Evaluation of sport participation objectives within a health-focussed social marketing sponsorship
Notice bibliographique
Résumé
Purpose The purpose of this paper is to empirically test O’Reilly and Madill’s (2012) process model to assess social marketing elements of a multi-year partnership between Coca-Cola Canada and a physical activity microgrant program, known as Teen Challenge, managed by ParticipACTION, a Canadian not-for-profit organization that champions sport and physical activity participation. ParticipACTION’s Teen Challenge is a multi-year initiative that was developed in 2008 and involves over 5,800 community organizations (COs) and over 500,000 Canadian teens across the nation (ParticipACTION, 2016). Design/methodology/approach A series of ten hypotheses related to the evaluation are tested using longitudinal data from event participants over a five-year period from 2009 to 2013. Findings The study revealed that sponsor and sponsee shared objectives around sport participation, including the three found in this study. Practically, this is a positive result for sport participation properties who might now consider this as part of a sponsorship sales strategy. The research found that both Coca-Cola Canada and ParticipACTION have the following shared objectives in this particular sponsorship: motivate and support youth to get active and live a healthy life; remove barriers that youth face in getting physically active; and encourage more COs to be involved in the program. Conceptually, this finding extends the discussion of shared social marketing objectives in sponsorship proposed by Madill and O’Reilly (2010). Research limitations/implications The findings support the sponsorship literature, in suggesting that shared objectives among sponsorship partners are important for the sponsorship to achieve successful outcomes (Cornwell et al. , 2001). Shared sponsorship objectives can be utilized as a strategic tool for the sponsee to demonstrate the effectiveness of the program and to build a long-term relationship. The results of the logistic regression analysis indicate that COs which viewed partners’ collaboration as positive agreed that the Teen Challenge program made physical activity more accessible and affordable for youth. Practical implications This study provides several important implications for non-profit organizations that aim to establish an effective social marketing campaign. One way for non-profit organizations to build a strong relationship with sponsors was through positive collaboration where the two partners work together (e.g. activation) to maximize the sponsorship’s effectiveness. First, it increased youth participation in the Teen Challenge program and made it affordable for teens to participate in other physical activity programs. Second, it enhanced Coca-Cola’s image as supporters of active lifestyle in the eyes of COs. Finally, it affected the likelihood that COs would recommend the program. Social implications Results of the survey of COs that are registered with the program provides us with another important finding that positive partner collaboration is only one component of overall effectiveness. Another component would be to take action to communicate to COs that positive collaboration indeed took place in the sponsorship. One way to achieve this goal is to demonstrate to COs the importance of funding that the sponsor provides as well as the impact of sponsorship partners’ positive collaboration on the overall program. The authors also found that sponsorship partners’ positive collaboration in the delivery of the Teen Challenge program played an important role in whether COs recommend the program to others. Originality/value The results of this research contribute to the evaluation of the sponsorship of a health-oriented social marketing sponsorship of a sport participation property or sponsee (ParticipACTION) and a major brand as sponsor (Coca-Cola).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,044 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».