SMEs in Collaborative Innovation Networks: A Maturity Model Evaluating their Absorptive Capacity
Bibliographic record
Abstract
SMEs increasingly engage in Collaborative Innovation Networks (CINs) to access valuable knowledge for innovation from complementary partners. They deploy their absorptive capacity (ACAP) to make efficient use of this new external knowledge. Despite the importance of ACAP to support the contribution of SMEs to innovation throughout the lifecycle of a CIN, there is no operational measure to guide them regarding ACAP implementation to reach the network common innovation goal. We propose to design a grid-based maturity model allowing SMEs to evaluate their ACAP given their embedding contexts in CINs. We follow a mixed methods’ Design Science approach to define the content of the maturity model and adjust it by predicting the ACAP aspects that an SME should primarily master considering its context in a CIN. Our results expand academic understanding on the contingent peculiarities of ACAP by unveiling the natures of its practices and its contextual variability for the examined SMEs. We improve practice by providing SMEs with an assessment tool to early spot their ACAP deficiencies and implement the relevant corrective actions.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".