Investigating the Role of Brand Equity in Predicting the Relationship Between Message Exposure and Parental Support for Their Child’s Physical Activity
Bibliographic record
Abstract
Social marketing researchers have identified brand equity as a potential mediator of the relationship between campaign message exposure and resulting behavior. This study examined whether message exposure and components of brand equity contribute to overall brand equity changes over the course of a 12-month campaign evaluation. In addition, we examine whether brand equity consistently accounts for covariance (i.e., mediation) in the relationship between message exposure and parental support (PS). Data were drawn from ParticipACTION’s “Think Again” campaign evaluations that targeted parents, specifically moms, with children between the ages of 5 and 11 years (three independent samples: March 2011, N = 702 [T1]; September 2011, N = 706 [T2]; March 2012, N = 670 [T3]). Univariate analyses of variance were used to examine changes in message exposure and components of brand equity over time, while structural equation modeling was used to examine the brand equity model relationship. Findings revealed that message exposure was greatest at T3 ( ps < .01) and that brand equity was greatest at T2 ( ps < .05). Model fit statistics revealed modest to good fit. Results demonstrated that Think Again message exposure was related to brand equity (standardized effects .10–.28) and that brand equity was related to PS (standardized effects .30–.40; ( ps < .01). Importantly, an indirect effect of message exposure on PS through brand equity (standardized effects .03–.09) emerged in all models ( ps < .05). This study demonstrates the utility of branding social marketing campaigns to increase campaign effectiveness.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".