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Record W2055962854 · doi:10.1080/08961530802129466

Consumers' Attitudes toward Marketing: A Cross-cultural Study of China and Canada

2008· article· en· W2055962854 on OpenAlexaboutno aff
Geng Cui, T. S. Chan, Annamma Joy

Bibliographic record

VenueJournal of International Consumer Marketing · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsConsumerismIndividualismChinaGovernment (linguistics)MarketingCross-culturalHofstede's cultural dimensions theoryBusinessPsychologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Previous research has attributed the differences in consumer attitudes toward marketing between countries to either the lifecycle of consumerism development or cultural values such as individualism. We conduct a cross-cultural study of China and Canada to test the two competing hypotheses. The survey results suggest that Chinese consumers are more positive about marketing and have a higher level of satisfaction than their Canadian counterparts. But the Chinese report more problems with marketing and less positive attitudes toward consumerism than the Canadians. While Chinese consumers are less likely to complain or engage in negative word-of-mouth, they are more supportive of government actions and public resolution. Consumerism and individualism have significant negative correlations with consumer attitudes toward marketing for the Canadians, but not for the Chinese. The cross-cultural variations may reflect the cultural values (i.e., individualism) and the role of government institutions, which are different between the two countries. These findings have significant implications for managing customer relationships in different countries and for interpreting the differences in consumer attitudes in cross-cultural studies.

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)0.002
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 score0.031
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.031
GPT teacher head0.288
Teacher spread0.257 · 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

Citations24
Published2008
Admission routes1
Has abstractyes

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