La capacité d’absorption, l’élément clé dans la compréhension de la relation entre information et innovation
Bibliographic record
Abstract
Plusieurs études rappellent que l’innovation dans les PME s’explique avant tout par l’apport systématique de l’information le plus souvent informelle provenant notamment des clients, des fournisseurs, des concurrents et de diverses sources plus formelles comme les revues d’affaires ou les foires industrielles. On peut toutefois favoriser celle-ci par une meilleure capacité de ces organisations à cibler et à transformer l’information en connaissance, en particulier en organisant mieux la veille et en améliorant la capacité d’absorption et de transformation de l’information. Dans une enquête exploratoire effectuée auprès d’une quarantaine de PME de trois secteurs industriels au Congo-Brazzaville et en tenant compte des caractéristiques d’un pays en développement, nous montrons que la relation entre les sources d’information, la capacité d’absorption et l’innovation joue autant que dans les pays industrialisés. En particulier, le niveau de formation de la direction et de quelques employés clefs semble constituer la variable la plus importante pour expliquer cette relation.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".