The impact of perceptual congruence on the effectiveness of cause‐related marketing campaigns
Bibliographic record
Abstract
Abstract In the cause‐related marketing (CRM) literature, the degree of fit between a firm and cause has been shown to positively impact the effectiveness of CRM campaigns. Throughout the literature, however, firm‐cause fit has been operationalized as the relatedness of conceptual attributes such as brand image and positioning (i.e., conceptual congruence). Across three studies, the authors demonstrate that the relatedness of perceptual attributes such as color (i.e., perceptual congruence) can also enhance the effectiveness of CRM campaigns. Study 1 shows that perceptual congruence between a firm and cause positively affects perceptions of overall fit and participation intentions. Study 2 provides evidence that perceptual congruence impacts CRM effectiveness through a fit‐as‐fluency mechanism. Finally, Study 3 demonstrates the moderating effect of elaboration on the relationship between fit type (perceptual vs. conceptual congruence) and participation intentions. Consistent with previous findings, elaboration positively affects participation intentions when the fit type is conceptual, but the results of Study 3 indicate that elaboration negatively impacts participation intentions when the fit type is perceptual.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".