Bibliographic record
Abstract
Research on managerial cognition and on organizational capabilities has essentially developed in two parallel tracks. We know much from the resource-based view about the relationship between capabilities and organizational performance. Separately, managerial cognition scholars have shown how interpretations of the environment shape organizational responses. Only recently have scholars begun to link the two sets of insights. These new links suggest that routines and capabilities are based in particular understandings about how things should be done, that the value of these capabilities is subject to interpretation, and that even the presence of capabilities may be useless without managerial interpretations of their match to the environment. This review organizes these emerging insights in a multi-level cognitive model of capability development and deployment. The model focuses on the recursive processes of constructing routines (capability building blocks), assembling routines into capabilities, and matching capabilities to perceived opportunities. To date, scholars have focused most attention on the organizational-level process of matching. Emerging research on the microfoundations of routines contributes to the micro-level of analysis. The lack of research on capability assembly leaves the field without a bridge connecting the macro and micro levels. The model offers suggestions for research directions to address these challenges.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".