Do Ownership Structure, Political Connections and Executive Compensation Have Multifaceted Effects on Firm Performance? An Alternative Approach
Bibliographic record
Abstract
This study investigates the multifaceted effects of board characteristics and ownership on firm performance. Using panel data for 130 listed firms over the period 2009-2016 and after applying the SORM-DEA to OLS, quantile and 3SLS regressions. We explore the first empirical evidence showing that board characteristics tend to have multifaceted effects in explaining firm performance. Executive compensation has a positive influence in expounding firm performance. In contrast, political connections have a negative impact on firm performance. Further, the findings indicate that foreign ownership and CEO chair duality are positively related to firm performance. These effects are more pronounced in periods of high oil prices, while foreign ownership and CEO chair duality fail to explain firm performance in a period of low oil price. The results also reveal that CEO educational background has a significant effect on performance in service firms compared with industrial firms. The outcomes of this study provide important implications for investors and policymakers.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".