External Threats and MNE Strategy: The Role of Exposure, Resources and Coping Mechanisms
Bibliographic record
Abstract
Multinational enterprises (MNEs), when threatened by political violence in a host country, make different choices; some stay, others exit. What determines who stays and who leaves? We combine insights from the resource-based view, real options theory, and the climate change and environmental sustainability literatures to predict the likelihood, timing and mode of foreign exit in extreme contexts. Exit is argued to be a function of the MNE subsidiary’s vulnerability, as determined by three factors: exposure to the threat, at-risk resources (i.e., their irreplaceability, which reduces the delayability of exit, and their immobility, which increases the irreversibility of exit), and coping mechanisms. Innovative geographic modeling of 626 Japanese MNE subsidiaries in 23 conflict-afflicted countries over 1986–2006 provides strong support for our arguments. Our findings suggest that valuable resources may become a firm’s “Achilles’ heel” in volatile environments, but that increased resilience is possible through the development of coping mechanisms.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".