Risk capital constraints to innovation in services
Bibliographic record
Abstract
Purpose – This paper aims to understand the factors associated with perceptions of venture capital as a barrier to innovation in an important subset of knowledge-intensive service firms – technology-based business services. A general and longstanding neglect of services in studies of innovation and a common focus of innovation studies on the availability of, and demand for, risk capital has been noted. Design/methodology/approach – In exploring these issues, the authors draw on survey data collected from 264 technology-based service firms located in Scotland and Northern England. The data are subjected to bivariate and multivariate statistical analyses to help explore the extent of demand-side risk capital concerns. Findings – It was found that smaller, faster growing and R&D-intensive firms perception greater equity barriers. Moreover, firms who are relatively happy about the managerial competencies available to them, but who identify deficiencies in marketing skills and the availability of external debt finance (which may say something broadly about their financial neediness), are shown to be “needy”. Originality/value – Studies of venture capital demand are relatively rare. Studies involving innovative service firms are rarer still. Given the prominent role of service firms in advanced economies and the changing perspective of the role of services in innovation, studies of financial constraints to innovation in services are timely. Innovation policy in advanced economies continues to be premised on patterns identified in manufacturing industries. This paper contributes to a broader perspective that views [technology-based] business services as dynamic innovation actors.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".