Is that an opportunity? An attention model of top managers' opportunity beliefs for strategic action
Bibliographic record
Abstract
Research summary: Exploiting opportunities is critical to a firm's competitive advantage. Not surprisingly, there has been considerable interest in the processes by which top managers allocate attention to potential opportunities. Although such investigations have largely focused on top‐down processes for allocating attention to the environment, some studies have explored bottom‐up processes. In this article, we consider both top‐down and bottom‐up processing to develop a model by which top managers form opportunity beliefs for strategic action depending on the allocation of transient and sustained attention. Specifically, this attentional model provides insights into how a top manager's attention is allocated to identify potential opportunities from environmental change and explores how different modes of attentional engagement impact the likelihood of forming beliefs about radical and incremental opportunities requiring strategic action . Managerial summary: Managers are interested in noticing and exploiting opportunities because the exploitation of an opportunity represents an important strategic action. Noticing and exploiting opportunities depends on how and where top managers allocate their attention. Managers can focus attention based on their knowledge and experience or as a result of something in the environment capturing their attention. In this paper, we consider both knowledge‐driven and environment‐driven processes for allocating attention to form opportunity beliefs. This opportunity belief arises from a two stage process. The first stage explains how a top manager identifies environmental changes as potential opportunities. The second stage explains how the top manager forms a belief that these identified environmental changes represent a radical or incremental opportunity worthy of exploitation . Copyright © 2016 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".