• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2006.05a
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• pp.371-376
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• 2006

서로 갈등적인 관계에 있는 다중 기준 하에서 다양한 국가 전력 시스템을 정량적으로 비교하는 데에는 전력 시스템의 비교 지표가 필요하다. 이러한 비교 지표를 산출하기 위하여 해석적 망형 과정(Analytic Network Process; ANP) 모델 가운데 상호 의존도 수중이 낮은 되먹임 모델 및 상호 의존도가 없는 독립성 모델이 개발되었다. 이러한 ANP 모델은 구성요소로 교점들(nodes)과 상호작용 관계를 표현하는 가지들(arcs)을 포함하고 있다. 의사결정 목표 교점에는 세 가지 유형의 리스크 성향이 포함되었다. 이러한 리스크 성향은 원자력 발전소 같은 위험 설비에 대한 전문가(그룹)의 리스크 성향이며, 더 구체적으로 말하면, 리스크 감수 성향, 리스크 혐오 성향, 리스크 중립 성향 등이다. 여기서 수행된 연구의 주요 목적은 ANP 모델을 구성하는 교점들 가운데 하나인 평가 기준 교점에서의 변화가 전력 시스템의 비교 지표에 미치는 영향을 해석하려는 것이다. 이러한 모델 변이가 비교 지표에 미치는 영향을 알아보기 위한 사례 연구에서 각 발전원의 특성을 비교할 평가 기준은 기준 사례와 비교 사례 각각에 대하여 상이하게 선정되었다: 기준 사례의 경우에는 보건성을 대표하는 생명 단축 [yr/TWh], 환경성을 대표하는 지구 온난화 [$g\;CO_{2}-eq./kWh$], 사회성을 대표하는 지속가능 정도[-], 경제성을 대표하는 발전 단가 [\/kWh] 등이 선정되었다; 반면에, 비교 사례의 경우에서는 보건성을 대표하는 사고 사망 [death/GWh]만이 다르고 나머지는 동일하게 선정되었다. 이러한 보건성을 대표하는 생명 단축 또는 사고 사망의 선정은 다음과 같은 비교 지표에 영향을 미친다는 것이 발견되었다: (1) 되먹임 모델에서는 성향 가중치 및 기준 등급에 영향을 준다. (2) 되먹임 모델과 독립성 모델에서는 시스템 등급에 영향을 준다. 향후에는 더욱 더 다양한 상호의존 모델들이 정량화될 필요성이 있다고 본다.

• 한국IT서비스학회:학술대회논문집
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• 2007.11a
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• pp.3-8
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• 2007

지난 2004년 6월 24일에 공표된 신BIS 자기자본규제제도에 의거 회원국 은행들에 대한 체계적인 위험관리가 강화되는 상황에서 우리나라도 2009년부터 모든 은행이 신BIS기준을 도입및 적용할 예정이다. 신BIS협약은 운영리스크(부적절한 내부절차, 직원, 시스템)에 대해서도 리스크를 측정하여 은행의 소요 자기자본에 반영토록 하고 있다. 따라서 본 연구자는 은행의 조직환경 변수들이 조직유효성에 영향을 미치고 이들이 또한 내부통제절차의 상시 유효성에 영향을 미치는 지 알아보기 위하여 국내 소재 은행원들을 대상으로 실증분석함으로써 은행들이 효율적인 내부통제를 통해 금융사고를 미연에 방지하고 은행자산의 건전성에 기여하기 위한 지침을 제시하는 관점에서 연구를 진행하였다. 연구결과, 연구모형에서 예상한 바와 같이 조직환경이 내부통제유효성에 직접적으로 영향을 미치지만 조직몰입도나 집단응집력과 같은 조직유효성을 매개로 하여 내부통제유효성에 더욱더 영향을 미치고 있었다. 이러한 점에 비추어 본 연구의 결과를 토대로 은행내 중간관리자의 성향에 따라 개별조직 구성원들의 행동양식이나 가치관에 영향을 미칠 수 있는 현실을 감안하여 장기적인 안목에서 양질의 리더십, 조직문화 그리고 분위기 등 조직환경을 리드해 나갈 수 있도록 관리자들에 대한 교육훈련 및 소양교육을 지속적으로 펼쳐나가는 것이 중요하다고 본다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.75-82
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• 2007

