Bibliographic record
Abstract
Abstract Research Summary What human capital do established organizations need to bring new ideas to market? Combining Danish registry and Community Innovation Survey data, we document a robust positive relationship between hiring former founders and firms' sales from innovation. Entrepreneurs join smaller, younger firms (which exhibit larger effects), managerial skills and external industry founding experience matter, while other selection or human capital‐based explanations appear muted. Founder hires especially enhance innovation when given middle management decision rights, for incremental offerings, and in innovation‐active firms. Our collective findings indicate startup experience equips founders with a generalist ability to acquire and mobilize resources around new ideas. By clarifying the nature of entrepreneurial human capital, we highlight a novel innovation input that helps firms unlock its commercial value. Managerial Summary As entrepreneurial careers proliferate, former founders represent a growing pool of potential employees with expertise in bringing new products and services to market. Can hiring entrepreneurs help established organizations enhance innovation? Using data from Denmark, we answer this question affirmatively and offer several explanations. Former founders gravitate towards younger, smaller established firms, where effects are stronger; they also bring valuable managerial skills and external industry founding experience. Notably, they generate more value when given broader authority as middle managers, for less obvious resource combinations, firms already active in innovation or research and development, and higher uncertainty contexts. Taken together, our findings suggest former founders' distinct combination of skills helps firms marshal resources around new offerings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".