• Health Policy and Management
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• v.33 no.4
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• pp.379-388
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• 2023

This study aims to propose the implementation of innovative payment models in Korea in order to promote the financial sustainability of the national health insurance system by reviewing the current status of the payment system in Korea and examining other countries' experiences with various innovative payment models. Korea primarily uses a fee-for-service payment system and additionally uses various payment systems such as case payment, per diem, and pay-for-performance. However, each payment system has its limitations. Many OECD (Organization for Economic Cooperation and Development) countries have pointed out the limitations of existing payment systems and have been attempting various innovative payment models (e.g., add-on payment, bundled payment, and population-based payment). Therefore, it is essential for Korea to consider innovative payment models, such as a mixed payment model that takes into account the strengths and weaknesses of each payment system, and to design and pilot these models. This process requires stakeholders to work together to build a social consensus on the implementation of innovative payment systems and to refine legal and systematic aspects, develop an integrated health information system, and establish dedicated organizations and committees. These efforts towards innovative payment models will contribute to developing a sustainable health insurance system that ensures the public's health and well-being in Korea.

• Journal of Information Technology Services
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• v.22 no.5
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• pp.17-30
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• 2023

A company's sustainable growth is a very important goal, and for this purpose, the company's business model is changing into a convergence of products and services. The purpose of PS-Offering is to maintain a long-term relationship with customers, and customer experience management is necessary for this. This study presents a service design methodology that can support customer experience management of the PS-Offering business model. The experience management portfolio analysis methodology consists of four steps: 1. Deriving service encounter through customer journey maps; 2. Identify the service structure of each service encounter in three forms (FFC, FSC, FSE). 3. Analyze the customer's emotional variables, that is, customer experience, at each service encounter, Finally, 4. After plotting the level of customer experience at the service encounter, the analysis is conducted with a customer experience management portfolio that seeks future strategic plans for this. The methodology presented in this study will help in the service design of the service encounter unit centered on customer experience. And it will improve the financial performance of the company by raising the service level of the business model.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.704-719
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• 2024

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies. Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance. In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more. As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices. Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm. For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications. In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers. The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection. As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• Asia pacific journal of information systems
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• v.31 no.1
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• pp.76-101
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• 2021

Digitalization and digital transformations have metamorphized the face of Financial Inclusion globally, more so, in cash obsessed economies like India. The purpose of our study is to empirically analyze the users' intention to adopt digital payment systems, post Demonetisation, during the COVID-19 pandemic in India. The conceptual framework for the study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) adoption model with added operationalized constructs of Perceived Risk and Stickiness to use Cash. A total of 326 respondents were surveyed using a pre-tested questionnaire during the Nationwide Lockdown 3.0 in India. These responses were analyzed using Partial Least Squares - Structural Equation Modelling (PLS-SEM) technique. The findings of the study revealed that performance expectancy and facilitating conditions directly influence the intention of individuals to use digital payment systems, whereas the effect of perceived ease of use on digital payment systems is mediated through the attitude towards the digital payment systems during COVID-19 pandemic situation. Implications of the proposed adoption model are discussed. This will enable the other developing economies to formulate a digital ecosystem, that is here to stay even after the pandemic.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_1
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• pp.257-268
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• 2024

Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. This paper utilizes various supervised classification machine learning models, including Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes. Moreover, based on our finding the KNN and Decision models achieved the highest accuracy rates at 70%, surpassing other models. The Random Forest model followed closely with an accuracy score of 62%. Among the evaluated models, the Naive Bayes model demonstrated the lowest accuracy at 56%. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.

• Management & Information Systems Review
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• v.30 no.2
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• pp.237-255
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• 2011

This empirical study is focused on practical application of Range-Based Volatility which is estimated by opening, high, low, closing price of overall asset. Especially proper forecasting period is what I want to know. There is four useful Range-Based Volatility(RV) such as Parkinson(1980; PK), Garman and Klass(1980; GK) Rogers and Satchell(1991; RS), Yang and Zhang(2008; YZ). So, four RV of KOPSI 200 index during 2000.5.22-2009.9.18 was used for empirical test. The emprirical result as follows. First, the best RV which shows the best forecasting performance is PK volatility among PK, GK, RS, YZ volatility. According to estimating period forcasting performance of RV shows delicate difference. PK has better performance in the period with financial crisis of sub-prime mortgage loan. if not, RS is better. Second, almost result shows better performance on forecasting volatility without sub-prime mortgage loan period. so we can say that forecasting performance is lower when historical volatiltiy is comparatively high. Finally, I find that longer estimating period in AR(1) and MA(1) model can reduce forecasting error. More interesting point is that the result shows rapid decrease form 60 days to 90 days and there is no more after 90 days. So, if we forecast the volatility using Range-Based volaility it is better to estimate with 90 trading period or over 90 days.

• Management & Information Systems Review
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• v.29 no.4
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• pp.333-357
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• 2010

Purposes of this paper are to investigate whether managers' compensation is sticky as accounting performance(ROA) vary or not and explore further what factors influence the managers' compensation stickiness. To empirically study the stickiness of managers' compensation, we used the financial data from manufacturing firms lised in the Korea Stock Exchange(1,000 firm-year data for 4 years). The results are as follows : First, managers' compensation is sticky with respect to change in accounting performance. That is, the increase in managers' compensation as accounting performance increases is greater than the decrease in managers' compensation in respect to equivalent decrease in accounting performance. Second, the degree of managers' compensation stickiness increases when managers have influence and contribution to firm value. Specifically, the degree of stickiness is positively associated with investment opportunity set, intangible assets' value, uncertainty of firms' operating environment, complexity of organizational hierarchy. But firms' size reversely impacts on the degree of managers' compensation stickiness.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.2
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• pp.221-232
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• 2016

The impact of the recent small business start-up competition in the market, has become overheated. It is effected by early retirement of a generation of youth employment. This study is a study on the impact of SEMAS(Small Enterprise and Market Service) system operating of the funding system, education support programs, and consulting support system on the business performance of small business owners. It has surveyed 272 business owners, in Gyeongsangnam-province. The study includes specific support system for usage frequency and satisfaction and conducted from January 2013 to September 2015. In addition, it analyzes characteristic that motivation, business model, item, owner's experience, sales and demographic by small business owner. Analysis results, the management performance of small business that uses financial support system and consulting support system is shown to be high. But education support system is the opposite effect. As a result, the management performance is related to industry experience. Therefore education support system need to be reorganized to the support depends on the development stage. This study was conducted to help small business owners entered the start-up market and a decision-making person with a policy decision.