Stick or Twist in the Tidal Wave? Subsidiary General Manager Succession During Political Transitions
Bibliographic record
Abstract
We examine how multinational enterprises (MNEs) manage foreign-subsidiary general manager (GM) succession during political transitions in host countries, i.e., political shifts along the democracy-autocracy continuum. Drawing on threat rigidity theory, we postulate that autocratic shifts in host countries – defined as political shifts toward concentration of power to the host country’s political leadership – limit the MNE’s latitude to act independently, thus posing a threat to the international organization, resulting in a lower likelihood of adaptation through foreign-subsidiary GM succession. We also hypothesize that this effect becomes weaker as firm international experience increases – as more internationalized MNEs will respond more swiftly and adapt to political alternations in host countries. Data from 2,151 GM foreign subsidiary successions in 2,201 foreign subsidiaries of Japanese MNEs support our predictions. Our results also evidence the asymmetric effects of regime shifts, suggesting that different directions of political transition (towards democratization vs. autocratization) generate different strategic staffing patterns. Overall, our study sheds light on how MNE leadership is affected by exogenous changes in host-country political environments, offering fundamental implications for IB theory and microfoundational research.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".