Relationship between Enterprise Capabilities and Value Creation in Kenyan Owned Enterprises
Bibliographic record
Abstract
Mining industry in countries such as Russia, Ukraine, United States of America, and Canada contribute highly to their GDP. In Africa countries such as South Africa, Namibia, and Tanzania, mining industry is continuously performing well. Studies show that Kenya has various types of gemstones, petroleum and minerals. However, reports indicate that Petroleum and Mining contributes less than 1% to GDP. This confirms that there are clear hindrances to value creation processes. The objective of this study was to establish the relationship between capabilities and value creation in the Kenyan owned mining enterprises. The study was a cross sectional survey. A questionnaire was used to collect data from Kenyan owned mining enterprise, where both semi-structured and open-ended questions were used. A quantitative approach was employed in data analysis. The findings showed that there is a statistically significant relationship between capabilities and value creation in Kenyan owned enterprises in mining industry in Kenya. The study concluded that Kenyan owned enterprises, human capital capabilities and prospecting knowledge capabilities have an association with value creation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".