Checking Our Blind Spots: The Most Common Mistakes Made by Social Marketers
Notice bibliographique
Résumé
Background: The work of social marketers and the environment in which they work is complex, which inevitably results in mistakes being made and sometimes, the failure of a social marketing program. Unfortunately, social marketers do not often report their own mistakes. Even when failures or mistakes are reported, it is usually for the purpose of one study, as opposed to a wider understanding of mistakes made by social marketers in the field. This is a significant gap in the development of social marketing practice since understanding the nature of the most common mistakes made by social marketers could assist them in assessing their own shortcomings and potentially lead to more effective programs. Focus: This article is related to research and evaluation of the social marketing field. Research Question: What are the perceptions of social marketing experts regarding the most common mistakes made by social marketers? Importance to the Field: A greater understanding of the common mistakes made by social marketers will allow practitioners to assess their own shortcomings, improve program outcomes, and raise the status of the social marketing field. Methods: This research is qualitative and exploratory, with a constructivist, grounded theory methodology. In-depth interviews with 17 social marketing experts were conducted. Experts were purposefully chosen based on a set of criteria including the number of years of experience they had in the field. Results: The interviews revealed nine mistake categories: inadequate research, poor strategy development, ad hoc approaches to programs, mismanagement of stakeholders, poorly designed program objectives, weak evaluation and monitoring, poor execution of pilots, inadequate segmentation and targeting, and poor documentation. Additionally, the interviews revealed two other emergent, crosscutting themes that affect the mistakes being made: external influences that the social marketer may not have direct control over and the social marketer’s own preconceptions that they bring to the program. Recommendations for Research or Practice: Future research may explore (1) the extent to which external influences lead to social marketing program success or failure, particularly in comparison to mistakes made by social marketers and (2) perspectives from the social marketing community as to the most common mistakes made by social marketers. Social marketers may consider being more reflexive in their work, including reporting their own mistakes and failed programs, as well as challenging the biases they may bring to the work that they do. Limitations: The sample size is small and therefore not generalizable to all social marketing experts or the social marketing community. Also, there are many parts of the world in which social marketers practice, but which are not represented by the social marketing experts. Additionally, the “mistakes” listed are based on opinion as opposed to direct observation, which may make them more susceptible to bias.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,055 | 0,179 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,014 |
| Communication savante | 0,006 | 0,011 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».