• Journal of Information Technology Services
• /
• v.6 no.3
• /
• pp.27-46
• /
• 2007

Korean homenetwork industry is now faced with problems like low profitability and unclear commercialilzation. In particular, low awareness of homenetwork usefulness is one of the main reasons why homenetwork industry is not developed. Therefore, this study tried to find what kinds of characteristics of homenetwork affected the value of apartment and how. we conducted a survey of residents of apartment with homenetwork installed in it. Surveyed data was analyzed based on the model of extended Technology Acceptance Model(TAM) including perceived enjoyment and perceived trust as new factors. The research result showed that all factors involved in suggested model had positive effects on the behavioral intention of using homenetwork and the value of apartment. Most importantly, homenetwork user's behavioral intention increased perceived housing value of the apartment. The research result can be used in explaining the advantage of homenetwork to the residence of apartment as well as in designing the homenwtwork install of apartment.

• Journal of Information Technology Applications and Management
• /
• v.27 no.6
• /
• pp.197-224
• /
• 2020

Mobile games have become one of the most important driving forces of the game industry. We focus on the continuous intention to use Chinese mobile games from the perspective of experiential marketing and network externalities. We integrate user experience, network externalities and flow theory into expectation confirmation model and explore the influencing factors of continuous usage intention of Chinese mobile game and propose a research model. Game experience, service experience, perceived enjoyment, social interaction, challenge, perceived number of users and perceived number of peers were employed as independent variables, while flow, perceived value and satisfaction as mediating variables and continuous intention as the dependent variable. After surveying 426 samples, the model is tested with structural equation model. The results reveal that perceived enjoyment significantly positively influences perceived value, flow, satisfaction, and continuous intention. The greater the enjoyment of the game, the greater the satisfaction of the game and the greater the willingness to use it continuously. Game experience has a significant direct effect on continuous intention, which indicates that a better game experience can retain more users. Service experience and perceive number of peers positively influence satisfaction. Another finding is that social interaction and perceived number of users positively influence perceived value and flow, which indicate that social attributes are critical roles for retaining users. Game challenge also positively influences flow. The proper level of challenge is more likely to cause users to enter the state of flow. Flow indirectly influences continuous usage intention through the satisfaction of the game, which indicates that satisfaction is driven by flow experience and further retaining users. Empirical results implied that mobile game companies need to focus on improving user experience, expectation satisfaction and extending network externalities to improve the continuous intention of using mobile game.

• Journal of Distribution Science
• /
• v.19 no.9
• /
• pp.5-17
• /
• 2021

Purpose: As mobile devices are commonly used and contact-free services are widespread due to the COVID-19 pandemic in the recent distribution environment, this study suggests retail strategies for consumers using high-speed railways. To this end, we analyzed how consumer perception on technologies necessary for use of mobile apps is related to the attitude that drives consumers to continue using the app services. Research design, data and methodology: Based on the extended unified theory of technology acceptance and use of technology model by Venkatesh, Morris, Davis and Davis (2003), we added variables proposed by existing theories that studied the technology acceptance model from multiple perspectives and empirically analyzed the relationship between user satisfaction and use intention with structural equation modeling. Results: As expected, factors necessary for the use of app services such as performance expectancy, social influence, price value, facilitating conditions, security, and aesthetics had positive effects on user satisfaction, whereas the effect of effort expectancy on user satisfaction was rejected. And user satisfaction was found to have a significant effect on intention to use. Conclusions: The results provide implications that strategic retail management of the above factors can motivate passengers to continuously use high-speed railways.

• Journal of the Korea Safety Management & Science
• /
• v.26 no.2
• /
• pp.93-105
• /
• 2024

In this study, based on previous research on personal broadcasting, we indirectly measured content quality, YouTuber attributes, user satisfaction, and behavioral intention, which are latent variables that cannot be directly measured, as measurement variables, and then measured theoretically between the latent variables. In order to analyze the causal relationship, we used a structural equation model to determine to what extent the content quality of the YouTube mukbang channel and the YouTuber's attributes influence behavioral intentions such as purchase, recommendation, and continued use according to viewers' satisfaction with use. We intend to analyze and verify the relationship between related variables. The research results are as follows. First, the value, relevance, timeliness, completeness, and data quantity of content quality were found to have no significant impact on user satisfaction. Second, the trustworthiness, expertise, attractiveness, and intimacy of YouTuber attributes were found to have a significant impact on user satisfaction, but the similarity of YouTuber attributes did not have a significant impact on usage. Third, user satisfaction was found to have a significant impact on behavioral intentions and purchase intentions. However, user satisfaction was not found to have a direct significant impact on recommendation intentions or continued usage intentions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.2060-2077
• /
• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.42 no.1
• /
• pp.183-194
• /
• 2018

