Developing the Innovation Capabilities of SMEs: The Role of Intermediary Firms in Knowledge Ecosystems
Bibliographic record
Abstract
Knowledge ecosystems drive growth by enabling firms to access diverse, specialized, and distributed resources from ecosystem members, allowing them to address complex product innovation challenges that would be difficult to tackle independently. This approach facilitates complementary value creation. However, small- to medium-sized enterprises (SMEs) encounter significant challenges within such ecosystems due to their limited size and limited resources. This article contributes to the extant studies on knowledge ecosystems by investigating how collaborations within these ecosystems enable SMEs to both explore and exploit knowledge, enhancing their innovation capabilities. Drawing on empirical data from 33 semistructured interviews and two focus groups involving multiple stakeholders (18 SMEs, 1 large firm, and 14 intermediary firms) from a knowledge ecosystem in Ostrobothnia, Finland, this article finds that knowledge cocreation through collaboration significantly improves SMEs’ technological and collaborative capabilities, leading to growth and market expansion. Intermediary firms play a dual role, going beyond knowledge brokering by providing capacity-building support that helps SMEs better contextualize and utilize external knowledge. This article advances both theoretical and practical understanding by demonstrating how intermediary firms function not only as facilitators but also as active capacity builders in the knowledge exploitation process. This nuanced understanding contributes to the ongoing discourse on ecosystem dynamics and SME innovation. From a practical perspective, SMEs should leverage core partners and intermediaries to address their inherent resource constraints and drive innovation performance. This approach enables them to expand their networks, codevelop technological solutions, and potentially secure future funding.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".