Matching the Project Manager's Roles to Project Types: Evidence From Large Dam Projects in Africa
Bibliographic record
Abstract
Large dam projects (i.e., those exceeding 15 m) often make headlines for their poor performance and their negative social and environmental impacts. The world register characterizes dams by height, purpose (e.g., irrigation), and type (e.g., rock-fill). Thus, large dams differ in many technical ways, but because practitioners still lack a framework to sort them into different types for management purposes, they tend to manage them in a one-size-fits-all manner. Shenhar and Dvir's NTCP (novelty, technology, complexity, and pace) model (2007) may be a good fit as large dams experience high unforeseen technological uncertainty. In this paper, through observations, a case study, a qualitative analysis of 42 interviews with project managers, and a quantitative analysis, we examined 30 large dam projects in Africa and sorted them into different categories according to the NTCP model. Going beyond the rather static NTCP, we identified their underlying NTCP characteristics and the variety of roles that their project managers played throughout the lifecycle, and highlighted the dynamic fit between the roles and NTCP characteristics. Since different characteristics and project manager's roles are prominent at different phases, project managers should sort dams into different types based on the NTCP model at different phases, and tailor their roles accordingly for more success.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".