Influence of Strategic Planning to Firm Performance in Agricultural Research Based Institutions of Kenya
Bibliographic record
Abstract
Strategic planning is a very crucial phenomenon in all organisations. It is the tool that determines the destiny of the firm. The objective of this study was to investigate the influence of financial resource strategic planning on firm performance, to determine influence of human capital strategic planning on firm performance, to analyze the influence of material resource strategic planning on firm performance and to determine influence of information resource strategic planning on firm performance in agriculture research based institutions of Kenya. Although there had been previous international studies in this field, no literature is evident on status of the same in agriculture based research institutions in Kenya. The study comprised of former 4 major agricultural based research institutes of Kenya, namely: Kenya Agricultural Research Institute (KARI), Coffee Research Foundation (CRF), Tea Research Foundation (TRF), and Kenya Sugar Research Foundation (KeSREF). The institutions had 2922 employees in the year 2015. The study employed descriptive research design. The sample size was 352 having been arrived at using Yamane’s (1967) formula. Results and conclusion of the study were that financial resources strategic planning, human capital strategic planning, material resource strategic planning and information resource strategic planning influence a firm’s performance in a great way.
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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 | 0.002 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".