Digital transformation in enhancing knowledge acquisition of public sector employees
Bibliographic record
Abstract
This research was conducted to determine knowledge acquisition by employing digital technology for public sector employees. This study adopts system quality, information quality, user satisfaction, service quality, and net benefit as the empirical considerations. The data analysis technique in this study used SEM (Structural Equation Modeling). Respondents in this study were 198 people consisting of public sector employees who used a learning management system. The results showed that service quality has a significant effect on user satisfaction. User satisfaction has a significant effect on net benefits. Meanwhile, system quality had no significant effect on user satisfaction, information quality had a significant effect on user satisfaction. The findings would imply the strategies to strengthen the implementation of e- learning is to increase user satisfaction. The finding managerially points out the necessity to evaluate the transformation of classical training programs in terms of face-to-face learning to blended learning by integrating online learning and face-to-face in the public sector knowledge acquisition model of training. The findings present an evaluation using empirical examination and highlights the importance of continuity to arrange action plans in public sector to synchronize knowledge acquisition model through training to obtain behavioral change and wider organizational impact of the training on public sector institutions.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".