DEVELOPING TRUST RECIPROCITY IN ELECTRONIC- GOVERNMENT: THE ROLE OF FELT TRUST
Bibliographic record
Abstract
At the present time, most people limit their use of government websites to downloading and printing forms while relying on other modes of communication (such as phone, face-to-face, and mail) for important transactions with the government. Many factors contribute to peoples’ reluctance to use online government tools. Privacy and security concerns are often cited as the two major reasons for lack of trust and cited as an important impediment to increased utilization of e-government. Although many studies have examined users’ trust in electronic media and examined information technology (IT) artifacts that can increase users’ perception of website trustworthiness, no studies have examined the impact of “felt trust” on e-government, or even on electronic business (e-business) in general. In other words, no study has yet examined how IT artifacts on websites make users feel trusted by the government and how that in turn could affect website trustworthiness. This study attempts to fill that gap by analyzing feedback collected from participants in a field study using an online survey. The results demonstrate the importance of felt trust as another way to build trust in e-government.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".