Examining the Relationship between Disproportional Ownership Mechanisms and Company Performance: An Empirical Research
Bibliographic record
Abstract
The paper explores the relation between the use of disproportional ownership mechanisms and firm performance in listed companies. Literature suggests that such tools deviate from the proportionality principle between cash-flow rights and control rights and negatively affect firm outcomes. Differently, some studies emphasize that disproportional ownership mechanisms have a positive effect on company performance as controlling shareholders can be motivated to maximize corporate outcomes, providing outperforming benefits than the potential minority expropriation risk. Moreover, it is still an open issue whether firm performance can be interpreted as determinant of disproportional ownership mechanisms. Therefore, it is an empirical question whether company performance can be considered as an implication and/or a driver of disproportional ownership devices. Moving from these premises, the article relies on a unique hand-collected dataset and examines a sample of Italian listed companies through regression analyses. Findings show that the use of disproportional ownership devices is affected by past company performance and highlight that these ownership tools worsen firm outcomes. The research has both theoretical implications for future studies and practical implications for policy makers.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".