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Record W4409164077 · doi:10.1016/j.ibusrev.2025.102447

Organizational legitimacy as a core concept for theorizing on business in emerging economies

2025· article· en· W4409164077 on OpenAlexafffund
Klaus E. Meyer, Caleb H. Tse

Bibliographic record

VenueInternational Business Review · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsWestern University
FundersIvey Business School, Western University
KeywordsLegitimacyPerspective (graphical)BusinessCore (optical fiber)Emerging marketsEconomic systemIndustrial organizationEconomicsPolitical scienceFinance

Abstract

fetched live from OpenAlex

Foreign multinational enterprises (MNEs) operating in emerging economies (EEs) face major challenges in attaining organizational legitimacy with local stakeholders, a precondition to successful operations in the country. This perspective explores the contingencies and perceptions that cause these legitimacy challenges and the actions MNEs employ to address these challenges, as identified in recent literature. We observe that legitimacy in EEs has many facets. Yet, it is often analyzed in a selective way – focusing on specific events or activities without considering the complexity of the phenomenon of how legitimacy is created, maintained and lost. We argue that organizational legitimacy is a very useful construct for international management research on EEs, and that the concept lends itself to more rigorous theoretical advancement than category-based concepts such as liability of foreignness. However, the IB literature has only partially embraced recent theoretical advances on the concept of legitimacy. We distinguish contingency, agency and judgement views of legitimacy to explore how applications of the concept may enhance our understanding of MNEs operating in EEs. This leads us to develop a future research agenda.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.297
Teacher spread0.275 · 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.

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

Citations24
Published2025
Admission routes2
Has abstractyes

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