Eliciting persona characteristics for risk-based decision making.

M'manga, Andrew; Faily, Shamal; McAlaney, John; Williams, Chris; Kadobayashi, Youki; Miyamoto, Daisuke

Abstract

Personas are behavioural specifications of archetypical users in Human Factors Engineering and User Interaction research, aimed at preventing biased views system designers may have of users. Personas are therefore nuanced representations of goals and expectations that should be addressed when designing systems. Previous work has shown how personas may be validated by grounding in qualitative models; however, more evidence is needed on the applicability for grounding models in risk decision making research. We present an approach for eliciting persona characteristics for risk-based decision making by using Observe Orient Decide Act (OODA) as a modelling baseline. The approach illustrates how modelling personas based on decision makers' understanding of risk facilitates designing for risk and uncertainty.

Citation

M'MANGA, A., FAILY, S., MCALANEY, WILLIAMS, C., KADOBAYASHI, Y. and MIYAMOTO, D. 2018. Eliciting persona characteristics for risk-based decision making. In Proceedings of the 32nd International BCS human computer interaction conference (HCI 2018), 4-6 July 2018, Belfast, UK. Swindon: BCS [online], article number 158. Available from: https://doi.org/10.14236/ewic/HCI2018.158

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 32nd International BCS Human Computer Interaction Conference (HCI)
• Start Date: Jul 4, 2018
• End Date: Jul 6, 2018
• Acceptance Date: May 18, 2018
• Online Publication Date: Jul 30, 2018
• Publication Date: Jul 30, 2018
• Deposit Date: Sep 19, 2021
• Publicly Available Date: Dec 6, 2021
• Publisher: BCS, The Chartered Institute for IT
• Peer Reviewed: Peer Reviewed
• Series Title: Electronic workshops in computing
• Series ISSN: 1477-9358
• DOI: https://doi.org/10.14236/ewic/HCI2018.158
• Keywords: User-centred design; User personas; Systems security; Risk-based decision-making; Risk analysis
• Public URL: https://rgu-repository.worktribe.com/output/1437884