Naming of International Joint Ventures: Local Legitimacy and Foreign Identity
Bibliographic record
Abstract
This study examines the naming of international joint ventures (IJVs) as a symbolic strategy to gain legitimacy. Drawing on prior knowledge of organizational names and the processes by which they were chosen, we argue that IJV naming is an exercise of agency to shape audience perceptions. Specifically, we develop hypotheses articulating the internal and external conditions in which IJVs deliberately signal their foreign identity by including the name of a multinational enterprise (MNE) partner to enhance their chances of being perceived as legitimate. In a sample of 380 IJVs in South Korea (2007–2015), we find that the likelihood of MNE name inclusion is higher in greenfield investments (vs. acquisitions) when the MNE has a large global presence and in industries with numerous foreign firms. Under these conditions, a foreign identity is likely to invoke positive evaluations from host country audiences. We also find that political affinity between the MNE’s home and host countries influences the likelihood of MNE name inclusion. Notably, IJVs operating under less favorable conditions for revealing their foreign identity leverage political affinity more frequently to gain legitimacy by including an MNE name. Overall, our results suggest that IJV naming is a deliberate act to achieve favorable classification and evaluation by host country audiences.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".