Investigating How Corporate Governance Affects Performance of Firm in Small Emerging Markets: An Empirical Analysis for Jordanian Manufacturing Firms
Bibliographic record
Abstract
This paper aims at exploring how the mechanisms of corporate governance (audit committee size, CEO duality, board size, female board members and board composition) affect the firm performance. Based on data from 66 out of 69 firms, which represents (95.6%) of Jordanian publicly quoted manufacturing firms covering a five-year period (2008–2012), the use of multiple regression analysis was done for assessing how each of the mechanisms of corporate governance relates to firm performance. The empirical findings of this study suggest that size of firm and Tobin's Q and ROA shows a significant positive correlation, while leverage and ROA show significant correlations. Results indicate that CEO duality and size of board have negative correlation with ROA, while non-executive directors' proportion shows a positive correlation with ROA. No relationship was recognized between the female board members' proportion and audit committee size and ROA. Conversely, the variables of corporate governance do not show a relation with measure of market performance, which supports the argument that market-based performance measures are impartial when economic circumstances are normal in context of emerging markets. The paper provides insight into better understanding how the various mechanisms of corporate governance are related to the performance of firm given the scenario of a small emerging market of non-oil-producing country.
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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 | 0.000 |
| 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) | 0.001 | 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".