An Exploratory Study of the Impact of Top Leadership on Effectiveness of Privatization of Hospitals Through Mergers and Acquisitions in Kenya
Bibliographic record
Abstract
This paper examines the privatization of hospitals through mergers and acquisitions (M&A) using Kenya as the country of focus. It shows that M&A activities are increasing in Africa and there is a history of privatization of state-owned enterprises (SOEs) / parastatals in Africa and Kenya in particular, which started in the 1990s. With the changing dynamics, increasing pressure to achieve universal health and looking at the history of mergers and acquisitions there is no doubt that this is going to become an important phenomenon in Kenya in the near future.Privatization of hospitals, including public and not-for-profit (NFP), has been popular since 1980s in North America (U.S., Canada) and Europe (Germany, England). Privatization and M&A activities of hospitals in other countries such as India, China, Saudi Arabia, Africa and Kenya have also increased. The reasons for these trends are industrialization of developing countries, changing lifestyles, aging populations, longer life expectancy, technological advancement, growth of the middle class, increase of non-communicable diseases (NCDs) and inefficiency of public health systems. With the changing dynamics, it would appear there is a need for African countries to expand their private sectors, and privatization of healthcare is an attractive area for private equity firms and private hospital chains. Due to growth of the economy and the middle class, higher demand for healthcare services and particularly expansion of NHIF (National Hospital Insurance Fund) coverage, privatization of hospitals makes economic sense in Kenya.Knowledge of M&A among top leadership is crucial in determining its success or failure. Therefore, the literature review focused on property right, transaction cost, and institutional theory. Relevant M&A theories such as process, synergy, efficiency and disturbance theory were also reviewed.The research philosophy, methodology and design of this study was based on exploratory, post-positivism, deduction and utilized mixed methods (qualitative and quantitative) with focus on verifying the hypothesis. The population of this research included Level 4, 5 and 6 hospitals in Kenya, totaling 268 hospitals with at least 50 beds; the sample size was 158 hospitals. Proportionate stratified random sampling methodology was used to determine the sample size of each hospital level (Level 4, 137 hospitals; Level 5, 14 hospitals; and Level 6, 7 hospitals).The hypothesis that there is no relationship between top leadership (X) and the effectiveness of privatization of hospitals (Y) through M&A was tested and there was a strong and positive relationship between the dependent and independent variables (r=0.821), and the regression model was found to be reliable. The null hypothesis was rejected because of the results of the T-test (β1=0.925, t=9.757, p<0.005).It is recommended that similar studies be conducted in East and South Africa to enable researchers to perform comparative analyses in order to improve the body of knowledge.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".