Living Labs: From Niche to Mainstream Innovation Management
Bibliographic record
Abstract
Living Labs have received increasing attention over the last decade. However, despite their growing popularity and ability to positively impact organisations’ innovation performance, mainstream innovation management literature has overlooked the diverse and promising Living Labs research landscape. In an effort to move the field forward, this study analyses extant Living Labs literature in the domain of innovation management. The study identifies conceptual bases informing Living Labs research, maps the collaboration between scholars in the field, examines prevailing themes influencing the debate and reveals the influence of Living Labs research on other domains. Bibliometric methods of co-authorship, keyword co-occurrence analysis as well as bibliographic coupling are employed on two databases. Database A includes 97 focal journal articles and Database B includes all cited sources of Database A, totalling 500 documents. This study reveals the rapid growth of the scholarly literature on Living Labs in the innovation management domain, driven by a core group of authors. However, other contributions from highly visible scholars have the potential to connect Living Lab research to mainstream innovation management studies. The study also identifies the influence of Living Labs research in different application fields and potential for its further evolution.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".