Assessing the Open Innovation Outcome in Living Labs
Bibliographic record
Abstract
This study introduces a model to assess the innovation outcome in living labs, which is a particular type of open innovation network. We propose that the outcome of open innovation activities in living labs can be assessed using a multiple linear regression model that builds on a set of empirically identified variables. Based on data from 26 living labs across four countries, we present a set of variables that can determine the conditions for co-creation in living labs and apply them in a multiple linear regression model to assess the innovation outcome of the living labs. The four pivotal variables include strategic intention, passion, knowledge and skills, and resources. All four variables have an equally positive effect on the innovation outcome. We also propose a maximum and an optimal number of participants to maintain passion in open innovation. While the paper advances scholarly research on open innovation by identifying and applying variables that impact the outcomes of innovation activities in living labs, practitioners can also apply the model to enhance innovation endeavours and targeted outcomes in living labs.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".