The Art of Lean Startup: A Conversation About Experimental Strategy and Cultural Entrepreneurship
Bibliographic record
Abstract
Two literatures point to strategies entrepreneurs can use to manage the uncertainty inherent in starting and scaling a new venture. The first literature falls under the umbrella of cultural entrepreneurship (Lounsbury & Glynn, 2001, 2019) and suggests that entrepreneurs tell stories and make coherent identity claims in attempt to reduce uncertainty from the perspective of potential resource providers. The second literature explores how entrepreneurs use lean startup practices and iterative experimentation to pivot in response to stakeholder feedback until product-market fit is achieved (Blank & Eckhardt, 2024; Shepherd & Gruber, 2021). However, considering these literatures together highlights an important and persistent theoretical puzzle: How do entrepreneurs craft coherent narratives of who they are and what they do when their initial efforts are often characterized by experimentation, change, and even failure? In this symposium, we assemble a panel of leading scholars to discuss their perspectives on this puzzle and highlight ongoing research that explores the intersection of entrepreneurial experimentation and cultural entrepreneurship.
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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 | 0.062 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.103 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.017 |
| 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".