Et si l’effectuation ne permettait pas de mieux pivoter en période de crise? Exploration des conduites de PME en contexte de COVID-19
Bibliographic record
Abstract
Although the health crisis hit SMEs and new companies hard, some of them survived by pivoting. In this study, we analyse the way in which these resilient companies have pivoted in response to this crisis, by focusing on the entrepreneurs’ modes of reasoning (effectuation or causation). To do this, we carried out a multiple-case study. The results of this analysis show that entrepreneurs’ responses to the crisis have varied according to their dominant model of reasoning. Entrepreneurs relying on a causal approach were able to proactively achieve a more complex pivot, while those who were predominantly effectuation-oriented achieved an adaptive pivot without really questioning their model. These results suggest that, in a context of radical ignorance, effectuation allows for a rapid adaptation, but would limit companies’ capacity for strategic renewal.
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How this classification was reachedexpand
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".