A structural equation modeling approach to examine the relationship between complexity factors of a project and the merits of project manager
Bibliographic record
Abstract
Nowadays, projects have become so widespread in the world that individuals and organizations are always involved in a variety of them.Recent advances in technology and fundamental changes in most scientific disciplines have had an essential impact on projects, and have made the nature and environmental conditions governing them to become more complex than before.With increasing complexity, the amount of information needed for project management increases.In general, the increasing complexity of projects is a growing source of project risks.It has been recognized that complexity affects the performance of a project and will be effective in its success.In this context, the traditional principles and practices of project management are no longer able to control the emerging complexity of projects.In addition, one of the key factors for the success of the projects is the appropriateness of the project manager's assignment.Many studies have been carried out in identifying the suitability criteria of the project manager and the methods of selecting the project manager.In most of these studies, the amount and type of complexity of the project are mentioned as factors influencing the design of an appropriate project manager.However, there has not yet been a specific approach for selecting the project manager with regard to the complexity of the project.Therefore, in this research, we try to investigate the relationship between the complexity of the project and the merits of the project manager by applying a structural equation modeling approach.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".