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Article
Publication date: 24 January 2025

Dania Bilal and Li-Min Cassandra Huang

This paper aims to investigate user voice-switching behavior in voice assistants (VAs), embodiments and perceived trust in information accuracy, usefulness and intelligence. The…

19

Abstract

Purpose

This paper aims to investigate user voice-switching behavior in voice assistants (VAs), embodiments and perceived trust in information accuracy, usefulness and 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 embodied voice 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 their voice 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 qualitative data. 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 invited or sampled to participate in a research study. There were 1,700 students enrolled in both courses. After the survey’s URL was posted in SONA, the authors received (n = 632) responses. Of these, (n = 150) completed the survey and provided valid responses.

Findings

Participants (43%) switched the voice interface in their VAs. They preferred American and British accents but trusted the latter. The British accent with a male voice was more trusted than the American accent with a female voice. Voice-switching decisions varied in the case of most and least preferred EVIs. Participants preferred EVIs that matched their characteristics. Most trusted their VAs’ information accuracy because they used the internet to find information, reflecting inadequate mental models. Lack of trust is attributed to misunderstanding requests and inability to respond accurately. A significant correlation was found between the participants’ perceived intelligence of their VAs and trust in information accuracy.

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 between the 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 people from diverse backgrounds, races, ethnicities, ages and genders. However, they could be examples for building prototypes to test in VAs.

Practical implications

Educators and information professionals should lead the way in offering artificial intelligence (AI) literacy programs to enable young adults to form more adequate mental models of VAs and support their learning and interactions. VA designers should address the failures and other issues the participants experienced in VAs to minimize frustrations. They should also train machine learning models on large data sets of complex queries to augment success. Furthermore, they should consider augmenting VAs’ personification with EVIs to enrich voice interactions and enhance personalization. Researchers should use a mixed research method 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 models of VAs. Failures in VAs could affect users’ willingness to use them 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 personalization beyond 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 study contributes to research on personifying VAs through EVIs with diverse characteristics to visualize voice interactions. Reasons for not switching the voice interface due to satisfaction with the current voice or a lack of knowledge of this feature did not support the status quo theory. Incorporating satisfaction and lack of knowledge as new factors could advance this theory. Switching the voice interface to avoid visualizing the least preferred EVIs in VAs is a new theme emerging from this study. Users’ trust in VAs’ information accuracy is intertwined with perceived intelligence and usefulness, but perceived intelligence is the strongest factor influencing trust.

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Article
Publication date: 23 December 2024

Wan-Chen Lee, Li-Min Cassandra Huang and Juliana Hirt

This study aims to understand fiction readers’ perspectives on the strengths and concerns of incorporating emojis into information systems for fiction. To solicit readers’…

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Abstract

Purpose

This study aims to understand fiction readers’ perspectives on the strengths and concerns of incorporating emojis into information systems for fiction. To solicit readers’ feedback, the authors adopted Cho et al.’s (2023) model of three families of fiction mood categories as the theoretical framework. Based on this framework, prototypes of interface designs that implemented textual mood descriptors and/or emojis were developed.

Design/methodology/approach

Eighteen adult fiction readers at a US public university were recruited for online interviews. The participants shared their insights into the prototypes and their fiction search and review experiences.

Findings

Most participants preferred designs that support both mood terms and emojis. The findings highlighted the potential of emojis to improve metadata inclusivity and serve diverse users’ needs. Technical challenges and accessibility issues for blind or visually impaired users were noted as limitations of emoji implementation.

Originality/value

Based on established theoretical frameworks and emoji mappings for mood categories, this study advances the progress of implementing emojis into information systems for fiction. The findings will inform user-centered interface designs that support the description, search and review of fiction.

Details

Journal of Documentation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0022-0418

Keywords

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Article
Publication date: 9 January 2024

Wan-Chen Lee, Li-Min Cassandra Huang and Juliana Hirt

This study aims to explore the application of emojis to mood descriptions of fiction. The three goals are investigating whether Cho et al.'s model (2023) is a sound conceptual…

220

Abstract

Purpose

This study aims to explore the application of emojis to mood descriptions of fiction. The three goals are investigating whether Cho et al.'s model (2023) is a sound conceptual framework for implementing emojis and mood categories in information systems, mapping 30 mood categories to 115 face emojis and exploring and visualizing the relationships between mood categories based on emojis mapping.

Design/methodology/approach

An online survey was distributed to a US public university to recruit adult fiction readers. In total, 64 participants completed the survey.

Findings

The results show that the participants distinguished between the three families of fiction mood categories. The three families model is a promising option to improve mood descriptions for fiction. Through mapping emojis to 30 mood categories, the authors identified the most popular emojis for each category, analyzed the relationships between mood categories and examined participants' consensus on mapping.

Originality/value

This study focuses on applying emojis to fiction reading. Emojis were mapped to mood categories by fiction readers. Emoji mapping contributes to the understanding of the relationships between mood categories. Emojis, as graphic mood descriptors, have the potential to complement textual descriptors and enrich mood metadata for fiction.

Details

Journal of Documentation, vol. 80 no. 2
Type: Research Article
ISSN: 0022-0418

Keywords

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