Patterns of Technological Innovation in Knowledge‐Intensive Business Services
Bibliographic record
Abstract
Employing data from a sample of 1,161 small firms, the paper draws broad comparisons between patterns of innovation expenditure and output, innovation networking, knowledge intensity and competition within Knowledge‐Intensive Business Services (KIBS; N = 563) and manufacturing firms (N = 598). In so doing, KIBS are further disaggregated along lines proposed by Miles et al. (1995 Miles, I., Kastrinos, N., Flanagan, K., Bilderbeek, R., den Hertog, P., Huitink, W. and Bouman, M. 1995. Knowledge Intensive Business Services: Their Role as Users, Carriers and Sources of Innovation EIMS Publication No. 15, Innovation Programme, DGXIII, Luxembourg [Google Scholar]). That is, as technology‐based KIBS (t‐KIBS; N = 264) and professional KIBS (p‐KIBS; N = 299). However, detailing such broad patterns is preliminary. The principal interest of the paper is in identifying the factors associated with higher levels of innovativeness, within each sector, and the extent to which such “success” factors vary across sectors. The results of the analysis appear to offer support for some widely held beliefs about the relative roles of “softer” and “harder” sources of knowledge and technology within services and manufacturing (Tether, 2004 Tether, B. 2004. Do Services Innovate (Differently)?, Manchester: University of Manchester. CRIC Discussion Paper 66 [Google Scholar]). However, some important qualifications are also apparent.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".