How Entrepreneurs Absorb Knowledge Spillovers During Innovative Product Development: Evidence From <scp>UK</scp> Start‐Ups
Bibliographic record
Abstract
ABSTRACT This study examines the impact of knowledge spillovers on product innovation performance within UK medium to high‐tech start‐ups. We propose a conceptual model that explains the relationship between incoming and network knowledge spillovers, potential and realized absorptive capacity, and exploratory and exploitative innovation performance, considering technological turbulence. Based on a PLS‐SEM analysis of 556 UK‐based medium to high‐tech start‐ups, our results show that during the potential absorptive capacity phase, start‐ups focus on acquiring incoming and networked knowledge spillovers. However, the exploitation of network knowledge spillovers occurs during the realized absorptive capacity phase. These findings contribute to the current understanding of the role of knowledge spillovers in absorptive capacity, while from a practical perspective they provide start‐ups with guidance on optimizing and exploiting knowledge spillover based on their firms' characteristics.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".