The DOI matches its Crossref record; 47 of the 48 references it cites were found in a registry or an index, and 1 are not checked yet.
At Your Service: Designing Voice Assistant Personalities to Improve Automotive User Interfaces
Open accessAbstract
This paper investigates personalized voice characters for in-car speech interfaces. In particular, we report on how we designed different personalities for voice assistants and compared them in a real world driving study. Voice assistants have become important for a wide range of use cases, yet current interfaces are using the same style of auditory response in every situation, despite varying user needs and personalities. To close this gap, we designed four assistant personalities (Friend, Admirer, Aunt, and Butler) and compared them to a baseline (Default) in a between-subject study in real traffic conditions. Our results show higher likability and trust for assistants that correctly match the user's personality while we observed lower likability, trust, satisfaction, and usefulness for incorrectly matched personalities, each in comparison with the Default character. We discuss design aspects for voice assistants in different automotive use cases.
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“In addition, customizing a voice assistant's personality to the user can lead to higher trust [7], while gender does not impact users' trust in a voice assistant [59].”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [7]
In the citing paper
“They found that users found metaphors that were similar to them (in terms of: openness, conscientiousness, extraversion, agreeableness, and neuroticism (OCEAN) [51]) were perceived to be more likable and trustworthy.”
From Examining Humanness as a Metaphor to Design Voice User Interfaces (2024) · cites this paper as [9]
In the citing paper
“A recent survey found that about 31% of users would accept recommendations from conver-sational AI, like ChatGPT, without verifying them.9”
From Examining Humanness as a Metaphor to Design Voice User Interfaces (2024) · cites this paper as [9]
In the citing paper
“Chatbot personality has been shown to affect trust [11,12], engagement [6], subjective satisfaction [13,14], and consumer behavior [6].”
From Interacting with a Chatbot-Based Advising System: Understanding the Effect of Chatbot Personality and User Gender on Behavior (2022) · cites this paper as [11]
In the citing paper
“Related works show that users trust chatbot personalities similar to theirs [11,58] in the context of voice-based e-commerce and driving assistant chatbot-based applications.”
From Interacting with a Chatbot-Based Advising System: Understanding the Effect of Chatbot Personality and User Gender on Behavior (2022) · cites this paper as [11]
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Metadata
- Authors
- Braun, M., Mainz, A., Chadowitz, R., Pfleging, B., & Alt, F.
- Affiliations
- BMW (Germany) (DE)
- Ludwig-Maximilians-Universität München (DE)
- Universität der Bundeswehr München (DE)
- Venue
- Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2019-05-02
- Pages
- 1-11
- OpenAlex
- W2941327948
- Cited by
- 182
- References
- 47
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 28 · 2023: 32 · 2022: 24 · 2021: 33 · 2020: 25 · 2019: 7
- FWCI
- 8.51
- Type
- conference paper
- Language
- en
- Open access
- green
- research.tue.nl
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- TU/e Research Portal (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- likeability, trust, user satisfaction
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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