Innovation capability building through intermediary organizations: cases of manufacturing small- and medium-sized enterprises from China's Zhejiang province
Bibliographic record
Abstract
Along with a continued expansion of the innovation landscape, collaboration with external partners becomes an increasingly critical strategy for organizations to acquire necessary resources from the outside when building their innovation capability. Based on three representative manufacturing small- and medium-sized enterprises (SMEs) in China's Zhejiang province, this study explores the evolution of the SMEs' unique organizational forms in response to such external collaboration. In particular, these organizational forms begin with a functional-level ‘technology department’ that subsequently evolves into a strategic-level ‘research centre’ that acts as a ‘quasi-intermediary organization’ within an existing organizational boundary and eventually becomes an independent professional ‘technology company’. Although, in general, the collaboration with universities and research institutes facilitates both exploratory and exploitative types of innovation capability building, this study further finds that such collaboration via a ‘technology department’ is the least effective. Furthermore, collaboration via a ‘research centre’ strengthens (weakens) the effect on the exploratory (exploitative) type of innovation capability building. Meanwhile, the collaboration via a wholly owned ‘technology company’ strengthens (weakens) the effects on the exploitative (exploratory) type of innovation capability building. Importantly, the collaboration via a joint-venture ‘technology company’ with universities and research institutes facilitates the ambidextrous exploratory and exploitative type of innovation capability building.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".