Analysis of axioms and assumptions of data envelopment analysis: application for efficiency measurement in project management contexts
Bibliographic record
Abstract
Data envelopment analysis (DEA) is increasingly used to measure projects' efficiency, as recent research contributions indicate. However, most studies in project management take the axioms and assumptions underlying DEA for granted or do not pay attention to the type of data they are working with. Lack of attention to these important factors leads to selection of inappropriate DEA models and, consequently, produces biased efficiency scores. In this paper, after arguing that DEA is an appropriate model for project efficiency measurement, the economic meaning of its axioms and assumptions is explained. We also explain how different data types require some modifications in the CCR model. As a result, a guideline is presented to help future project management scholars select an appropriate DEA method tailored for their specific situation. Further, to highlight the importance of paying attention to these issues, we empirically demonstrate the high sensitivity of DEA results to the applicability of underlying DEA axioms and assumptions.
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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 | 0.071 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".