Once bitten twice shy? <scp>E</scp> xperience managing violent conflict risk and <scp>MNC</scp> subsidiary‐level investment and expansion
Bibliographic record
Abstract
Research summary : Researchers have increasingly emphasized the need to better understand how context affects the value of experiential learning. We address this gap by investigating when corporate‐level experience can be leveraged across borders and when experience needs to be country‐specific to be valuable. We test our hypotheses using a unique multi‐source panel dataset of 379 large MNCs from 29 home countries and their subsidiaries in 117 host countries over a 10‐year period, 1999–2008. In contrast to prior research, we find that the ability of a firm to leverage its experience with political risk across borders is limited by the type of risk involved. Experience with nonstate violent conflicts may be transferrable, but only country‐specific experience appears to yield measureable benefits for conflicts involving the host country government . Managerial summary : Violent conflicts not only increase social unrest but also impose added costs of doing business. For managers who find themselves in the midst of violent conflicts or who wish to survive and potentially gain a competitive advantage in operating in such challenging environments, is it possible to learn to manage such a seemingly “unmanageable” problem? In contrast to studies that have examined other types of political risk, we find that the ability of a firm to leverage its experience with violent conflict risk across borders is limited. Specifically, only country‐specific experiential knowledge about how the host government prepares and manages such conflict risks yields measureable economic benefits for MNCs and their subsidiaries operating in countries during conflict . 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".