Bibliographic record
Abstract
The paper explores the limitations of innovation in clusters, proposing that innovation advantages of clusters are contingent upon technological paradigms. Technological paradigms manifest in the heuristics of ‘how to do things’ and ‘how to improve them’ in a domain, embedded in organizational routines. The paper argues that new product development routines can be enacted in clusters, turning into cluster-based routines. Cluster-based routines are efficient in guiding search for rapid solutions within established technological trajectories but become ineffective during paradigm shifts. Consequently, cluster-based routines tend to promote paradigm-bound innovation rather than paradigm-setting innovation. Using an original, product-level database of mobile handsets in China from 2007 to 2016 — a period which witnessed a paradigm transition from feature phones to smartphones — the study presents robust evidence that being in a dominant cluster in Shenzhen has a positive impact on product innovation in the feature phone regime but casts significantly negative effects on paradigm transition and subsequent innovation in the smartphone era. The findings indicate that the temporal and spatial processes of innovation are deeply interwoven. • New product development routines can be enacted in clusters. • Cluster-based routines facilitate paradigm-bound, not paradigm-changing, innovation. • Innovation policies need to support non-cluster areas.
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.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".