The effect of network structure on radical innovation in living labs
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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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".