Developing country perceptions of high‐ and low‐involvement products manufactured in other countries
Bibliographic record
Abstract
Purpose The aim of this paper is to examine the quality perceptions of developing country consumers in Malaysia and Papua New Guinea (PNG), on a high‐ and low‐involvement product (personal computer and shoes) produced by the manufacturing countries of origin of the USA, Australia, Italy and Brazil. Design/methodology/approach The country‐of‐origin (COO) effect on quality perceptions was measured by exploring interactive effect differences, using analysis of variance. Findings The findings from this study were first, that consumers in PNG evaluated their homemade products less favourably than foreign‐made products. Second, that COO effects influence consumers' preferences differently in the case of high‐ and low‐involvement products and third, that analyses using overall mean values instead of interaction effects can lead to incorrect interpretations. The results also supported the widely held view that consumers hold stereotypical views of products made in different foreign countries but disagreed about the nature of such stereotypical views. Practical implications The main implications of this study are first, that more attention needs to be paid to a product's COO when marketing to consumers in Malaysia and PNG. Second, that in the case of high‐ and low‐involvement products, marketing managers should take special care to examine the impact of COO effects. Third, that COO research should take care to correctly evaluate and use interaction effects since the simple use of overall mean values can produce very different and incorrect interpretations. Originality/value This paper makes important contributions to the COO and consumer ethnocentrism research.
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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 | 0.001 | 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 | 0.001 | 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) | 0.005 | 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".