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Record W4200005777 · doi:10.1080/08961530.2021.2020702

Lifestyle and Purchase Intention: The Moderating Role of Education in Bicultural Consumers

2021· article· en· W4200005777 on OpenAlexaboutno aff
Iliana E. Aguilar-Rodríguez, Leopoldo G. Arias‐Bolzmann

Bibliographic record

VenueJournal of International Consumer Marketing · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsPsychographicTheory of planned behaviorPsychologyStructural equation modelingOptimismSocial psychologyVariance (accounting)Norm (philosophy)MarketingControl (management)Political scienceBusinessEconomics

Abstract

fetched live from OpenAlex

This research is the first to analyze the relationship between lifestyles and purchase intentions in first-generation bicultural consumers residing in Canada. It applies the Activities, Interests, and Opinions (AIO) model and the Theory of Planned Behavior (TPB) and includes education level as a moderating variable to find differences in consumption between the country of origin and the host country. A total of 194 personal surveys were administered. The data were analyzed using structural equation modeling and one-way analysis of variance (ANOVA), suggesting that subjective norm predicts purchase intention, being positively related to Health and Optimism and Household Oriented and Industrious, and negatively related to Self-reliance and Leadership. A negative impact was found between the education level and the subjective norm, which was significant concerning the host country. The study also revealed that the subjective norm is positively related to Health and Optimism and negatively related to Self-reliance and Leadership, improving the model’s predictive accuracy when the educational level is involved. The findings demonstrate the usefulness of TPB and provide marketers with better identification of psychographic market segments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.257
Teacher spread0.245 · 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

Citations16
Published2021
Admission routes1
Has abstractyes

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