The Relationship of Consumer Ethnocentrism, Purchase Intention, and Lifestyle in First-generation Bicultural Ethnic Groups
Bibliographic record
Abstract
This study analyzed the relationship between first-generation Colombian – Canadian bicultural ethnic consumers, their purchase intention, and lifestyles. These common types of consumers were to make purchases depending on the lifestyle, bicultural ethnic group, and the ethnocentric trends that might differ from the country of origin or the host country. There were 158 surveys administered in Toronto, Canada. Structural Equation Modeling was applied, using the Unweighted Least Squares Estimates and Maximum Likelihood Methods. An Exploratory Factor Analysis was run using the principal axis method and Promax rotation after conducting the multivariate normality tests, reliability, and discriminant and convergent validity tests. The Confirmatory Factor Analysis guaranteed an adequate measurement model. The purchase intention was explained as 85.2% for lifestyle factors, such as self-reliance and leadership, nurturing and family orientation, household oriented and industrious, and ethnocentrism in Colombia and Canada. It was found that lifestyle factors were not significantly related to purchase intention. However, ethnocentrism positively impacted Canadian product perceptions and a negative impact on Colombian products. Results showed that lifestyles (activities, interests, and opinions [AIO]) are not always key elements in consumer’s purchase intentions. Bicultural consumer ethnocentric trends are stronger in the host country because consumers in a developing country accept more developed countries. The study supported the theory of social identity (Tajfel, 1982) and optimal distinction (Brewer, 1979), which suggests consumers would have a bias towards the country with which they identify or experience dual or divided loyalties between the country of origin and the host country.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".