E-Cash management System in the Public Sector in Sri Lanka: With Special Reference to the Ministry of Public Administration & Home Affairs
Bibliographic record
Abstract
E-cash Management System enhances cash management in the private sector and the public sector in a fast pace of organizational environment change. Though it has not a universal framework for e-cash management, the public sector in many countries has provided its usefulness by dint of their results of application. Concurrently, the public sector in Sri Lanka has also initiated e-cash management at ministerial and departmental levels parallel to the existing setup. Despite many attempts made in improving e-cash management system in the public sector, little progress has been made. However, there is a lack of research on e-cash management in the public sector in Sri Lanka. Thus, this study focuses to fill the gap in this regard. This study seeks to contribute knowledge in this area by addressing related questions. The first research question is as follows: Despite many reformsmade in the administration system in the public sector, whydo the issues in respect of e-cash management system abide or persist or prevail to be addressed? Secondly, what are the factors that have adversely affected the progress of e-cash management? Hence the study was carried out to elite answer to the aforementioned questions.Combinations of Qualitative and Quantitative research strategies were applied in the research process which is commonly known as mixed method research. Random sampling method was applied. Samples of 500 managers in the public sector were selected and only 317 responded. Data were collected by self-administered Questionnaire and interviewed concurrently. Using the data from the survey analyses was completed. The results of the study revealed that the existing level of e-cash management owing to administrative reforms and implementations have not effectively addressed the deep-rooted social, political and cultural issues which pose as crucial barriers for the success of e-cash management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".