5 research outputs found
A Robot is a Smart Tool: Investigating Younger Users' Preferences for the Multimodal Interaction of Domestic Service Robot
The degree that domestic service robots are generally accepted mainly depends on the user experience and the surprise that the design brings to people. To make the design of robots to follow the trend of interactions of smart devices, researchers should have insights into young people's acceptance and opinions of emerging new interactions. The main content of this study is a user elicitation through which the users' suggestions for commanding a robot in specific contexts are gathered. Accordingly, it sheds light on the features of user preferences for human-robot interaction. This study claims that younger users regard service robots merely as intelligent tools, which is the direct cause of the above interaction preferences. Keywords: Service robot, Interaction design, User preferenc
Do We Blame it on the Machine? Task Outcome and Agency Attribution in Human-Technology Collaboration
With the growing functionality and capability of technology in human-technology interaction, humans are no longer the only autonomous entity. Automated machines increasingly play the role of agentic teammates, and through this process, human agency and machine agency are constructed and negotiated. Previous research on βComputers are Social Actors (CASA)β and self-serving bias suggest that humans might attribute more technology agency and less human agency when the interaction outcome is undesirable, and vice versa. We conducted an experiment to test this proposition by manipulating task outcome of a game co-played by a user and a smartphone app, and found partially contradictory results. Further, user characteristics, sociability in particular, moderated the effect of task outcome on agency attribution, and affected user experience and behavioral intention. Such findings suggest a complex mechanism of agency attribution in human-technology collaboration, which has important implications for emerging socio-ethical and socio-technical concerns surrounding intelligent technology
Robot-Mediated interviews: A field trial with a potential real-world user
Β© 2020 John Benjamins Publishing Company. This a peer reviewed, authorβs accepted manuscript. Contact John Benjamins Publishing Company permission to re-use or reprint the material in any form.In recent years the possibility of using humanoid robots to perform interviews with children has been explored in a number of studies. This paper details a study in which a potential real-world user trialled a Robot-Mediated Interviewing system with children to establish if this approach could realistically be used in a real-world context. In this study a senior educational psychologist used the humanoid robot Kaspar to interview ten primary school children about a video they had watched prior to the interview. We conducted a pre and post interview with the educational psychologist before and after using the system to establish how the system worked for him and the perceived potential for real-world applications. The educational psychologist successfully used the system to interview the children and believed that principally using a small humanoid robot to interview children could be useful in a real-world setting provided the system was developed further.Peer reviewe
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ν.This dissertation aims to propose a user evaluation model to evaluate social AI personal assistants in the early stage of product development. Due to the rapid development of personal devices, data generated from personal devices are increasing explosively, and various personal AI services and products using these data are being launched. However, compared to the interest in AI personal assistant products, its market is still immature. In this case, it is important to understand consumer expectations and perceptions deeply and develop a product that can satisfy them to spread the product and allow general consumers to easily accept the product promptly. Accordingly, this dissertation proposes and validates a user evaluation model that can be used in the early stage of product development.
Prior to proposing this methodology, main characteristics of social AI personal assistants, the importance of user evaluation in the early stage of product development and the limitations of the existing user evaluation model were investigated in Chapter 2. Various technology acceptance models and evaluation models for social AI personal assistant products have been proposed, evaluation models that can be applied in the initial stage of product development were insufficient, however. Moreover, it was found that commonly used evaluation measures for assessment of hedonic value were much fewer compared to measures for utilitarian value. These were used as starting points of this dissertation.
In Chapter 3, the evaluation measures used in previous studies related to social AI personal assistant were collected and carefully reviewed. Through systematic review of 40 studies, the evaluation measures used in the past and limitation of related research were investigated. As a result, it was found that it was not easy to develop a prototype for evaluation, so it was possible to make the most of the products that have already been commercialized. In addition, all evaluation items used in previous studies were collected and used as the basis for the evaluation model to be proposed later. As a result of the analysis, considering the purpose of the social AI personal assistant, the role as supporting the user emotionally through social interaction with the user is important, but it was found that the evaluation measures related to hedonic value that are commonly used were still insufficient.
In Chapter 4, evaluation measures that can be used in the initial stage of product development for social AI personal assistant were selected. Selected evaluation measures were used to evaluate three types of social robots and relationship among evaluation factors were induced through this evaluation. A process was proposed to understand to various opinions related to social robots and to derive evaluation items, and a case study was conducted in which a total of 230 people evaluated three social robots concept images using the evaluation items finally selected through this process. As a result, it is shown that consumersβ attitude toward products was built through the utilitarian dimension and the hedonic dimension. In addition, there is positive relationship between ease of use and utility in the utilitarian dimension, and among aesthetic pleasure, attractiveness of personality, affective value in the hedonic dimension. Moreover, it is confirmed that the evaluation model derived from this study showed superior explanatory power compared to the previously proposed technology acceptance model.
