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Record W1988131661 · doi:10.1108/13555850910997544

Perceptions of countries based on personality traits: a study in China

2009· article· en· W1988131661 on OpenAlexaff
Alain d’Astous, Dong Li

Bibliographic record

VenueAsia Pacific Journal of Marketing and Logistics · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsOriginalityPersonalityMultinational corporationChinaScale (ratio)Context (archaeology)BeijingSample (material)Value (mathematics)Product (mathematics)PerceptionPsychologyBig Five personality traitsMarketingPosition (finance)Social psychologyBusinessPolitical scienceGeographyComputer scienceCreativity

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine country perceptions in China from the point of view of the personality concept. Design/methodology/approach A country personality scale developed in a Western country was adapted to the Chinese social context and used to position 11 different countries, including China, on six personality dimensions. This was accomplished by means of a survey of 184 adult Chinese people from the city of Beijing. Findings The results show that the adapted scale has good psychometric properties, that it behaves appropriately with respect to some theoretical expectations, and that it brings about results that are consistent with common sense and with previous country image research. Research limitations/implications The study should be replicated with a more representative sample of Chinese people and a larger array of country stimuli. Originality/value The paper shows that the country personality scale can be used to better understand how Chinese consumers think of a product's country of origin and suggest appropriate product positioning strategies to help multinational corporations define their strategic actions with respect to China.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.099
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.021
GPT teacher head0.266
Teacher spread0.244 · 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

Citations30
Published2009
Admission routes1
Has abstractyes

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