Seeking Greater Practitioner and Managerial Use of DEA for Benchmarking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it