Who Do You Prefer? The Effect of Age, Gender and Role on Users’ First Impressions of Embodied Conversational Agents in eHealth

Abstract

Embodied conversational agents may be used to engage users in adopting eHealth applications. The aim of this research is to investigate which design features establish a positive first impression of an agent in this context. A set of eight static agent images, different in age, gender and role, were subjected to testing in an online questionnaire. Respondents (n = 155) selected their preferred design and rated the characteristicsfriendliness, expertise, reliability, involvement and authorityand the likeliness of following the agent's advice for all designs. In addition, focus groups (n = 13) were conducted for detailed understandings supporting these impressions. Our results show that, for both a general and elderly population, (1) people seem to prefer images of young, female agents over old, male agents, (2) the (a) age, (b) gender and (c) role of the agent image affect the perception of the agent's characteristics and the likeliness of following the agent's advice, and that (3) both the general and elderly population prefer an agent image that is similar in (a) age and (b) gender. A next step would be to investigate how the characteristics of the agent designs are perceived after interaction with the agent.

Embodied Conversational Agent Appearance for Health Assessment of Older Adults: Explorative Study

“…Although research on short-term interaction with an agent indicates that an agent’s appearance, including clothing [ 18 ], racial concordance with the user [ 30 , 31 ], and similarity with the user [ 30 , 32 ], could affect users’ perceptions of the agent, to the best of our knowledge there is no research on agent authority after short interaction in particular. From a previous study [ 16 ], we see that at first glance, static images of male and older agents are indeed seen as more authoritative than female and young agents, respectively. In addition, the study shows that the differences found in authority are often higher compared with differences found for other characteristics tested, which could explain why the difference in authority level is still present after short interaction.…”

“…But research shows that in human-human interaction, first impressions, formed within milliseconds [ 28 ], are difficult to lose. Therefore, we assumed that the differences in perceptions of characteristics of a static image of a young female agent and an older male agent, as found in a previous study [ 16 ], would still be present after a short interaction with these agents. An explanation for this inconsistency could be that impressions in human-agent interaction differ from impressions in human-human interaction.…”

“…By a peer agent, we mean an agent who is not a medical expert. Agent designs were selected based on findings from a previous study [ 16 ], in which the static images of eight agents were evaluated. The agent images differed on three features: age (young or old), gender (male or female), and role (experts had a high level of health expertise, and peers had a low level of health expertise).…”

“…Although ECAs have the potential to be used as eHealth apps such as digital frailty assessments, little is known about how these agents should be designed and how the design affects our impressions of the agents, and no design guidelines exist [ 15 ]. In one study, ter Stal et al [ 16 ] identified people’s first impressions of agents varying in age, gender, and role using a zero acquaintance approach: there was no interaction involved, and participants rated static agent images at first glance. The study shows that characteristics of older and male agents were perceived differently than characteristics of young and female agents, respectively.…”

“…The aim of this study is to assess how an agent’s appearance, particularly age and gender, affects the users’ likeliness of following agent advice and users’ perceptions of the agent’s characteristics and feeling of rapport after short interaction with the agent. This study builds on previous work [ 16 ] by studying users’ impressions of agents at first glance (using the zero acquaintance approach) and after a short interaction with the agents (using the thin-slicing approach). As a secondary aim, we investigate the potential of a frailty assessment app with an agent by evaluating its usability and intention to use.…”