Evidence-based management for today’s “ambidextrous” organizations
Bibliographic record
Abstract
Purpose This paper examines how evidence based management (EBM) can help managers build more flexible organizations. In the context of this article, we define the need to build for this capacity around the challenge of “ambidexterity”, or the need for companies to continue operations while also allowing for innovation. We present a framework to help managers create strategies that help them build ambidexterity in their organizations, whether they operate in highly regulated, compliance driven or un-regulated, non-compliance climates. Design/methodology/approach This paper identifies four organizational design strategies each of which represents a different leadership and organization consideration that may focus on how evidence based management practices are linked to competency building (i.e., exploitation), the need innovation, or an equal balance between the two (i.e., ambidexterity). Findings Our findings reveal that an organization’s use of data given these four strategic orientations reflect different uses of data (verifiability and codification concerns) and ways of embedding compliance and ambidexterity (exploitation vs. exploration) considerations. Practical implications These four strategies help managers expose biases in their current decision-making practices, and how they subsequently may affect lifecycle, change management, and data practice in ambidexterity development. Originality/value While EBM acknowledges the importance of utilizing evidence, it remains limited toward understanding how it might be used to build for ambidexterity in organizations.
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 | 0.136 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".