Bibliographic record
Abstract
Purpose – This study aims to investigate the effects of type of message (information/buy), the moderating effects of fit (high/low) and salience (brand vs cause) and the mediating effects of attributions of partner motives in cause marketing advertisements. Design/methodology/approach – Two experiments, one with students and the second with a more representative sample of the population were used to investigate the effects. ANOVA and structural equation modeling were used to test the relationships. Findings – Fit and salience were found to be key moderators on the effect of type of message on consumer responses. While brands can use a buy message when they are salient, this benefits them only when fit is high. For informational messages, cause salience leads to positive outcomes, especially when fit is low. Further, consumer attributions of partner motives mediate responses to the advertisement. Research limitations/implications – Type of message is an important variable that needs to be selected with care. However, the moderating effects of fit and salience and the mediating effects of consumer attributions of partner motives may be able to overcome type of message. Practical implications – Initial partner selection is critical for the brand. A second key factor is inferences due to the specific message, fit and salience. Nonprofit firms have less to worry about fit compared to brands as attitude and behavioral intentions are high under both fit conditions. Social implications – Cause marketing can be used successfully to benefit both brand and cause simultaneously. Originality/value – This study examines the effects for both brands and causes and suggests ways in which both can benefit, leading to a win–win situation. This is an important contribution to the cause marketing field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.193 | 0.048 |
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".