Users’ voice-switching behaviors, embodiments and perceptions of voice assistants
| Date | 24 January 2025 |
| Pages | 29-55 |
| DOI | https://doi.org/10.1108/ILS-07-2024-0081 |
| Published date | 24 January 2025 |
| Author | Dania Bilal,Li-Min Cassandra Huang |
Users’voice-switching behaviors,
embodiments and perceptions of
voice assistants
Dania Bilal
School of Information Sciences, The University of Tennessee Knoxville, Knoxville,
Tennessee, USA, and
Li-Min Cassandra Huang
Department of Library of Information Science, National Taiwan University,
Taipei, Taiwan
Abstract
Purpose –This paper aims to investigate user voice-switching behavior in voice assistants (VAs),
embodiments and perceivedtrust in information accuracy, usefulnessand intelligence. The authors addressed
four research questions: RQ1. What is the nature of users’voice-switching behavior in VAs?RQ2: What are
user preferences for embodiedvoice interfaces (EVIs), and do their preferred EVIs influence their decision to
switch the voice on their VAs? RQ3: What are the users’perceptions of their VAs concerning: a. information
accuracy, b. usefulness,c. intelligence and d. the most important characteristics they must possess? RQ4:Do
users prefer theirvoice interface to match their characteristics (age, gender,accent and race/ethnicity)?
Design/methodology/approach –The authors used a 52-question survey questionnaire to collect
quantitative and qualitativedata. The population was undergraduate students(freshmen and sophomores) at a
research university in the USA. The students were enrolled in two required courses with a research
participation assignment offered for credits. Students must register for research participation credits in the
SONA Research Participation System www.sona-systems.com/platform/research-management/ Registered
students cannot be invitedor sampled to participate in a research study. Therewere 1,700 students enrolled in
both courses. Afterthe survey’s URL was posted in SONA, the authors received (n= 632) responses.Of these,
(n= 150) completedthe survey and provided valid responses.
Findings –Participants(43%) switched the voice interface in their VAs.They preferred American and British
accents but trustedthe latter. The British accent with a male voice was moretrusted than the American accent
with a female voice.Voice-switchingdecisions varied in the case of most and least preferredEVIs. Participants
preferred EVIs that matched theircharacteristics. Most trusted their VAs’information accuracy because they
used the internet to find information, reflecting inadequate mental models. Lack of trust is attributed to
misunderstandingrequests and inability to respond accurately.A significant correlation was found between the
participants’perceivedintelligence of their VAsand trust in informationaccuracy.
Research limitations/implications –Due to the wide variability in the data (e.g. 84% White, 6% Asian and
6% Black), the authors did not perform a statistical test to identify the significance betweenthe selected EVIs and
participants’races or ethnicities. The self-reported survey questionnaire may be prone to inaccuracy. The
participants’interest in earning research credit for participation in this study and using SONA is a potential bias.
The EVIs the authors used as embodiments are limited in their representation of peo ple from diverse backgrounds,
races, ethnicities, ages and genders. However,they could be examples for building prototypes to test in VAs.
Practical implications –Educators and informationprofessionals should lead the way in offering artificial
intelligence (AI) literacy programsto enable young adults to form more adequate mental models of VAsand
support their learning and interactions. VA designers should address the failures and other issues the
participants experiencedin VAs to minimize frustrations.They should also train machine learning models on
large data sets of complex queries to augmentsuccess. Furthermore, they should consider augmenting VAs’
This research was partially funded by the University of Tennessee School of Information Sciences
Faculty Research Fund. The authors thank the School for supporting this research.
Information and
Learning Sciences
29
Received15 July2024
Revised4December 2024
Accepted10 De cember 2024
Informationand Learning
Sciences
Vol.126 No. 1/2, 2025
pp. 29-55
© Emerald Publishing Limited
2398-5348
DOI 10.1108/ILS-07-2024-0081
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2398-5348.htm
personification with EVIs to enrich voice interactionsand enhance personalization. Researchers should use a
mixed researchmethod with data triangulation instead of only a survey.