In Chapter 5, the model was validated again by applying the evaluation measure and the relationship among evaluation factors derived in Chapter 4 to other products. 100 UX experts with expertise in the field of social AI personal assistants and 100 users who use the voice assistant service often, watched two concept videos of the voice assistant service to help users in the onboarding situation of mobile phones and evaluated these concepts. As a result of the evaluation, there is no significant difference in the evaluation results between the UX expert and the real user group, so the structural equation model analysis was conducted using all the data obtained from the UX expert and the real user group. As a result, results similar to those in Chapter 4 are obtained, and it is expected that the model could be generalized to social AI personal assistant products and applied for future research.
This dissertation proposes evaluation measure and relationship among evaluation factors that can be applied when conducting user evaluation in the initial stage of social AI personal assistant development. In addition, case studies using social AI personal assistant products and services were conducted to validate it. With the findings of this study, it is expected that researchers who need to conduct user evaluation to clarify product concepts in the early stages of product development will be able to apply evaluation measures effectively. It is expected that the significance of this dissertation will become clearer if further research is conducted comparing the finished product of social AI personal assistants with the video type stimulus in the early stage of development.λ³Έ λ
Όλ¬Έμ μ΅κ·Ό λΉ λ₯΄κ² λ°μ νκ³ μλ social AI personal assistantμ κ°λ° μ΄κΈ° λ¨κ³μ νμ© κ°λ₯ν μ¬μ©μ νκ° νλͺ©μ κ°λ°νκ³ νκ° νλͺ© κ°μ κ΄κ³λ₯Ό κ²μ¦νλ κ²μ λͺ©νλ‘ νλ€. κ°μΈ λλ°μ΄μ€μ λ°λ¬λ‘ μΈν΄, κ° λλ°μ΄μ€μμ μμ±λλ λ°μ΄ν°κ° νλ°μ μΌλ‘ μ¦κ°νκ³ μκ³ , μ΄λ₯Ό νμ©ν κ°μΈμ© AI μλΉμ€ λ° μ νμ΄ λ€μνκ² μ μλκ³ μλ€. νμ§λ§ κ·Έ κ΄μ¬μ λΉν΄, social AI personal assistant μ νμ μ€μ μμ₯μ μμ§ μ±μνμ§ μμ λ¨κ³μ΄λ€. μ΄λ¬ν μν©μμ μ νμ λΉ λ₯΄κ² νμ°μν€κ³ μΌλ° μλΉμλ€μ΄ μ½κ² μ νμ μμ©ν μ μκ² νκΈ° μν΄μλ, μλΉμμ κΈ°λμ μΈμμ μΆ©λΆν μ΄ν΄νκ³ κ·Έλ₯Ό μΆ©μ‘±μν¬ μ μλ μ νμ κ°λ°νλ κ²μ΄ μ€μνλ€. μ΄μ λ°λΌ λ³Έ μ°κ΅¬μμλ μ ν κ°λ° μ΄κΈ° λ¨κ³μ νμ©ν μ μλ μ¬μ©μ νκ° νλͺ©μ μ μνκ³ νκ° νλͺ© κ° κ΄κ³λ₯Ό λμΆνλ κ²μ λͺ©νλ‘ νλ€.
λ¨Όμ 2μ₯μμλ social AI personal assistantμ νΉμ§, μ ν κ°λ° μ΄κΈ° λ¨κ³μμ μ΄λ£¨μ΄μ§λ μ¬μ©μ νκ°μ μ€μμ± λ° κΈ°μ‘΄ μ¬μ©μ νκ° λͺ¨λΈμ νκ³μ μ μ‘°μ¬νμλ€. κΈ°μ‘΄μ κΈ°μ μμ© λͺ¨λΈ λ° AI personal assistant μ νμ νκ° λͺ¨λΈλ€μ΄ λ€μνκ² μ μλμ΄ μμΌλ, μ ν κ°λ° μ΄κΈ° λ¨κ³μ νμ©ν μ μλ νκ° λͺ¨λΈμ λΆμ‘±νμκ³ , μ ν μ λ°μ νκ°ν μ μλ νκ° λͺ¨λΈμ λΆμ¬λ‘ λλΆλΆμ κΈ°μ‘΄ μ°κ΅¬μμλ λ κ°μ§ μ΄μμ νκ° λͺ¨λΈμ κ²°ν©, μμ νμ¬ μ¬μ©ν κ²μ μ μ μμλ€.
