How Strategic Human Resource Planning Influence Performance of Agricultural Research Institutes in Kenya
Bibliographic record
Abstract
Strategic planning is a tool that determines the destiny of an individual, institution or organization. Globally, competition defines strategies encompassed by institutions due to industrial economy that had been experienced to pass toward knowledge resource. Efficiency is achieved by successful utilization of resources. Organisations seek a more competitive edge at all costs and turn to more innovations in information technology. Effective measures provide decision makers with feedback on the effect of deliberate actions and influence critical decisions such as resources allocations, as well as appropriate action as may be necessary. This study was carried out in Kenya Agricultural and Livestock Research Organisation (KALRO) to determine how strategic human resource influence the performance of agricultural research organisations. It was anchored on the theory of transformational leadership theory. Information on whether research organisations apply strategic human resource planning in management was scarce. This was partly due to the little attention that was drawn on quality of services offered and feedback. Although there had been previous international studies in this field, no similar work had been conducted in agricultural-based research organisations in Kenya. This study therefore sought to establish whether strategic human resource planning could influence performance in agricultural research organizations. Survey research design was used. The study comprised of four agricultural research institutes. The Institutes had a total of 2922 employees in 2016. A structured questionnaire was administered to collect primary data. A pilot test was conducted on 10% of the total respondents to test reliability and validity. Reliability of the instrument was determined by use of Cronbach’s alpha coefficient. The Pearson’s product moment correlation was used to establish test for linearity using Statistical Package for Social Sciences, (SPSS 2018), while Analysis of Variance was used to test hypothesis. Results showed that strategic human resource management contributes to increased agricultural research performance in KALRO institutes. It is recommended that the findings of this study be embraced by other agricultural research institutions in Kenya.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.000 |
| 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".