A CROSS-CULTURAL COMPARISON OF CONSUMER ATTITUDES TOWARD CAUSE- RELATED MARKETING
Bibliographic record
Abstract
This study examines consumer attitudes toward cause-related marketing (CRM) in four countries: Canada, Australia, Norway, and Korea. It investigates the role of consumer values in shaping these attitudes toward CRM. The study finds that attitudes toward cause-related marketing differ significantly across countries. Attitudes toward CRM appear to be less positive in countries where it is less established (e.g., Korea) and more positive in countries where CRM is well established (e.g., Canada). Differences in attitude toward CRM are also directly correlated with personal values, specifically with internal and external values. IMPLICATIONS FOR SOCIAL MARKETING Social marketers can benefit greatly from involvement with corporations in cause-related marketing programs, particularly as CRM begins to extend globally. However, a good understanding of consumer attitudes toward CRM in different countries is essential to ensure that social mar-keters benefit from these corporate relationships. The growth of CRM in various countries around the world and the personal values that are associated with it are, there-fore, of significant interest to social marketers. BACKGROUND Cause-related marketing (CRM) usually represents an opportunity for nonprofit organizations to benefit finan-cially from a partnership with a for-profit corporation. It can also allow nonprofit organizations to benefit from a corporation’s advertising spending and promotional sup-port for their cause (Berger, Cunningham, & Kozinets, 1998). This may, in many cases, include social marketing messages encouraging behavior change in line with the D ow nl oa de
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".