Ownership Structure and Firm Technical Innovation： a Theoretical and Empirical Analysis on Chinese Enterprises
Bibliographic record
Abstract
Taking more than 600 enterprises as example, this paper analyzes the influence of managers’ features on the relationship between ownership structure and technical innovation of the firm. The authors find that the managers’ care to owners benefit (Emc) and the managers’ talent(Ta) have positive influence on firm technical innovation (Inte), and the ownership share of different kinds of owners have different effects on Emc and Ta. Therefore, ownership structure can not only directly influence the technical innovation of the enterprises, but also influence it indirectly. Key words: ownership structure, firm technical innovation, managers’ care to owners benefit, the managers’ talent Resume: Prenant en exemple plus de 600 entreprises, ce document present analyse l’ influence des caracteristiques des managers sur les relations entre la structure possessive et l’innovation technique des entreprises. Les auteurs trouvent que l’attention des managers aux interets des possedants (Emc) et le talent des managers (Ta) effectue une influence positive sur l’ innovation techniquel des entreprises(Inte), et le partage possessif de possedants de toutes les sortes a de differents effects sur Emc et Ta. Cependant, la structure possessive peut influencer l’innovation technique des entreprises directement a la fois indirectement. Mots cles: structure possessive, innovation technique des entreprises, attention des managers aux interets des possedants, talent des managers
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".