Political Connections and Business Strategy in Dynamic Environments: How Types and Destinations of Political Ties Affect Business Diversification in Closed and Open Political Economic Contexts
Bibliographic record
Abstract
Research summary This article studies how the strategic benefits of political ties change as a closed political‐economic system opens, focusing on types and destinations of connections between business leaders and political actors. We examine how formal and informal ties (types) to party leaders, government officials, and elected legislators (destinations) facilitated diversification by business groups in Taiwan between 1986 and 1998. During this period, Taiwan underwent political and economic liberalization that led to changes in tie accountability, public scrutiny, and diffusion of power over resources. We show that formal position interlocks with dominant party or senior government officials provide greatest benefits in a closed political‐economic system, while informal social ties to a wider range of political actors, particularly legislators, provide greater benefits as the system becomes more open. Managerial summary As a closed political economy becomes more open, the types and destinations of political ties that generate strategic benefits for firms change. In closed markets, the greatest benefits arise from formal ties to central political leaders. In more open markets, the benefits shift to informal ties with a broader range of political actors, including legislators. We demonstrate the change in targets for strategic benefits by studying how political ties influenced business diversification in Taiwan as the country moved from closed markets in the 1980s to more open markets in the 1990s. Copyright © 2016 Strategic Management Society
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".