What's the Evidence on Evidence-Based Management?
Bibliographic record
Abstract
Executive Overview In this article, we respond to recent calls for increased use of evidence-based management (EBMgt) by conducting a systematic review of the literature to answer the following questions: (1) Is there a substantial literature concerning the concept of evidence-based management? (2) What is the quality of evidence (where it exists) regarding evidence-based management? and (3) Is there evidence that employing evidence-based management will improve organizational performance? We applied an assessment rubric based on ranking systems developed in evidence-based medicine to evaluate the strength of evidence. We found that a large number of articles are published on the topic, but most provide encouragement to adopt EBMgt based on opinion and anecdotal information. We call for increased research to generate stronger evidence related to the impact of EBMgt on organizational performance.
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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 | 0.185 | 0.519 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".