Project management in the international development industry
Bibliographic record
Abstract
Abstract Purpose – The purpose of this paper is to analyze the empirical relationship between project management (PM) efforts (the extent to which national project coordinators (NPCs) – the project managers in the aid industry sector – make use of available PM tools), project success, and success criteria. Design/methodology/approach – Data were collected by way of questionnaires delivered by mail to 600 recipients in 26 different countries in Africa. Findings – The research results suggest that project success is insensitive to the level of project planning efforts but a significant correlation does exist between the use of monitoring and evaluation tools and project "profile," a success criterion which is an early pointer of project long‐term impact. Research limitations/implications – This paper contributes to PM research by exploring the relationship between the use of PM tools and project success in the non‐traditional PM – although project oriented – aid industry sector. The paper highlights self‐perceptions of NPCs and should not be interpreted in other ways. Practical implications – This paper highlights the importance of PM tools in practice. Further, it suggests that NPCs (who are in fact only involved in project execution) put a lot of effort into monitoring and evaluation. In so doing, they strive to ensure project performance and accountability throughout project lifecycle, and this contributes to project "profile." Originality/value – This is the first study that offers insights into the relationship between PM efforts and project success in the aid industry sector. The paper calls for further research on PM practices in the aid industry sector where projects remain important instruments for aid delivery.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".