Social implications –There is a dire need to teach young adults AI literacy skills to enable them to build
adequate mental modelsofVAs. Failures in VAs could affect users’willingness to usethem in the future. VAs
can be effective teaching and learning tools, supporting students’autonomous and personalized learning.
Integrating EVIs with diverse characteristics could advance inclusivity in designing VAs and support
personalizationbeyond language, accent and gender.
Originality/value –This study advances research on user voice-switching behavior in VAs, which has
hardly been investigated in VA research. It brings attention to users’experiential learning and the need for
exposure to AI literacy to enable them to form adequate mental models of VAs. This studycontributes to
research on personifying VAs through EVIs with diverse characteristics to visualize voice interactions.
Reasons for not switching the voiceinterface due to satisfaction with the current voice or a lack of knowledge
of this feature did not supportthe status quo theory. Incorporatingsatisfaction and lack of knowledge as new
factors could advancethis theory. Switching the voice interface to avoid visualizingthe least preferred EVIs in
VAsis a new theme emerging from this study. Users’trust in VAs’information accuracy is intertwinedwith
perceivedintelligence and usefulness, but perceivedintelligence is the strongest factor influencingtrust.
Keywords Voiceassistants, Embodied voice interfaces (EVIs), Interface mirroring,
Perception of VAintelligence, Perception of VA usefulness, Trust in VA information accuracy,
Voice-switchingbehavior
Paper type Research paper
Introduction
Voice assistants (VAs) are software programs that use artificial intelligence (AI), natural
language processing, speech recognition and large language models to provide information,
perform various tasks, engage in conversations with users and generate responses based on
text or speech inputs (Terzopoulos and Satratzemi, 2020). They offer flexible features,
allowing users to switch the defaultvoice interface by language, accent and gender or choose
the preferred voice and gender while settingup the device (Bilal and Barfield, 2021a). These
features have intrigued researchers’interest in exploring voice-switching behavior as a new
form of interaction behavior with VAs. Nonetheless, we still know very little about this
behavior,especially from young adult users’(i.e. college students) perspectives.
VAs have been implemented in many disciplines, including education, to enhance teaching
and learning and provide students access to coursework, augment autonomous and personalized
learning, and promote computational thinking (Al Shamsi et al., 2022). Students learn to use
VAsthrough trial and error or other experiential experiences. Eliciting young adults “perceptions
of VAs”information accuracy, intelligence, trust and usefulness. Eliciting the students’
perceptions will unveil their VAs’literacy knowledge and skills and whether interventions are
needed to enable them to use these devices effectively and efficiently.
Studies revealed that users assign VAs anthropomorphic attributes (Calahorra-Candao and
Martín-de Hoyos, 2024), treat them as humans (Ki et al., 2020;Seymour and Van Kleek, 2021;
Pitardi and Marriott, 2021), personify them by ascribing friendship (Lopatovska et al., 2019;
Pradhan et al., 2019;Schweitzer et al., 2019;Wienrich et al., 2021), trust (Girouard-Hallam and
Danovitch, 2022;Seymour and Van Kleek, 2021;Wienrich et al., 2021) and personality traits
(Bilal and Barfield, 2021a;Lopatovska and Williams, 2018;Snyder et al., 2023). Users also
prefer their VA’s voice to match their gender, language and accent (e.g. Bilal and Barfield,
2021b;Brahnam and De Angeli, 2021). In the case of chatbots and embodied voice interfaces
(EVIs), users prefer the characteristics of the agents EVIs to match their race and ethnicity (e.g.
Bilal and Barfield, 2021b;Bonfert et al., 2021;Liao and He, 2020).
With the growth of Generative AI (GenAI), such as ChatGPT and similar tools,VAs are
increasing in sophisticationby performing more conversational than transactional tasks (e.g.
ILS
126,1/2
30
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