An Empirically Grounded Search for a Typology of Project Management Offices
Bibliographic record
Abstract
This article uses an empirical contribution to better understand the project management office (PMO). PMOs are an important aspect of project management practice. Their design and management is complicated by the great variability found among PMOs in different organizations. Lack of consensus on their structure and the roles they undertake prevent the establishment of formal standards on PMOs. Having a typology of PMOs can make the great variability much more manageable. However, the typology should be grounded in reality. The aim of this article is to exploit a rich database of descriptions of 500 PMOs to identify patterns in the data that can form the bases for one or more typologies of PMOs. Data on both the organizational context and the characteristics of PMOs were explored. The search for the bases of a typology relies on the identification of statistical associations (1) between the characteristics of PMOs and characteristics of their organizational context, (2) between the different characteristics of PMOs themselves, and (3) between the performance of PMOs and the characteristics of both PMOs and their organizational context. The analysis explores each of these avenues successively in the search for characteristics that are good or poor candidates for forming the basis of a typology of PMOs. The results of the analysis are then integrated into a model.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".