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Record W3172236787 · doi:10.1111/ijcs.12720

How to craft humorous advertisements across diverse cultures? Multi‐country insights from Brazilian, Chinese and American consumers

2021· article· en· W3172236787 on OpenAlexaff
Ying Zhu, Valerie Lynette Wang, Yong J. Wang, Joicey Wei

Bibliographic record

VenueInternational Journal of Consumer Studies · 2021
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsUncertainty avoidanceCollectivismIndividualismAffect (linguistics)ChinaPsychologyMulticulturalismCraftSocial psychologyAdvertisingHofstede's cultural dimensions theoryCultural diversityCognitionSociologyBusinessPolitical scienceGeography

Abstract

fetched live from OpenAlex

Abstract In today's multicultural environment, the influence of cultural orientations on humorous advertising outcomes is increasingly significant for researchers and practitioners. Drawing on a theoretical framework from culture dimensions and theories of humour in advertising, this research examines the moderating role of cultural orientation on the relationship between humour styles and consumers' attitudes towards humorous ads. Empirical results from studies conducted in Brazil, China and the United States show that uncertainty avoidance moderates the effect of cognition‐evoking humorous ads on consumers' responses to the ads, while individualism–collectivism moderates the effect of affect‐evoking humorous ads on these responses. Specifically, consumers from countries with high uncertainty avoidance (e.g., Brazil) have more favourable attitudes towards and perceive greater humour from cognition‐evoking humorous ads with closure than those from countries with low uncertainty avoidance (e.g., China, United States). In addition, consumers from collectivist countries (e.g., China, Brazil) have more favourable attitudes towards and perceive greater humour from affect‐evoking humorous ads with closure than do those from individualist countries (e.g., United States).

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.412
Teacher spread0.384 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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