Matching consumers' country and product image perceptions: an Australian perspective
Bibliographic record
Abstract
Purpose Country‐of‐origin (COO) effects are concerned with buyers' opinions regarding the relative qualities of goods and services produced in various countries. It is the aim of this study to test a framework for investigating the match/mismatch between consumers' product category and country image perceptions. Specifically, the paper seeks to examine whether consumers perceive all products emanating from a particular country favourably simply because consumers associate favourable attributes with that country or whether this effect is specific to particular product categories. Design/methodology/approach The study employed a structured survey administered through mall intercepts. Data were collected from a sample of 188 Australian consumers. While Australian consumers were the focal country of study, countries selected for evaluation included Japan, Korea, the USA, Canada, China and New Zealand. The products selected included beer, automobiles, watches, leather shoes and stereos. Findings The findings suggest that when a strong favourable match exists between country and product image then COO will positively influence product evaluation and willingness to buy. Conversely, when an unfavourable mismatch is evident COO would negatively influence consumers' product evaluations and willingness to buy. Originality/value Given that most products originating in foreign countries are subject to country stereotyping or image effects, it is important for marketers and retailers to understand and manage the potential impact of COO effects. This study tests a framework that can be applied by marketers to determine the effect of product and country matches in relevant domestic or international markets.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".