Effectiveness of Nepal’s E-Government Service Delivery: A Case of Department of Passport (DoP)
Bibliographic record
Abstract
The COVID-19 pandemic situation made governments and citizens take part in the use of technology to render services. The use of technology has revitalized and revolutionized public administration in many countries. All technology-driven services delivery also enhanced the trust of citizens towards the e-government systems. Also, the Government of Nepal (GoN) is moving towards the system of online service rather than service through physical presence because the pandemic situations forced to adopt online platforms for requesting public services. At the same time, the engagement on online activities increased during the pandemic for communication and other service-related activities. ‘Passport’ is an essential document issued by the government of residence required to travel other countries. In order to get a passport an applicant submits the filled-up form to the Department of Passport (DoP) with verified information through District Administration Office (DAO). In Nepal for a quick processing of the passport, the applicants can visit DoP and do the processing from there too. The information verification of the applicants is a sensitive and lengthy process in Nepal, and it is a paper-based. The DoP has an online pre-enrollment system that facilitates passport application processing. We assume that the introduction of technology improves service delivery. In this paper, the online pre-enrollment system of DoP is analyzed using E-Government Maturity Models (E-GovMM), and SWOT analysis. The views of the officials from DoP on the use of the system is collected through a purposive survey. Our findings from the study showed that the maturity level of the pre-enrollment system is in alignment with the maturity model, and the online pre-enrollment system implementation in the DoP has improved the traditional passport processing process. The pandemic situations also highlighted the need of online systems so that the social distancing could be practiced avoiding the spread of virus. Such pandemic situations can be more pressing and frequent in upcoming time. The maturity of the existing systems is necessary for which government should upgrade policy, human resources and technology. There has to be collaboration among various government agencies to provide single window online service delivery, which can be a sustainable solution to provide public service delivery in any disruptive situation. The government should embrace the concept of system thinking to develop whole of a government through collaborative effort in improving the public service delivery though E-Government implementation.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".