Decomposition of cross‐country differences in consumer attitudes toward marketing
Bibliographic record
Abstract
Purpose Previous studies have found significant differences in consumer attitudes toward marketing between countries and attributed such variations to differences in the stage of consumerism development and cultural values. This study aims to test these competing hypotheses using econometric decomposition to identify the source of such cross‐country variations. Design/methodology/approach Using survey data of consumer attitudes toward marketing from China and Canada, this study adopts econometric decomposition to examine the cross‐country difference in consumer attitudes toward marketing. Findings The results show that Chinese consumers have more positive attitudes toward marketing than Canadians and the two countries differ significantly across all predictor variables. However, the results of decomposition suggest that consumerism, individualism and relativism do not have any significant effect on the country gap in consumer attitudes toward marketing, while idealism has a significant coefficient effect. Research limitations/implications The study finds different effects of cultural values on consumer attitudes across countries and has meaningful implications for international marketing strategies. Originality/value The study investigates the sources of cross‐national differences in consumer attitudes toward marketing using rigorous analyses to improve the accuracy of cultural attribution for international marketing and cross‐cultural consumer 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".