Managing Corruption During Regime Change
Bibliographic record
Abstract
Abstract We examine how foreign-owned multinational enterprises (MNEs) respond to changing corruption practices in a sudden transition of political systems in one of the world’s largest emerging markets. We draw on the political science and corporate political activity literatures in explaining how local subsidiary firms (subsidiaries) of Western European MNEs adapted their political activities during the transformation of Indonesia’s political landscape from an autocratic regime to a democratic and decentralized system. Based on four case studies of political activities, we found that the political strategies of our subsidiaries changed in response to evolving power structures in an environment of arbitrary and pervasive corruption. Under Suharto’s regime (1967–1998) MNEs sought to avoid informal transaction costs by developing relations with his supporters. When Suharto fell, MNEs began to conduct transitioning strategies by partnering with competitors, leveraging political networks, and outsourcing corrupt practices. Later, however, MNEs developed ethical political strategies by leveraging government partnerships, supporting national interests, and forming relationships with local communities. We theorize that corruption in times of political system change can be dynamic and evolving and that the nature of this corruption offers MNEs more agency than previously understood in managing corrupt demands in ethical ways.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".