Organizational legitimacy as a core concept for theorizing on business in emerging economies
Bibliographic record
Abstract
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.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.033 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".