Structural equation modeling of critical success factors in the programs of development regional
Bibliographic record
Abstract
The Structural Equation Modeling SEM using SmartPLS.V3 software was used in this study to model the priority of CSFs of program management under four categories (program planning, strategy of the organization, stakeholders, and construction program performance) associated with Regional Development Programs RDPs in Iraqi provinces. Based on the literature review, the identified CSFs of program management have been explored through a systematic review approach. This model investigated the relationship and effect of CSFs on program management of regional development. The measurement model underwent three iterations to fulfil the threshold criterion, which included Cronbach's a being more than 0.7, CR being greater than 0.7, and AVE being more significant than 0.5. As a result, the model met the convergent validity. For every path modelling and hypothesis, the structural model is evaluated. The model produced a GoF of (0.524), regarded as sufficiently high to be considered for obtaining adequate global PLS model validity. The model's global GoF performance was also assessed, and the findings met the criteria. It is clear from the final model that there may be a connection between the main program management groups, as shown by the path model confirmed.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".