The influence of perceived host country political risk on foreign subunits' supplier development strategies
Bibliographic record
Abstract
Abstract Recent protectionist trends (e.g., trade wars, Brexit) have challenged the stability of the global supply chains (SCs) of multinational corporations. While the SC and operations management (SC/OM) literature has examined how such sources of political risk influence strategic SC/OM decisions at the global level, we know little about how host country political risk influences the SC/OM practices of foreign subunits at the local level. We conducted an exploratory, multiple‐case study of western subunits operating in China to investigate whether managers of foreign subunits in a host country perceive political risk; and if so, how they adapt their SC/OM strategic choices to reduce its impact on their subunits. Our empirical findings suggest that variations in political risk perceptions create different political legitimacy goals that subunits attempt to meet by adapting their supplier development strategies. We induct a process model and identify three archetypes of supplier development strategy—self‐centered, collaborative, and voluntary—adopted by subunits to shape their legitimacy and mitigate political risk. We contribute to the SC/OM literature by theorizing SC/OM managers' perceived political risk, showing how foreign subunits may use supplier development to influence how the host government views them, and by complementing traditional “corporate” legitimacy‐seeking strategies with new “SC/OM” legitimacy‐seeking strategies.
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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 | 0.015 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".