Bibliographic record
Abstract
Purpose Few scholars have been cited as frequently as Pinto, Slevin, and Prescott for their contributions to project success and related critical success factors (CSF) in the 1980s. Studies since then built on their articles to broaden and refine our understanding of the topic. The purpose of this paper is to discuss the reasons for the impact of these seminal contributions and how the topic of project success continues to evolve. Design/methodology/approach The paper analyses the popularity of Pinto and his colleagues' contributions to project success and reviews the development of this field of research since then. Findings Project success remains a vibrant school of thought as do the earlier definitions, measurement scales and dimensions, and assessment techniques that Pinto and his colleagues developed. The authors view success more broadly and think of it strategically because they consider longer‐term business objectives. Some research is now based on managerial or organizational theories and reflects the multi‐dimensional and networked nature of project success. Practical implications Practically, the classic contributions in project success continue to be valid. The authors see diversity in how success is defined and measured. The CSFs vary by project types, life cycle phases, industries, nationalities, individuals, and organizations. Originality/value The paper relates earlier understandings of project success to subsequent research in the field and underscores the significant findings by Pinto, Slevin, and Prescott.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".