Human Resource Management Practices and Firm Performance: A Study of Manufacturing Firms in Kenya
Bibliographic record
Abstract
Manufacturing in Kenya account for the greatest share of industrial production output characterized by relatively low value addition of 7.5 per cent recorded in 2010 to 2.3 per cent recorded in 2011, low employment and capacity utilization and a paltry 25 percent export volumes. However, the share of Kenyan products in the regional market is only 7 percent of the US $11 billion regional market and its contribution to the GDP has remained at about 10 percent since the 1960s. This has given rise to the concern that practicing managers have put little effort to improve the situation. This study therefore sought to establish the relationship between Human Resource Practices and firm performance in the manufacturing firms in Kenya. Used a census survey of the 68 medium and large manufacturing firms whose core activities involved in production and marketing of edible oils, soaps and detergents, beverages or sugar registered in the Kenya Association of Manufacturers directory 2012. Data was collected through self administered questionnaires sent to the Production Manager, Brand Manager, Human Resource Manager, Marketing Manager, or the relevant manager dealing with innovations. The main findings of this study reveals that manufacturing firms apply human resource management practices to different extents. For instance, some models of human resource management practices such as licensing are not commonly used, while others like hiring of skilled employees and teaching company schemes are very common with average composite mean score of 4.00 and 4.08 out of the best score of 5.0 respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".