‘Can you see it?’ how one-for-one promotions increase imagery vividness and promotion effectiveness in cause-related marketing
Bibliographic record
Abstract
This paper investigates consumer response to one-for-one promotions in cause-related marketing (CRM), relative to promotions with cash. Three empirical studies explore how one-for-one promotions enhance mental imagery vividness, leading to improved promotional outcomes. Study one demonstrates one-for-one promotions generate superior promotion effectiveness compared to monetary donations. Study two is an extension demonstrating one-for-one promotions amplify imagery vividness associated with the donated product and its usage context, improving consumer responses. Study three reveals this effect is moderated by consumers’ product usage experience, such that one-for-one (vs. monetary) promotions produce a stronger effect among consumers with limited experience with the product. This research contributes to the CRM literature by integrating Construal Level Theory and relational processing theories, highlighting imagery vividness as a key cognitive mechanism driving consumer responses. Marketers can apply these findings by deploying strategies to enhance imagery in promotions and targeting consumers with less product experience, who are especially responsive to one-for-one promotions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".