Motivational and Cognitive Structures of Greek Consumers in the Purchase of Quality Food Products
Bibliographic record
Abstract
Following the means-end chain (MEC) analysis methodology, the study attempts to identify the psychologically-based, personal values-related motives of high-quality food purchasing in Greece. It is hypothesized that high social class consumers are quality-and health-conscious and exhibit a strong preference for high-quality foods. Olive oil is selected as the target-product of the survey, due to its importance for the everyday diet of Greeks and the agricultural economy of the country. A number of intrinsic and extrinsic quality cues are used as important product quality features. The application of the MEC methodology starts with the selection of a sample with the above-defined profile. It continues with the selection of very important olive oil quality attributes and the practical implementation of laddering-type interviews. The ultimate task of the study includes the development of sample's cognitive map, where the links between olive oil quality attributes, use benefits and consumers' values are clearly designated. The overall conclusion is that “high quality,” “healthiness/safety,” “tastiness,” “convenience” and “ethical consciousness” constitute the main motivational areas of high income and educational level consumers behind the selection of quality olive oil brands.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".