‘Closed Open Innovation’ or ‘Openly Closed Innovation’ – Which way is for world-first innovations?
Bibliographic record
Abstract
‘Open innovation’ strategy suggests the purposive use of knowledge inflows and outflows to accelerate internal innovation, and expand the markets for external use of innovation. The literature on ‘open innovation’ tends to agree that there are two dimensions of R&D projects can be used to evaluate their openness: (1) how much it relies on external knowledge resources; and (2) if it is internally or externally developed. To date, the literature on ‘open innovation’ has neither deliberated theoretically on nor tested empirically the effects of these two dimensions simultaneously in relation to innovation outcome measures. Therefore, the aim of this paper is to fill in this research gap in both theory and empirics. My typology is theoretically based on the Schumpeterian innovation types (Mark I and Mark II) on the one hand and the rich literature on firms’ external knowledge searching on the other hand. The Closed Open Innovation refers to those innovations that are internally developed but draw heavily on external scientific/technical knowledge. The Openly Closed Innovation refers to those innovations that are externally developed but meanwhile draw mainly on internal scientific/technical knowledge. The dataset used in this study is the Canadian Technological Innovation Dataset, which was obtained from a population survey for 1635 major industrial innovations in Canada during the period from 1945 to 1980. Based on a binary logistic regression model, the results show that totally closed innovation modes were positively associated with the likelihood of an innovation being worldfirst; meanwhile, the findings reveal an overall trend that innovation modes for world-first innovation had become less and less closed and a mix of open modes had become equally effective as the closed ones.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".