Exploring the Relationship Between Managerial Cognitive Capabilities and Dynamic Managerial Capabilities
Bibliographic record
Abstract
The micro-foundations research agenda's primary motivation in strategy is to dissect macro-level constructs in terms of actions and organizational members' interactions to the micro-level. This work seeks to evolve the understanding of these micro-foundations to explain the relationship between Managerial Cognitive Capabilities and Dynamic Managerial Capabilities. We conducted a laboratory experiment with a sample of 111 participants, divided into two groups, containing 57 and 54 participants, each one. The results revealed that Sensing Opportunity and Seizing Opportunity, components of the Dynamic Managerial Capability, and the Language and Communication, which are part of the Cognitive Managerial Capability, can be predictive of the ability to Reconfigure Tangible and Intangible Assets. Our research contributes by extending central literature on micro-foundations through an experiment. We empirically show that managerial and cognitive dynamic capabilities can be a preeminent field to improve the comprehension of dynamic capabilities' micro-foundations.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".