Explicating the role of innovation intermediaries in the “unknown”: a contingency approach
Bibliographic record
Abstract
Purpose Innovation intermediaries have become key actors in open innovation (OI) contexts. Research has improved the understanding of the managerial challenges inherent to intermediation in situations in which problems are rather well defined. Yet, in some OI situations, the relevant actor networks may not be known, there may be no clear common interest, or severe problems may exist with no legitimate common place where they can be discussed. The purpose of this paper is to contribute to the research on innovation intermediaries by showing how intermediaries address managerial challenges related to a high degree of unknown. Design/methodology/approach The authors draw upon the extant literature to highlight the common core functions of different types of intermediaries. The authors then introduce the “degree of unknown” as a new contingency variable for the analysis of the role of intermediaries for each of these core functions. The authors illustrate the importance of this new variable with four empirical case studies in different industries and countries in which intermediaries are experiencing situations of high level of unknown. Findings The authors highlight the specific managerial principles that the four intermediaries applied in creating an environment for collective innovation. Originality/value Thereby, the authors clarify what intermediation in the unknown may entail.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".