Organizational Factors as Predictors of Knowledge Management Practices in Federal University Libraries in Nigeria
Bibliographic record
Abstract
Abstract Objective – University libraries in Nigeria are facing challenges arising from poor funding, increasing user demands, and a competitive information environment. Knowledge management has been accepted by information professionals as a viable management tool, but issues surrounding its application require empirical investigation. The aim of this study is to determine the organizational factors that are correlates and predictors of knowledge management practices in federal university libraries in Nigeria. Methods – The study was based on a correlational research design. Twenty heads of university libraries in Nigeria responded to a structured questionnaire developed by the researcher. The questionnaire was validated by experts and its internal reliability was 0.78 obtained through Cronbach’s alpha procedures. The data collected were analyzed using Mean, Standard Deviation, One-Way ANOVA, Pearson’s Product Moment Correlation Coefficient, and regression analysis. Results – The study found that management support and collaboration were the most significant predictors of knowledge management practices in federal university libraries in Nigeria. Even though human resources policy and rewards systems had positive correlations with knowledge management practices, their correlation coefficients were not significant. Conclusion – The success of knowledge management in university libraries in Nigeria depends on some contextual factors such as the support given by the management staff and the extent of collaboration among staff.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".