Transforming construction: heterarchical megaproject ecologies and the management of innovation
Bibliographic record
Abstract
As the digital transformation impacts many sectors of the economy, actors in the construction industry—and more specifically in megaproject initiatives—have to adapt to new technologies and processes. Megaprojects are commonly undertaken to build essential infrastructures such as roads, dams, buildings, or even smart cities or districts, and usually involve complex and hybrid organizational forms. Moreover, digitalization transforms megaprojects, presenting megaproject teams with opportunities, but also challenges. This conceptual paper explores the characteristics of heterarchical megaproject ecologies in order to identify ways to address the impacts of the current digital transformation. Our aim is to analyze how a heterarchical form of governance contributes to transforming the management of innovation. While heterarchies can lead to severe dysfunctions, we propose strategies to manage them, paying specific attention to the governance of a common-pool-resource scenario, network roles, knowledge articulation and learning. The main contribution of this paper is to provide a renewed conceptualization of megaproject governance and to propose a conceptual framework that can be used to study the management of innovation in empirical megaproject settings.
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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 | 0.005 | 0.008 |
| 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 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".