The Effects of Modality, Device, and Task Differences on Perceived Human Likeness of Voice-Activated Virtual Assistants

Cho, Eugene; Molina, María de Miguel; Wang, Jinping

doi:10.1089/cyber.2018.0571

Cited by 51 publications

(55 citation statements)

References 25 publications

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“…This is further illustrated by our findings that security concerns were highest among participants who were exposed to voice recommendations on a smart speaker and by the findings of a recent interview study showing that users of smart speakers would feel uncomfortable if their smart speaker voice commands were used to target adverts. 14 This finding also extends previous recent findings that the modality (voice vs. screen) of a virtual assistant influences the perceived human-likeness of the assistant, 37 whether people feel comfortable using an assistant, 56 and the ease of understanding the information provided 57 by showing that modality also influences concerns that people may have, and the persuasiveness of the assistant.…”

Section: Discussionsupporting

confidence: 77%

“…With regard to utilitarian tasks, Cho et al demonstrated that people had more positive attitudes toward a virtual assistant that was voice-rather than textbased because it was perceived as more human like. 37 This more intuitive way of communicating may make people engage in less in-depth information processing 30 and, according to the MAIN model, people make snap judgments of information that are usually positive. 38,39 Therefore, con-sumers may be more easily persuaded via voice than via screens.…”

Section: Modality: Different Types Of Voice and Screen-based Assistantsmentioning

confidence: 99%

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How Social Cues in Virtual Assistants Influence Concerns and Persuasion: The Role of Voice and a Human Name

Voorveld

Araujo

2020

Cyberpsychology, Behavior, and Social Networking

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The aim of this study was to test how two important types of social cues used by virtual assistants today can affect consumer concerns and persuasion. These two cues are modality (voice-based via smart speaker, voicebased via a smartphone, or text-based on a smartphone screen) and the adoption of a human name rather than no name. An online scenario-based experiment (n = 180) has shown that participants who were exposed to a voicebased recommendation via a smart speaker were the most concerned about security and found text-based recommendations on a screen to be the most persuasive. Participants who were exposed to a virtual assistant with a human name were less concerned about their autonomy and were more strongly persuaded than those exposed to an assistant without a human name.

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Section: Discussionsupporting

confidence: 77%

Section: Modality: Different Types Of Voice and Screen-based Assistantsmentioning

confidence: 99%

How Social Cues in Virtual Assistants Influence Concerns and Persuasion: The Role of Voice and a Human Name

Voorveld

Araujo

2020

Cyberpsychology, Behavior, and Social Networking

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“…In this sense, when prompted to describe the usefulness of such technology, respondents portray their VAs as being helpful as far as they are learning how to be helpful. Thus, functional and hedonic attributes are interpreted as characteristics that VAs can more/less develop based on their social interactions and the resulting learning process (Cho et al, 2019; Nass & Brave, 2005). This process helps in understanding why users' trust is mainly driven by the perceived social components of such technology.…”

Section: Study 2: Method Data Analysis and Resultsmentioning

confidence: 99%

“…As VAs are characterized by a mode of interaction (i.e., voice), that are usually reserved to human-to-human exchanges (Nass & Brave, 2005), it is reasonable to expect that they are more likely to be perceived as sociable (Cho et al, 2019). However, there is a general threat that users may perceive advanced technology as being less empathetic, based on the degree of display of human-like characteristics (Davenport et al, 2020).…”

Section: Social Motivators Trust and Attitudementioning

confidence: 99%

Alexa, she's not human but… Unveiling the drivers of consumers' trust in voice‐based artificial intelligence

Pitardi¹,

Marriott

2021

Psychology and Marketing

230

192

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With the development of deep connections between humans and Artificial Intelligence voice‐based assistants (VAs), human and machine relationships have transformed. For relationships to work it is essential for trust to be established. Although the capabilities of VAs offer retailers and consumers enhanced opportunities, building trust with machines is inherently challenging. In this paper, we propose integrating Human–Computer Interaction Theories and Para‐Social Relationship Theory to develop insight into how trust and attitudes toward VAs are established. By adopting a mixed‐method approach, first, we quantitatively examine the proposed model using Covariance‐Based Structural Equation Modeling on 466 respondents; based on the findings of this study, a second qualitative study is employed to reveal four main themes. Findings show that while functional elements drive users' attitude toward using VAs, the social attributes, being social presence and social cognition, are the unique antecedents for developing trust. Additionally, the research illustrates a peculiar dynamic between privacy and trust and it shows how users distinguish two different sources of trustworthiness in their interactions with VAs, identifying the brand producers as the data collector. Taken together, these results reinforce the idea that individuals interact with VAs treating them as social entities and employing human social rules, thus supporting the adoption of a para‐social perspective.

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“…Because satisfied customers tend to continue using a service (Bhattacherjee, 2001) or to buy repeatedly (Bartl et al, 2013), satisfaction is an appropriate construct to evaluate the effect of CAs' distinct interaction modalities on service encounters. Indeed, existing research comparing speech-and text-based interactions shows that speaking generates positive attitudes toward the CA (Cho et al, 2019;Novielli et al, 2010;Schroeder & Schroeder, 2018). These attitudes are driven by both utilitarian and hedonic values: Studies on users' perceived efficiency find that speaking is faster than typing, which increases users' productivity (Le Bigot et al, 2007;Ruan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Voice Assistant vs. Chatbot – Examining the Fit Between Conversational Agents’ Interaction Modalities and Information Search Tasks

2021

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Owing to technological advancements in artificial intelligence, voice assistants (VAs) offer speech as a new interaction modality. Compared to text-based interaction, speech is natural and intuitive, which is why companies use VAs in customer service. However, we do not yet know for which kinds of tasks speech is beneficial. Drawing on task-technology fit theory, we present a research model to examine the applicability of VAs to different tasks. To test this model, we conducted a laboratory experiment with 116 participants who had to complete an information search task with a VA or a chatbot. The results show that speech exhibits higher perceived efficiency, lower cognitive effort, higher enjoyment, and higher service satisfaction than text-based interaction. We also find that these effects depend on the task’s goal-directedness. These findings extend task-technology fit theory to customers’ choice of interaction modalities and inform practitioners about the use of VAs for information search tasks.

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The Effects of Modality, Device, and Task Differences on Perceived Human Likeness of Voice-Activated Virtual Assistants

Cited by 51 publications

References 25 publications

How Social Cues in Virtual Assistants Influence Concerns and Persuasion: The Role of Voice and a Human Name

How Social Cues in Virtual Assistants Influence Concerns and Persuasion: The Role of Voice and a Human Name

Alexa, she's not human but… Unveiling the drivers of consumers' trust in voice‐based artificial intelligence

Voice Assistant vs. Chatbot – Examining the Fit Between Conversational Agents’ Interaction Modalities and Information Search Tasks

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