Measuring Project Value: A Review of Current Practices and Relation to Project Success
Bibliographic record
Abstract
Achieving a higher project value for all project participants is a major concern in the construction industry and reflects the extent to which projects are successful.The major struggle, however, is in the ability to both identify and measure the tangible and intangible project value requirements.Having different interpretations of what project value constitutes, the literature offers a variety of practices and suggestions for measuring project value.However, since the offered methods are fragmented and do not build on one another, a further investigation is required.Accordingly, this research provides a review of the measures discussed in the literature and suggests new directions for evaluating project value.The research targets the construction industry in addition to other industries that also provide effective strategies to create and measure value in customer-based product developments.The study revealed a lack of a sufficient approach for quantifying value on projects.Consequently, this research aims at providing combined effective ways to help measure project value in an effort to align stakeholders' needs, increase stakeholders' satisfaction, and thus realize successful projects.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".