Managing Perceived Project Value: An Exploratory Study into the Designer’s Role within the C-K Theory
Bibliographic record
Abstract
Enhancing value on projects has been a major objective within the construction industry. Design management practices have focused on exploring value generation and management during the project development to ensure successful completion of projects. Additionally, many research studies discussed the role of the designer in achieving a higher project value through collaboration with different project stakeholders. However, many projects are still executed with an inconvenient level of satisfaction, leaving hidden value often unlocked. This study uses concepts discussed in literature, specifically the concept-knowledge theory (C-K theory), which touches on creative thinking and knowledge generation. The research comprises an exploratory study on current design management practices and suggests consistency in dealing with value optimization strategies. A proposed model is developed focusing on the responsibility of the designer in knowledge generation and innovation. The model is then tested with a panel of experts to validate its applicability. The proposed approach represents a novel way for dealing with design innovation and for managing the perceived project value in comparison to current practices. This research aims at providing owners and designers with a better understanding of value enhancement and management strategies.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".