Program and Performance Budgeting System in Public Sector Organizations: An Analytical Study in Saudi Arabian Context
Bibliographic record
Abstract
In order to improve the budget system, a number of approaches and techniques have been adopted in public sector organizations such as Program and Performance Budgeting System (PPBS), Performance Based Budgeting System (PBBS) and Zero-Based Budgeting (ZBB). The present study is an extension in the line of very few researches which had been conducted in developing countries in regard to implementation of “One Budgetary Approach” which is known as “Program and Performance Budgeting System PPBS”. The study concentrates its focus on the support which it may find in case of “budgetary format” is adopted by the “Public Sector Organizations” in the kingdom of Saudi Arabia. The study explores several dimensions such as familiarity, acceptability and adoptability of PPBS, “degree of contribution of Accounting System followed by “public sector organizations” to adopt PPBS”, the benefits that might be realized and the obstacles that probably might be faced if this approach of budgeting is adopted by Public Sector Organizations” in Al-Kharj region. The study came up with the following main findings; there is a fair familiarity and understanding of PPBS by financial managers and accountants working in the “public sector organizations”, the accounting system followed by “public sector organizations” contributes to adopt PPBS successfully, there are certain benefits could be obtained while adopting PPBS by public sector organizations, and finally certain obstacles have been discovered which are standing as stumbling-stone to adopt PPBS in “public sector organizations” in Al-Kharj region effectively.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".