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Who is online? A latent class analysis of internet activities and determinant characteristics of older people

Articolo
Data di Pubblicazione:
2023
Abstract:
As Italy is the European country with the highest percentage of adults aged over 60, growing concerns have emerged about how the ageing population is living in an era of increasing digitalisation. According to the capability approach framework, Internet services can enable older adults to achieve social inclusiveness and independence, only if the variety of digital activities includes a large number of active ageing parameters. This is the first study that explores Internet use by Italian older adults and identifies latent groups of Internet users. We elaborated 13,597 responses from an Italian representative annual population survey and 40 different online activities were analysed using an Exploratory Factor Analysis and further elaborated together with sociodemographic variables in a Latent Class Analysis. Three classes of older Internet users were identified: Utilitarian, Familiar and Enjoyment users. The findings support the existence of heterogeneous older Internet users, at the same time showing the importance of personal characteristics to predict class membership. Being female, widowed, having a low income, being poorly educated, living alone, and having various comorbidities predicted fewer online activities. From a policy perspective, this study highlights that targeted training programmes together with improvements to digital infrastructures are essential to increase the level of Internet activities in later life as is the need for policies in favour of the disadvantaged groups in the older population.
Tipologia CRIS:
Articolo su Rivista
Keywords:
Aging; Internet use; Digital inclusiveness; Exploratory factor analysis; Latent class analysis; Capability approach
Elenco autori:
Pantelaki, E; Maggi, E; Crotti, D
Autori di Ateneo:
CROTTI DANIELE
Centro di Ricerca per l'Invecchiamento di Successo
MAGGI ELENA
Link alla scheda completa:
https://irinsubria.uninsubria.it/handle/11383/2157831
Link al Full Text:
https://irinsubria.uninsubria.it//retrieve/handle/11383/2157831/229017/Manuscript%20CHB_pre-print.pdf
https://irinsubria.uninsubria.it//retrieve/handle/11383/2157831/229018/CHB-2023-107830.pdf
Pubblicato in:
COMPUTERS IN HUMAN BEHAVIOR
Journal
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URL

https://www.sciencedirect.com/science/article/pii/S0747563223001814?via=ihub
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