Corporate governance dynamics of political tie formation in emerging economies: Business group affiliation, family ownership, and institutional transition
Bibliographic record
Abstract
Abstract Research Question/Issue Despite the importance of political ties to firm strategy and performance, little is known about why and how firms form political ties. This study examines how firm‐specific governance structures function as important determinants of the formation of political ties and how the impact of governance structures evolves during institutional transition of emerging economies. Research Findings/Insights Our empirical analysis is based on a longitudinal data set involving manually coded political ties of listed firms in Taiwan between 1996 and 2005. We find that business group affiliation and family ownership facilitate the formation of political ties but that such effects diminish as market infrastructures and regulatory institutions develop. Theoretical/Academic Implications This study systematically examines how corporate governance structures affect the formation of political ties. It identifies an overlooked explanation for the dynamics of political ties that resides in the internal governance structures of firms. It contributes to the corporate governance research by demonstrating how governance structures other than the board of directors can provide resources to facilitate strategic actions such as political tie establishment. It also enriches research on corporate governance bundles by showing the interdependence among multilevel governance mechanisms in the context of political tie formation. Practitioner/Policy Implications This study offers insights for business executives interested in managing interdependence with government. While business group affiliates and family firms are better able to link to politicians, such advantages diminish during institutional transition. Hence, group leaders and family owners should consider other political activities to effectively manage political risks as institutions develop.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".