Top Management Team Characteristics, Strategy Implementation and Performance of Tea Factory Companies in Kenya
Bibliographic record
Abstract
The study was designed to examine the indirect influence of top management team characteristics on performance of tea factory companies in Kenya. This study was guided by the upper echelons theory and the institutional theory. The objective of the study was to examine the influence of strategy implementation on the relationship between top management team characteristics and performance. A descriptive cross-sectional survey design was adopted. The population of the study comprised tea factory companies in Kenya managed by the Kenya Tea Development Agency Limited. Primary data were collected using structured questionnaire targeting factory unit manager, production manager, finance manager and field services manager. Rigour was tested by examining reliability scores and factor analysis. The reliability scores were high and within acceptable range for purposes of analysis. Results of factor analysis demonstrate high presence of construct and convergent validity. Data were analyzed through regression analysis. Our results support the supposition of indirect influence of top management team characteristics on performance. We empirically demonstrate that strategy implementation fully mediates the relationship between top management team characteristics and performance. The study contributes to the stock of knowledge by challenging the previously held belief that top management team characteristics directly influences performance. We conclude that organizational performance is the result of effective deployment of top management skills and experiences in the execution of strategy rather than the mere constellation of characteristics held by top managers.
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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 | 0.002 |
| 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".