Managing the Legitimacy of ‘Sinful’ Companies in Extreme Institutional Environments
Bibliographic record
Abstract
We examine how multinational enterprises (MNEs) manage their legitimacy in extreme institutional environments. Building on the legitimacy-as-process perspective, we investigate the legitimation activities of a century-old local subsidiary of a beer-brewing MNE in Indonesia, the world’s largest Muslim country. Drawing on a triangulated dataset that includes a series of interviews with company directors and related market and non-market actors, we present a longitudinal case study of how the subsidiary continued to negotiate its legitimacy and nonmarket influence in an unstable environment where alcohol consumption is proscribed. Based on this case we present a process model that suggests how foreign-owned businesses may maintain legitimacy in extreme institutional environments despite their engagement in ‘sinful’ products. Our study contributes to the nonmarket strategy literature and notably to research on managing the legitimacy of foreign firms in ‘sin’ industries. These contributions have implications for political risk management in inherently extreme institutional contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".