Lean Startup and Learning Loops in Entrepreneurial Ventures: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it