5 research outputs found
Virtual Assistance in Any Context - A Taxonomy of Design Elements for Domain-Specific Chatbots
Several domain-specific assistants in the form of chatbots have conquered many commercial and private areas. However, there is still a limited level of systematic knowledge of the distinctive characteristics of design elements for chatbots to facilitate development, adoption, implementation, and further research. To close this gap, the paper outlines a taxonomy of design elements for chatbots with 17 dimensions organized into the perspectives intelligence, interaction and context. The conceptually grounded design elements of the taxonomy are used to analyze 103 chatbots from 23 different application domains.Through a clustering-based approach, five chatbot archetypes that currently exist for domain-specific chatbots are identified. The developed taxonomy provides a structure to differentiate and categorize domain-specific chatbots according to archetypal qualities that guide practitioners when taking design decisions. Moreover, the taxonomy serves academics as a foundation for conducting further research on chatbot design while integrating scientific and practical knowledge
Virtual Assistance in Any Context: A Taxonomy of Design Elements for Domain-Specific Chatbots
Several domain-specific assistants in the form of chatbots have conquered many commercial and private areas. However, there is still a limited level of systematic knowledge of the distinctive characteristics of design elements for chatbots to facilitate development, adoption, implementation, and further research. To close this gap, the paper outlines a taxonomy of design elements for chatbots with 17 dimensions organized into the perspectives intelligence, interaction and context. The conceptually grounded design elements of the taxonomy are used to analyze 103 chatbots from 23 different application domains. Through a clustering-based approach, five chatbot archetypes that currently exist for domain-specific chatbots are identified. The developed taxonomy provides a structure to differentiate and categorize domain-specific chatbots according to archetypal qualities that guide practitioners when taking design decisions. Moreover, the taxonomy serves academics as a foundation for conducting further research on chatbot design while integrating scientific and practical knowledge
Chatbotsโ extroverted or introverted personalityโs influence on consumersโ purchase intention depending on consumersโ extroversion extent
The purpose of this study is to investigate the influence of chatbot personality on consumer
purchase intention, depending on the consumer's level of extroversion. Through an
experiment, this study will analyze the effects of extroverted and introverted chatbot
personalities on consumers with different levels of extroversion. The findings will provide
valuable insights into the impact of chatbot personality on consumersโ purchase intention and
the potential for personalized communication strategies in e-commerce. Results indicate that
chatbot perception significantly influences purchase intention and that a match in personality
between the chatbot and the consumer leads to higher purchase intention. Additionally,
extroverted chatbots are found to lead to higher purchase intention than introverted ones.
