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Record W4416916404 · doi:10.1080/14680777.2025.2585278

Consumer responses to femvertising: evidence from a cross-cultural study

2025· article· en· W4416916404 on OpenAlexaff
Claudia L. Gomez‐Borquez, Edgar Centeno‐Velázquez, María Eugenia López-Pérez, Anna Török, Erzsébet Malota, Ernesto del Castillo

Bibliographic record

VenueFeminist Media Studies · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsConsumer behaviourPerceptionVariety (cybernetics)Affect (linguistics)

Abstract

fetched live from OpenAlex

This study examines the moderating role of support for women’s rights and feminist self-identification in the brand-related effects of femvertising in a cross-cultural context. This research investigates how consumers from countries with varying gender equality indices respond to femvertising. A survey was conducted in two countries, Spain, which represents southern Europe and has a higher gender equality index, and Mexico, which represents Latin America and has a lower gender equality index. Two relevant contributions emerged from this study. First, it provides a comparative framework for identifying the main empirical findings on the brand-related effects of femvertising across different cultures. Second, it highlights that in countries with high gender equality, such as Spain, support for women’s rights negatively moderates the relationship between general attitude towards femvertising and evaluations of the presented femvertising messages. This finding suggests that consumers in these regions may be more critical and sensitive to feminist messaging. The practical implications suggest that brands should tailor their femvertising campaigns to align with the socio-cultural context of each market to ensure authenticity and consumer resonance.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.131
GPT teacher head0.404
Teacher spread0.272 · 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.

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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