Developing a Framework for Assessing Team Alignment in Construction Using TVD
Bibliographic record
Abstract
Customers' value is crucial to the success of a construction project, and team alignment is required to steer projects toward their intended value.Alignment is when the right people work together on a project to generate and achieve values that are consistently communicated and accepted.In the architecture, engineering, and building industry, teamwork challenges are inevitable.The existence of a team does not guarantee the success of the project, and a dysfunctional team might result in project failure, wasting resources such as time, money, and energy.Target value design (TVD) is a lean approach that leads the design and construction phases to meet project objectives while adhering to team and project limits.Based on their values, each project has different conditions, facts, or impacts that help strengthen team alignment (factors).Additionally, a team that is aligned has particular qualities that are recognized as attributes.Measuring and assessing team performance based on TVD using factors is complex.This research fills the gap in the literature review concerning the measurement and assessment of team alignment.The process and its results could help construction project leaders regularly assess and identify team strengths and weaknesses to improve team alignment.A case study is also presented to apply the proposed framework to measure team alignment on a construction project, to improve team performance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".