Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the role of perceived food quality and consumer ethnocentrism and potential trade-offs between these two concepts in Russian consumers’ food purchase decisions after the implementation of the Russian import ban. Design/methodology/approach Survey data were collected via in-person interviews in the City of Perm, which is one of the largest and most industrial cities in Russia. A double-bounded dichotomous-choice contingent valuation model is utilized to estimate willingness to pay (WTP) and to analyze factors that affect consumers’ choice. Findings The results suggest that most respondents do not consider domestically produced cheese as a risky product in terms of food safety but simply of lower quality than imported cheese. However, the average respondent’s WTP discount for domestic cheese compared to imported cheese is 8 percent, which is relatively small. This corresponds to participants’ opinion that buying domestic cheese is the right thing to do since it supports Russian farmers and producers. The results indicate further that with increasing education and income levels, individuals are less likely to prefer domestically produced cheese for either economic or quality reasons. This effect is stronger for the quality preference. Research limitations/implications The results indicate that if the Russian government aims at expanding the domestic food market further, more attention needs to be paid to ensuring the quality of domestic food products in order to increase consumer acceptance and WTP. Originality/value This is the first study providing empirical evidence on Russian consumers’ attitudes and perceptions of domestically vs imported food products after the implementation of the Russian import ban, which can be considered as an external policy shock.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".