Marketing Physical Activity? Exploring the Role of Brand Resonance in Health Promotion
Bibliographic record
Abstract
Social marketing campaigns promoting moderate to vigorous physical activity (MVPA) may be more successful when associated with strong brands. Little is known regarding how brand constructs such as brand resonance may be related to MVPA and its antecedents (e.g., having a physical activity identity). A better knowledge of these constructs and their relationships can reveal how to strengthen brands to make them more useful for interventions. The purpose of this study was to test a model linking ParticipACTION (a Canadian social marketing organization) brand constructs (brand affective attitude, identification, resonance), MVPA antecedents (behavioral affective attitude, identity), and MVPA. This study used a cross-sectional online survey design with a representative Canadian adult sample of 1,475 people (M age = 49.36; 49.1% female). Path analysis was conducted to test the model. Overall, the model fit the data well, demonstrating positive associations between brand affective attitude and identification, identification and resonance, resonance and both behavioral affective attitude and identity, behavioral affective attitude and both identity and MVPA, and identity and MVPA. The results suggest that building brand resonance is important for linking branding to MVPA variables. Brand management activities designed to target brand affective attitude may be crucial to helping people feel strong resonance with a brand in a way that supports behavior change.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".