Advancing the Fifth Hand explanation of project cost misperformance
Bibliographic record
Abstract
Cost misperformance in projects from contract award, expressed through cost growth and margin erosion, is a problem that confronts construction organisations worldwide. While several conceptual theories have attempted to explain cost misperformance, they often fail to account for how and why it arises in specific project contexts. The Fifth Hand has emerged as a pragmatist explanatory construct, but it has yet to be empirically tested. This paper extends its application by drawing on Evidential Pluralism, the idea that no single form of evidence suffices for causal inference, and epistemic causality, which focuses on how causal knowledge is justified. Integrating these perspectives strengthens the Fifth Hand’s foundations for context-sensitive, evidence-informed explanations. Using an explanatory case study, we address two research questions: (1) What associations and causal mechanisms contribute to cost misperformance in construction projects? And (2) How can a pluralistic, evidence-based analysis advance the Fifth Hand’s explanatory power? Statistical analysis for a sample of 67 projects, totalling $3.22 billion in value, delivered by a construction organisation, revealed associations between cost growth and margin gap (negative) and project size (positive), with unapproved subcontract variations predicting margin erosion. Qualitative analysis identified organisation-wide mechanisms, temporal discounting, disengagement from systems, and overconfidence, as well as project-specific mechanisms such as external design-related ambiguity and internal planning failures. Theoretically, this paper extends the Fifth Hand by integrating pluralistic approaches to evidence and causal reasoning. Practically, it offers construction organisations insights into behavioural and systemic vulnerabilities that contribute to cost misperformance.
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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 | 0.025 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".