Navigating Political System Change in a Transitional Economy
Bibliographic record
Abstract
We examine how multinational enterprises (MNEs) respond to a sudden transition of political systems in the emerging market of Indonesia. We draw on the political science and nonmarket strategy literature to explain how local subsidiary firms of MNEs (‘subsidiaries’) adapted their nonmarket strategy during the transformation of Indonesia’s political landscape from an autocratic regime (1967-1998) to the current democratic and decentralized system. Based on multiple qualitative case studies of Western European subsidiaries, we found that MNEs adapted their business strategies to relate with multiple layers of government in an environment of rampant corruption. Under Suharto, MNEs sought to develop relations with his regime, while also seeking to avoid informal transaction costs. From 1998, MNEs began to conduct survival strategies by partnering with local firms and leveraging local political networks. Later however MNEs adopted proactive, innovative strategies that replaced corrupt activities, for example, by leveraging government-level partnerships and forming long-term relationships with local communities. These findings suggest that MNEs need active agency and proactive nonmarket strategies to address the negative challenges of unstable political environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".