A Retrospective look at our Evolving Understanding of Project Success
Bibliographic record
Abstract
Our views on project success have changed over the years from definitions that were limited to the implementation phase of the project life cycle to definitions that reflect an appreciation of success over the entire project and product life cycle. This paper assesses our evolving understanding of project success over the past 40 years and discusses conditions for success, critical success factors and success frameworks. The paper concludes with a holistic view of project success and its implications for practice. This is an important topic because projects are an increasingly common way of work, and the lines between project and process work are harder to discern. Increasingly, more project managers work in companies using program and portfolio management as a means to organize project-related work. The success of individual projects, therefore, impacts the wider organization in several dimensions and makes the concept of project and project management success that much more relevant. The topic is also important because it has a bearing on the future directions of project management in the strategic context.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".