Innovation Propensity in the Specialized Suppliers Industry
Bibliographic record
Abstract
The paper focuses on the effects of technology-push and demand-pull determinants on firm's innovation propensity comparing start-ups and established firms in the specialized suppliers' industry. Specifically, it explores technology-push and demand-pull effects in isolation and in their interaction using a sample of European firms in the period 2007-2009. Our main results show that either the technology-push and demand-pull determinants exert a positive impact on innovation propensity in both start-ups and established firms, Moreover, in start-ups, we discovered that the demand-pull determinant plays a strong moderating role in the relationship between innovation propensity and the technology-push determinant. The paper contributes in making managers more aware of the effect that some choices concerning the composition of the firm’s workforce may produce on the firm’s innovation propensity. There are also implications for policy makers whose overemphasis on demand pull incentives may disempower the positive effect of the technology determinant on the innovation propensity of start-ups.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".