Functionality, Emotion, and Acceptance of Artificial Intelligence Virtual Assistants

Du, Xiaomin; Zhao, Xinran; Wu, Chia‐Huei; Feng, Kesha

doi:10.4018/jgim.290418

Cited by 12 publications

(3 citation statements)

References 49 publications

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“…Prior research indicates that behavioral intention may not always accurately predict the actual use (Chang et al, 2021c;Laukkanen, 2016), which may also depend on other influencing factors such as trust and risk perception (Arruda Filho et al, 2022). Besides, several other critical motivational factors also impact the successful adoption of technologies, particularly for fintech platforms (Hanafizadeh et al, 2014a, b), for instance, users' emotions (Chen et al, 2022;Du et al, 2022;Hung et al, 2021). Therefore, it is necessary to propose a comprehensive model based on users' perceptions, emotions and behavioral intentions to better explain the essential motivation of robo-advisors adoption by users.…”

Section: Investors' Willingness To Use Robo-advisors 2857mentioning

confidence: 99%

Exploring investors' willingness to use robo-advisors: mediating role of emotional response

Xia,

Zhang,

Zhang

et al. 2023

IMDS

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PurposeThis paper aims to investigate investors' willingness to use robo-advisors from customers' perspectives and analyzes the factors that drive them to use robo-advisors, including perceived usefulness and emotional response.Design/methodology/approachThe authors extend the Cognition-Affect-Conation (CAC) framework to the behavioral domain of robo-advisor users on financial technology platforms and conduct an empirical study based on 248 valid questionnaires.FindingsThe authors find two types of factors driving the willingness to use robo-advisors: perceived usefulness, trust and perceived risk as external driving forces and investor sentiment as an internal driving force. Trust has a significant positive effect on willingness to use, and arousal in emotional response plays a mediating role between perceived usefulness and willingness to use.Originality/valueThis research provides valuable insights for financial institutions to engage in robo-advisor innovation from customers' perspectives.

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Section: Investors' Willingness To Use Robo-advisors 2857mentioning

confidence: 99%

Exploring investors' willingness to use robo-advisors: mediating role of emotional response

Xia,

Zhang,

Zhang

et al. 2023

IMDS

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“…The core technology of VAs is semantic analysis, which allows them to interpret human speech. As this technology has advanced, VAs' comprehension of human instructions has improved significantly, expanding from the interpretation of simple semantics to the emotions contained in human instructions (Du et al, 2021;Ma et al, 2022). This increase in VA comprehension has contributed to the increase in VA usage.…”

Section: Introductionmentioning

confidence: 99%

Semantic Technology and Anthropomorphism

Hsu

Lee

2023

Journal of Global Information Management

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A long-standing debate exists on whether robots need personality. With voice assistants (VAs) (i.e., Google Assistant and Apple's Siri) as the research context, this study employed the stimulus-organism-response (SOR) model and the theory of reasoned action to investigate how the personalities that VAs display (i.e., humanlike traits and behavior traits) influence perceived risk, perceived enjoyment, trust, attitude to use, and continued usage intention. The results show that when VAs have more humanlike linguistic traits, such as tone and phrasing, and more positive behavior traits, such as politeness and helpfulness, users enjoy using VAs more, have more trust in VAs, and display a greater willingness to continue using VAs. Unlike past studies focusing on technical aspects, the results of this study provide decision-makers with a new perspective, showing that using more humanlike designs and giving VAs unique personalities can build user trust and increase willingness to use VAs.

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“…On the one hand, researchers, such as Du et al [9], have employed and further extended existing technology acceptance models to better understand peoples' preferences regarding functional elements of social agents (e.g., their perceived usefulness and perceived ease of use). To this end, seniors were a particularly often-investigated target group as here CAs are considered a means to tackle potential issues of loneliness and companionship (e.g., [10][11][12]).…”

Section: Introductionmentioning

confidence: 99%

Perspectives on Socially Intelligent Conversational Agents

Brinkschulte

Schlögl

Monz

et al. 2022

MTI

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The propagation of digital assistants is consistently progressing. Manifested by an uptake of ever more human-like conversational abilities, respective technologies are moving increasingly away from their role as voice-operated task enablers and becoming rather companion-like artifacts whose interaction style is rooted in anthropomorphic behavior. One of the required characteristics in this shift from a utilitarian tool to an emotional character is the adoption of social intelligence. Although past research has recognized this need, more multi-disciplinary investigations should be devoted to the exploration of relevant traits and their potential embedding in future agent technology. Aiming to lay a foundation for further developments, we report on the results of a Delphi study highlighting the respective opinions of 21 multi-disciplinary domain experts. Results exhibit 14 distinctive characteristics of social intelligence, grouped into different levels of consensus, maturity, and abstraction, which may be considered a relevant basis, assisting the definition and consequent development of socially intelligent conversational agents.

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Functionality, Emotion, and Acceptance of Artificial Intelligence Virtual Assistants

Cited by 12 publications

References 49 publications

Exploring investors' willingness to use robo-advisors: mediating role of emotional response

Exploring investors' willingness to use robo-advisors: mediating role of emotional response

Semantic Technology and Anthropomorphism

Perspectives on Socially Intelligent Conversational Agents

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