This study investigated the characteristics of Omni Channel and examined the influence of them on consumers' perceived functional value and emotional value as well as the effect of perceived functional value and emotional value on user's intention of use through Omni Channel. To verify the research model, the questionnaire survey was conducted on 20's to 40's men and women residing in Seoul and the metropolitan area by convenience sampling. The number of copies used for data analysis was 696. To verify the research model, factor analysis, reliability analysis, and structural equation model analysis were performed using AMOS 20.0. First, Omni Channel characteristics consisted of four factors: instant connectivity, location-based provability, interactivity, and entertainment. Second, the instant connectivity, location-based provability and entertainment positively influenced functional value and emotional value; however, the interactivity was significant only to the emotional value. Third, consumers' perceived value of Omni Channel characteristics had a significant effect on attitude. Fourth, the more favorable the attitude toward Omni Channel is higher for the intention to use Omni Channel.

• IE interfaces
• /
• v.24 no.1
• /
• pp.1-7
• /
• 2011

The purpose of this research was to develop an integrated interface for the usability test of systems or products in the design process. It is capable of automatically creating GOMS models which can predict human task performances. It can generate GOMS models to be interacted with the prototype interfaces. It can also effectively manage various design information and various usability test results to be implemented into the new product and/or system design. Thus we can perform usability test for products or system prototypes more effectively and also reduce time and effort required for this test. For usability tests, we established an integrated interface based on GOMS model by the LabVIEW program. We constructed the system that the linkage to GOMS model is available. Using this integrated interface, the menu structure of mobile phone can be constructed easily. User can design a depth and a breath that he want. The size of button and the label of the button is changable. The path to the goal can be defined by the user. Using a designed menu structure, the experiment could be performed. The results of GOMS model and the actual time are presented. Besides, values of operators of GOMS model can be defined as the value that user wants. Using the integrated interface that we developed, the optimal menu structure deducted. The menu structure that user wants can be established easily. The optimal layout and button size can be decided by comparison of numerous menu structures. User can choose the method of usability test among GOMS model and empirical data. Using this integrated interface, the time and costs can be saved and the optimal menu structure can be found easily.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.26 no.4
• /
• pp.105-121
• /
• 2015

PDA model is new collection development method not only corresponding user's demand, but also expensing acquisition budget effectively. In the PDA model, when the trigger that indicates user's demand reaches up to treshold, e-book is automatically purchased without librarian's interrogation. This study examines concept, basic principles, and current condition of operation about PDA and finds a way that could applicate to Korean university library foreign E-book consortium. Current model of university library e-book acquisition pursuit reduction of budget and utility value, but the selected collection is not based on user's demand. Therefore, it could be considerable to apply PDA or evidence based purchase model to foreign e-book acquisition consortium.

• Journal of Information Technology Services
• /
• v.21 no.5
• /
• pp.15-28
• /
• 2022

Recently, government agencies are actively adopting the platform model as a means of public policy. However, existing studies on the public platform are minimal and have focused on user experiences or the possibility of public usage of the platform model. Now the research concerning building governance structure and utilizing network effects of the platform after adopting the platform model in the public sector is keenly required. This study intended to ignite academic dialogue on the governance of public platforms in the context of digital transformation. This study focused on a case of the 'Special delivery,' a public delivery app established by Gyeonggi-do. In order to analyze the characteristics of the public platform and its governance structure, data were collected from press releases, policy reports, and news articles. Data was analyzed using the frame of Hagui's platform design factors and Ansell & Gash's collaborative governance model. The results of the public platform analyses showed 1) incompleteness in the value trade-off accounting, which was designed for platform business based on general cost-benefit analysis, and 2) a closed governance structure that limits direct participation of diverse user groups(i.e., service provider, customer) in order to enhance providers' utility by preventing customers' excessive online activities. The results of this study provided theoretical and policy implications regarding designing the strategy for accounting for value trade-offs and functioning governance structure for public platforms.

• Journal of KIISE:Software and Applications
• /
• v.37 no.5
• /
• pp.363-369
• /
• 2010

Demonstration-based learning has the advantage that a user can easily teach his/her robot new task knowledge just by demonstrating directly how to perform the task. However, many previous demonstration-based learning techniques used a kind of attribute-value vector model to represent their state spaces and policies. Due to the limitation of this model, they suffered from both low efficiency of the learning process and low reusability of the learned policy. In this paper, we present a new demonstration-based learning method, in which the relational model is adopted in place of the attribute-value model. Applying the relational instance-based learning to the training examples extracted from the records of the user demonstrations, the method derives a relational instance-based policy which can be easily utilized for other similar tasks in the same domain. A relational policy maps a context, represented as a pair of (state, goal), to a corresponding action to be executed. In this paper, we give a detail explanation of our demonstration-based relational policy learning method, and then analyze the effectiveness of our learning method through some experiments using a robot simulator.