Challenges Associated with the Implementation of Knowledge Management in Nigerian Tertiary Institutions
Bibliographic record
Abstract
This study examined the challenges associated with knowledge management implementation and academic staff retention in selected tertiary institutions in South East, Nigeria. The research design adopted was a cross-sectional survey design. The main instrument used for data collection was questionnaire. The population consisted of 7,423 academic staff of the 10 randomly selected institutions in the South East Nigeria. A total sample size of 555 was drawn from the population. The instrument was checked for reliability using Cronbach method and the reliability co-efficient result of 0.915 showed that the instrument had high degree of item reliability. The hypothesis formulated was tested using Friedman chi-square statistics. The findings indicate that there is positive significant challenge in knowledge management implementation and academic staff retention in selected tertiary institution. The study concluded that lack of Knowledge Management implementation has posed a great challenge in academic staff retention in tertiary institutions. It was advised that tertiary institutions in Nigeria should adequately manage the process of knowledge acquisition, sharing and development so as to enjoy a Stella performance
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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 | 0.007 | 0.024 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".