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Record W3086701384 · doi:10.5539/ibr.v13n10p53

Globalization or Localization: Global Brand Perception in Emerging Markets

2020· article· en· W3086701384 on OpenAlexvenueno aff
Lily C. Dong, Chunling Yu

Bibliographic record

VenueInternational Business Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsEmerging marketsGlobalizationMarketingBusinessGlobal marketingPerceptionBrand managementAdvertisingEconomicsPsychologyMarket economy

Abstract

fetched live from OpenAlex

With the globalization of world economy, more brands from emerging markets have entered the international market, which brought changes to the competitive landscape previously dominated by global brands from developed countries. It becomes more critical for marketing managers to understand consumers’ perceptions of the two types of global brands: traditional global brands from developed countries and emerging global brands from developing countries, and to uncover the changes in consumers’ purchase intentions in the new competitive environment. This study attempts to identify factors influencing consumers’ purchase intentions concerning the aforementioned two types of global brands. The results indicated that consumers’ interpretation of global brands is becoming increasingly complicated. In addition to the already established pathway of “perceived brand globalness (PBG)” influencing consumers’ brand attitude (hereinafter referred to as “BA”) and purchase intentions, there emerged a new pathway of “perceived brand localness (PBL)” influencing consumers’ brand perception and purchase intentions. These two pathways have different effects on traditional global brands and emerging global brands. Specifically, for traditional global brands, PBG has greater influence than PBL; for emerging global brands, PBL has more influence than PBG.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.099
GPT teacher head0.375
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

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

Citations13
Published2020
Admission routes1
Has abstractyes

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