The Speed of the Effects of Publicly Funded Research on Business R&D, Innovation and Innovation Behaviour: Evidence from UK Firms
Bibliographic record
Abstract
Abstract This study contributes to the understanding of the speed of the effects of publicly funded research – R&D grants – on firm R&D, innovation and innovation behaviour. We argue that while the speed of the positive effects on R&D (i.e. input additionality; IA) tightly relates to public support programmes’ requirement of private contribution in R&D spending, the positive effects on innovation (i.e. output additionality; OA) and innovation behaviour (i.e. behavioural additionality; BA) are temporally delayed, linked to the explorative nature of learning associated with publicly funded projects and obstacles to changes in the innovation behaviour of firms. We empirically test our theoretical framework for a sample of R&D‐intensive UK firms to find that while IA occurs in the year following receipt of R&D grants, BA takes 3 years after receipt of R&D grants to occur. We do not find that grants have a positive effect on either product or process innovation. In measuring BA, we employ a ‘classical’ treatment and control group evaluation without relying on firms’ perceptions of how the receipt of grants affected their innovation behaviour. Our findings have significant implications for both management practice and policy.
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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 | 0.006 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".