How Interactions Among Peripheral and Central Actors Shape Action in Health Innovation Ecosystems.
Bibliographic record
Abstract
We examine how interactions between peripheral and central actors facilitate innovation efforts in innovation ecosystems through an exploratory, qualitative case study of a new type of central leader and its surrounding SMEs in the healthcare context. Our study makes a threefold contribution: we identify and describe an understudied type of ecosystem leader (a faciliatory ecosystem leader); we propose a framework describing the interactions between the central leader and peripheral actors; and we identify the presence and intertwining of emotions that arise from these interactions. We show how interactions between the ecosystem leader and SMEs that have varying levels of fit in the ecosystem can influence innovation ecosystems to better meet the innovation needs of SMEs. Our study extends the current knowledge on peripheral actors in innovation ecosystems and their role in ecosystem change. We draw attention to the potential benefits of retaining ecosystem members with poor fit and identify internal pressures for ecosystem change. Finally, our study identifies opportunities for future research on the impact and management of heterogeneous peripheral actors in innovation ecosystems.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".