The dark side of strengthened minority voting power: An innovation perspective
Bibliographic record
Abstract
Abstract Based on the 2014 regulatory reforms aimed at strengthening the protection of legitimate rights and interests of minority investors in China, we investigate minority shareholders’ short‐termism and how minority voting impacts firm innovation. We find that the 2014 reforms effectively motivate minority shareholders to attend shareholder meetings and greatly enhance their voting influence. We also find that enhanced minority voting power after the reforms lowers the number of firms’ patent applications, and this effect is more pronounced for the firms that see the greatest increase in shareholder attendance at shareholder meetings. Moreover, enhanced minority voting power boosts executive turnover‐performance sensitivity, thereby undermining firm innovation. Finally, we show that different types of minority shareholders have distinct impacts on firm innovation, depending on their investment horizons. The negative effect of minority voting power is more pronounced for state‐owned enterprises (SOEs) than for non‐SOEs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".