Stakeholders Integration in Offsite Construction Supply Chain: Flows and Relationships for Different Project Delivery Methods
Bibliographic record
Abstract
Modular and Off-site Construction (MOC) is a construction methodology that can meet the demands of a rapidly growing population by overcoming productivity challenges associated with conventional construction.However, the adoption of MOC remains limited, partly due to a lack of understanding of the MOC Supply Chain's (MOC-SC) structure, particularly regarding material and information flows and stakeholder roles.Furthermore, previous research has not investigated the impact of different project delivery methods (PDMs) on stakeholder relationships.This paper addresses this challenge with a two-fold approach.First, a high-level MOC-SC process map for a panelized MOC was developed based on data collected from collaborative meetings with industry practitioners and a literature review.The high-level process map visualizes the MOC-SC's structure, as well as material and information flows throughout the MOC-SC.Second, this paper applies the salience model, a stakeholder analysis tool utilized widely in project management, to analyze how three PDMs (i) Design-Bid-Build (DBB); (ii) Design-Build (DB) with in-house prefabrication; and (iii) DB with outsourced prefabrication affect the stakeholders.The analysis of the MOC-SC under the selected PDMs has shown the influence of PDMs on communication channels.Consequently, the Design-Build PDM with in-house prefabrication fosters direct communication links between stakeholders, enhancing communication and facilitating successful project completion.These insights can help practitioners develop stakeholder management strategies tailored to their needs.Moreover, a deeper understanding of the MOC-SC can reduce industry hesitancy and facilitate broader adoption of MOC by the construction industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".