Human Capital Availability, Competitive Intensity and Manufacturing Priorities in a Sub-Saharan African Economy
Bibliographic record
Abstract
Several studies have been done on the relationships between human resources management (HRM) practices and manufacturing activities. However, most of these studies have been confined to well-developed economies where the focus of HRM practices is mostly on the investment in human capital to facilitate the use of advanced manufacturing technology. In less developed economies, the primary HRM concern is attracting and retaining skilled, knowledgeable and experienced labor. In this study, we examine the relationships between human capital availability, competitive intensity and their interactive effects on manufacturing priorities in a Sub-Saharan African economy — Ghana. We found that competitive intensity is an important determinant of the emphasis firms plan to place on manufacturing priorities (low-cost, quality, flexibility, and delivery). However, human capital availability affects the emphasis firms plan to place on low-cost and delivery. Furthermore, competitive intensity moderates the relationship between human capital availability and the emphasis that firms plan to place on the manufacturing priorities of low-cost and quality.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".