Predicting technology acceptance and adoption by the elderly: a qualitative study

Renaud, K. and Van Biljon, J. (2008) Predicting technology acceptance and adoption by the elderly: a qualitative study. In: Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists, Wilderness, South Africa, 6-8 Oct 2008, pp. 210-219. (doi: 10.1145/1456659.1456684)

Abstract

Technology adoption has been studied from a variety of perspectives. Information systems, Sociology and Human-Computer Interaction researchers have come up with various models incorporating factors and phases to predict adoption that, in turn, will lead to persistent use. Technology acceptance by the elderly mobile phone user has received less attention and no model currently exists to predict factors influencing their technology adoption. A literature study yielded a set of acceptance factors (derived mostly from quantitative studies) and adoption phases (derived mostly from qualitative studies) that could influence and predict mobile phone adoption by the elderly user. We confirmed a subset of these factors by consulting findings from research into the context of senior mobile phone users, including the needs and limitations of these users. We then verified the factors qualitatively by means of structured interviews with senior mobile phone users. The interviews included the use of scenarios as well as a mobile phone design activity. Triangulating the quantitative findings from literature with the qualitative findings from this study led to a set of interlinked acceptance factors and adoption phases that we present as the Senior Technology Acceptance & Adoption model for Mobile technology (STAM. This paper makes a contribution to understanding technology acceptance by senior users and should be of interest to researchers, designers and decision-makers on technology adoption, especially mobile features and services.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Renaud, Professor Karen
• Authors: Renaud, K., and Van Biljon, J.
• College/School: College of Science and Engineering > School of Computing Science