La réalité de l’apprentissage par l’échec en entreprise : une approche behavioriste enrichie des émotions
Bibliographic record
Abstract
Dans cette recherche, nous envisageons les échecs en matière d’innovation comme des stimuli, susceptibles de déclencher un processus d’apprentissage organisationnel, que nous cherchons à modéliser, à travers l’analyse du lancement raté d’un nouvel espace de vente dans un Grand Magasin français. Ce faisant, nous tentons de comprendre pourquoi une entreprise apprend – ou n’apprend pas – à la suite d’un échec, même si nous refusons d’adopter une approche strictement binaire. Pour atteindre cet objectif, nous inscrivons la question des échecs commerciaux dans le cycle d’apprentissage par l’expérience de March et de ses coauteurs, que nous complétons par l’ajout de deux facteurs : l’engagement affectif et les émotions.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".