• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.167-184
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• 2009

New internet trend, such as Web 2.0, has brought about new roles of users and changes in the web ecosystem. In the age of information overload like nowadays, activities in the brand community sites help to make more reasonable decisions in choosing certain brands and products. In order to examine organizational strategic aspects of brand community, this study examine (1) what difference exists in user satisfaction according to the brand community types (the user-initiated type and the corporate-established type), and (2) which dimensions of service quality affect brand community users' satisfaction. Research findings show that types of brand community influence perceived user satisfaction, and tangibility, reliability, interactivity, and stability dimensions of service quality make a positive influence on user satisfaction as well. Practical implications for managing brand community sites in marketing perspectives are further discussed.

• The Research Journal of the Costume Culture
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• v.17 no.5
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• pp.791-806
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• 2009

Internet brand community's message makes significant contributions for fashion industry. Therefore, if we understand and implement the characteristics of the message on the Internet brand's community, it can be useful for future marketing strategy. This study was to classified the internet community and to examine messages with regard to direction, write motive and prior knowledge. The messages were taken from February, 20th, to August, 7th in 2008. Content analysis method was used for this study. The results were as follows: First, Fashion brand community divided into user initiative type and enterprise establishing type. Second, User initiative type had positive direction message more than enterprise establishing type had. Third, User initiative type had large volumes of information searching or sharing related message, but enterprise establishing type had large quantities of personal experience related message about write motive. Fourth, There was no difference numerical prior knowledge between user initiative type and enterprise establishing type, and then both had a high rate of prior knowledge.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.703-720
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• 2022

Based on the love styles theory used in psychology, this paper classifies users(Passionate Love, Game-playing Love, Friendship Love, Practical Love, Possessive Love, Altruistic Love) and investigates satisfaction with the motivation for using TikTok love videos(Entertainment, Social Relationship, Love skills-learning, Self-verification, Problem-solving) according to the theory of use and satisfaction. First, 414 users were selected to conduct TikTok surveys to collect data. Then, through the analysis of the research results, among the six love types, game-playing type and possessive type have a positive (+) impact on entertainment motivation and love skill-learning motivation. Game-playing type also have a positive (+) impact on social relationship motivation and self-verification motivation. In addition, altruistic type and possessive type are also factors to strengthen the motivation of self-verification. The altruistic type, possessive type and practical type will improve the problem-solving motivation. Finally, through hierarchial multiple regression analysis, it is confirmed that game-playing love type, entertainment motivation, love skill-learning motivation and self-verification motivation can improve user satisfaction. The above results enrich the research of user classification as well as providing inspiration for improving the quality and communication efficiency of TikTok's video and enhancing user experience.

• Science of Emotion and Sensibility
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• v.10 no.3
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• pp.403-410
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• 2007

The most common method in the psychological measuring method for measuring user's emotions is to indirectly measure the user's emotion by using adjectives, called emotional words. The previous research, in order to observe user's emotional changes while they interact with a product, has extracted some emotional words and representative emotions, and made a set of subjective evaluation scale. In addition to adjective checklists, self-report questionnaire-type scales have been extensively used to assess user's emotions. This research suggested a self-report questionnaire-type scale using the representative emotions and a set of subjective evaluation scale made in the previous research. Also the reliability of the suggested self-report questionnaire-type scale was confirmed through the analysis of Cronbach's coefficient alpha. Therefore, the self-report questionnaire-type scale extracted through this research can be used in various ways to measure a user's user's emotions naturally expressed while using a product.

• Korean Journal of Computational Design and Engineering
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• v.17 no.6
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• pp.417-425
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• 2012

In recent years, great attention has been paid to using simple physical objects as tangible objects to improve user interaction in augmented reality (AR) environments. In this paper, we address AR-based user interaction using tangible objects, which has been used as a key component for virtual design evaluation of engineered products including digital handheld products. We herein consider the use of two types (product-type and pointer-type) of tangible objects. The user creates input events by touching specified parts of the product-type object with the pointer-type object, and the virtual product reacts to the events by rendering its visual and auditory contents on the output devices. The product-type object is used to reflect the geometric shape of a product of interest and to determine its position and orientation in the AR environment. The pointer-type object is used to recognize the reference position of the pointer (or finger) in the same environment. The rapid prototype of the product is employed as a good alternative to the product-type object, but various alternatives to the pointer-type object can be considered according to fabrication process and touching mechanism. In this paper, we present four alternatives to the pointer-type object and investigate their strong and weak points by performing experimental comparison of their various aspects including interaction accuracy, task performance, and qualitative user experience.

