The role of consumer ethnocentrism in a buy national campaign in a transitioning country
Bibliographic record
Abstract
Purpose The goal of this paper is to examine the link between consumer ethnocentrism (CE) and the attitudes of two consumer groups to a buy local campaign in a transitioning economy, Slovakia. Design/methodology/approach Using a structured questionnaire, data were collected from 211 non‐students at shopping malls in Banská Bystrica (non‐student group) and from 209 students at the University of Matej Bela, Banská Bystrica (student group) in Slovakia. Ethnocentrism was measured using the consumer ethnocentric tendencies scale (CETSCALE) while attitudinal statements were used to measure the attitudes toward locally made products and a buy local campaign. The attitudinal data were factor analysed and the correlations between the CETSCALE and the attitudinal statements were examined. Summative scales based on the factor analysis results were also developed. Findings A significant finding of this paper is the role of the government and industry in encouraging Slovakians to buy local. The nonstudent consumers to be less ethnocentric than the student group are found. The attitudinal statements of both groups toward Slovakian products are generally similar. Originality/value This research was designed to contribute to the discussion of CE by linking it to attitudes to a buy local campaign in a transitioning 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".