Model for developing trust on US construction projects
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the factors found on US construction projects that are perceived by contractors to strengthen or weaken trust between contracting stakeholders and to develop a framework for evaluating these relationships. Design/methodology/approach A comprehensive framework containing a number of factors (54) that could impact trust on construction projects was first developed. A survey questionnaire was then developed and administered via phone to contractors selected from the Engineering News Record top 400 US construction companies. The survey findings were then used to develop a trust model and case studies were used to validate and revise the trust model. Findings A trust model is developed that helps large US contractors measure and improve trust with other stakeholders on their projects. Practical implications Large US contractors are now provided with a tool not previously available to help them measure and improve trust between the different contracting parties on construction projects which can help them decrease project time and costs, and improve project results. Originality/value The proposed trust model adds a number of different dimensions to the existing trust models found in the literature and as such improves the contractor’s ability to foster and enhance trust on a US construction project.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".