General Manager Turnover in MNC Subsidiaries: The Roles of Board Heterogeneity and CEO Network Size
Bibliographic record
Abstract
Subsidiary general managers (GMs) play a central role in foreign subsidiary governance. While their exit represents a loss of human capital for multinational companies (MNCs), we know relatively little about the dynamics underlying the phenomenon of subsidiary GM turnover. In this study, we thus address the subject by exploring the effects of MNC board tenure heterogeneity and CEO network size on GM turnover rates. Grounded in the tenets of upper echelons theory and using a longitudinal dataset with 6,577 observations across 1,603 foreign subsidiaries of Japanese MNCs between 2004 and 2020, we argue and show that greater heterogeneity in board tenures reduces the likelihood of subsidiary GM turnover. This finding supports the value-in-diversity perspective that MNC boards with greater tenure heterogeneity can enable better-informed decisions on foreign subsidiary GM turnover, thereby reducing the likelihood of premature dismissals or public scapegoating. However, we further find that CEOs with larger network sizes can override the attenuating effect of board tenure heterogeneity on subsidiary GM turnover rates, which we attribute to three interrelated mechanisms: relational power, signaling inclination, and informal knowledge-sharing. Together, these findings advance understanding of strategic leadership interfaces and the interdependent roles of top managers in international business contexts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".