The Core Competencies of Effective Project Execution: The Challenge of Diversity
Bibliographic record
Abstract
The successful planning and execution of large projects relies on the flexibility of engineering-construction-procurement (EPC) firms. It is argued that the effective management of this flexibility depends on the acquisition and development of a set of core competencies. Field and archival research in the USA, Canada, the United Kingdom, France, Malaysia, and Japan, are used to modify and extend current core competency theory to the execution of large projects. The research discloses four distinct groups of core competencies: entrepreneurial, technical, evaluative, and relational. These core competencies support core project processes that structure activities and routines involved in project development and delivery. We describe each core competency, and we examine how they impact core processes and through them project performance. We argue that the strategy of EPC firms evolves under the pressure of two opposing forces. Firms experience pressure to seek project opportunities in diverse areas and regions with a view to creating a robust project portfolio, and they experience pressure to remain close to their core competencies in order to minimize costs and maximize the probability of gaining individual contracts. Three types of strategies develop in response to these opposing pressures: focussing strategy which is competency driven; switching strategy which is opportunity driven; and combining strategy which attempts to strike a balance between the two imperatives
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".