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Naming of International Joint Ventures: Local Legitimacy and Foreign Identity

2025· article· en· W4416006667 on OpenAlexaff
Yunok Cho, Jisun Yu, Seung‐Hyun Lee

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsConcordia University
Fundersnot available
KeywordsMultinational corporationLegitimacyLeverage (statistics)Identity (music)PoliticsInternational joint ventureSample (material)Foreign direct investment

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.629
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.258
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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