Patterns of Technological Innovation in Knowledge‐Intensive Business Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it