Earticle

초록

영어

Recently there were many Artificial Intelligence speakers that got established and introduced. Most well-known devices include world famous Amazon Echo. Despite their popularity, worldwide promotion and high awareness, previous research has found that usage of such voice based Artificial Intelligence assistants did not reach its expectations and consumers are not using home voice assistants frequently. Such can be attributed to the fact that people see AI speaker as yet another redundant interface within the home, and not one that’s critical to their daily experience, thus it adoption rate is not dramatically increased yet compared with recent technology advancement on devices. The primary emphasis of our research is motivated by the fact that UI/UX design for Artificial Intelligence devices should be studied differently with traditional UI/UX research, and the Voice-based UI design of artificial intelligence devices was intended to be understood in terms of psychological interaction. In this paper, we provide key factors for successful Voice user interface design based on delphi method and data analysis about Amazon Echo. This study confirms that future research on artificial intelligence devices should be done in terms of psychological interaction, unlike previous UI/UX studies, and suggests that VUI is important in the development of artificial intelligence devices. Since this study presented the success factors of voice-based chatbot design, it can be used to develop the analysis of consumer needs in the VUI aspect of artificial intelligence devices in the future. Also, it is consistent with the direction that the Human-Computer Interaction (HCI) study has been paying attention to.

목차

ABSTRACT
1. Introduction
1.1 Voice User Interface (VUI)
1.2 Voice User Experience (VUX)
1.3 Voice Chatbot Principle
2. Literature Review
1.4 The Common Barriers to VUI
1.5 Text mining
1.6 LDA Topic Modeling
3. Framework
4. Methodology
4.1 Delphi Study on Voice User Interface
4.2 Data collection
4.3 Data separating
4.4 Text preprocessing
4.5 LDA Topic Modeling
5. Result of Analysis
6. Discussion
7. Implications
References

키워드

저자

• Kyung Jin CHA [ Hanyang University ]
• Young Bum KIM [ Hanyang University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658