Virtual Assistance in Any Context

Janssen, Antje Henriette Annette; Passlick, Jens; Cardona, Davinia Rodríguez; Breitner, Michael H.

doi:10.1007/s12599-020-00644-1

Cited by 57 publications

(37 citation statements)

References 34 publications

(74 reference statements)

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“…Previous literature has proposed general guidelines for designing voice user interfaces [8] and accessible conversational user interfaces for different disability groups [9], as well as virtual assistants for specific purposes such as teaching [10] and in-vehicle assistance [11]. Optimal design techniques are likely to depend on the purpose of the virtual assistant [12,13]; therefore, recommendations specifically in the context of health are needed. Although research has examined methods of assessing the usability of virtual assistants in the health domain [14], clear guidelines on maximizing the user experience of virtual health assistants are lacking.…”

Section: Introductionmentioning

confidence: 99%

Improving User Experience of Virtual Health Assistants: Scoping Review

Curtis¹,

Bartel²,

Ferguson³

et al. 2021

J Med Internet Res

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Background Virtual assistants can be used to deliver innovative health programs that provide appealing, personalized, and convenient health advice and support at scale and low cost. Design characteristics that influence the look and feel of the virtual assistant, such as visual appearance or language features, may significantly influence users’ experience and engagement with the assistant. Objective This scoping review aims to provide an overview of the experimental research examining how design characteristics of virtual health assistants affect user experience, summarize research findings of experimental research examining how design characteristics of virtual health assistants affect user experience, and provide recommendations for the design of virtual health assistants if sufficient evidence exists. Methods We searched 5 electronic databases (Web of Science, MEDLINE, Embase, PsycINFO, and ACM Digital Library) to identify the studies that used an experimental design to compare the effects of design characteristics between 2 or more versions of an interactive virtual health assistant on user experience among adults. Data were synthesized descriptively. Health domains, design characteristics, and outcomes were categorized, and descriptive statistics were used to summarize the body of research. Results for each study were categorized as positive, negative, or no effect, and a matrix of the design characteristics and outcome categories was constructed to summarize the findings. Results The database searches identified 6879 articles after the removal of duplicates. We included 48 articles representing 45 unique studies in the review. The most common health domains were mental health and physical activity. Studies most commonly examined design characteristics in the categories of visual design or conversational style and relational behavior and assessed outcomes in the categories of personality, satisfaction, relationship, or use intention. Over half of the design characteristics were examined by only 1 study. Results suggest that empathy and relational behavior and self-disclosure are related to more positive user experience. Results also suggest that if a human-like avatar is used, realistic rendering and medical attire may potentially be related to more positive user experience; however, more research is needed to confirm this. Conclusions There is a growing body of scientific evidence examining the impact of virtual health assistants’ design characteristics on user experience. Taken together, data suggest that the look and feel of a virtual health assistant does affect user experience. Virtual health assistants that show empathy, display nonverbal relational behaviors, and disclose personal information about themselves achieve better user experience. At present, the evidence base is broad, and the studies are typically small in scale and highly heterogeneous. Further research, particularly using longitudinal research designs with repeated user interactions, is needed to inform the optimal design of virtual health assistants.

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Section: Introductionmentioning

confidence: 99%

Improving User Experience of Virtual Health Assistants: Scoping Review

Curtis¹,

Bartel²,

Ferguson³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

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“…Second, we contribute to CA research by presenting a measure that has the potential to make contemporary chatbots more successful (Janssen et al, 2020 ). Our work shows that inoculation theory, and inoculation messages in particular, is effective for fostering user acceptance after a CA response failure.…”

Section: Discussionmentioning

confidence: 99%

Immunizing with information – Inoculation messages against conversational agents’ response failures

Weiler¹,

Matt

Heß³

2021

Electron Markets

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Conversational agents (CAs) are often unable to provide meaningful responses to user requests, thereby triggering user resistance and impairing the successful diffusion of CAs. Literature mostly focuses on improving CA responses but fails to address user resistance in the event of further response failures. Drawing on inoculation theory and the elaboration likelihood model, we examine how inoculation messages, as communication that seeks to prepare users for a possible response failure, can be used as an alleviation mechanism. We conducted a randomized experiment with 558 users, investigating how the performance level (high or low) and the linguistic form of the performance information (qualitative or quantitative) affected users’ decision to discontinue CA usage after a response failure. We found that inoculation messages indicating a low performance level alleviate the negative effects of CA response failures on discontinuance. However, quantitative performance level information exhibits this moderating effect on users’ central processing, while qualitative performance level information affected users’ peripheral processing. Extending studies that primarily discuss ex-post strategies, our results provide meaningful insights for practitioners.

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“…Also, some approaches that focus on influencing factors and challenges during a chatbot project are available [11,12]. Further studies bring together the existing scientific results in chatbot research in the form of literature reviews [2][3][4][30][31][32] or to create a taxonomy of chatbots [33]. However, they categorize the results with a scientific focus and often do not provide guidance for practitioners.…”

Section: Related Researchmentioning

confidence: 99%

Chatbot Introduction and Operation in Enterprises – A Design Science Research-based Structured Procedure Model for Chatbot Projects

Wolff¹,

Hobert²,

Schümann³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Chatbot research has become an emerging research area. Researchers survey the technology behind and the whole ecosystem from different perspectives, e.g., human-computer interaction, design research, or anthropomorphism. To foster the transfer from research to practice, a comprehensive structured procedure model is missing yet. Due to this, the transfer of the research results into real-world settings in enterprises is often complicated. Hereto, we propose a comprehensive structured procedure model to guide practitioners in chatbot projects based on a Design Science Research study. In doing so, necessary project steps are pointed out and corresponding research results are highlighted to make them reusable for practice in a targeted manner. Thus, we provide structured support for chatbot projects in enterprises.

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Virtual Assistance in Any Context

Cited by 57 publications

References 34 publications

Improving User Experience of Virtual Health Assistants: Scoping Review

Improving User Experience of Virtual Health Assistants: Scoping Review

Immunizing with information – Inoculation messages against conversational agents’ response failures

Chatbot Introduction and Operation in Enterprises – A Design Science Research-based Structured Procedure Model for Chatbot Projects

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