How to Improve Interface Management Behaviors in EPC Projects: Roles of Formal Practices and Social Norms
Bibliographic record
Abstract
Interface management (IM) has emerged as an effective strategy to reduce interface-related issues and risks by facilitating communication and coordination among diverse parties, particularly in engineering, procurement, and construction (EPC) projects. This study developed and tested a theoretical model to investigate how formal IM practices, social norms (i.e., management norms and project norms regarding IM), and personal attitudes interactively affect individuals’ IM behaviors. The results show that an individual’s IM behaviors are directly driven not only by formal IM practices but also by management and project norms regarding IM. Additionally, formal IM practices have significantly positive effects on management norms, project norms, and personal attitudes toward IM. The findings of this research contribute to the IM body of knowledge by offering insights into the relationships among interface participants’ IM behaviors, formal IM practices, social norms, and personal attitudes in EPC projects. Understanding these in-depth underlying relationships can help to develop effective strategies (e.g., developing and maintaining favorable management and project norms) for motivating and supporting IM behaviors.
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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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".