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 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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".