Once bitten twice shy? <scp>E</scp> xperience managing violent conflict risk and <scp>MNC</scp> subsidiary‐level investment and expansion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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 it