• Korean Journal of Computational Design and Engineering
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• v.16 no.4
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• pp.277-284
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• 2011

In tangible augmented reality (AR) environments for virtual prototyping, the user interacts with virtual products by manipulating tangible objects with his or her hands, but the user often encounter awkward situations in which his or her hands are occluded by augmented virtual objects, which reduces both immersion and ease of interaction. In this paper, we present how to resolve such hand region occlusion in order to enhance natural interaction and immersive visualization. In the AR environment considered, we use two types (product-type and pointer-type) of tangible objects for tangible user interaction with a virtual product of interest. Holding the tangible objects with his or her hands, the user can create input events by touching specified regions of the product-type tangible object with the pointer-type tangible object. We developed a method for resolving hand region occlusion frequently arising during such user interaction, It first detect hand region in a real image and refines the rendered image of the virtual object by subtracting the hand region from the rendered image, Then, it superimposes the refined image onto the real image to obtain an image in which the occlusion is resolved. Incorporated into tangible AR interaction for virtual prototyping of handheld products such as cellular phones and MP3 players, the method has been found by a preliminary user study that it is not only useful to improve natural interaction and immersive visualization of virtual products, but also helpful for making the users experience the products' shapes and functions better.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.55-60
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• 2020

Adaptive recommendation systems have been developed with big data processing as a system that provides services tailored to users based on user information and usage patterns. Deep learning can be used in these adaptive recommendation systems to handle big data, providing more efficient user-friendly recommendation services. In this paper, we propose a system that uses deep learning to categorize and recommend tourism types to suit the user's personality. The system was divided into three layers according to its core role to increase efficiency and facilitate maintenance. Each layer consists of the Service Provisioning Layer that real users encounter, the Recommendation Service Layer, which provides recommended services based on user information entered, and the Adaptive Definition Layer, which learns the types of tourism suitable for personality types. The proposed system is highly scalable because it provides services using deep learning, and the adaptive recommendation system connects the user's personality type and tourism type to deliver the data to the user in a flexible manner.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.217-238
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• 2006

In this study, we propose a virtual community recommendation model based on user behavioral models. It is designed to recommend optimal virtual communities for an active user by applying case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extensions. Also, it is designed to filter its case-base by considering the user's needs type before applying CBR. To test the usefulness of our model, we conduct two-step validation - experimental validation for the collected data, and survey validation for investigating the actual satisfaction level. Experimental results show that our model presents effective recommendation results in an efficient way. In addition, they also show that the information on the user's needs type may generate opportunities for cross-selling other commercial items.

• Journal of the Korean Institute of Educational Facilities
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• v.26 no.6
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• pp.19-27
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• 2019

This study aims to design educational space that will respond to changing educational curriculum and creative educational activities. The study was conducted with user centered design method in which students and teachers who actually use space participate directly in space design, moving away from the one-sided approach by experts. The design of educational space, which is the subject of this study, was carried out by the opinions of users and the opinions of users were collected through the descriptive and mapping measure forms by analyzing previous researches and actual space cases on user centered design. Based on the opinions collected by the survey form, total four spaces, Type A, two Type B, and Type C, were designed. The fact that the user directly commented on the space plan to be used and actually participated in the space design would be helpful for users' deep interest in the results of this study and students' academic improvement.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.449-451
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• 2006

With the increasing danger of personal information being exposed, a technique to protect personal information by identifying a non-user in case it is exposed. A study to construct a neural network recognizer for developing a economical and effective user protecting system. For this, time variables regarding user typing patterns from a pattern extraction device. With the variations in the standard deviation for the collected time variables, non-user patterns were generated. The recognition performance increased with the increase in the standard deviation and a higher recognition was achieved at 2.5. Also, five types of training data were generated and the recognition performance was examined as a function of the number of non-user patterns. With the increase in non-suer patterns, the recognition error quantified in the root mean square error (RMSE) was reduced. The smallest RMSE was obtained at the type 5 and 90 non-user patterns. In overall, the type 3 model yielded the highest recognition accuracy Particularly, a perfect recognition of 100% was achieved at 45 non-user patterns.