The origin and nationality of general manager successors in local-market-seeking MNE subsidiaries
Bibliographic record
Abstract
The extant literature suggests that it is sensible to deploy a host-country national (HCN) general manager (GM) successor in the local-market-seeking subsidiaries of multinational enterprises. However, limited attention has been paid to whether subsidiary GMs come from outside or inside an organization. By simultaneously considering the nationality and origin of subsidiary GM successors, our case-based study provides a sharper theory of succession decision-making in the context of local-market-seeking subsidiaries. We demonstrated that the use of HCN GMs is not always the best strategy and can even be the worst option because ex post opportunism may arise for HCN GM successors promoted from within the subsidiary. Using HCNs from outside the subsidiary can limit ex post opportunism but may entail a new bounded reliability issue resulting from identity-based discordance. Our interview data revealed a managerial safeguard, which we term ex ante socialization, to address this issue.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".