How to make sponsorship activations memorable: the power of meaningfulness
Bibliographic record
Abstract
Purpose Building on memory-dominant logic, this study proposes a theoretical model explaining the antecedents and consequences of memorable activation experience. Design/methodology/approach Through a qualitative research methodology – a narrative review, an analysis of current activation trends, eight interviews and content validity evaluation by five experts – this study hypothesized that six antecedents (positive emotions, novelty, meaningfulness, involvement, experience intensification and serendipity) positively influence memorable activation experience, which in turn significantly affects two managerial outcomes: positive word-of-mouth and sponsor recall. To evaluate the proposed hypotheses, this study analyzed data collected from consumers who participated in a sponsorship activation by Lafleur (n = 215) and Salon de jeux de Québec (n = 306) using partial least squares structural equation modeling (PLS-SEM). Findings The results indicate that all antecedents, except for novelty, have a significant impact on memorable activation experience, which positively impacts positive word-of-mouth and sponsor recall. Originality/value By extending the scope of memorable experience to the sponsorship field, this study enhances its conceptualization and application. It also contributes to a better understanding of designing activations to increase the likelihood of memorability, thus improving their effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.041 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".