chesbrough, 2003). However, there are differences: if the elements mentioned by the respondents are described in literature, their representation of OI and its components allows us to affirm that this practice is not generalised and that it is often open to interpretation. That emphasises the importance of the role LLs can play as intermediaries to accompany private stakeholders in the OI process. Private stakeholders look for a guide to develop their OI know-how and find their way in the OI ecosystem. Originality/value – The value of this paper is to bridge the research on OI done with private organisation and the research on LLs. The research literature did not pay much attention to the representation of the private stakeholders in the OI ecosystem. This paper has provided the start to open up that field.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".