Evaluation of the role of project management office (PMO) at P.XYZ based on risk to improve project performance
Bibliographic record
Abstract
The Project Management Office (PMO) has different roles, but if it is concluded that the existence of this PMO will be needed by the project. Factors contributing to project performance include support from the PMO. PMO at PT. XYZ will begin to be implemented starting in 2019. From 2019 to 2022 work on 3 project assignments from the local government. In practice, there were problems where 2 of the three projects experienced delays in completion and 1 other project experienced payment delays, where the risks to the project have not been optimally managed. This study aims to identify the role of the project management office (PMO) owned by PT. XYZ, identify risks in PMO management and identify the role of PMO that has the most influence on risk-based project performance at PT. XYZ. The research method used in this study is a survey method for several respondents where the previous questionnaire was validated by experts and a pilot survey was carried out and the results of the questionnaire will be analyzed using the SEM method. This is to be able to provide results if risk control in PMO management is carried out effectively or on target so that it can improve project performance at PT. XYZ.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".