How Cause-Related Marketing Might Improve Purchase Intention in Emerging Countries: A Two-Lag Study in the Finance Service Sector
Bibliographic record
Abstract
For businesses in emerging countries, often confronted with important social and development challenges, cause-related marketing (CRM) might help to improve their market share in demonstrating corporate social responsibility. This study explores the relationship between CRM bringing up the issue of COVID-19 and purchase intention among consumers of banking services in an emerging country. We collect data through two survey studies (303 and 506 respondents) using a 3-month time-lagged design. Results show the relevance of associative learning and signal theories in confirming (1) the positive sequential mediating effect of brand association and commitment on the relationships between CRM bringing up the issue of COVID-19 and purchase intention and (2) the moderating positive impact of corporate image on the relationships between CRM bringing up the issue of COVID-19 and consumer brand association. Our findings encourage firms in emerging countries to use CRM, but governments should also incentivize or support adopting socially responsible practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".