How to Evaluate Managerial Cognition and Avoid Traps in Open Innovation
Bibliographic record
Abstract
Organisations find it difficult to assess ‘purposeful’ knowledge sourcing and sharing (KSS) for open innovation (OI), limiting full potential of KSS. We present thematic insights from 22 cognitive interviews with OI managers exploring their beliefs, perceptions, and actions during KSS activities and propose a practical framework by aggregating perception and knowledge of best practices. With illustrative quotes, a practical example case, and visual cues, we situate discussions on the common OI traps and provide clear implications for managers and organisations contemplating engaging in OI. We hope our framework provides new insights and pathways at the OI-cognition nexus.
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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 | 0.055 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".