3μ₯μμλ AI personal assistant κ΄λ ¨ κΈ°μ‘΄ μ°κ΅¬μμ νμ©λ νκ° νλͺ©μ κ²ν νμλ€. μ΄ 40κ°μ μ°κ΅¬λ₯Ό 리뷰νμ¬, κΈ°μ‘΄μ νμ©λκ³ μλ νκ° νλͺ©μ μ’
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4μ₯μμλ social AI personal assistant μ ν κ°λ° μ΄κΈ° λ¨κ³μμ νμ© κ°λ₯ν νκ° νλͺ©μ μμ§ λ° μ μνκ³ , νκ° νλͺ©μ νμ©νμ¬ social robotsμ νκ°ν λ€ μ΄λ₯Ό ν΅ν΄ νκ° νλͺ© κ°μ κ΄κ³λ₯Ό λμΆνμλ€. Social robots κ΄λ ¨ μ견μ λ€μνκ² μ²μ·¨νκ³ νκ° νλͺ©μ λμΆνλ νλ‘μΈμ€λ₯Ό μ μνμμΌλ©°, λ³Έ νλ‘μΈμ€λ₯Ό ν΅ν΄ μ΅μ’
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μ λν νκ°λ₯Ό μ§ννμλ€. νκ° κ²°κ³Ό UX μ λ¬Έκ°μ μ€μ¬μ©μ κ·Έλ£Ή κ°μλ νκ° κ²°κ³Όμ μ μλ―Έν μ°¨μ΄λ₯Ό 보μ΄μ§ μμκΈ° λλ¬Έμ, UX μ λ¬Έκ°μ μ€μ¬μ©μ κ·Έλ£Ήμμ μ»μ λ°μ΄ν° μ 체λ₯Ό νμ©νμ¬ κ΅¬μ‘° λ°©μ μ λͺ¨λΈ λΆμμ μ§ννμλ€. κ·Έ κ²°κ³Ό 5μ₯κ³Ό μ μ¬ν μμ€μ κ²°κ³Όλ₯Ό μ»μκ³ , μΆν ν΄λΉ λͺ¨λΈμ social AI personal assistant μ νμ μΌλ°ννμ¬ νμ©ν μ μμ κ²μΌλ‘ νλ¨νμλ€.
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Όλ¬Έμ social AI personal assistant κ΄λ ¨ μ ν λ° μλΉμ€μ κ°λ° μ΄κΈ° λ¨κ³μμ μ¬μ©μ νκ°λ₯Ό μ§νν λ νμ© κ°λ₯ν νκ° νλͺ© λ° νκ° νλͺ© κ°μ κ΄κ³λ₯Ό λμΆνμλ€. λν μ΄λ₯Ό κ²μ¦νκΈ° μνμ¬ social AI personal assistant μ ν λ° μλΉμ€λ₯Ό νμ©ν μ¬λ‘μ°κ΅¬λ₯Ό μ§ννμλ€. λ³Έ μ°κ΅¬ κ²°κ³Όλ μΆν μ ν κ°λ° μ΄κΈ° λ¨κ³μμ μ νμ 컨μ
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ννκ² μ μν μ μμ κ²μΌλ‘ μκ°λλ€.Chapter 1 Introduction 1
1.1 Background and motivation 1
1.1 Research objectives 5
1.2 Dissertation outline 7
Chapter 2 Literature review 9
2.1 Social AI personal assistant 9
2.2 User centered design process 13
2.3 Technology acceptance models 16
2.4 Evaluation measures for social AI personal assistant 22
2.5 Existing evaluation methodologies for social AI personal assistant 27
Chapter 3 Collection of existing evaluation measures for social AI personal assistants 40
3.1 Background 40
3.2 Methodology 43
3.3 Result 51
3.4 Discussion 60
Chapter 4 Development of an evaluation model for social AI personal assistants 63
4.1 Background 63
4.2 Methodology 66
4.2.1 Developing evaluation measures for social AI personal assistants 68
4.2.2 Conducting user evaluation for social robots 74
4.3 Result 77
4.3.1 Descriptive statistics 77
4.3.2 Hypothesis development and testing 80
4.3.3 Comparison with existing technology acceptance models 88
4.4 Discussion 93
Chapter 5 Verification of an evaluation model with voice assistant services 95
5.1 Background 95
5.2 Methodology 98
5.2.1 Design of evaluation questionnaires for voice assistant services 99
5.2.2 Validation of relationship among evaluation factors 103
5.3 Result 108
5.3.1 Descriptive statistics 108
5.3.2 Hypothesis development and testing 111
5.3.3 Comparison with existing technology acceptance models 118
5.4 Discussion 121
Chapter 6 Conclusion 124
6.1 Summary of this study 124
6.2 Contribution of this study 126
6.3 Limitation and future work 128
Bibliography 129
Appendix A. Evaluation measures for social AI personal assistant collected in Chapter 4 146
Appendix B. Questionnaires for evaluation of social robots 154
Appendix C. Questionnaires for evaluation of voice assistant service 166λ°
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