사용자 경험 데이터를 활용한 스마트 제품 특성에 대한 질적 연구

학위논문 (박사)-- 서울대학교 대학원 : 산업·조선공학부, 2015. 8. 윤명환.We are surrounded by products that have minds of their own. Smart products share the ability to collect, process, and produce information and can be described as thinking for themselves. The smart design has been applied to so many products so frequently, and in so many different contexts, that it is becoming more of a marketing claim than a well-defined technical description. Which consumer devices should be categorized as smart products? While the rapid growth in embedded computing power and the absolute number of microcontroller chips are well-documented facts, the definition of a smart product is still evolving. Definitions and characteristics differ considerably depending on the point of view of researchers. The purpose of this study is to identify product smartness and users implicit needs more effectively and efficiently for developing new ideas/concepts for smart products. The overall procedure of developing idea/concept for new smart products/services is as follow. In phase I, product smartness is identified. A conceptual model of product smartness was identified based on the literature review and the expert interview. As a result, five main dimensions of product smartness are selected (Autonomy, Adaptability, Multi-functionality, Connectivity, and Personalization). Also, to explore the relationship between product smartness and user experience, user experiences of smartphones were classified according to the reason of emotions (positive/negative) utilizing social media data (Twitter). According to the results, there were many positive experiences for all of dimensions, but there were negative experiences only for multi-functionality and connectivity. In phase II, to identify users needs, a Day Reconstruction Method (DRM) and a Self-Organizing Map (SOM) were conducted. To collect more natural and longitudinal user experience, the DRM was conducted with a case study on smart TVs. Also, to analyze the collected user experience data more effectively and efficiently, a clustering analysis was conducted using the SOM. As a result, similarity between episodes could be identified by analyzing a two-dimensional map, and 15 groups were classified from 330 episodes of user experience on smart TVs. The results from the two phases are integrated in phase III, where new ideas/concepts for smart products/services are developed with relationship analysis. From the case study of developing new idea/concept for smart TV, a total of seven detail concepts were developed with five extracted user experiences of smart TV. Also, to explore the relationship between idea quality, product smartness and satisfaction of new ideas/concepts, the evaluation experiments were conducted by practitioners and researchers. Idea quality metrics were collected from previous studies, and then they were re-organized into three dimensions: Workability, Relevance, and Attractiveness. To validate the conceptual model, an evaluation experiment was conducted. According to the results of the experiment, the relationship between idea quality, product smartness, and satisfaction on new idea/concepts were identified and explored. In the idea quality, Relevance is relatively more important than the others. It is found that Workability has no significant influence on satisfaction. Also, in the product smartness, Autonomy is the most important factor for satisfaction of new idea/concept. However, Multi-functionality shows no significant influence on satisfaction of new idea/concept. This study will motivate researchers and practitioners to develop and improve smart products and its applications. Even though developed new idea/concept is difficult to be implemented or acceptable to users, users and developers will be satisfied if the idea/concept is relevant and attractive. Also, developers may want to implement their ideas for multi-functional products in a stepwise manner and to provide consumers with the opportunity to get used to certain levels of product smartness.ABSTRACT i CONTENTS v List of Tables ix List of Figures xi I. INRODUCTION 1 1.1 Background and Problem Definition 1 1.2 Purpose and Motivation of this Study 5 1.3 Organization of the Thesis 10 II. BACKGROUND 13 2.1 Smart Product 13 2.1.1 Ubiquitous computing and context-awareness 14 2.1.2 Definitions of smart product 16 2.1.3 Characteristics of smart products 18 2.2 New Product Development for Smart Product 30 2.2.1 Identifying user needs and user experience (UX) 31 2.2.2 Idea/concept generation 34 2.2.3 Idea/concept evaluation 43 III. IDENTIFYING PRODUCT SMARTNESS 49 3.1 Overview 49 3.2 Conceptual Model of Product Smartness 50 3.2.1 Method 50 3.2.2 Result of re-organization 50 3.3 Analysis of User Experience of Smartphones utilizing Social Media Data 59 3.3.1 Method 59 3.3.2 Result of classification 61 3.4 Discussion 66 IV. IDENTIFYING USER NEEDS 67 4.1 Overview 67 4.2 Eliciting Users Implicit Needs using a Diary-based Behavior Analysis 68 4.2.1 Method 68 4.2.2 Results of DRM 76 4.2.3 Results of clustering analysis 87 4.3 Discussion 91 V. DEVELOPING SMART PRODUCT IDEA/CONCEPT 95 5.1 Overview 95 5.2 Method 96 5.2.1 Phase I: Identifying product smartness 96 5.2.2 Phase II: Identifying user needs 97 5.2.3 Phase III: Developing new idea/concept for smart products 97 5.3 Case Study 100 5.2.1 Phase I: Identifying product smartness 100 5.2.2 Phase II: Identifying user needs of smart TV 100 5.2.3 Phase III: Developing idea/concept for smart TV 101 5.4 Discussion 104 VI. EVALUATING SMART PRODUCT IDEA/CONCEPT 105 6.1 Overview 105 6.2 Analysis of Relationship between Idea Quality and Satisfaction on New Idea/Concept for Smart Products 106 6.2.1 Conceptual model and hypothesis 106 6.2.2 Method 112 6.2.3 Results 113 6.3 Analysis of Relationship between Product Smartness and Satisfaction on New Idea/Concept for Smart Products 117 6.3.1 Conceptual model and hypothesis 117 6.3.2 Method 118 6.3.3 Results 119 6.4 Discussion 122 VII. DISCUSSION AND CONCLUSION 125 7.1 Summary of Findings 125 7.2 Contribution of this Study 128 7.3 Limitation and Further Study 130 BIBLIOGRAPHY 133 APPENDIX 153Docto

