Corruption and international development: a review of project management challenges
Bibliographic record
Abstract
Purpose The outcomes and quality of international development projects (IDPs) remain highly controversial, especially with perception of corruption by various stakeholders. This study aims to integrate findings from both social and administrative sciences to focus attention on the governance challenges involved in both business and public administration of such projects. It also asks to what extent Project Management (PM) methods can effectively be harmonized with broader anti-corruption initiative in both donor and receiving countries. Design/methodology/approach Taking a transdisciplinary viewpoint, this study proposes a review and synthesis of the literature on the theoretical, methodological, and epistemological issues in researching corruption as a construct in PM as applied to IDPs. Findings Some experts recognize the inefficacy of applying classical PM tools and processes. By contrasting the literature, this study concludes that an alternative approach to overcome the taboos and prejudice in studying corruption is to ask a different research question. As opposed to studying “who and why” about corruption occurrences (ex-post), given the challenge of unveiling its practices and motivations, PM researchers can to ask instead “where and how” it occurs and help understand methods to mitigate its effects on projects (ex-ante). Originality/value A research agenda is proposed for the several disciplines and fields concerned with solving this phenomenon. To guide PM research on development projects, focusing on the “where and how” of corruption requires addressing how actors build their knowledge management capabilities and address the social and cultural challenges inherent to IDPs.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".