The moderating role of cultural traits in consumer reaction to CRM campaigns : a comparative study of Chinese and Canadians of European descent
Bibliographic record
Abstract
Cause Related Marketing has grown tremendously as a marketing tool since the 1980s.Numerous studies have been conducted in North America, Europe and Australia with mostly consumers of European Descent, but no such study has been done with Chinese consumers.This study explicitly measures how Chinese consumers perceive CRM and how their reactions compare with their European-Canadian counterparts.In essence, this study evaluates the moderating role of cultural traits (individualism/collectivism and low-context/high-context) in shaping consumer reaction to CRM.A total of 302 people responded to a pre-designed questionnaire.Overall, the results suggest that Chinese consumers are aware of and favorable to CRM, though less than European-Canadians.Chinese females illustrate a more positive reaction to CRM than their male counterparts in terms of general CRM attitude and behavior intention.It is concluded that cultural traits have a significant effect on consumers' awareness of CRM and attitudes toward CRM firms, but not on their attitudes toward CRM brands, toward CRM in general or their behavior intention.Importantly, cultural traits moderate consumers' attitudes to CRM firms, toward CRM brands and their product purchase intention, but not in terms of awareness of CRM, attitude toward CRM in general or brand choice intention.This paper contributes to an understanding of the relationship between cultural traits and consumer reaction to CRM campaigns.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".