The new-normal challenges and IPD solutions: a Canadian case study
Bibliographic record
Abstract
Purpose According to the construction literature, the number of projects applying integrated project delivery (IPD) principles is expected to increase in the new-normal era. However, given that the pandemic is not yet fully over, accurate and measurable data are not yet available. Also, there is a lack of empirical studies that could provide guidelines as to the application of IPD principles at the various stages of construction projects. Thus, the goal of this paper is to address this knowledge gap through case studies. Design/methodology/approach This paper follows a multi-step research methodology, namely, a literature review, case study and focus group discussions in the context of Canadian construction projects. Findings Based on the conducted literature review and focus group discussions, the authors identified: (1) new challenges in the various stages of the construction projects' life cycle, (2) their related proximity aspects (technological, organizational, geographical and cognitive) and (3) IPD principles that can address the identified challenges within their associated proximities. The results show that IPD relational principles can improve a project's organizational and cognitive proximities, while IPD digital integrative principles can enhance a project's geographical and technological proximities. Originality/value This study contributes to the theoretical checklists of challenges that the construction industry has experienced since the beginning of the pandemic, and to the practical guidelines of implementation of IPD principles to meet these challenges. The conducted case studies are timely and relevant, and their results provide new insights for key project stakeholders into the application of IPD to tackle new-normal challenges based on their proximity perspectives.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".