The Relationship among Ownership Structure, Managers’ Factors and Firm Technical Innovation: a Case Study
Bibliographic record
Abstract
The empirical research on the relationship between firm ownership structure and technical innovation, is the weak tache in existing research. Based on existing research result, this article empirically studies the impact of managers on relationship between ownership structure and technical innovation by analyzing large sample database. And it proves that firm manager is the important link between firm ownership structure and technical innovation. Key words: ownership structure, attitude of manager, talent of manager, firm technical innovation Resume L’etude positiviste du mecanisme de lien entre la structure de la propriete et l’innovation technologique de l’entrprise est un point faible dans les recherches actuelles. Donc, inspire de fruits de recherches des predecesseurs, l’auteur fait, a partir des donnees de grands echantillons, une analyse positiviste de l’influence des facteurs du gerant sur les relations entre la structure de la propriete et l’innovation technologique de l’entrprise. De ce fait, il prouve que le gerant est un maillon important du mecanisme de lien entre la structure de la propriete et l’innovation technologique de l’entrprise Mots-cles: La structure de la propriete, l’attitude de travail du gerant, la competence du gerant, l’innovation technologique de l’entrprise 摘 要 企業所有權結構與企業技術創新聯繫機制的實證研究，是現有研究中的一個薄弱環節；為此，文章借鑒前人有關研究成果，採用大樣本資料，實證分析了經營者因素對企業所有權結構與企業技術創新相互關係的影響，從而證明經營者因素是企業所有權結構與企業技術創新聯繫機制中的重要環節。 關鍵詞： 所有權結構；經營者工作態度；經營者能力；企業技術創新
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".