Social Activism and Corporate Nonmarket Performance: Evidence from Nuclear Power Generation
Bibliographic record
Abstract
In this paper, we theorize about nonmarket performance outcomes in contentious environments, i.e. in settings that are characterized by stakeholder disapproval targeting the firm and by social movement mobilization against its activities. More specifically, we posit that firms that are targeted by protests due to their involvement in stigmatized activities should experience worse nonmarket performance outcomes. This is because politicians–and by extension, regulators–depend on public consensus to stay in office, and as such they are especially sensitive to mobilization; for this reason, we argue that they will be less likely to behave favorably towards firms that are overtly opposed by activists in public arenas. We find support for this idea through a study of electric utilities that were involved in nuclear power generation in the United States between 1970 and 1995, using the approval of increased rates of return (ROR) by public utilities’ commissions (PUC) as a dependent variable. We also find this effect to be especially strong: 1) when there is ideological alignment between activists and regulatory bodies or between activists and the state’s legislative and executive bodies; 2) when the extent of involvement of the firm in controversial activities is greater; 3) when protests targeting the firm’s activities are better organized. We discuss the contributions of our results to the literature on nonmarket strategy and elaborate on their implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".