Lean Startup and Learning Loops in Entrepreneurial Ventures: A Systematic Review
Bibliographic record
Abstract
The lean startup embraces experimentation and validated learning as part of the entrepreneurial search effort. Scholars situate it within the Learning School of Strategy (Bortolini et al., 2018; Mintzberg, 1978) and report that it intersects with multiple organizational learning areas (York, 2022). Of interest is the relationship of lean startup, its iterating and pivoting actions, and continuous experimentation with learning loops (single-, double-, and triple-loop) in the entrepreneurial setting. This systematic review, with guidance from Tranfield et al. (2003), Preferred Reporting Items for Systematic and Meta-Analyses (Moher et al., 2010), and the International Journal of Management Reviews, identified evidence around these relationships. This effort used preset criteria to screen citations from three portals (ABI/Inform, EBSCO, and SCOPUS) and Snowball collection per Wohin (2014). This effort identified 41 publications (19 systematic, 22 snowball). This review finds direct and suggestive evidence concerning the interrelationships of lean startup, its actions, and processes with the learning loops. Also, it posits a model involving lean startup and the three learning loops and offers questions for further exploration.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".