Abstract

Background: Conversational agents (CAs) are a novel approach to delivering digital health interventions. In human interactions, terms of address often change depending on the context or relationship between interlocutors. In many languages, this encompasses T/V distinction—formal and informal forms of the second-person pronoun “You”—that conveys different levels of familiarity. Yet, few research articles have examined whether CAs' use of T/V distinction across language contexts affects users' evaluations of digital health applications.Methods: In an online experiment (N = 284), we manipulated a public health CA prototype to use either informal or formal T/V distinction forms in French (“tu” vs. “vous”) and German (“du” vs. “Sie”) language settings. A MANCOVA and post-hoc tests were performed to examine the effects of the independent variables (i.e., T/V distinction and Language) and the moderating role of users' demographic profile (i.e., Age and Gender) on eleven user evaluation variables. These were related to four themes: (i) Sociability, (ii) CA-User Collaboration, (iii) Service Evaluation, and (iv) Behavioral Intentions.Results: Results showed a four-way interaction between T/V Distinction, Language, Age, and Gender, influencing user evaluations across all outcome themes. For French speakers, when the informal “T form” (“Tu”) was used, higher user evaluation scores were generated for younger women and older men (e.g., the CA felt more humanlike or individuals were more likely to recommend the CA), whereas when the formal “V form” (“Vous”) was used, higher user evaluation scores were generated for younger men and older women. For German speakers, when the informal T form (“Du”) was used, younger users' evaluations were comparable regardless of Gender, however, as individuals' Age increased, the use of “Du” resulted in lower user evaluation scores, with this effect more pronounced in men. When using the formal V form (“Sie”), user evaluation scores were relatively stable, regardless of Gender, and only increasing slightly with Age.Conclusions: Results highlight how user CA evaluations vary based on the T/V distinction used and language setting, however, that even within a culturally homogenous language group, evaluations vary based on user demographics, thus highlighting the importance of personalizing CA language.

Highlights

  • Designing Conversational Agents for HealthcareConversational agents (CAs) are intelligent computer programs that engage users in human-like conversations and include text-based chatbots, voice-activated assistants, and embodied conversational agents [1]

  • We examine a particular term of address cue: T/V distinction or Tu/Vous distinction [40], which refers to the use of different second-person pronouns (“You”) in some languages, denoting a combination of less (T form) or more (V form) formality, distance, or emotional detachment [41]

  • The MANCOVA model specified using all outcome variables showed no significant main effects, interaction effects were found for T/V Distinction and Gender (Wilks’ λ = 0.924, F(11, 258) = 1.939, p = 0.035, ηp2 = 0.076), T/V Distinction, Gender and Age (Wilks’ λ = 0.921, F(11, 258) = 2.014, p = 0.027, ηp2 = 0.079), T/V Distinction, Language, and Gender (Wilks’ λ = 0.907, F(11, 258) = 2.404, p = 0.007, ηp2 = 0.093), and T/V Distinction, Language, Gender, and Age (Wilks’ λ = 0.903, F(11, 258) = 2.531, p = 0.005, ηp2 = 0.097) showing effect sizes ranging from medium to small [83]

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Summary

Introduction

Designing Conversational Agents for HealthcareConversational agents (CAs) are intelligent computer programs that engage users in human-like conversations and include text-based chatbots, voice-activated assistants, and embodied conversational agents [1]. Research has demonstrated how visual, conversational, and identity-related cues trigger “humanness heuristics” [18, 19] and affective states in users similar to natural human communication [20]. Design factors such as physical appearance [21], gender [22, 23], and speech dialect [24] can be tailored to match users’ cultural and demographic background and help to establish rapport [15, 20] and perceptions of a CA’s personality [25]. Few research articles have examined whether CAs’ use of T/V distinction across language contexts affects users’ evaluations of digital health applications

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