An Empirical Identification of Project Management Toolsets and a Comparison among Project Types
Bibliographic record
Abstract
This article presents the results of an empirical investigation of project management practice. Practice is investigated through the study of the extent of use of a large number of practices, tools, and techniques specific to project management. A sample of 2,339 practitioners participating in a large-scale international survey is used for this article. The sample size and the diversity of contexts in which the respondents are working render the analysis feasible and the results reliable. The data is analyzed to identify patterns of practice. More specifically, using principal component analysis, the research identifies patterns that demonstrate that practitioners use project management tools and techniques in groups or “toolsets.” A brief attempt is made to compare results with A Guide to the Project Management Body of Knowledge (PMBOK® Guide) (PMI, 2008) Knowledge Areas and Process Groups. The article also shows how practice varies with the management of different types of projects: engineering and construction; business and financial services; information technology (IT) and telecommunications; and software development projects. The identification of these variations has important consequences for practice and for the study of practice.
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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 | 0.016 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".