Start‐ups' use of knowledge spillovers for product innovation: the influence of entrepreneurial ecosystems and virtual platforms
Bibliographic record
Abstract
Entrepreneurial ecosystems have been explored widely in entrepreneurship, management and social sciences literature. The Knowledge Spillover Theory of Entrepreneurship (KSTE) aims to uncover the effects of information on start‐ups co‐located in diverse locations, such as urban areas, science and technology parks, incubators, and accelerator programs. Extant research has focused on how entrepreneurs launch start‐ups and develop patents over a 5–10 years timespan from a regional perspective. However, studies into the development processes of start‐ups and the creation of entrepreneurial ecosystems in physical and virtual environments in high‐tech start‐ups, are limited. As a result, this paper aims to identify the development processes undertaken by high‐tech entrepreneurs at the individual level and evaluate the absorption and implementation of knowledge in physical and virtual clusters within entrepreneurial ecosystems. A multiple case study of 32 start‐ups that have attended incubator and accelerator programs in London, United Kingdom, is presented. Semi‐structured interviews were conducted with Chief Executive Officers (CEOs) and Founders of start‐ups to propose the Model of Knowledge Spillovers and Entrepreneurial Ecosystems. The themes identified during interviews highlight the mechanisms employed by start‐ups to capture tacit and explicit knowledge spillovers. Theoretically, the findings of this study contribute to the KSTE by questioning the flexibility of entrepreneurs to access knowledge without the limitation of geographical proximity to sources of knowledge. Practically, our findings provide entrepreneurs with proven mechanisms required to capture tacit knowledge spillovers within entrepreneurial ecosystems and use virtual platforms to obtain explicit knowledge spillovers towards product innovation.
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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 | 0.003 | 0.013 |
| 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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".