Local Open Innovation: How Spatial Closeness Facilitates Profiting from Distant Search
Bibliographic record
Abstract
In this paper, we complement the dominant focus of open innovation (OI) research on global networks with a local perspective. Prior research has developed and evaluated multiple OI techniques and approaches to connect an innovating organization effectively with research institutions, entrepreneurs, academia, and firms from different industries, for example using the crowdsourcing mechanism. Yet, despite the fruitful access to a global network of knowledge sources and potential collaboration partners, firms face manifold barriers to profiting from such a distant search. Therefore, we propose a Local Open Innovation (LOI) approach, purposefully reducing the spatial distance between knowledge seekers and solution providers to balance between the benefits of distant search and local closeness. Spatial proximity, i.e. collaborative face-to-face group work and problem-solving experiences, positively affects trust within a local innovation network, increasing the chances for further collaborations after an initial crowdsourcing activity. Our research is grounded in an extensive, longitudinal qualitative study of several LOI event facilitated by a Canadian intermediary who developed and implemented a LOI approach. Our analysis finds that LOI can overcome innovation challenges of incumbent companies, stimulate creativity, foster distant search, and, as our findings show, in many cases lead to organizational development and change towards openness and new internal structures for innovation management.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".