Effectuation, stalled: action learning and the Commitment Readiness Gap for creative entrepreneurs
Bibliographic record
Abstract
Early-stage creative entrepreneurs (CEs) often bring strong skills and compelling visions, yet their ventures stall before building real momentum. This qualitative study followed six such entrepreneurs over a ten-week Action Reflection Learning (ARL) programme, using session recordings, artefacts, and journal notes, and analysed them thematically to explore how reflection is converted into actionable commitments. Participants were introduced to effectuation, a decision-making logic used by expert entrepreneurs, as practical heuristics for acting under uncertainty. Although effectuation is widely used to teach novice entrepreneurs, participants experienced multiple points of stall, revealing a Commitment Readiness Gap. The ARL programme helped bridge these stalls by providing structured interventions that made resources and goals visible, rehearsed stakeholder interactions, and supported small, actionable steps. In addition to introducing the Commitment Readiness Gap, this study adds a readiness layer to the effectuation model, offering insights and practical interventions to help entrepreneurs convert reflection into commitments under uncertainty.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".