Finally, perceived ease of chatbot use is shown to increase purchase intention. These findings
suggest that providing chatbots with a personality can effectively enhance purchase intention,
particularly if matching the consumerโs personalityO objectivo deste estudo รฉ investigar a influรชncia da personalidade do chatbot na intenรงรฃo de
compra do consumidor, dependendo do nรญvel de extroversรฃo do consumidor. Atravรฉs de uma
sรฉrie de experiรชncias, este estudo irรก analisar os efeitos de personalidades extrovertidas e
introvertidas de chatbots sobre consumidores com diferentes nรญveis de extroversรฃo. Os
resultados proporcionarรฃo valiosos conhecimentos sobre o impacto da personalidade de
chatbot na intenรงรฃo de compra dos consumidores e o potencial para estratรฉgias de
comunicaรงรฃo personalizadas no comรฉrcio electrรณnico. Os resultados indicam que a percepรงรฃo
do chatbot influencia significativamente a intenรงรฃo de compra e que uma correspondรชncia na
personalidade entre o chatbot e o consumidor leva a uma maior intenรงรฃo de compra. Alรฉm
disso, verifica-se que os chatbots extrovertidos levam a uma intenรงรฃo de compra mais elevada
do que os introvertidos. Finalmente, a percepรงรฃo de facilidade de utilizaรงรฃo do chatbot รฉ
aumenta a intenรงรฃo de compra. Estas conclusรตes sugerem que dar uma personalidade aos
chatbots pode efectivamente aumentar a intenรงรฃo de compra
Designing Personality-Adaptive Conversational Agents for Mental Health Care
Millions of people experience mental health issues each year, increasing the necessity for health-related services. One emerging technology with the potential to help address the resulting shortage in health care providers and other barriers to treatment access are conversational agents (CAs). CAs are software-based systems designed to interact with humans through natural language. However, CAs do not live up to their full potential yet because they are unable to capture dynamic human behavior to an adequate extent to provide responses tailored to usersโ personalities. To address this problem, we conducted a design science research (DSR) project to design personality-adaptive conversational agents (PACAs). Following an iterative and multi-step approach, we derive and formulate six design principles for PACAs for the domain of mental health care. The results of our evaluation with psychologists and psychiatrists suggest that PACAs can be a promising source of mental health support. With our design principles, we contribute to the body of design knowledge for CAs and provide guidance for practitioners who intend to design PACAs. Instantiating the principles may improve interaction with users who seek support for mental health issues
ํฌ์ค์ผ์ด๋ฅผ ์ํ ๋ํํ ์ธ๊ณต์ง๋ฅ์ ๋ชจ์ฌ๋ ํ๋ฅด์๋ ๋์์ธ ๋ฐ ์ฌ์ฉ์ ๊ฒฝํ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์ธ๋ฌธ๋ํ ํ๋๊ณผ์ ์ธ์ง๊ณผํ์ ๊ณต, 2021.8. ์ด์คํ.๋์งํธ ํฌ์ค์ผ์ด(Digital Healthcare) ๊ธฐ์ ์ ๋ฐ์ ์ ์ผ์ ํฌ์ค์ผ์ด ์์ญ์์์ ํ์ ์ ์ฃผ๋ ํ๊ณ ์๋ค. ์ด๋ ์ํ ์ ๋ฌธ๊ฐ๋ค์ ์ ํํ ์ง๋จ, ์ง๋ณ์ ์น๋ฃ๋ฅผ ๋์ธ ๋ฟ๋ง ์๋๋ผ ์ฌ์ฉ์๊ฐ ์ค์ค๋ก ์ผ์์์ ์๊ธฐ๊ด๋ฆฌ๋ฅผ ํ ์ ์๋๋ก ๋๋๋ค. ๋์งํธ ํฌ์ค์ผ์ด ๊ธฐ์ ์ ๋ํ์ ์ธ ๋ชฉํ ์ค ํ๋๋ ํจ๊ณผ์ ์ผ๋ก ํฌ์ค์ผ์ด ์๋น์ค๋ฅผ ๊ฐ์ธํ ์ํค๋ ๊ฒ์ธ๋ฐ, ์ด๋ฌํ ์ธก๋ฉด์์ ๋ํํ ์ธ๊ณต์ง๋ฅ(Conversational AI)์ ์ฌ์ฉํ๊ธฐ ์ฝ๊ณ ํจ์จ์ ์ธ ๋น์ฉ์ผ๋ก ๊ฐ์ธํ๋ ์๋น์ค๋ฅผ ์ ๊ณตํ ์ ์๊ธฐ์ ์ฃผ๋ชฉ๋ฐ๊ณ ์๋ค.
