Success in Interrelated Supply Chain: an Analysis of the Human Behaviour Under Crisis
Bibliographic record
Abstract
A wealth of studies is available on the key success factors of managing interrelated projects in a construction supply chain. The human factor, however, is often overlooked in normative success solutions. According to classical management theory, individuals are expected to act rationally and maximise their utility. Although, due to an individual's computational and cognitive abilities, decision-makers often choose the first satisfactory course of action rather than searching for the optimal course of action, particularly during times of crisis. This study adopted a surrogate model to conduct a series of laboratory simulations that involved human behaviour. A comprehensive literature review was conducted to determine the experiment design, followed by sixteen hours of experiments that spanned two countries investigating decision-making behaviour within two prominent management models: the traditional and collaborative models. In order to identify patterns in the perception of the participants regarding real success factors, a content analysis was performed on their questionnaire responses. This analysis identified three key characteristics of construction success and the top characteristics required to succeed under each model investigated. By sharing these insights and lessons learned, teams can gain a deeper understanding of what it takes to succeed in a competitive environment.
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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 | 0.000 | 0.000 |
| 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".