• The Journal of Information Systems
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• v.32 no.3
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• pp.19-39
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• 2023

Purpose This study aims to identify user experience factors that can enhance both metaverse utilization and satisfaction based on the honeycomb model. For this we presented two research questions: first, what are the experience factors of metaverse users? Second, do metaverse user experience factors impact satisfaction? Design/methodology/approach To address these questions, a mixed-methodology approach is employed, including text mining techniques to analyze online reviews and quantitative econometric analysis to reveal the relationship between user experience factors and satisfaction. A total of 69,880 reviews and ratings data were collected. Findings The analysis revealed eight metaverse user experience factors: entertainment, operability, virtual reality, immersion, economic activity, visual performance, avatar, and sociality, all of which were found to have a positive impact on user satisfaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 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 Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.1113-1120
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• 2010

In this paper, we have proposed Architecture Development methodology which can connect both operational view and system view. The Functional Architecture can connect both user and developer, and it is located between Activity analysis and System analysis. We suggest the new architecture methodology using the Functional Architecture and it provides effect to analyze the connection between user(military) and developer(enterprise) in Defence R&D and the new Architecture with the feedback analyze activity on a point of system view and the new architecture make the functional architecture.

• Proceedings of the KSR Conference
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• 2003.10b
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• pp.210-216
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• 2003

This study focuses on the physical environment and human activity of the user of train toilets, on the analysis of factors needed to obtain reasonable toilet design and it also focuses on the understanding of the interface between its user. Moreover it proposes a module of the development process and methods of approaching the User Toilet Interface. The study so presents a design standard under which the concrete data of the characteristics and practicable range and the convergent demands accelerate to the module could be confirmed and criticized.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.125-138
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• 2019

Purpose: The present research was designed to explore a causal relationship among SNS prosumer characteristics, relationship quality, user satisfaction, and loyalty intention, and we intended to explore mediating role of relationship quality in the causal relationship. Methods: As survey tool, questionnaire that had obtained validity and reliability through literature survey and pretest survey, and sample 214 was analyzed using SEM analysis method. Results: All theoretical relationships, except the relationship between information provision and relational quality, proved to be significant. The relationship quality plays an important intermediary role in the research model. Conclusion: The characteristics of SNS prosumer activity can be summarized by interaction and informational provision. To increase user satisfaction and loyalty, it is necessary to support these characteristics and strengthen relationships with customers.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.4
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• pp.958-972
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• 2013

The purpose of this study is to analyze on high school teachers' stage of concern(SoC) on 'Creative-Experience Activity' in 2009 revised national curriculum and investigate the improvements of their levels. The subjects for this study were 234 high school teachers through out all part of the Korea country. The instrument for this study was developed according to the Hall & Hord(2006)'s stage of concerns questionnaire of Stage of Concern. The Data were analyzed by Profiling of teachers' concern and one-way ANOVA. The major findings of this study were as follows: First, 85.25% of the high school teachers were in stage of Awareness. 6.34% of teachers were in stage of Information. Most of high school teachers' concern about 'Creative-Experience Activity' was generally 'non-user' stage that unconcerned. Second, there were not significant differences in teachers' stage of concern according to their sex and teaching career except for region. In region, the teacher in metropolis tent to interest getting new information than small and midium size city. We need to make an effort to transfer from 'non-user' stage to 'early-user' stage of and 'impact' stage. To do so, we may sublate too much curriculum revise and start teacher training for 2009 revised 'creativity-experience activity' curriculum. The result of the study provide that there are not curriculum implementation success without teachers' concern about revised curriculum.

• Journal of the Korean Institute of Landscape Architecture
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• v.19 no.3
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• pp.87-97
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• 1991

The primary purpose of this study is to investigate factors and variables which have significant effects on user's satisfaction with recreational activities in Daeshin Natural Park, thereby establishing directions of development of urban parks. To test the causal models of this research, the data were gathered by self-administered questionnaires form 627 households in Pusan City which were selected by the multi-stage probability sampling method. The analysis of the data primarily consists of two-phase: The first analysis dealt exploratory factor analysis which identified major factors involved in satisfaction with recreational activities in Daeshin Natural Park and the second analysis tested the fit of the causal models of this research by employing LISREL methodology. The factor analysis identified that three factors are involved in satisfaction with recreational actitives. The three factors of satisfaction with recreational activities are facilities for health and phisical exercise, group recreational activity, maintenance activity. The second phase analysis tested the fit of the causal models for satisfaction with recreational activity to the data and identified statistically significant causal linkage among overall satisfaction with Daeshin Natural Park, other endogenous factors and exogenous variables.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.123-128
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• 2007

It has been argued that Enterprise systems (ES) implementations are overshadowed by a high failure rate despite their promised benefits. One of the commonly cited reasons for ES implementation failures in the context of mandatory use is end-user's unwillingness or sabotage to adopt or use systems. Considering that the appropriate management of expectations may play an important role in making positive behavior toward newly implemented systems, this study examines the effect of outcome expectations on the system use activity in the mandatory use context of information systems from the Social Cognitive Theory perspective. Structural equation model analysis using LISREL 8.7 provides significant support for the proposed relationships. The empirical results suggest that outcome expectations and user satisfaction have positive effects on system use activity conceptualized by immersion, reinvention, and learning. Theoretical and practical implications of the study shed some light on how to improve system use activity in the mandatory use context of information systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.9-17
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• 2011

This paper proposes an algorithm that predicts the state of user's next actions, exploiting the HMM (Hidden Markov Model) on user profile data stored in the ubiquitous home network. The HMM, recognizes patterns of sequential data, adequately represents the temporal property implicated in the data, and is a typical model that can infer information from the sequential data. The proposed algorithm uses the number of the user's action performed, the location and duration of the actions saved by "Activity Recognition System" as training data. An objective formulation for the user's interest in his action is proposed by giving weight on his action, and change on the state of his next action is predicted by obtaining the change on the weight according to the flow of time using the HMM. The proposed algorithm, helps constructing realistic ubiquitous home networks.