Information Communication Technology (ICT) Utilization in Private Universities in Uganda: Exploring Strategies to Improve. A Case of Uganda Christian University
Bibliographic record
Abstract
The study was an extension of a similar study by [1] conducted among local government staff in Uganda and was extended among staff at Uganda Christian University. A total of 108 respondents out of the accessible 130 representing 83.0% response rate was realized after administering the study instruments over a period of two (2) weeks. Of the 108 respondents, 70.4% constituted staff in administration, 8.0% heads of sections and 21.6% academic staff. Data was analyzed using different statistical techniques which included descriptive statistics, mainly mean and standard deviation, Pearson Product Moment Correlation Coefficient to establish the relationships between variables and multiple regression analysis to establish causal influence of factors on ICT utilization. At bivariate level, organizational support systems, ICT infrastructure and users’ perceptions had a strong relationship on ICT utilization. Multiple regression analysis revealed that only user perceptions (perceived ease of use) and user knowledge and skills had a significant causal influence on ICT utilization. The researcher Recommended that UCU strategic management cater for personnel ICT capacity building to improve their skills and therefore enhance ICT usability. Further research may consider segmenting staff by faculties and study how utilization levels differ using ANOVA. A comparative study can also be undertaken to assess UCU and any other private university.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".