Microfoundations of sensing capabilities: From managerial cognition to team behavior
Bibliographic record
Abstract
Scanning the environment for information about competitors, technology trends, or customer needs allows firms to sense opportunities and threats, which supports dynamic capabilities and helps firms remain competitive over time. There has been significant theoretical development on the cognitive antecedents of dynamic capabilities—so-called dynamic managerial capabilities . In this study, I propose a novel mechanism through which managerial cognition can scale to a collective level in support of sensing capabilities and consider how organizational design may influence this relationship. Specifically, I posit that high-construal managers engage in more environmental scanning than low-construal managers do, because their mental horizons are broader and encompass further alternatives, and that over time their behavior is modeled by their team. I also suggest that managers’ degree of task-related interdependence with peer managers across the firm influences the direction of this relationship, with low interdependence reversing it. I find support for my theory using multiple-source, time-lagged data gathered from 88 managers and their team, thereby offering key implications for theory and practice.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".