E-Government Systems to Improve Citizen-to-Public Administration Communication: A Literature Review
Bibliographic record
Abstract
Cities are growing worldwide, overloading public infrastructure and causing delays and inefficiencies. In this context, public services are increasingly transitioning online, facilitating citizen engagement and feedback. This digital transformation offers numerous advantages, including reduced response times, enhanced citizen satisfaction, and cost savings. The aim of this work is to conduct a comprehensive survey of e-government systems, emphasizing their role in improving communication between citizens and public administration, and to provide insights into trends, gaps, and best practices in this evolving field. This research emphasizes conversational technologies and adopts the Digital Governance framework, which examines how digital tools reshape public administration by improving services, transparency, participation, and efficiency. The review used a structured manual process via Excel, applying criteria to ensure transparent screening and categorization, enabling a focused and adaptable analysis. We found research in several countries indicating that it is a global research trend. The study examines various aspects of e-government systems, including their objectives, implementation level, communication interface, commercial development technology, evaluation method, and metrics utilized. Overall, this review provides valuable insights into the current landscape of e-government systems to improve citizen-to-public administration communication and identifies trends and gaps in this evolving field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".