Modelling Perceived Quality in Fruit Products
Bibliographic record
Abstract
Quality is in the eye of the beholder. Therefore, the firm's marketing strategy must be carried out by taking into consideration not only the consumers' objectively measurable needs and expectations but also their subjective perceptions as to what actually constitutes a quality product. Turning to Olson and Jacoby's distinction regarding the difference between a product's intrinsic and extrinsic attributes, the authors performed the estimation of structural equation models in order to assess the contribution of fruit product attributes to the Spanish consumers' perception of quality. In this article, the authors demonstrate that: (a) perceived quality in fruit products is a multidimensional concept depending on both intrinsic and extrinsic attributes; (b) intrinsic attributes exert a greater influence on perceived quality in fruit products than do extrinsic attributes; and (c) a very limited number of attributes (only seven out of twenty) stand out as being statistically significant to the consumers' perception of quality in fruit products. Finally, they provide statistical estimates pertaining to the relative contribution of the most significant intrinsic and extrinsic attributes to perceived quality in fruit products.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".