Do the Board Characteristics influence the Firm Performance? An Experience with the Capital-Intensive Industries Listed in the Saudi Stock Exchange (TADAWUL)
Bibliographic record
Abstract
The objective of the researchers in this article is to explore the relationship of board characteristics (board size, board meeting, number of board committees, board independence) on the firm performance (ROA & Tobin’s Q) in Saudi Capital-Intensive Industries for the data period of 2017-2020. Many researchers have tried to measure this relationship in earlier research papers, but the Capital-Intensive Industries have not been exclusively tested so far. This paper aims at filling this gap and measure the relationship of exclusive board characteristics and firm performance Capital Intensive Industries listed in Saudi Stock Exchange (TADAWUL). We find board size influences the firm performance in an opposite direction. On the other hand, board meeting influences the firm performance in a positive direction and both the results are statistically significant. The other board characteristics are not influencing the firm performance in this study. Additionally, the firm size is influencing the firm performance (positively with ROA and negatively with Tobin’s Q).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".