Seeking Greater Practitioner and Managerial Use of DEA for Benchmarking
Bibliographic record
Abstract
It is interesting to observe how long it takes to move a powerful new technology from the academic to the practitioners’ world and to understand what constitutes acceptance and adoption by professionals. Data Envelopment Analysis (DEA) was introduced in 1978 but has been only sporadically used in real world applications by consultants and analysts. We believe its current limited use is far below its potential. The reasons for this and the possible paths to increase awareness and use of DEA are explored in this paper. Impediments arise from the research community’s underselling or poorly communicating the power and flexibility of DEA, possibly because that is not their key motivation in developing this methodology. At the same time, industry participants are typically slow to learn and accept a new technology, particularly if they feel there are risks associated with trying something new, if the new technology might be complex and challenge their ability to understand new concepts, or if the suggested results might seem threatening. Academic papers on DEA have effectively adapted to meet the requirements of editors of academic publications as evidenced by many thousands of published papers. We suggest that another distinct line of research could focus on adapting DEA to make it more accessible and responsive in addressing managerial problems. Ideally, this would generate enthusiasm for DEA in the management community that parallels its success in academia. We explore alternate strategies to increase awareness of DEA’s capabilities by practitioners and managers to extend or augment the success of applications of DEA in benefitting businesses, governments and the non-profit sectors. We invite responses to this paper by those who can offer additional, viable approaches that can augment the use of DEA in the commercial world.
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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 | 0.224 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".