Product Diversification and the Financial Performance of Manufacturing Companies in Kenya
Bibliographic record
Abstract
This study sought to establish the impact product diversification strategies as used by manufacturing entities in Kenya on the financial performance of these entities focusing on the earnings before interest and tax (EBIT) and return on assets (ROA). Limited research has been carried out on how manufacturing entities in Kenya manage operational risks despite these entities facing high volatility in the operating environment. The objectives of the study therefore focus on how product diversification as a risk management strategy influences the financial performance of manufacturing entities in Kenya. The research was based on the modern portfolio theory as by carefully choosing of investments to be included in a portfolio; an investor can effectively minimize the risk exposure and in the process maximize the portfolio expected return. The study used ten year panel data for the period spanning 2007 – 2016 from a sample of forty nine companies. From the findings, the null hypotheses of the study were not rejected implying that product diversification does not have a significant influence on the financial performance of manufacturing entities in Kenya when measured against both EBITS and ROA.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".