The influence of information technology, administrative management and knowledge management practices on the success of e-government in Indonesia
Bibliographic record
Abstract
The high quality of public services is a guarantee for the public of easy and efficient access. Good quality public services can help increase people's productivity, reduce unnecessary bureaucracy, and encourage more active participation from all walks of life. In addition, government transparency serves as the main basis for building a relationship of mutual trust between the government and society. Through the application of information and communication technology, E-Government allows the government to provide public services more efficiently, quickly and easily. This research aims to analyze the influence of information technology, administrative management, and knowledge management practices on the success of E-Government. This type of research is quantitative research using a questionnaire. Respondents were selected using a random sampling method from various groups in the public sector in the DKI Jakarta province. A total of 380 questionnaires were distributed to respondents, and 264 questionnaires were successfully returned. However, there were 21 questionnaires that were not filled in completely. Finally, 243 questionnaires were analyzed further. Questionnaire measurements used a Likert scale of 1 - 7. The data in this study were analyzed using SmartPLS 4 software. The results of this study conclude that the implementation of information technology has a significant relationship with knowledge management practices. However, the relationship between information technology and e-government in this study was not proven to be significant. Administrative management has a significant relationship with knowledge management practices but has no significant effect on E-Government. Knowledge management practices have a significant influence on E-Government and there is interaction between information technology and knowledge management practices, as well as between administrative management and knowledge management practices on E-Government.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".