Understanding the Disruptiveness of Integrated Project Delivery (IPD) in the AEC Industry
Bibliographic record
Abstract
The Architecture, Engineering, and Construction (AEC) industry is plagued by persistent challenges such as low productivity, cost overruns, and frequent project delays. Integrated Project Delivery (IPD) has emerged as a potential solution, offering collaborative approaches to improve project outcomes. This study proposes a two-tiered methodology for evaluating the disruptiveness of innovations in the AEC industry, with a particular focus on IPD as a disruptive innovation. In the first tier, a multidimensional framework is developed to systematically assess the disruptiveness of innovations in the AEC sector. This framework, informed by a thorough literature review and disruptive innovation theory, includes dimensions such as business models, processes, and anticipated outcomes. The second tier applies the framework by analyzing the disruptiveness of IPD. The assessment draws on data from three comprehensive studies, including ethnographic research, interviews, and focus groups, which examine IPD’s impact on different stakeholder groups such as clients, consultants, and contractors. Findings reveal that IPD has the potential to significantly disrupt traditional business models, processes, and project outcomes, particularly at the project level. Notable disruptive characteristics include shifts in collaboration dynamics, redefined project financing models, and improved efficiency. However, several barriers hinder IPD adoption, including resistance to change and misalignment with conventional contractual structures. Expert interviews support these results, indicating that IPD represents a fundamental shift in the AEC industry. This research contributes to the existing body of knowledge by offering a structured framework for assessing the disruptiveness of AEC innovations and demonstrating its practical application. In this way, AEC organizations, projects, and practitioners can better strategize for the adoption of any new disruptive innovation and thus pursue a strategic advantage in the highly competitive industry market.
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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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".