Mobile phone adoption: do existing models adequately capture the actual usage of older adults?

  • Authors:
  • Helene Gelderblom;Tobie van Dyk;Judy van Biljon

  • Affiliations:
  • UNISA, Pretoria;UNISA, Pretoria;UNISA, Pretoria

  • Venue:
  • SAICSIT '10 Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists
  • Year:
  • 2010

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Abstract

This paper investigates mobile phone adoption by older adults to better understand this user group's current level of adoption and to assess how well existing adoption models capture the acceptance levels of elderly users. The rationale is that mobile technology is continually improving and despite efforts to address their needs, this section of the population is known to be a largely untapped market. Data was captured on mobile phone usage of a sample of 46 users aged 60 to 87 through observation, a questionnaire and informal interviews. Using mostly qualitative and some quantitative data analysis techniques, the participants' levels of acceptance of the technology were determined and related to their age, how they acquired the phone, how long they have owned it, how often they use it and which functions they use. Our findings indicate that most participants in the sample have not reached full adoption but have not rejected the technology either. We found that a significant number of users stagnate in a phase of limited use with no inclination towards full adoption or complete rejection. This is contrary to the adoption presented by existing models that assume a user will reach a point of either rejection or acceptance of the technology. Therefore we conclude that existing technology and mobile phone adoption models do not adequately capture the mobile phone adoption patterns of the elderly. We have thus established the need to reconsider mobile phone adoption models. Furthermore, we have identified the specific age, the length of ownership, and the manner in which the phone was acquired as good predictors of where a user will end on the acceptance-rejection scale.