Influence of Capital Structure on Growth in Wealth of Investment Groups in Kenya
Bibliographic record
Abstract
Investment groups are formed with the aim of growing and maximizing wealth for the members. However, some have failed making it difficult for them to be sustainable. The main objective of the study is to establish the influence of capital structure on growth in wealth of investment groups in Kenya, and to establish the moderating effect of group size on the relationship between capital structure and the growth in wealth of investment groups in Kenya. The study used cross sectional survey research design. The population of interest was 4020 investment groups registered by Kenya association of Investment groups. Stratified random sampling method was used and 364 investment groups were selected proportionate to the size of the strata. The survey instrument was a questionnaire administered to the group members and their officials. Pilot test was done using 36 respondents who were drawn from target population but not be included in the main study sample. Cronbach alpha was used to test reliability of the instrument, factor analysis was used in the testing of construct validity by considering average variances extracted and squared correlations of the constructs. Analysis of the data was done using descriptive statistics and inferential statistics. Descriptive statistics involved computations measures of central tendency and presented in frequency tables, pie charts and graphical charts. Inferential statistics was done using the multiple regression. Inferential analysis involved fitting of regression models. The regression analysis results obtained from the study show capital structure had a significant influence on growth in wealth. A moderated multiple regression was fitted to test the moderating effect of size on the relationship between capital structure and growth in wealth. The Moderated Multiple regression model results showed that size has a significant influence on the relationship between capital structure and growth in wealth. The findings and conclusions of this study are of significance to the investment groups. They are able to appreciate how growth in wealth of their groups is influenced by the study variables. Based on the findings the management can be able to understand the strategies to be taken in order to improve the growth of the respective investment groups.
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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 | 0.003 |
| 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) | 0.002 | 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".