• Journal of Korean Institute of Industrial Engineers
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• v.27 no.2
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• pp.181-189
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• 2001

The proliferation of the Internet and related technologies and applications has led to a new form of market place known as the electronic store. In this paper, we study competition between two shopping channels, an electronic store and traditional retailers. Based on the circular spatial market model, we derive the Nash and Stackelberg equilibria as a function of the efficiency of the electronic store. The result shows that the Stackelberg equilibrium is always superior to the Nash equilibrium for both channels. It is also shown that, in some cases, the electronic store has incentive to decrease its efficiency to gain more profit.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.655-671
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• 2021

This study analyzes the premium risk of insurers in Korea, which is expected to experience the fastest population aging in the world. Based on the Lee-Carter model, we generate 10,000 scenarios for the number of future survivors in the group of the 10,000 policyholders of life annuity. According to the result of simulation study, the probability of insurer's loss for both groups of male and female policyholders is very low. This result indicates that the premium risk of insurers is not as great as the insurer's concern. This study also suggests introduction of the longevity swap as an alternative to manage the premium risk for the insurer which sells life annuity products. The longevity swap allows insurers to hedge premium risk and reduce capital burden due to the premium risk inherent in life annuity. This study also shows through examples that the counterparty of swap deal may have excess profit in exchange for taking premium risk.

• KDI Journal of Economic Policy
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• v.40 no.4
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• pp.47-63
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• 2018

Government R&D grants for SMEs have risen to three trillion Korean won a year, placing Korea second among OECD nations. Indeed, analysis results have revealed that government support has not only expanded corporate R&D investment and the registration of intellectual property rights but has also increased investment in tangible and human assets and marketing. However, value added, sales and operating profit have lacked improvement owing to an ineffective recipient selection system that relies solely on qualitative assessments by technology experts. Nevertheless, if a predictive model is properly applied to the system, the causal effect on value added could increase by more than two fold. Accordingly, it is important to focus on economic performance rather than technical achievements to develop such a model.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.27-34
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• 2020

This investigation was carried out in Liaoning, Shandong, and Shaanxi where classified most of their geological organizations into profit organizations, which means they must implement enterprise-oriented reform immediately. The valid 311 questionnaires were collected and used to verify the serial mediating model by AMOS 23.0. Results verified the crucial mediating effects of structural and psychological empowerment between external-focused organizational culture and openness for change. Adhocracy culture positively affects employees' openness for change through three indirect paths, including one mediator and two mediators. Market culture impacts individuals' openness for change through two indirect paths, one is through structural empowerment and another one is through two mediators. The findings provide managers in geological organizations with an empowering management practice model which could promote geological industry reform effectively.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.239-248
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• 2007

In the highly competitive construction industry, a slight inaccuracy of estimation can easily cause the loss of a project. Erroneous experience-based cost estimates or allocations of on-site supervisory manpower often offset the profit gained from the project and may jeopardize the management processes. To counter these types of problems, we develop a model using mathematical analysis and case-based reasoning to automate the allocation of on-site supervisory manpower and estimate construction site costs. The method is founded upon laborious data collection processes and analysis by matching statistical assumptions, and is applicable to construction projects. In the modeling the costs and allocation of on-site supervisory manpower are quantified for both owners and contractors before initiating or bidding on the projects. The findings confirm that the degree of variation of the model predictions has an accuracy rate at 88.47%. Single-site construction projects can be accurately predicted and the assignment of supervisory manpower feasibly automated.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.463-467
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• 2015

Mega-shock means a sporadic event such as the earning shock, which occurred by sudden market changes, and it can cause serious problems of profit loss of international construction projects. Therefore, the early response and prevention by analyzing and predicting the Mega-shock is critical for successful project delivery. This research is preliminary study to develop a prediction model that supports market condition analysis and Mega-shock forecasting. To avoid disadvantages of classic statistical approaches that assume the market factors are linear and independent and thus have limitations to explain complex interrelationship among a range of international market factors, the research team explored the Fractal Theory that can explain self-similarity and recursiveness of construction market changes. The research first found out correlation of the major market factors by statistically analyzing time-series data. The research then conducted a base of the Fractal analysis to distinguish features of fractal from data. The outcome will have potential to contribute to building up a foundation of the early shock warning system for the strategic international project management.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.181-187
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• 2013

