Socio-political legitimacy: An integrative and interdisciplinary review and agenda for theory development in unit and programmatic approaches
Bibliographic record
Abstract
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.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".