Performance of Firm and Board Attributes Nexus: Using Hausman Test Analysis
Bibliographic record
Abstract
With the rise of corporate failures and the conflict of interest arising from shareholders and the management, there have been growing concerns in corporate governance (CG). It is there is ponsibility of the board of director in CG is to oversee the management as well as the firm performance and to make the management accountable to shareholders. Hence this research examines the connection between firms’ performance and board features using board size, board independence in addition to board age as a proxy for board characteristics and turnover as a proxy for firm performance. A sample size of 16 consumer goods firms out of a population of 20 consumer goods firms listed in the NSE from 2016 to 2019 was used using a judgmental sampling technique. Secondary data employed was taken out from the sampled firms’ annual reports. Hausman test analysis was used to select the appropriate regression model, which is the fixed effect regression model that was utilized to analyse the connection between firms’ performance in addition to board characteristics. It is found that firm performance and board independence of the consumer services goods companies in Nigeria are significantly related.The results also confirmed that firm performance and board size of the consumer services goods companies in Nigeria are significantly related. The result indicates firm performance and board education of the consumer services goods companies in Nigeria are not significantly related. Consequently, overall lthe study concluded that firms’ performance and board characteristics are related. Also, board characteristics increase board performance which will lead to increase in firms’ performances, there by maximizing profit and ensuring efficiency. The study concluded that a company with good board characteristics would help to ensure the maximization of both the shareholders and stakeholders wealth. Hence a proper board characteristic helps to solve the problem of both agency theory and stakeholders’ theory.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".