Navigating Political Currents: CEO Ideology and Government Influence on Corporate Divestitures
Bibliographic record
Abstract
Extending the resource dependence theory, this study explores how the ideological currents of governmental leadership at both the federal and state levels impact corporate strategies, particularly focusing on transformative divestitures aimed at long-term energy transition. Centering on the U.S. oil and gas sector, this study finds that firms are significantly more likely to avoid divestitures under Republican leadership at both federal and state levels of government. Moreover, it identifies a critical moderating role of CEO political ideology in shaping these relationships. The findings demonstrate that the more Republican-leaning the CEO, the greater the likelihood that oil and gas companies will engage in divestitures under a Republican federal administration as opposed to a Democratic one. This suggests a complex interplay where CEO ideology exerts a conditional influence depending on the prevailing governmental ideology. These findings not only extend our understanding of how external political environments influence corporate strategic decisions, but also highlight the nuanced role of executive leadership in navigating these decisions. By elucidating the dynamic interactions between government policies and corporate strategies, this research offers valuable insights for policymakers and business leaders aiming to foster more sustainable energy practices.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".