Effectiveness of human resource management practices in developing countries :
Bibliographic record
Abstract
Dunning (2006) asserted that international business research focused heavily on the physical assets of organizations and nations, thus neglecting the human environment of organizations and nations.Research has shown “the most important driver for economic advancement is knowledge” and is drawn from the human environment (Zhu et al., 2011, p. 312). The human environment is defined as the “human assets (i.e. creativity leading to innovation; experience, skills and knowledge of employees) and the skills and abilities those assets possess within a given location” (Zhu et al., 2011, p. 312).Thus, how an organization, including government, manages its human resources (HR), drawn from the human environment in which it operates, will significantly impact success or failure (Barney, 2001; Kong & Thomson, 2009).We contend that although there has been a great deal of research on human resource management (HRM) as a competitive advantage for firms, there has been little work done on the analysis of HRM practices in government and its influence on a nation’s competitive advantage. In a qualitative study of a developing nation in the Caribbean we interviewed 12 senior level employees. Our analysis revealed that little attention was paid to HRM, which resulted in the ineffectiveness of the application of government policies. The data revealed that issues started with the recruitment and selection processes.This paper focuses on the recruitment and selection processes utilized by government agencies that cause institutional voids which lead to the failure to utilize public service employees as a source of competitive advantage.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".