Navigating Investor-Firm Dynamics: Mutual Dependence and Power Imbalance Fossil Fuel Divestitures
Bibliographic record
Abstract
This study examines how mutual dependence and power imbalance between institutional investors and firms influence strategic decisions, focusing on fossil fuel divestitures in the U.S. oil and gas sector. Drawing on resource dependence theory (RDT), we propose that firm- institutional investor dynamics shape the likelihood of two distinct types of divestitures: neutral divestitures, which maintain a steady business scope, and transformative divestitures, which significantly alter the firm’s strategic direction. Our analysis of 197,151 firm-institutional investor-year observations from 240 publicly traded U.S. oil and gas companies (2000–2021) reveals that mutual dependence fosters alignment, encouraging transformative divestitures under long-term institutional investors (LIIs), while power imbalances disrupt this collaboration. In contrast, short-term institutional investors (SIIs) exhibit limited influence on transformative divestitures, highlighting the heterogeneous impact of investor types. By advancing firm-level dyadic measures of mutual dependence and power imbalance, this study extends RDT’s application to investor-firm relationships and demonstrates the strategic relevance of divestitures in managing resource dependencies. These findings contribute to the corporate restructuring literature by framing divestitures as proactive strategies for navigating ESG pressures and external dependencies, offering practical implications for firms and institutional investors navigating sustainability-driven transitions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".