From process-based to technology-driven: a study on functionalities as key elements of collaborative planning methods for construction projects
Bibliographic record
Abstract
With the advancement of emerging technologies, significant attempts have been made to develop collaborative planning methods and to involve as many project stakeholders as possible in the construction project planning and control process. However, inadequate consideration has been paid to the characteristics, goals, and principles underlying these methods to meet the needs of collaboration for project planning between project teams. To deal with this, a multi-stage methodology was carried out to achieve the aims of this study. The first step was identifying collaborative planning methods and their functionalities in the construction sector. Further, a quantitative analysis based on Social Network Analysis (SNA) was conducted to determine the most frequently utilized functionalities in collaborative planning methods. The results revealed that process-based collaborative planning methods’ functionalities prioritized process and people-related characteristics such as team trust and promise, as well as social interactions, whereas technology-driven methods highlighted visualization along with collaboration and communication as a key element of collaborative planning. Subsequently, this study contributes to the body of construction project planning and control knowledge from both theoretical and practical perspectives by enhancing the understanding and sensemaking of project stakeholders towards the underlying concepts and objectives of collaborative planning methods.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| 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".