How Can a Firm Suppress Shareholders’ Punitive Reaction to Its Disengagement from a Geopolitically Uncertain Market?
Bibliographic record
Abstract
Heightened geopolitical tensions have increased firms’ uncertainty about some geographical markets; in response, firms may announce their disengagement from these markets. Such announcements may lower the firm's future revenue and thus elicit negative reactions from shareholders. The authors theorize that managers can frame announcements to impress shareholders and suppress their punitive reactions. In the context of firms’ announcements of disengagement from Russia following its invasion of Ukraine, the authors show that an announcement's market emphasis (i.e., mentions of product-market activities and stakeholders) is positively related to the shareholders’ reaction. Further, the announcement's social emphasis (i.e., mentions of employees, environment, and community) and a delay in announcing the disengagement weakens the market emphasis's positive association with shareholder reactions. This research highlights that linguistic framing in disengagement announcements can shape shareholders’ reactions to such announcements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".