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A structured approach to assessing and developing integrated project delivery: capability maturity and readiness evaluation

2025· other· en· W7134145440 sur OpenAlexaboutno aff
Ahmad J. Arar

Notice bibliographique

RevueEspace École de technologie supérieure (École de technologie supérieure) · 2025
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCapability Maturity ModelImplementationIntegrated project deliveryBest practiceMaturity (psychological)Work (physics)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,047
score de la tête « metaresearch » (Gemma)0,077
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,047
Score d'incertitude au seuil0,249

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0470,077
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0140,009
Études des sciences et des technologies0,0020,003
Communication savante0,0070,006
Science ouverte0,0020,006
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0070,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,027
Tête enseignante GPT0,314
Écart entre enseignants0,287 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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