Wood, X; Ghimire, P; Kim, S; Barutha, P and Jeong, H D (2024) Framework for evaluating the success of integrated project delivery in the industrial construction sector: A mixed methods approach & machine learning application. Construction Economics and Building, 24(1-2), pp. 94-118. ISSN 2204-9029
Abstract
Integrated project delivery (IPD) has gained traction as a collaborative approach to managing complexity and uncertainty in large industrial capital projects. While IPD emphasizes team integration and process alignment to drive better outcomes, the lack of standardized benchmarks to evaluate its performance relative to traditional methods persists as a barrier. To bridge this gap, this study developed a practical, and unbiased Project Success Framework (PSF) for IPD on industrial projects. A mixed methods research approach including subject matter experts' survey, research charrette, and validation survey was conducted to build and validate the PSF. In addition, this study proposed a machine learning (ML)-based application tool embedding PSF to enhance the practicality and applicability of PSF. The machine learning-based application tool was validated by comparing the results with the PSF suggested in this research. The PSF developed in this study allows researchers and practitioners to empirically evaluate the integrated project delivery's efficacy on key industrial project outcomes. In addition, it offers a method to compare project delivery methods across diverse projects, aiding organizations in precise selection using empirical evidence for optimal results. Moreover, this framework aids clients in crafting shared risk/reward models that foster successful outcomes by encouraging desirable behaviors.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | industrial construction; integrated project delivery; machine learning; mixed methods research; project success framework |
| Index terms: | project delivery, industrial construction, practitioner, machine learning application, integrated project delivery, validation, evidence, project success, mixed method, integration, traction, charrette, capital project, machine learning, survey, complexity, project outcome |
| Subjects: | design process, professional development, evaluation and assessment methods, artificial intelligence, transportation engineering, project management theory and practice, building construction, contractual arrangements, organizational analysis, systems engineering, project completion, data collection methods, project delivery, strategic project management, practitioner |
| Topics: | Organizational Design, Research Practice, Project Management, Roles and Professions, Engineering Principles, Procurement, Digital Applications, Information Management, Design Practice |
| Descriptive scope: | 4 PCEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here