Bibliographic record
Abstract
This paper provides a global theoretical explanation for why country of origin effects vary in consumers' quality evaluation-a puzzling issue in the research-based on the framework of accessibility-diagnosticity and information integration. Findings of judgment weight changes, quality assessment speed, and perceived quality support the hypotheses of the framework that country of origin effects vary not only by the diagnosticity of the country of origin, but also by the accessibility-diagnosticity of its accompanying cues. The findings also demonstrate strong competing relationships among quality-signaling cues in the quality evaluation, such that even with the same country of origin, country of origin effects are weaker when accompanied by more diagnostic accompanying cues (e.g., strong brands, distinctive prices) compared with less diagnostic accompanying cues (e.g., weak brands, less distinctive prices). This paper discusses the application of the framework to country of origin management and suggests ways to manipulate a variety of quality-signaling cues (e.g., brand, price, product design, and product warranty) to minimize less favorable or to maximize favorable countries of origin.
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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 | 0.005 | 0.031 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".