EMPLOYEE RECOGNITION PRACTICES AND TEACHER PERFORMANCE IN PUBLIC SECONDARY SCHOOLS IN KENYA: A CASE OF BUSIA COUNTY
Bibliographic record
Abstract
Scholars assert that organizations that effectively implement employee recognition practices are likely to gain a competitive edge against rivals due to high motivation and retention rates of skilled and talented employees. The purpose of the study was to examine the relationship between employee recognition practices and teacher performance. The Systems theory and the Ability-Motivation-Opportunity theory informed this study. The study was anchored on a pragmatic paradigm and adopted a mixed methods approach to address the research questions. The study targeted 185 Principals, 185 HODs, and 1 CDE. The study categorized respondents into 3 strata (CDE, Principals, and HODs). Simple random sampling was then utilized to select a sample proportionate to each stratum. Schools were clustered based on 7 Sub- Counties in Busia County. The sample size of the study was 126 HODs, 19 Principals and 1 CDE. Questionnaires and interview schedules were used to collect data. Piloting was conducted in 37 schools to refine instruments. Descriptive statistics (frequencies, percentages, mean, and standard deviation) and inferential statistics (correlation and regression) were employed. Analyzed data was represented in APA tables and Pie charts. The study found that there is a lack of a clear, systematic and comprehensive implementation approach to employee recognition in most schools in Busia County. It is clear that recognition and rewards are critical factors towards the establishment of a quality culture that appreciates and values the contribution of teachers and their accomplishments in service delivery. The findings of the study found that (r = 0.917, p<0.01) an indication that there is a positive significant relationship between employee recognition and teacher performance. It was found that an adjusted R square value of 0.77 implies that employee recognition accounted to nearly 77% of the total variation in teacher performance. It was found that ( = 0.917, t= 3.371, p<0.05), implying that employee recognition statistically and significantly predicted teacher performance; hence the study rejected the null hypothesis. The study concluded that a unit improvement in employee recognition is likely to result in an improvement in teacher performance by 91.7% (β= 0.917). The study recommended that there is a need for TSC and school management to strengthen the existing recognition system by developing mechanisms for frequent identification, recognition and administration of rewards in a more consistent, prompt, impartial and transparent manner in all public schools with a view to encourage improvement, promote creativity and innovation and enhance performance. Article visualizations:
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".