E-government Service Adoption by Citizens: A Literature Review and a High-level Model of Influential Factors
Bibliographic record
Abstract
E-government services refer to services offered by governments using information technology (IT). Many governments around the globe are investing heavily in IT to enhance service delivery to their citizens. However, citizens do not always use these services so that they often forgo their potential benefits because of key interconnected considerations that are perceived to transpire from their use. Over the years several studies examined IT adoption in e-government services contexts, building a rich albeit fragmented body of knowledge in the process. Indeed, the diversity found in these studies and the fast and continuous change that characterizes IT in general, make the identification and the synthesis of the main factors influencing citizens’ adoption of E-government services a relevant and timely endeavor. For this reason, this study builds on the findings of a systematic literature review to provide a high-level framework that conceptually structures the state of knowledge on the topic, and that informs both researchers and practitioners on the main factors influencing e-government services adoption by citizens.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".