Abstract
Objective: Internet use is increasing among all age groups. However, older people are less likely to adopt the internet. This study aims to examine the cumulative impact of sociodemographic variables enabling case finding of non-users among community dwelling older people.
Methods: This study uses data from the Belgian Ageing Studies. The sample consists of 64,553 people aged 60 and older. Age, gender, marital status, education, monthly household income and internet use are included. Logistic regression analyses and CHAID-analyses are performed to determine the impact of sociodemographic characteristics on internet use.
Results: According to logistic regression analyses, being female, high age, low education, low income and being widowed showed to be related with lower odds of internet use. CHAID-analyses revealed education, income and age, respectively, as the strongest predictors of non-use. Based on the cumulative impact of these sociodemographic characteristics, diverse subgroups composed
almost entirely of non-internet-users could be determined. The highest proportion of non-users (98.6%) was observed in the subgroup of people with no degree/only primary education, who had a net household income of less than 999 euro/month and were 80 years and older.
Conclusions: The lower the education and income and the higher the age, the higher the possibility of being non-internet-user. These results can assist policy makers and internet training providers to identify non-users when deploying e-inclusion initiatives.
Methods: This study uses data from the Belgian Ageing Studies. The sample consists of 64,553 people aged 60 and older. Age, gender, marital status, education, monthly household income and internet use are included. Logistic regression analyses and CHAID-analyses are performed to determine the impact of sociodemographic characteristics on internet use.
Results: According to logistic regression analyses, being female, high age, low education, low income and being widowed showed to be related with lower odds of internet use. CHAID-analyses revealed education, income and age, respectively, as the strongest predictors of non-use. Based on the cumulative impact of these sociodemographic characteristics, diverse subgroups composed
almost entirely of non-internet-users could be determined. The highest proportion of non-users (98.6%) was observed in the subgroup of people with no degree/only primary education, who had a net household income of less than 999 euro/month and were 80 years and older.
Conclusions: The lower the education and income and the higher the age, the higher the possibility of being non-internet-user. These results can assist policy makers and internet training providers to identify non-users when deploying e-inclusion initiatives.
Original language | English |
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Pages (from-to) | 112-130 |
Number of pages | 19 |
Journal | Journal of Aging and Innovation |
Volume | 11 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2022 |