Seize the Day: Executive Thought Self-leadership and Heterogeneity Among Dynamic Managerial Capability Underpinning Cognitive Capabilities
Bibliographic record
Abstract
Extant literature has established the importance of individual dynamic managerial capabilities to the enterprise level sensing, seizing, and reconfiguring capacities of an organization. Despite theorization that heterogeneity in executive thought processes and thinking disposition stands causal for the oft observed differences in managerial capability between executives, little is known about the individual level antecedents of this cognitive heterogeneity which ultimately influences the direction of the entire firm. In response to calls for future investigation into this critical gap, the present paper draws upon a micro-level theory heretofore underutilized in the strategic realm – self-leadership – to examine how executives’ cognitive processes impact their entire firm. In pursuit of this goal, the cognitive-based thought self-leadership theory is utilized to more thoroughly explain the drivers of heterogeneity among the underlying cognitive capabilities of managers’ crucial dynamic managerial capabilities. In this way, the present study theorizes how specific individual executive cognitive processes (thought self-leadership strategies – e.g., self-talk, mental imagery) can influence the firm-level strategic decisions of innovation and expansion and thus impact overall organizational performance, through the bolstering of individual cognitive capacities and resulting managerial capabilities.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".