Innovation and knowledge-intensive business service: the contribution of knowledge-intensive business service to innovation in manufacturing establishments
Bibliographic record
Abstract
It is well established that knowledge-intensive business service (KIBS) firms can be innovators in their own right. It is also well established that KIBS can contribute to innovation in their client firms. This role of KIBS has been theorised, and some of the processes by which KIBS contribute to innovation have been scrutinised by way of case studies. However, there are few, if any, large-scale analyses that permit the two following questions to be addressed: (i) Do firms that use KIBS systematically introduce more innovations than those that do not? (ii) Is recourse to certain types of KIBS associated with certain types of innovation? Our survey of KIBS use across 804 manufacturing establishments in Quebec shows that KIBS contribute to their client's innovation – thereby confirming in a more general way what has been observed in case studies – but also that different types of KIBS contribute to different types of innovation.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".