๊ธฐ์กด์ ๊ฐ์ธํ๋ ์ผ์ด ์๋น์ค๋ค์ ๊ฒฝ์ฐ๋ ๋๋ถ๋ถ ์๋ฃ๊ธฐ๊ด์ ์ง๋ณ์น๋ฃ ์๋น์ค๋ค์ ํฌํจ๋์๋๋ฐ, ๋ํํ ์ธ๊ณต์ง๋ฅ์ ์ด๋ฌํ ๊ฐ์ธํ๋ ์ผ์ด ์๋น์ค์ ์์ญ์ ์ผ์์์์ ์ง๋ณ ์๋ฐฉ์ ์ํ ๊ด๋ฆฌ๋ก ํ์ฅํ๋๋ฐ ๊ธฐ์ฌํ๋ค. ์ผ๋์ผ ๋ํ๋ฅผ ํตํด ๋ง์ถคํ ๊ต์ก, ํ
๋ผํผ, ๊ทธ์ธ์ ๊ด๋ จ ์ ๋ณด ๋ฑ์ ์ ๊ณตํ ์ ์๋ค๋ ์ธก๋ฉด์์ ์ผ์ ํฌ์ค์ผ์ด์ ์ ํฉํ ๋์งํธ ํฌ์ค์ผ์ด ๊ธฐ์ ๋ก์ ํ์ฉ๋๊ฐ ๋๋ค. ์ด๋ฌํ ์ด์ ์ผ๋ก ์ธํด ๋ค์ํ ์ญํ ์ ๊ฐ์ง ๋ํํ ์ธ๊ณต์ง๋ฅ๋ค์ ๊ฐ๋ฐ์ด ์ด๋ฃจ์ด์ง๊ณ ์๋ค.
๊ทธ๋ฌ๋, ์ด๋ฌํ ๋ํํ ์ธ๊ณต์ง๋ฅ๋ค์๊ฒ ์ฌ์ฉ์์ ์ ํธ๋์ ์ ํฉํ ํ๋ฅด์๋๋ฅผ ๋ถ์ฌํ๋ ์ฐ๊ตฌ๋ ๋๋ฌผ๊ฒ ์ด๋ฃจ์ด ์ง๊ณ ์๋ค. ๋ํํ ์ธ๊ณต์ง๋ฅ์ ์ฃผ์ ๊ธฐ๋ฅ์ธ ์์ฐ์ด ๊ธฐ๋ฐ ์ํธ์์ฉ์ CASA ํจ๋ฌ๋ค์(CASA Paradigm)์์ ์ ๊ธฐํ๋ ์ฌ์ฉ์๊ฐ ์์คํ
์ ์์ธํํ๋ ๊ฒฝํฅ์ ๋์ธ๋ค. ๋๋ฌธ์ ํ๋ฅด์๋์ ๋ํ ์ฌ์ฉ์์ ์ ํธ๋๊ฐ ์ง์์ ์ธ ๋ํํ ์ธ๊ณต์ง๋ฅ์ ์ฌ์ฉ๊ณผ ๋ชฐ์
์ ์ํฅ์ ๋ฏธ์น๋ค. ๋ํ ๋ํํ ์ธ๊ณต์ง๋ฅ์ ์ฅ๊ธฐ์ ์ธ ์ฌ์ฉ์ ์ํด์ ์ ์ ํ ์ฌ์ฉ์์์ ์ฌํ์ , ๊ฐ์ ์ ์ํธ์์ฉ์ ๋์์ธ ํด ์ฃผ์ด์ผ ํ๋๋ฐ, ์ธ์ง๋ ํ๋ฅด์๋์ ๋ํ ์ฌ์ฉ์์ ์ ํธ๋๊ฐ ์ด ๊ณผ์ ์๋ ์ ์๋ฏธํ ์ํฅ์ ๋ฏธ์น๋ค. ๋๋ฌธ์ ์ง์์ ์ธ ์ฐธ์ฌ๊ฐ ๊ฒฐ๊ณผ์ ํฐ ์ํฅ์ ๋ฏธ์น๋ ์ผ์ ํฌ์ค์ผ์ด ์์ญ์์ ๋ํํ ์ธ๊ณต์ง๋ฅ์ ํ์ฉํ๋๋ฐ ๊ฐ์ธํ๋ ํ๋ฅด์๋ ๋์์ธ์ด ๊ธ์ ์ ์ธ ์ฌ์ฉ์ ๊ฒฝํ ๋ฐ ์ฌ์ฉ์ ๊ฑด๊ฐ ์ฆ์ง์ ๊ฐ๋ฅ์ฑ์ ๋์ผ ๊ฒ์ผ๋ก ๋ณธ ์ฐ๊ตฌ๋ ๊ฐ์ ํ๋ค. ๊ฐ์ธํ๋ ํ๋ฅด์๋ ๋์์ธ์ ์ํด ์ฌ์ฉ์์ ํ์ค์์ ์น๋ฐํ ๊ด๊ณ์ ์๋ ์ค์กด์ธ๋ฌผ(ํธ์คํธ)์ ํ๋ฅด์๋๋ฅผ ๋ํํ ์ธ๊ณต์ง๋ฅ์ ์ ์ฉํ๊ณ ํ๊ฐํ๋ ๊ฒ์ด ๋ณธ ์ฐ๊ตฌ์ ํต์ฌ์ ์ธ ์์๋์ด์ด๋ค.
