Beyond Bias and Error: Institutionalizing the Fifth Hand to Explain Infrastructure Project Cost Misperformance
Bibliographic record
Abstract
Cost misperformance, when final construction costs exceed a client's approved budget, continues to be a challenge when procuring infrastructure delivery worldwide. Traditional explanations often emphasize individual and organizational errors and/or biases, while overlooking the institutional contexts that shape decision-making. Our paper extends the Fifth Hand, an emergent theory of project behavior, by embedding it within a neo-institutional perspective, linking micro-level ecological rationality with meso-level organizational pressures and macro-level institutional logics. We integrate isomorphic mechanisms (e.g., coercive, mimetic, normative) with institutional logics (e.g., professional [cost control]; market [competitiveness], collaborative [risk-sharing]) to explain the recurrence and legitimacy of behavioral patterns across infrastructure projects. The Fifth Hand is thus mapped to isomorphic pressures and institutional logics, illustrating how they shape a project's cost performance. By adopting a multi-level, institutional lens, we strengthen the Fifth Hand's explanatory foundation by connecting individual and organizational decisions to broader structural and normative forces. We conclude by outlining research directions to explore how isomorphic mechanisms and institutional logics evolve across projects, interact over time, and sustain 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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".