Living Labs: From Niche to Mainstream Innovation Management
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it