Demystifying the dynamic link between project value and design team networks: A socio-technical lean management framework
Bibliographic record
Abstract
Construction projects are complex networks of people with various backgrounds, perceptions, objectives, and dynamically changing project value expectations. Attaining common project objectives that satisfy all stakeholders’ value propositions requires collaboration whether formally or informally. Although the need for collaborative approaches on projects seems like an intuitive thought, the actual foundations and drivers needed to achieve effective implementation of a collaborative and value-based environment are usually plagued with hurdles. Moreover, previous research work and common industry practices remain mostly focused on the ‘product’ or technical end of the project while marginalizing the human-centric processes that can make or break the project. This research aims to help project managers attain higher value on projects through analyzing and managing the social context of project teams. Specifically, this research investigates the links between design team communications and project value performance as well as analyzing the evolving dynamics of value and network structures. Data analytics was used to reveal a potential correlation between the communication structures of project teams and value fulfilment on projects. Findings revealed that the inherent social dynamics and social network composition can reflect the team’s reported level of fulfilling value on projects. Such structures affect a team's ability to effectively exchange knowledge and coordinate design tasks. The research's contribution lies in introducing a sociotechnical approach for delivering value on projects through developing value-based social networks that help improve design communications and presenting a reproducible framework that enables teams to advance and align value propositions among different stakeholders.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".