์ด๋ฅผ ๊ฒ์ฆํ๊ธฐ ์ํด์ ํด๋น ํ์ ๋
ผ๋ฌธ์ ์ด ์ธ ๊ฐ์ง์ ์ธ๋ถ ์ฐ๊ตฌ๋ฅผ ํฌํจํ๋ค. ์ฒซ์งธ๋ ์ค์กด์ธ๋ฌผ์ ํ๋ฅด์๋ ์ค์์๋ ์ผ์ ๊ฑด๊ฐ๊ด๋ฆฌ์ ์ ํฉํ ํธ์คํธ์ ํ๋ฅด์๋๋ฅผ ํ์ํ๋ ์ฐ๊ตฌ์ด๋ค. ๋์งธ๋ ํธ์คํธ์ ํ๋ฅด์๋๋ฅผ ๋ํํ ์ธ๊ณต์ง๋ฅ์ ์ ์ฉํ๊ธฐ ์ํด ๊ณ ๋ คํด์ผ ํ ์ธ์ด์ ์์๋ค์ ์ ์ํ๋ ์ฐ๊ตฌ์ด๋ค. ๋ง์ง๋ง์ผ๋ก๋ ์์ ๊ณผ์ ์ ํตํด ๊ฐ๋ฐ๋ ์ค์กดํ๋ ์ธ๋ฌผ์ ํ๋ฅด์๋๋ฅผ ๊ฐ์ง ๋ํํ ์ธ๊ณต์ง๋ฅ์ด ์ผ์ ํฌ์ค์ผ์ด ์์ญ์์ ์ค์ ํจ๊ณผ๋ฅผ ๋ณด์ด๋์ง๋ฅผ ํ๊ฐํ๋ ์ฐ๊ตฌ์ด๋ค. ๋ํ ํด๋น ํ์๋
ผ๋ฌธ์ ์ผ๋ จ์ ์ฐ๊ตฌ๋ค์์ ๋ฐ๊ฒฌํ ๊ฒฐ๊ณผ๋ค์ ๋ฐํ์ผ๋ก ์ฌ์ฉ์์ ์น๋ฐํ ๊ด๊ณ์ ์๋ ํ๋ฅด์๋๋ฅผ ์ผ์ ํฌ์ค์ผ์ด๋ฅผ ์ํ ๋ํํ ์ธ๊ณต์ง๋ฅ์ ์ ์ฉํ ๋ ๊ณ ๋ คํด์ผํ ๋์์ธ ํจ์์ ๋ค์ ๋์ถํ๊ณ ๊ฐ์ด๋๋ผ์ธ์ ์ ์ํ๋ค.Advance in digital healthcare technologies has been leading a revolution in healthcare. It has been showing the enormous potential to improve medical professionalsโ ability for accurate diagnosis, disease treatment, and the usersโ daily self-care. Since the recent transformation of digital healthcare aims to provide effective personalized health services, Conversational AI (CA) is being highlighted as an easy-to-use and cost-effective means to deliver personalized services.
Particularly, CA is gaining attention as a mean for personalized care by ingraining positive self-care behavior in a daily manner while previous methods for personalized care are focusing on the medical context. CA expands the boundary of personalized care by enabling one-to-one tailored conversation to deliver health education and healthcare therapies. Due to CA's opportunities as a method for personalized care, it has been implemented with various types of roles including CA for diagnosis, CA for prevention, and CA for therapy.
