Cultural dimensions and materialism: comparing Canada and China
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine levels of materialism in Canada and China and how they vary with differences in culture. Design/methodology/approach Data were gathered from students and the general public with self‐completed surveys using measures for materialism and culture. Findings In the first stage of analysis, levels of materialism were examined across countries. Overall, materialism was higher for the Chinese than the Canadians on all Richins and Dawson's dimensions except acquisition centrality. To investigate these unexpected results, levels of Hofstede's cultural dimensions were compared across country, age, and gender and it was seen that the Chinese outscored Canadians on all dimensions except uncertainty avoidance. Finally, the association between the components of materialism and dimensions of culture was examined and a cultural explanation for at least part of the difference in level of materialism between the two countries found. Research limitations/implications Data were collected in specific regions of the countries. Owing to the characteristics of the two regions, a more general approach to data sampling would likely produce even more pronounced differences than those noted here. Practical implications A better understanding of the nature of materialism and how it varies across cultures should enable marketers, policy makers, and social planners to act more effectively. Originality/value This paper finds some unexpected differences in materialism and goes on to find that the cultural differences between Canada and China have changed since the original Hofstede data were collected.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".