The construction market condition is getting worse because of global constructions slow down, low profit, market contraction and so on. For these reason, most construction companies depend on public construction projects which possible to protect construction fee, known as progress payment, by laws. Despite this law, problems of progress payment are constantly occurring and it has been main factors that hinder the construction cost's cash-flow in construction project. To solve this problem, many researchers suggested various solutions but most of solutions were focused on specific target as owner, general contractor, and subcontractor. So, most of solutions were insufficient consider about interaction between contractors. Because of these reasons, it was hard to reflected policy. This research aimed to use system dynamics to develop the model for the application and payment based on the regulations and papers. Also, performed a developed model's verification based on progress payment regulation's basic objectives.

• Journal of Korean Academy of Nursing
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• v.23 no.1
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• pp.68-89
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• 1993

This study was conducted to develop a model of a fee schedule for nursing services.'Regardless of the demand for skilled and professional nursing service today, the Korean health insurance system does not furnish a chapter for the nursing service fee schedule. A nation-wide survey of hospital nursing service fee schedules was to provide practical and realistic data about how the variety of nursing services are being charged. From September 1990 to April 1991, data from the fee schedule used by twenty hospitals located in eight large cities which are designated large medical regions in the Korea Health Care and Patient Referral System were collected. Nursing services and the fees charged for them were analyzed. The nursing services were subjected to a secondary analysis with referrence to reports on “nursing services to be charged in Korea”. The total number of nursing services recommended by the literatures was 177 : finally 141 types of nursing services were selected by investigator as chargable nursing services. In addition, data on managerial characteristics of the hospitals were collected to discover influential variables for a nursing fee schedule model. Under the assumption that all the managerial characteristics of the hospitals influenced the fee schedule, the following model was tested : Fee of nursing services (C) = f(A₁, A₂, A₃, A₄, A/sub 5/, A/sub 6/, A/sub 7/, A/sub 8/,) When, A₁ = number of nurses A₂ = the first salary of a nurse educated in a four year A₃ = scale of nursing management division A₄ = location of the hospital A/sub 5/ = the type of hospital management (profit / non-profit) A/sub 6/ = number of hospital beds A/sub 7/ = years of hospital operation A/sub 8/ = number and kinds of clinical divisions The results showed that the model should be built as follows : C = f (A₁, A/sub 4/, A/sub 5/) Each nursing service was applied to the fee schedule with consideration for the professional level and time-taken to provide the services. Detailed fee schedules were presented in the related tables. Of the 141 kinds of nursing services, 24.8% were chargeble to the Korea Health Insurance, 32.6％ of the nursing services were being paid directly by the patienty. The rest of nursing services (42.6％) were not being charged to any source. It was recommened that the Korea Health Insurance Reimbursement system should add a classification system for nursing services that can be used in the national health care program. Further study is needed about how to include 32.6% of the nursing services now being paid for directly by the patients in the health insurance system.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.1-16
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• 2023

Due to the progress of the 4th industrial revolution and the COVID-19 pandemic, the subscription economy was rapidly expanding. In particular, the subscription economy was expected to expand further as the servicing of products(servitization) rapidly progresses. In this study, we tried to empirically analyze the factors that promote and hinder the spread of the subscription economy from the consumer's point of view. To this end, based on the Service Profit Chain (SPC) model, which identified mechanisms leading from quality to satisfaction, loyalty, and performance, a research model was established by combining the framework of the Value-based Adoption Model (VAM), which covers both benefit and sacrifice factors. Usefulness and convenience were derived as benefit factors, and perceived risks and perceived costs were derived as sacrifice factors. The effects of these factors on satisfaction and continuous use intention were analyzed. For empirical analysis, a survey was conducted targeting people who have experience in subscription economy, and 300 effective samples were analyzed. The analysis was performed as a structural equation model using AMOS 24. As a result of the empirical study, it was found that convenience had a significant positive (+) effect on satisfaction. Perceived risk and perceived cost were analyzed to have a negative (-) effect on satisfaction. On the other hand, usefulness was found to have no significant effect on satisfaction. The influences affecting satisfaction were in the order of perceived cost, convenience, and perceived risk. Satisfaction was found to have a significant positive (+) effect on continuous use intention. The results of this study were considered meaningful in that they broadened the horizons of research by combining existing validated models at the academic level and testing their validity, and found that perceived cost was still an important factor at the practical level.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.