However, there lacks study on the personalization of healthcare CA to meet user's preferences on the CA's persona. Even though the CASA paradigm has been applied to previous studies designing and evaluating the human-likeness of CA, few healthcare CAs personalize its human-like persona except some CAs for mental health therapy. Moreover, there exists the need to improve user experience by increasing social and emotional interaction between the user and the CA. Therefore, designing an acceptable and personalized persona of CA should be also considered to make users to be engaged in the healthcare task with the CA. In this manner, the thesis suggests an idea of applying the persona of the person who is in a close relationship with the user to the conversational CA for daily healthcare as a strategy for persona personalization. The main hypothesis is the idea of applying a close person's persona would improve user engagement. To investigate the hypothesis, the thesis explores if dynamics derived from the social relationship in the real world can be implemented to the relationship between the user and the CA with the persona of a close person.
To explore opportunities and challenges of the research idea, series of studies were conducted to (1) explore appropriate host whose persona would be implemented to healthcare CA, (2) define linguistic characteristics to consider when applying the persona of a close person to the CA, and (3)implement CA with the persona of a close person to major lifestyle domains. Based on findings, the thesis provides design guidelines for healthcare CA with the persona of the real person who is in a close relationship with the user.Abstract 1
1 Introduction 12
2 Literature Review 18
2.1 Roles of CA in Healthcare 18
2.2 Personalization in Healthcare CA 23
2.3 Persona Design CA 25
2.4 Methods for Designing Chatbotโs Dialogue Style 30
2.4.1 Wizard of Oz Method 32
2.4.2 Analyzing Dialogue Data with NLP 33
2.4.3 Participatory Design 35
2.4.4 Crowdsourcing 37
3 Goal of the Study 39
4 Study 1. Exploring Candidate Persona for CA 43
4.1 Related Work 44
4.1.1 Need for Support in Daily Healthcare 44
4.1.2 Applying Persona to Text-based CA 45
4.2 Research Questions 47
4.3 Method 48
4.3.1 Wizard of Oz Study 49
4.3.2 Survey Measurement 52
4.3.3 Post Interview 54
4.3.4 Analysis 54
4.4 Results 55
4.4.1 System Acceptance 56
4.4.2 Perceived Trustfulness and Perceived Intimacy 57
4.4.3 Predictive Power of Corresponding Variables 58
4.4.4 Linguistic Factors Affecting User Perception 58
4.5 Implications 60
5 Study 2. Linguistic Characteristics to Consider When Applying Close Personโs Persona to a Text-based Agent 63
5.1 Related Work 64
5.1.1 Linguistic Characteristics and Persona Perception 64
5.1.2 Language Component 66
5.2 Research Questions 68
5.3 Method 69
5.3.1 Modified Wizard of Oz Study 69
5.3.2 Survey 72
5.4 Results 73
5.4.1 Linguistic Characteristics 73
5.4.2 Priority of Linguistic Characteristics 80
5.4.3 Differences between language Component 82
5.5 Implications 82
6 Study3.Implementation on Lifestyle Domains 85
6.1 Related Work 86
6.1.1 Family as Effective Healthcare Provider 86
6.1.2 Chatbots Promoting Healthy Lifestyle 87
6.2 Research questions 94
6.3 Implementing Persona of Family Member 95
6.3.1 Domains of Implementation 96
6.3.2 Measurements Used in the Study 97
6.4 Experiment 1: Food Journaling Chatbot 100
6.4.1 Method 100
6.4.2 Results 111
6.5 Experiment 2: Physical Activity Intervention 128
6.5.1 Method 131
6.5.2 Results 140
6.6 Experiment 3: Chatbot for Coping Stress 149
6.6.1 Method 151
6.6.2 Results 158
6.7 Implications from Domain Experiments 169
6.7.1 Comparing User Experience 170
6.7.2 Comparing User Perception 174
6.7.3 Implications from Study 3 183
7 Discussion 192
7.1 Design Guidelines 193
7.2 Ethical Considerations 200
7.3 Limitations 206
8 Conclusion 210
References 212
Appendix 252
๊ตญ๋ฌธ์ด๋ก 262๋ฐ