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Record W2527562764 · doi:10.1509/jm.15.0206

How Does Local–Global Identity Affect Price Sensitivity?

2016· article· en· W2527562764 on OpenAlex
Huachao Gao, Yinlong Zhang, Vikas Mittal

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Marketing · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMindsetIdentity (music)Position (finance)Affect (linguistics)Sensitivity (control systems)MarketingContrast (vision)SacrificeAdvertisingBusinessSocial psychologyMicroeconomicsEconomicsPsychologyComputer scienceAestheticsGeography

Abstract

fetched live from OpenAlex

The authors propose that when consumers’ local identity is accessible, they are less likely to be price sensitive because of a sacrifice mindset. Six studies using divergent measures of the independent and dependent variables as well as diverse samples (students and nonstudents, U.S. and Chinese residents, primary and secondary data) produce consistent results. Furthermore, the authors demonstrate the mediating role of a sacrifice mindset by both measuring and manipulating this construct; they also identify boundary conditions of the association between a consumer's local identity and price sensitivity. Previous research has shown that consumers with a local identity display lower price sensitivity to brands with a local origin. In contrast, the results from this research show that consumers with a local identity display lower price sensitivity even to products with an ambiguous origin. Firms using a globalization strategy can try to activate consumers’ local identity to make them less price sensitive to their brands, without having to position the brands as local.

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.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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