"But at the age of 85? Forget it!": Internalized ageism, a barrier to technology use

J Aging Stud. 2021 Dec:59:100971. doi: 10.1016/j.jaging.2021.100971. Epub 2021 Sep 15.

Abstract

The COVID-19 pandemic has underscored how everyday information and communication technology (EICT), such as online banking, e-shopping, or e-mail, are essential for individuals of all ages to maintain activity engagement, health, and well-being. Yet, older adults are often stereotypically portrayed as incapable, technophobic, or unwilling to engage in EICT. This may further contribute to the digital divide, as age stereotypes have the power to act like self-fulfilling prophecies and impede older adults' engagement in complex everyday life tasks. This study aimed to shed light on internalized ageism as manifested in older non-users' narrations about EICT use. It further explored how age stereotypes in the context of EICT are constructed and perpetuated through disempowering and ageist environments. A qualitative approach was applied, performing semi-structured interviews in participants' homes (N = 15). Data were analyzed following the principles of qualitative content analysis, applying both deductive categorization and inductive coding. Internalized ageism appeared to be an omnipresent element in older adults' narrations about EICT non-use. This was reflected in the four subcategories "competence and learning", "relevance and use", "technology design", and "intergenerational contact". Ageism, as manifested in the social environment and the design of technology, seemingly contributed to the internalization of age stereotypes and low EICT engagement. This research calls for inclusive technology designs, ageism-free EICT learning settings, and awareness campaigns about lifelong learning to help close the digital divide and ensure optimal aging experiences for older people.

Keywords: Ageism; Digital divide; Self-perceptions of aging; Self-stereotyping; Stereotype threat; Technology.

MeSH terms

  • Aged
  • Ageism*
  • Aging
  • COVID-19*
  • Humans
  • Pandemics
  • SARS-CoV-2
  • Stereotyping
  • Technology