A structured approach to assessing and developing integrated project delivery: capability maturity and readiness evaluation
Bibliographic record
Abstract
Integrated practices and delivery models are increasingly being seen as the way forward for the construction industry to maximize the value generated and increase the likelihood of project success. Most prominently, Integrated Project Delivery (IPD) is an innovative way of project delivery that breaks away from traditional delivery practices and is deemed effective in integrating stakeholders, enabling collaboration, and enhancing project outcomes. As a relatively novel approach, the IPD framework and practices still lack essential pieces to enable the full potential of this delivery method, which, in turn, represents the motivations for this research. Namely, the theoretical motivations stem from the scattered research domain, and the absence of a widely recognized research and development framework that defines the constitute parts of IPD and bridges the academic work with practical implications which hinder further development in this approach. The practical motivations, on the other hand, derive from the lack of structured tools that enable an informed evaluation of IPD practices at the different stages of a project, which is deemed necessary for enhancing its practices and facilitating continuous improvement. Therefore, the central aim of the research presented in this PhD dissertation is to enhance the theoretical foundation and practical implementations of IPD, ultimately constructing a path for more standardized and consistent approaches to IPD implementation, thus enhancing its effectiveness and fostering its adoption across the industry. This aim was achieved through research progress that includes establishing a research and development framework for IPD alongside the development of capability maturity and readiness models. The Research and Development Framework (IPD R&D) defines the constituted elements of IPD and organizes them in a framework that corresponds to its practical implementation. It also consolidates scattered research efforts, organizing the research and development domain around IPD, and guides future scholarly inquiry and practical exploration in IPD. In addition, this research introduces a structured approach for evaluating and enhancing IPD practices by developing dual models: an IPD Maturity Model and an IPD Readiness Model, each tailored to evaluate and enhance the effectiveness of IPD practices at different project stages. The Capability Maturity Model (IPDCMM) and its tool are designed to inform and assess the maturity of IPD practices at the end of the project, providing projects and teams valuable insights into the effectiveness of their implementation through a set of indicators and metrics for five levels of maturity, derived from both established frameworks and empirical data from three IPD case studies. Concurrently, the Capability Readiness Model (IPDCRM) and its tool evaluate project readiness to start implementing IPD, ensuring that critical plans, resources, tools, and necessary efforts are in place and aligned for a successful IPD implementation. This model identifies key readiness indicators, which are evaluated against a structured checklist to determine the readiness level among five established levels and guide projects in aligning and enhancing their preparedness. The overarching methodological framework that guided this research was Design Science Research (DSR), underpinned by a pragmatic philosophy that forms the epistemological and ontological basis for both the approach and the findings of this study. The pragmatic philosophy influenced the research approach and directed the methodological choices, prioritizing research methods based on their practicality and flexibility. It emphasizes achieving practical and applicable results, namely artifacts, that benefit the construction industry. This research framework embraces a dynamic interaction between theory and empirical data, systematically iterating between model development, testing, and refinement. Through this methodological lens, this study employs mixed-methods data collection across five Canadian case studies in four Canadian provinces that offer diversity in project type, size, and jurisdictions impacting IPD adoption. This richness provided an empirical base to validate the proposed models and tools. This research contributes to the field of project delivery and construction management by extending the theoretical understanding of IPD through a structured approach to research and development, capability, maturity, and readiness. In addition, it contributes to the practical application of IPD by operationalizing these frameworks into practical tools that can enable informed evaluation of IPD practices, enhance its implementation, and facilitate continuous improvement. This research concludes with a call for further validation of the tools proposed across broader industry segments to ensure their generalizability and to continue advancing collaborative and innovative practices within the construction industry.
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 | 0.047 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".