After the startup: A collection to spur research about entrepreneurial growth
Bibliographic record
Abstract
Abstract Research Summary Entrepreneurship researchers have made great strides toward understanding who discovers and/or creates opportunities, how they validate a business model, and how they attract resources, but far less is known about what happens next. Beyond gathering resources, how do entrepreneurs build a growing organization once customer enthusiasm has been demonstrated? What has been learned is fragmented across theoretical perspectives and activities related to growth. We describe a collection of articles that spotlight different theories and organize the articles according to key activities: (1) building internal resources and capabilities, (2) leveraging partnerships, (3) taking strategic actions, and (4) managing interactions among resources, partners, and actions. Juxtaposing these activities with theories from the collection, we offer a research agenda designed to spur research to fill gaps in understanding of how entrepreneurs successfully manage growth. Managerial Summary The period of an organization's development between the startup stage and becoming an established firm presents unique challenges. We spotlight a set of articles that have provided insights into how organizations can overcome these challenges. We then add our own perspective by providing research ideas that scholars can investigate in order to generate additional insights.
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 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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.035 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".