Literature Review on Collaborative Project Delivery for Sustainable Construction: Bibliometric Analysis
Bibliographic record
Abstract
This paper aims to conduct a bibliometric analysis and traditional literature review concerning collaborative project delivery (CPD) methods, with an emphasis on design-build (DB), construction management at risk (CMAR), and integrated project delivery (IPD) Methods. This article seeks to identify the most influential publications, reveal the advantages and disadvantages of CPD, and determine the most suitable CPD methods for sustainable construction. This research involves the application of bibliometric instruments in R, which is a powerful statistical computing language that can be used to perform complex data analyses and visualizations on bibliographic data to scrutinize academic journals retrieved from the Scopus database. Google Scholar is also utilized for an in-depth analysis as part of this study. Relevant articles are identified and screened for review. Our analysis is grounded on an extensive dataset of 927 journal articles collected from the year 2000 up to September 2023, providing a robust foundation for a comprehensive examination. Citation analysis identified highly cited publications that have significantly influenced the discourse on CPD. The analysis further established the advantages and disadvantages of CPD methods to suggest the most suitable CPD technique for sustainable construction. The results of this analysis offer insights into future directions and opportunities for further research through a comprehensive overview of the existing discourse on the subject. The paper classifies CPD through collaborative contracting, particularly through early contractor involvement (ECI), groups the design-build and construction manager at risk methods under CPD, and aligns their advantages with the critical success factors for sustainable construction in order to select the most suitable CPD technique. This research can serve as a guide for industry professionals, researchers, and policymakers, providing a structured path for collaborative endeavors and facilitating coordinated efforts toward collaborative project delivery methods and sustainable construction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.029 | 0.214 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".