This study determined the factors that influenced the usability of a mobile-based intelligent agent called “AskRed.” The design-related factors were evaluated in terms of performance, accuracy... Show More

This study determined the factors that influenced the usability of a mobile-based intelligent agent called “AskRed.” The design-related factors were evaluated in terms of performance, accuracy, responsiveness, aesthetics, and completeness. The usability of the software was determined in terms of satisfaction and intention to re-use the software. The software received favorable ratings from the students. Experts' software evaluation recommended strengthening the design of the intelligent agent in terms of security, performance, completeness, and ease-of-use. Multiple regression analyses showed that performance and completeness influenced satisfaction and intention to re-use. Aesthetics and responsiveness influenced satisfaction but not intention to re-use. Responsiveness had a negative impact on satisfaction. The predictive powers of the regression equations are 58% and 73%. This study provided empirical evidence on the predictors of usability of an intelligent agent used in a university setting.

This article considers the visual appearance of a virtual agent designed to take over the driving task in a highly automated car, to answer the question of which visual appearance is appropriate for... Show More

This article considers the visual appearance of a virtual agent designed to take over the driving task in a highly automated car, to answer the question of which visual appearance is appropriate for a virtual agent in a driving role. The authors first selected five models of visual appearance thanks to a picture sorting procedure (N = 19). Then, they conducted a survey-based study (N = 146) using scales of trust, anthropomorphism, and likability to assess the appropriateness of those five models from an early-prototyping perspective. They found that human and mechanical-human models were more trusted than other selected models in the context of highly automated cars. Instead, animal and mechanical-animal ones appeared to be less suited to the role of a driving assistant. Learnings from the methodology are discussed, and suggestions for further research are proposed.