Notice bibliographique
Résumé
Marketing to aged consumers: a marketplace challenge Ageing consumers are becoming an increasingly relevant and prominent consumer group for marketersyet there is a lack of research in the marketing literature (Rybaczewska and Sparks, 2021).This special section, i.e. marketing to aged consumers, and the associated track at Global Marketing Conference, 2020 are an attempt to stimulate this research and bring the topic to the fore.We hope that it delivers and stimulates further research that will help marketers and researchers to better recognise ageing consumers as legitimate consumers who need legitimate markets to better meet their growing and dynamic needs.Research for the past two decades has continuously suggested that ageing consumers are the fastest growing consumer group in developed countries and have the highest level of spending power and net worth of any aged group and average spending on products and services is more than ever before (Chen and Shoemaker, 2014).Researchers note that many of these ageing people expect to maintain their well-being and quality of life over several decades of ageing and make decisions accordingly (Bateson, 2021).Whilst they start preparing for the future, few actually relate "their" ageing with being frail and needing permanent care but foresee an attractive lifestyle, being independent and feeling supported (Bowling and Iliffe, 2011).Yet, life satisfaction and thus well-being in old age are low (Baird et al., 2010) and people report general anxiety about ageing (Sargent-Cox et al., 2014 andKillen andMacaskill, 2020).There is agreement amongst researchers that for older people, social ties, connections and activities are important contributors to well-being (Shankar et al., 2015) and quality of life (Orden et al., 2020).Practising marketers have also been slow to respond to the ageing population opportunity in most markets and some researchers suggest that marketers often insult this target market through inappropriate marketing (Rosenthal et al., 2021).For marketers, it is important to acknowledge that older consumers are comprised individuals with unique needs that transition as they agecognitively or physically.Ageing affects all of the facets of consumers' life from decision-making, evaluation, media behaviour, socialisation, sensitivity and even reactions to marketing activities (Rybaczewska and Sparks, 2021).This opens up challenges and opportunities to recognise and fulfil their needs in ways that enhance well-being.To support practising marketers, as well as ageing people and their stakeholders, marketing researchers are exploring and testing many aspects of marketing to ageing consumers including dealing with stereotypes (Westberg et al., 2020), developing models of life-course (Moschis, 2021), service design (Bateson, 2021), marketing and policy (Nunan and Di Domenico, 2019), technologies for ageing people (Wilson-Nash and Tinson, 2021) and many more.In this section, we are delighted to present three papers on marketing to aged consumers.Our section begins with a paper titled "Lifestyle segmentation of older Koreans: A longitudinal comparison of segments and life satisfaction" from Kwon Jung (KDI School of Public Policy and Management) and Jihye Jung (University of Texas) where they adopt a longitudinal research design to explore segmentation of ageing consumers based on their lifestyle and value orientations and then how this might impact consumer satisfaction.We also include a paper titled "Aging consumers and their brands: A customer journey perspective" from Chao-Chin, Huang (National Pingtung University of Science and Technology) where the author explores the ageing consumer journey and how this might be different between age-denial brands and age-adaptive brands.Finally, we include a paper titled "Using the life course paradigm to study financial well-being in late life" from Pattharanitcha Prakitsuwan Editorial 1
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.
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 | 0,003 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,010 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,056 | 0,053 |
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 ».