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Record W4416076989 · doi:10.1016/j.jwb.2025.101691

Socio-political legitimacy: An integrative and interdisciplinary review and agenda for theory development in unit and programmatic approaches

2025· article· en· W4416076989 on OpenAlexaff
Huda Khan, Zaheer Khan, Geoffrey Wood, Oded Shenkar

Bibliographic record

VenueJournal of World Business · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Realism in Sociology
Canadian institutionsWestern University
FundersEconomic and Social Research CouncilEuropean Commission
KeywordsUnit (ring theory)Development (topology)Development theory

Abstract

fetched live from OpenAlex

Firms operating across national borders are subject to scrutiny in both home and host countries. These multiple regimes of scrutiny increase their vulnerability to legitimacy crises that can significantly impact their operations and reputations. Over the past two decades, scholars in international business (IB) have investigated socio-political legitimacy (SPL), primarily through an institutionalist lens. However, despite extensive research across IB, political science, and sociology, the literature remains fragmented, characterized by diverse theoretical frameworks and modes of inquiry. This paper seeks to synthesize these disparate perspectives and identify converging themes. Specifically, it examines legitimacy through three core dimensions: property, perception, and process. Drawing on 250 studies from IB and management, sociology, and political science journals, this integrative review offers a comprehensive understanding of SPL, highlighting key theories, themes, and methodological trends. Furthermore, it introduces a “theory-on-theory” agenda aimed at advancing legitimacy both as a unit theory and in its relative role within institutional theory as a programmatic theory. The paper lays the foundations for future theorizing and empirical research on legitimacy-building strategies across diverse institutional contexts.

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)0.002
metaresearch head score (Gemma)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.088
GPT teacher head0.429
Teacher spread0.342 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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