The effect of network structure on radical innovation in living labs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study aims to focus on living labs as a means of achieving radical innovation by discussing the differences in their network structure and its effect on the type of innovation outcome. Design/methodology/approach This research analyses 24 living labs in four countries using qualitative methods. Findings A specific network structure referred to as a distributed multiplex supports radical innovation in living labs, while distributed and centralized network structures support incremental innovations. Also, the results suggest that radical innovation depends on the driving actor and objectives in a living lab. Research limitations/implications A bias on the perceived novelty of innovation may exist when analyzing data collected through interviews with a limited number of living lab participants compared to a large number of informants. This study proposes a two-dimensional framework based on the network structure to investigate innovation in living labs. Practical implications This paper offers a classification tool to identify, categorize and make sense of organizations’ participation in open innovation networks and in living labs. Originality/value The study provides evidence that, although the distributed multiplex network structure supports the emergence of radical innovations, the distributed and centralized network structures support incremental innovation. A combination of a provider- or utilizer-driven living lab and a distributed multiplex network structure, with a clearly defined and future-oriented strategic objective, offers good potential for radical innovation to occur.
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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 | 0.008 |
| 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 | 0.000 |
| 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 it