Building Citizen Trust towards E-Government Services: Do High Quality Websites Matter?
Bibliographic record
Abstract
E-governments are increasingly becoming a familiar fixture in virtual landscapes. Yet, the lack of citizen trust brought on by the novelty and uncertainty of online transactions has inhibited the widespread acceptance for public e-services. Ascribing to the perspective of technology as a social actor with whom the customer interacts and transacts, we put forward a research model that accentuates the pivotal role of e-government service quality as a salient driver of citizens' trustworthiness beliefs towards e-government Web sites, which in turn promotes the corresponding adoption of public e-services. E-government service quality, as conceptualized in this study, borrows from the popularized SERVQUAL constructs in deriving prescriptive design principles to guide the development of e-government Web sites. Data collected from a sample of 647 e-government service participants substantiates all 14 hypothesized relationships, thereby suggesting that high quality e- government Web sites do matter in building citizen trust towards public e-services.
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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 | 0.009 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".