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Record W4415329568 · doi:10.1177/23294884251384721

From Voice to Action? A Survey on Organizational and Individual Factors Impacting Employee Activism Intentions

2025· article· en· W4415329568 on OpenAlexfundno aff
Sara Conti, Yijing Wang, Silvia Ravazzani

Bibliographic record

VenueInternational Journal of Business Communication · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of CanadaSanofi
KeywordsEmployee voiceEmployee engagementOrganizational cultureOrganisation climateEmployee researchCorporate communicationOrganizational communicationPolitics

Abstract

fetched live from OpenAlex

Activism is increasingly shaping social and political arenas and influencing the corporate world. This study examines the factors that drive employee activism within organizations, focusing on how organizational and individual factors influence both communicative and actionable activism intentions. Drawing on a survey of 324 Italian public and private sector employees, we investigate the impact of: (a) organizational factors, specifically climate of voice, transparent leadership communication, and perceived corporate authenticity; and (b) individual factors, specifically perceived external prestige, organizational identification, and employee engagement. Additionally, we explore the moderating role of (c) corporate and CEO activism, as leaders’ active involvement in social, political, and environmental issues may influence employee activism intentions. Findings underscore that a supportive voice climate, authentic alignment of corporate values, and transparent leadership communication empower employees toward activism. This study provides strategic implications for integrating purpose-driven values into internal communication and leadership practices to cultivate an organizational culture that encourages proactive employee engagement and advocacy.

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)0.002
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 score0.011
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.053
GPT teacher head0.320
Teacher spread0.267 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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