Factors in consumer choice of Russian retail chains’ private labels: The role of green attributes
Bibliographic record
Abstract
The issues of green marketing, its impact on consumer behaviour, and the evolution of retail chains’ private labels (PLs) are well-covered in scientific literature. However, there is a lack of comprehensive research addressing the interconnection of these three aspects. The paper explores the factors affecting consumer decisions to purchase green private labels and identifies what product attributes Russian consumers associate with being green, i.e., safe for both the consumer and the environment at all stages of production, sales and disposal. The methodological basis of the study resides in relationship marketing theory, consumer choice theory, value perception theory and the concept of sustainable development. The data for the empirical study were obtained from in-depth expert interviews with retail chains’ representatives and respondents’ survey. The data were processed using correlation and regression analysis, as well as content analysis. The research results indicate that the price of private labels is not the sole factor in consumer choice: it is influenced by both socio-demographic characteristics of respondents and additional properties of PLs, including a set of green attributes, informative packaging, social orientation of private labels, etc. The findings of the study are valuable for planning marketing campaigns to promote PLs of retail chains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".