Patterns of Innovation Capabilities in KIBS Firms: Evidence from the 2003 Statistics Canada Innovation Survey on Services
Bibliographic record
Abstract
The aim of this paper is to shed light on complementarities and substitutions between various types of innovation capabilities in knowledge-intensive-based service (KIBS) firms. The data used in this study are the responses of 2,625 innovative firms to the 2003 Statistics Canada Innovation Survey on services. The empirical results suggest the presence of three patterns of complementary innovation capabilities, one pattern of substitute activities and finally, four patterns of innovation capabilities that are independent from each other. Hence, the results suggest the presence of complementarities: first, between internal R&D, external R&D, acquisition of equipment and machinery, and marketing activities; second, between external R&D, acquisition of equipment and machinery, acquisition of external knowledge and marketing activities; third, between acquisition of equipment and machinery, acquisition of external knowledge and marketing activities. Such complementarities lead to the conclusion that, in practice, managers of KIBS firms consider the consolidation of these capabilities jointly instead of separately. The paper also discusses issues related to patterns of capabilities that are substitutes and independent from each other. The results of this study also show significant heterogeneity in the determinants of the different patterns of innovation capabilities.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".