Corporate Political Connections: A Multidisciplinary Review
Bibliographic record
Abstract
Corporate political connections (CPCs)—ties that firms forge with political actors—directly affect firms, political actors, and various stakeholders in societies. This topic has been studied extensively in multiple disciplines, including management, economics and finance, political science, and sociology. However, this body of research remains rather fragmented within the confines of each discipline or field, and synergies in theoretical and empirical domains remain underexploited. Differences between CPCs and other forms of corporate political activities are also often unclear. This article develops a focused, comprehensive, and theoretically deep review of the rapidly growing but disparate literature on CPCs in multiple disciplines and fields and distinguishes, compares, and connects multiple, heterogeneous theoretical perspectives that have been adopted in these different literatures. By conducting an extensive literature search of the articles published between 1990 and 2020 in 24 leading peer-reviewed journals in management, economics and finance, political science, and sociology, we build our review framework by organizing the reviewed articles into three groups of topics based on their logical connections: the conceptualization of CPCs, the antecedents of CPCs, and the outcomes of CPCs. Within each group, we distinguish two primary angles—the firm and the political actor—that correspond to the two entities joined by CPCs. On the basis of this framework, we identify major gaps and suggest avenues for future research. Our review works together with a companion review on corporate political activity, published in this same issue, to offer a wholistic perspective on the boundary between corporations and political actors.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".