Erratic strategic decisions: when and why managers are inconsistent in strategic decision making
Bibliographic record
Abstract
Abstract While decision makers in organizations frequently make good decisions rooted in stable and consistent preferences, such consistency in outcomes is not always the case. In this study, we adopt a psychological perspective of judgment to investigate managers' erratic strategic decisions, which we define as a manager's inconsistent judgments that can shape the direction of the firm. In a study of 2,048 decisions made by 64 CEOs of technology firms, we examine how both metacognitive experience and perceptions of the external environment (hostility and dynamism) could affect the extent to which managers make erratic strategic decisions. The results indicate that managers with greater metacognitive experience make less erratic strategic decisions. The results also indicate that in hostile environments managers make more erratic strategic decisions. But contrary to our expectations, in dynamic environments managers make less erratic strategic decisions. Similarly, hostility and dynamism interact in their effect on erratic strategic decisions in that the positive relationship between environmental hostility and erratic strategic decisions will be less positive for managers experiencing high environmental dynamism than those experiencing low environmental dynamism. These results have important implications for strategic decision‐making research. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.010 | 0.086 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".