Identifying Forces Driving PMO Changes
Bibliographic record
Abstract
Project management offices (PMOs) are dynamic organizational entities, frequently in transition from one charter and structure to the next. Within this article, we present empirical results on the nature and reasons for this transition. The article reports the second of a series of studies aimed at understanding the dynamics of PMOs. It addresses the mistaken paradigm that PMOs change because characteristics or functions in an existing PMO are wrong and require a new PMO charter or structure that can last for a long time. Instead of that, the article proposes a process view on the transformation of the PMO as being triggered by conditions within the external and/or internal context and producing outcomes in terms of impacts from the transformation. A global web-based questionnaire on PMO transitions in structure and charter yielded 184 responses. Factor analysis and correlation analyses revealed that the transition of a PMO from one configuration to the next is not a question of being right or wrong. PMOs in transition can rather be understood as a multilevel dynamic process anchored in a specific organizational context change. From the academic viewpoint, the authors believe that this research filled a large gap in the under-standing of the reasons for and nature of PMOs to transition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".