A Capability Maturity Model for Integrated Project Delivery
Bibliographic record
Abstract
As the adoption of integrated project delivery (IPD) progresses, increasing evidence has highlighted its potential to improve project outcomes. However, as an emerging practice, there remains a lack of structured mechanisms to evaluate the maturity of its implementation, which can limit opportunities for learning and improvement. Therefore, this study introduces an IPD Capability Maturity Model (IPDCMM) to evaluate the maturity of IPD implementation at the project level. This model enables organizations to benchmark their IPD capabilities against established best practices, facilitating structured development and continuous improvement. This model is designed as a post-project assessment tool that evaluates the maturity of IPD practice upon project completion, providing critical insights for learning and future project enhancements. The methodology, underpinned by a pragmatic philosophy and guided by the principles of design science research (DSR), prioritizes achieving practical outcomes (artifact). It combines insights from IPD frameworks, maturity models from other fields, and three case studies. The IPDCMM was developed alongside the IPD Maturity Assessment Tool (IPD-MAT), an artifact validated via evaluation sessions and feedback interviews with key stakeholders of IPD case studies. This model provides a structured framework for assessing IPD implementation maturity and facilitates a pathway for enhancing IPD practices and achieving efficiency in project delivery.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".