Critical success factors for international development projects in Afghanistan: An exploratory factor analysis
Bibliographic record
Abstract
This study aims to identify and evaluate the critical success factors (CSFs) for international development projects (IDPs) from the perspective of key IDP stakeholders in Afghanistan. The study adopts a quantitative cross-sectional survey research design. Thirty-one success factors were identified and shortlisted through literature reviews and validated by experts and IDP management practitioners. The study's target population is the IPD senior management, IDP team members, and the general public. Amongst 500 questionnaires distributed, a total of 217 were returned and considered for analysis. The result of Exploratory Factor Analysis (EFA) revealed five key CSFs, namely: project cycle management, effective recruitment, continuous learning and adapting, project management method, and clear project goals and objectives. Besides, one-way ANOVA results revealed no statistically significant differences in the ranking of CSFs by the three groups of respondents. However, the post hoc test result indicated that the CSF 'continuous learning and adapting' was relatively rated lower by the general public. The findings of the study would assist the international community, their implementing partners, and IDP management practitioners in better management and successful implementation of IDPs in developing countries. It will also contribute to the CSFs theories and IDPM body of knowledge. The research is the first of its kind to examine the CSFs for IDPs in Afghanistan.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".