Unpacking the Constituents of Dynamic Capabilities: A Microfoundations Perspective
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
This position paper updates about methodological and epistemological issues on the micro-foundations perspective. We propose that conceptual divergences between the different streams of contributions to dynamic capabilities (DCs) research (the Eisenhardt versus Teece divide) hide discrepancies about methodological aspects, and about the locus of DCs. We zoom out from current epistemological debates about the microfoundations to explain the necessity of multi-level approaches, and to clarify the interpretation of the Boudon-Coleman “bathtub”. We elaborate on these aspects to explain how to enhance research on DCs, and more generally in strategic management. We discuss specific issues in relation with the selection of units of analysis and with the elaboration of field research protocols. We also propose practical recommendations adhering to the micro-foundations approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it