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Record W4407229315 · doi:10.1111/1467-8551.12900

Conditional Silence: Organizational Status and Under‐Communication of Environmental Performance

2025· article· en· W4407229315 on OpenAlexaff
Yinglin Huang, Claude Francœur, Shafu Zhang, Stephen Brammer

Bibliographic record

VenueBritish Journal of Management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsConformityBusinessGreenwashingReputationCorporate social responsibilityAccountingStakeholderMarketingPublic relationsPsychologyPolitical scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

Abstract Do corporate communications regarding firms’ environmental performance accurately reflect their environmental actions and impacts? While substantial research has focused on greenwashing, less attention has been given to companies’ under‐reporting of their environmental performance. Building on middle‐status conformity theory, this study examines the relationship between organizational status and environmental disclosure and performance. We find that only middle‐status firms fully disclose their environmental performance, while both high‐ and low‐status firms under‐report their achievements: high‐status firms to minimize risk to their reputation, and low‐status firms to avoid additional conformity costs. Firms in stakeholder‐sensitive industries, with higher institutional ownership, and those with corporate social responsibility committees are less likely to under‐report. Moreover, middle‐status firms are particularly vulnerable to penalties for concealing environmental information. Overall, our findings suggest that organizational status plays a significant role in shaping firms’ propensity to disclose environmental achievements.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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

Citations8
Published2025
Admission routes1
Has abstractyes

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