Les technologies de l’information, la gestion des connaissances et un avantage concurrentiel soutenu : une analyse par la théorie des ressources
Bibliographic record
Abstract
La théorie des ressources présente une approche stratégique par laquelle une organisation a recours à ses ressources internes en vue d’obtenir un avantage concurrentiel durable. En effet, depuis une vingtaine d’années, les gestionnaires considèrent que certaines ressources et certaines capacités spécifiques des entreprises sont cruciales pour expliquer la performance en affaires. Un des défis à relever, pour ces gestionnaires, est d’identifier, de développer, de protéger et de déployer ces ressources dans une direction qui assure un avantage concurrentiel durable. Or, la connaissance est reconnue comme l’une de ces rares ressources organisationnelles intangibles susceptibles d’accorder un avantage concurrentiel durable. Ainsi, reprenant l’étude de cas de Cooper, Watson, Wixon et Goodhue (2000), cette note de recherche applique la théorie des ressources en gestion des connaissances et en technologies de l’information et contribue à conscientiser les gestionnaires sur le potentiel stratégique et l’importance de développer un leadership spécifique en gestion des connaissances et en technologies de l’information.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".