Government to Citizen: Advocacy of Government On-line Systems and Their Acceptance among Citizens
Bibliographic record
Abstract
Government on-line systems under the e-service project were launched in 2000. The ongoing objectives are to improve internal government office efficiency as service delivery to its citizens. Since its launch ten years ago, the use of this service by the citizens has beens relatively low, especially on the transaction side. Mostly, citizens use e-services merely to check their Road Transport Department (JPJ) and Royal Police Malaysia (PDRM) traffic summonses, to take their driving tests, to check their electrical and telephone bills and to check compound and tax issues with the Kuala Lumpur City Hall (DBKL). Citizens’ use of the e-service, mainly to do routine checking more than to conduct transactions, could subsequently influence further expansion of e-service. These issues lead to form the objectives of the study: firstly, to examine the factors that influence the use of government’s e-service, and secondly, to measure the strength of influence among the variables. The results reveal that the crucial factors that influence the intentions and behavior of citizens in accepting government’s e-services are “attitude” and perceived “behavioral control,” while subjective “norm” is not as evident.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".