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Record W4416006035 · doi:10.5465/amproc.2025.267bp

Negotiating with Environmental Activists in the Shadow of Regulation

2025· article· en· W4416006035 on OpenAlexaff
Kartik Rao, Adam Fremeth, Guy L. F. Holburn

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsWestern UniversityTrent University
Fundersnot available
KeywordsNegotiationRegulatory stateShadow (psychology)Context (archaeology)DiscretionFlexibility (engineering)Environmental regulationRegulatory focus theory

Abstract

fetched live from OpenAlex

We study interactions between firms and environmental activists that occur in the regulatory domain to examine the conditions under which they reach cooperative outcomes to pre-empt formal regulatory contestation. We argue that a munificent regulatory environment plays a central role in enabling cooperative outcomes by providing firms greater access to resources and regulatory flexibility that allows them greater discretion in addressing the demands of environmental activists. We further contend that regulatory munificence mitigates the effects of firms’ environmental impact, activists’ inclination to adopt contentious tactics, and pressures from competing stakeholders – attributes that hinder the ability of firms and environmental activists to achieve cooperative outcomes. We empirically test our predictions in the context of U.S. electric utility sector, where state-level regulatory proceedings provide stakeholders, including environmental activists, the opportunity to participate and influence regulatory outcomes. Statistical analysis of the outcomes of negotiations between firms and environmental activists in these state regulatory proceedings between 1990 to 2015 provides strong support for our predictions. Our study contributes novel insights to the research on firm-stakeholder relations by examining interactions that occur between them in the relatively understudied, but critical, regulatory domain.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.226
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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