Toward a theory of evidence based decision making
Bibliographic record
Abstract
Purpose The purpose of this paper is to integrate existing body of knowledge on evidence‐based management, develop a theory of evidence, and propose a model of evidence‐based decision making. Design/methodology/approach Following a literature review, the paper takes a conceptual approach toward developing a theory of evidence and a process model of decision making. Formal research propositions amplify both theory and model. Findings The paper suggests that decision making is at the heart of management practice. It underscores the importance of both research and experiential evidence for making professionally sound managerial decisions. It argues that the strength of evidence is a function of its rigor and relevance manifested by methodological fit, relevance to the context, transparency of its findings, replicability of the evidence, and the degree of consensus within the decision community. A multi‐stage mixed level model of evidence‐based decision making is proposed with suggestions for future research. Practical implications An explicit, formal, and systematic collaboration at the global level among the producers of evidence and its users akin to the Cochrane Collaboration will ensure sound evidence, contribute to decision quality, and enable professionalization of management practice. Originality/value The unique value contribution of this paper comes from a critical review of the evidence‐based management literature, the articulation of a formal theory of evidence, and the development of a model for decision making driven by the theory of evidence.
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.
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 | 0.222 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.027 | 0.027 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".