Local Open Innovation: How Spatial Closeness Facilitates Profiting from Distant Search
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
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.005 |
| 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