Les facteurs stratégiques affectant l'innovation technologique dans les PME manufacturières
Bibliographic record
Abstract
Résumé Malgrk une riche littkrature sur l'innovation, les variables relikes am stratkgies de E'entreprise ont ete rarement examinkes comme dkterminants de l'innovation. Cet article ktudie l'impact de l'exercice du management stratkgique sur la propension & innover et le degrkde nouveautk des innovations dkveloppkes par les PME manufacturi2res. L a rksultats de l'ktude sont basks sur l'estimation de deux moddes kconomktriques utilisant des donnkes empiriques collectkes en 2005 aupr2s de 247 PME manufacturi2res de la rkgion du Bas-Saint-Laurent au Qukbec. Ils sugg2rent que les variables relikes aumanagement stratkgique sont des dkterminants importants de l'innovation. b u r impact dkpasse celui de certains dktemzinants classiques de l'innovation tels que la recherche et dkveloppement (R&D), la taille de l'entreprise, et 1 ' intensitk technologique de 1 'industrie. In spite of an extensive literature on innovation, the variables related to firm strategy have rarely been examined as determinants of their capacity to innovate. This arti- cle investigates the impact of strategic management on the propensity to innovate and the degree of novelty of innovations developed by SMEs in the manufacturing sectol: The results are based on an estimation of two econometric models using emprical data collected in 2005 from 247 SMEs located'in the Bas-Saint-Laurent region of Quebec. They suggest that strategic manage- ment variables are important determinants of innova- tion. Their impact exceeds that of some traditional deter- minants of innovation such as research and development (R&D), firm size, and the technological intensity of the industry.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".