A Novel Framework for Identifying Customers' Unmet Needs on Online Social Media Using Context Tree

Customer needs and user contexts play an important role in generating ideas for new products or new functions. This study proposes a novel framework for identifying customers' unmet needs on online social media using the Context Tree through the Hierarchical Search of Concept Spaces (HSCS) algorithm. The Context Tree represents the hierarchical structure of nodes associated with related keywords and corresponding concept spaces. Unlike other methods, the Context Tree focuses on finding the unmet needs of customers from online social media. The proposed framework is applied to extract customer needs for home appliances. Identified customer needs are used to make user scenarios, which are used to develop new functions of home appliances.Y

Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet Allocation

Accidents related to electric kick scooters, which are widespread globally, are increasing rapidly. However, most of the research on them concentrates on reporting accident status and injury patterns. Therefore, while it is necessary to analyze safety issues from the user's perspective, interviewing or conducting a survey with those involved in an accident may not return enough data due to respondents' memory loss. Therefore, this study aims to identify the risk factors in the context-of-use for electric kick scooters based on a topic modeling method. We collected data on risk episodes involving electric kick scooters experienced by users in their daily lives and applied text mining to analyze text responses describing the risk episodes systematically. A total of 423 risk episodes are collected from 21 electric kick scooter users in South Korea over two months from an online survey. The text responses describing risk episodes were classified into nine topics based on a latent Dirichlet allocation. From the result, four risk factors can be identified by analyzing the derived topics and the cause of the risk according to the context. Moreover, we suggested design improvement directions. This study can be helpful for designing safer electric kick scooters considering safety.Y

A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives.

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation

ANALYSIS OF RELATIONSHIP BETWEEN BRAND PERSONALITY AND CUSTOMER SATISFACTION ON A VEHICLE EXHAUST SOUND

This study aims to understand the brand personality of vehicle exhaust sounds and how these elements affect the customer satisfaction in regards to exhaust sounds. Thus, a research model for exploring the relationship between brand personality and customer satisfaction was developed. This study was conducted on nine vehicles’ exhaust sounds which were engine acceleration sounds when the speed of the vehicle increased from 0 to 100km/h (zero to 100). The evaluation was conducted among 40 participants who each have a minimum of ten years’ driving experience and have no impairment in their hearing. The findings partly support the research model and confirm that brand personality dimensions are influencing factors in satisfaction for the vehicle exhaust sound. ‘Sophistication’ and ‘Confidence’ are more important for customer satisfaction of V6 and V8 cylinder exhaust sounds than the other brand personality scales. The research advances the understanding of the effect of brand personalities on customer satisfaction for vehicle exhaust sounds

Semantic Scene Graph Generation Using RDF Model and Deep Learning

Over the last several years, in parallel with the general global advancement in mobile technology and a rise in social media network content consumption, multimedia content production and reproduction has increased exponentially. Therefore, enabled by the rapid recent advancements in deep learning technology, research on scene graph generation is being actively conducted to more efficiently search for and classify images desired by users within a large amount of content. This approach lets users accurately find images they are searching for by expressing meaningful information on image content as nodes and edges of a graph. In this study, we propose a scene graph generation method based on using the Resource Description Framework (RDF) model to clarify semantic relations. Furthermore, we also use convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to generate a scene graph expressed in a controlled vocabulary of the RDF model to understand the relations between image object tags. Finally, we experimentally demonstrate through testing that our proposed technique can express semantic content more effectively than existing approaches

TARGET SIZE, POSITION AND MOVEMENT DIRECTION EFFECTS ON THE DRAG TASK PERFORMANCE OF A GAZE CONTROL DEVICE

An eye mouse is an alternative input device which can perform most of the tasks that a conventional mouse can, it is usually used by disabled people. Although the usability of an eye mouse has not been investigated exhaustively, a few studies have focused on the drag tasks of an eye mouse. This study evaluated the performance of drag tasks by an eye mouse according to target size, position, and movement direction. Twenty-seven participants were recruited to perform drag tasks. The results indicated that the diagonal movement worked better than horizontal movement, which worked better than vertical movement. The upper horizontal movement worked better than the lower one. Also, the eye mouse worked better with large targets than with small or medium ones. The results of this study should help in designing interfaces or applications for an eye mouse

Semantic Scene Graph Generation Using RDF Model and Deep Learning

Over the last several years, in parallel with the general global advancement in mobile technology and a rise in social media network content consumption, multimedia content production and reproduction has increased exponentially. Therefore, enabled by the rapid recent advancements in deep learning technology, research on scene graph generation is being actively conducted to more efficiently search for and classify images desired by users within a large amount of content. This approach lets users accurately find images they are searching for by expressing meaningful information on image content as nodes and edges of a graph. In this study, we propose a scene graph generation method based on using the Resource Description Framework (RDF) model to clarify semantic relations. Furthermore, we also use convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to generate a scene graph expressed in a controlled vocabulary of the RDF model to understand the relations between image object tags. Finally, we experimentally demonstrate through testing that our proposed technique can express semantic content more effectively than existing approaches

PRISMA flow diagram of hybrid BCIs.

<p>PRISMA flow diagram of hybrid BCIs.</p

Frequency of performance measures used in the reviewed articles.

<p>Frequency of performance measures used in the reviewed articles.</p