Bridging the Maturity Gap: Introducing BIAMM, a Simplified Project Management Maturity Model Aligned with PMI Domains for Developing Contexts.
Bibliographic record
Abstract
Project Management Maturity Models (PMMMs) are critical tools for assessing and improving project management processes within organizations.However, existing models primarily target organizations with established project management practices, which creates a gap for organizations at the early stages of adopting project management.This study reviews eight prominent project management methodologies (PMMMs) and evaluates their suitability for organizations in the early stages of adopting project management.Using a structured comparative analysis framework based on accessibility, scalability, implementation complexity, and contextual adaptability, the research identifies significant limitations in the applicability of these models for organizations in the early stages of adopting project management practices.The findings reveal that most PMMMs are overly complex, prescriptive, and resource-intensive for novice organizations, limiting their practical adoption.The paper recommends tailoring or simplifying maturity models to better serve the needs of beginners with more inclusive and adaptable frameworks.The paper concludes by proposing a new model, the Basic Intermediate Advanced Maturity Model (BIAMM).BIAMM offers a straightforward yet effective framework with three maturity levels: Basic, Intermediate, and Advanced.Its flexibility in accommodating various methodologies, including Waterfall, Agile, and Hybrid, makes it an ideal choice for organizations seeking to develop strong project management capabilities.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".