Identifying and characterising diversity of the older car driver.

Bradley, Michael D., Keith, Suzette, Kolar, Irena ORCID logoORCID: https://orcid.org/0000-0001-7486-642X and Whitney, Gill ORCID logoORCID: https://orcid.org/0000-0002-3333-2154 (2007) Identifying and characterising diversity of the older car driver. In: Proceedings of the Ergonomics Society Annual Conference, 17-19 April 2007, Nottingham UK. Bust, Philip D., ed. Taylor & Francis. ISBN 9780415436380. [Book Section]

Abstract

A further paper from the EPSRC/SPARC funded ‘An investigation into the advanced technology desires, needs and requirements of older drivers' project, this describes a new taxonomy for the older driver, based on technology usage.

Developed from the data collected from older drivers who became part of the user group for the project, it provides an alternative method for categorisation of older users, which takes into account their exposure and frequency of use of other technologies at home, in the office and in the car. The paper highlights the wide diversity of older drivers, both in terms of the traditional views of age and ability, but also in user behaviours with respect to driving and technology use. This method allowed the formation of focus groups, interviews, design reviews and user trials with participants of known technology exposure rather than the traditional age, gender and ability based categorisations. This novel step benefited the project research through allowing appropriate participant selection for specific events, such as providing focus groups with participants of similar levels technology exposure, with consequent high levels of engagement and discussion. The paper also challenges the stereotyping of older drivers, through unexpected examples of the diversity of the user group. The Ergonomics Society annual conference is a refereed international event.

Item Type: Book Section
Research Areas: A. > School of Science and Technology
A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 456
Useful Links:
Depositing User: Repository team
Date Deposited: 13 Nov 2008 15:35
Last Modified: 16 May 2023 08:48
URI: https://eprints.mdx.ac.uk/id/eprint/456

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