Country images of technological products in Taiwan
Bibliographic record
Abstract
This article presents the results of a survey of 202 male Taiwanese consumers. In this study, consumer judgements of two technological products varying in their level of complexity made in highly, moderately, and newly industrialised countries were obtained in a multi‐attribute context. The results show that the country‐of‐origin image of moderately and newly industrialised countries was less negative for technologically simpler products (i.e. a television) than they were for technologically complex products (i.e. a computer). It appears that the negative image of moderately and newly industrialised countries can be attenuated by making Taiwanese consumers more familiar with products made in these countries and/or by providing them with other product‐related information such as brand name and warranty. Newly industrialised countries were perceived more negatively as countries of design than as countries of assembly, especially in the context of making technologically complex products. The image of foreign countries as producers of consumer goods was positively correlated with education. The more familiar consumers were with the products of a country, the more favourable was their evaluation of that country. Consumer involvement with purchasing a technologically complex product such as a computer was positively associated with the appreciation of products made in moderately industrialised countries. Managerial and research implications are derived from these results.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".