ECI Impacts on Reducing the Causes of Disputes in Construction Projects
Bibliographic record
Abstract
This study investigates the complexity and the context of the construction industry and conditions where causes of disputes arise. The construction project life cycle needs management approaches that help to reduce conflicts in the first place, reduce risk and improve performance. According to lean, disagreements and disputes are waste; if eliminated, costs would be reduced, performance improved, and the health of the working environment would be sustained. Emerging collaborative project delivery methods involve key participants very early in the project, frequently even before the design phase. It is characterized with a multiparty contractual agreement that allow risk and rewards sharing among the stakeholders to mitigate them collaboratively. Although partnering may be a helpful solution to improve the situation by getting people to work together; however, it does not analyze the underlying issues that make the conditions difficult and contribute to uncertainty and disputes. Taking inspiration from lean and collaborative delivery methods, ECI is a project delivery method that can fill the partnering deficit gap. It would specify the time of contractor involvement to improve the design, increase productivity, reduce risk, improve performance, and sustain a healthy environment through constructability reviews, design assistance, or even taking over the design process. This study contributes to filling that missing gap by evaluating the impact of ECI that, if brought to the construction projects, would reduce the cause of disputes occurrence, and improve performance and relationships. According to the research findings, there are several pathways for ECI implementation, which indicates that there is no one strict rule, neither for its procurement evaluation nor for its contractual design. This means that ECI can be used in two stages for the same contractor, or it can use different contractors for each stage. It could also be done in different ways, such as with traditional DBB, DB, management contracting, project partnering, or alliancing. This study offers research opportunities and agendas to help academics and construction practitioners gain better knowledge that can help them design an appropriate ECI pathway that can improve the project’s performance and reduce surprises that might lead to conflicts and disputes. ECI has been shown to reduce various types of claims such as design, time extension, scope, liability, and termination claims. The results were confirmed with actual data from a case study in Canada, “170 Street over YHT—Bridge Rehabilitation”. Moreover, the study analyzes the ECI influence on the contractual risks through a comparison of two ECI contracts: CCGC contract form as adopted by the City of Edmonton in Canada and JCT (MC) as adopted in UK.
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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 | 0.036 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".