Managing Geopolitical Tensions: Firm Strategies Under Conflicting Demands
Bibliographic record
Abstract
Geopolitics introduces significant challenges for multinational companies (MNCs), shaping strategic decisions in unprecedented ways. This symposium focuses on firms’ performance and response to conflictual demands of various actors, which reshape their institutional environments. While extant research has explored the adverse effects of geopolitics on firm strategies, it often overlooks the complexities arising from such conflicting demands. By integrating theoretical and empirical perspectives, this symposium investigates how MNCs manage competing pressures from multiple stakeholders (e.g. home and host countries) and explores how their investments in third-party countries can capitalize on the rivalries between major global powers. Leaving the Backseat: The Active Role of Multinational Firms in China-US Decoupling Author: Anne Jamison; Copenhagen Business School Author: Harald Puhr; Universität Innsbruck Flying Under the Radar: Host Country Geopolitical Animosity and MNE Strategic Disassociation Author: Joao Albino Pimentel; University of South Carolina Author: Gianni De Bruyn; Author: Grazia D. Santangelo; Copenhagen Business School Contested Inter-Governmental Organizations and Foreign Investment Author: Si (Coco) Cheng; Copenhagen Business School Author: Raffaele Conti; ESSEC Business School Author: Srividya Jandhyala; ESSEC Business School Does it Pay to Take a Stand Abroad? Consumers’ Reactions to MNCs’ Sociopolitical Activism Author: Fangwen Lin; National University of Singapore Author: Ishva Minefee; University of Illinois at Urbana-Champaign Author: Lori Qingyuan Yue; Columbia University in the City of New York
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".