The Impact of Serial Entrepreneur’s Technological and Financial Experience on Venture Exploration
Bibliographic record
Abstract
This study examines learning from novelty creation and its interplay with financial success in the context of serial entrepreneurship. Specifically, we highlight how the novelty creation and financial success of serial entrepreneurs’ prior ventures influence the exploratory behavior of their subsequent ventures. By conceptualizing two separate types of venture exploration—entry into new knowledge domains and the search for distant knowledge—we examine the multifaceted features of how entrepreneurs learn from their prior ventures’ technological and financial experiences. We hypothesize that the novelty creation of prior ventures will facilitate subsequent ventures’ entry into knowledge domains different from those of prior ventures and will increase the propensity of subsequent ventures to search for knowledge distant from their technology space. This relationship is contingent on prior ventures’ financial success. When the prior venture experienced financial success, serial entrepreneurs have the financial slack to accelerate their ventures to enter new knowledge domains but simultaneously fall into the success trap that develops cognitive inflexibility, preventing distant search. Thus, under the condition of prior ventures’ financial success, the positive effect of novelty creation on entering new knowledge domains will be strengthened, and the positive effect of novelty creation on searching for distant knowledge will be weakened. We find broad support for our hypotheses using Crunchbase for entrepreneur and venture data worldwide from 1967 to 2018 and patent data from USPTO. The results have important implications for research on entrepreneurial learning and, more broadly, on what makes entrepreneurs more exploratory.
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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 | 0.021 |
| 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 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".