The Effect of Human Resource Management Practices on Corporate Performance: A Study of Graphic Communications Group Limited
Bibliographic record
Abstract
In developing economies such as Ghana, the influence of governments in state-owned entities renders many human resource management best practice principles ineffectual. Graphic Communications Group Limited (GCGL) is a state-owned entity. Its human resource practices can be crucial to its performance. The purpose of this study therefore was to assess whether GCGL’s human resource management practices, particularly recruitment and selection, performance appraisal, remuneration, and training and development practices influence its performance. Simple random sampling was used to select one hundred employees from GCGL. T-tests were carried out to examine the relationship between the selected HR practices and corporate performance. The results revealed that, from the perceptions of the respondents, there exists a positive relationship between effective recruitment and selection practices, effective performance appraisal practices and GCGL’s corporate performance. The research did not gather sufficient evidence to conclude on how remuneration, training and development practices influence GCGL’s performance. The study recommends that the management of GCGL continues to ensure that the company’s HR policy, effective recruitment and selection practices, as well as effective performance appraisal practices are upheld.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".