이 논문의 공급업체의 불확실성하에서의 신문팔이 문제를 다루고 있다. 즉 공급업체의 공급량이 소매업체가 주문한 양을 충족하기 못하는 상황을 고려한 것이다. 여기서 우리는 기대 이익을 최대화하는 최적의 주문량을 구하였고, 기존의 연구와 달리 소매업체의 위험 성향을 고려하기 위해 하향 라스크에 대한 제약 조건을 추가하여, 위험 성향이 높을수록 더 많은 주문량을 사용한다는 결과를 사례 연구를 통해 확인할 수 있었다.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.287-308
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• 2022

The explosive growth of cryptocurrency, led by Bitcoin has emerged as a major issue in the financial market recently. As a result, interest in cryptocurrency investment is increasing, but the market opens 24 hours and 365 days a year, price volatility, and exponentially increasing number of cryptocurrencies are provided as risks to cryptocurrency investors. For that reasons, It is raising the need for research to reduct investors' risks by dividing cryptocurrency which is not suitable for recommendation. Unlike the previous studies of maximizing returns by simply predicting the future of cryptocurrency prices or constructing cryptocurrency portfolios by focusing on returns, this paper reflects the tendencies of investors and presents an appropriate recommendation method with interpretation that can reduct investors' risks by selecting suitable Altcoins which are recommended using Apriori algorithm, one of the machine learning techniques, but based on the similarity and association rules of Bitocoin.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.29-56
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• 2018

The purpose of this research is to examine whether investment portfolio composition affects the technological performance of corporate venture capital (CVC). The stages of investment are categorized from "start-up/seed", "early", and "expansion", to "later" stage. We posit and test that the investment stage composition in a portfolio is highly correlated with the growth potential and downside risk of the portfolio, which in turn influences an investor's innovation performance. To test this hypothesis, we used negative binomial panel regression with 21 years of deal data from 70 cases of CVC. The results show that there is an inverted U shaped relationship between investment portfolio composition and technological performance. This means that the more seed or early stage investment within the investment portfolio, the higher the innovation performance; however, if the amount of seed or early stage investment is over a certain level, the performance decreases. Further, this study finds that the external partners of a venture negatively moderate the inverted U shaped relationship between portfolio composition and innovation performance. We believe that corporate planners, venture capitalists, and policy makers will be helped by these results showing that companies can maximize their investment performance by considering the investment stage and progress of investments.

• Communications for Statistical Applications and Methods
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• v.17 no.5
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• pp.653-666
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• 2010

Under the retirement pension plan enforced since December 2005, retirees can just choose the payout strategy either of a lump sum allowance or of an annuity in receiving the retirement benefit. Therefore, it is imperative to review and introduce the program withdrawal system enforced by countries with mature pension plan, and complement the limitations of the current payout strategy in the future. In this study, the appropriateness of each of the payout strategies related to the program withdrawal system is examined in terms of shortfall risk and bequest fund per each risk propensity through the expected utility model that reflects the age of the retiree.

• Journal of Radiation Protection and Research
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• v.33 no.4
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• pp.127-134
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• 2008

Potential economic impact of a hypothetical severe accident at a nuclear power plant(Uljin units 3/4) was estimated by applying the Delphi method, which is based on the expert judgements and opinions, in the process of quantifying uncertain factors. For the purpose of this study, it is assumed that the radioactive plume directs the inland direction. Since the economic risk can be divided into direct costs and indirect effects and more uncertainties are involved in the latter, the direct costs were estimated first and the indirect effects were then estimated by applying a weighting factor to the direct cost. The Delphi method however subjects to risk of distortion or discrimination of variables because of the human behavior pattern. A mathematical approach based on the Bayesian inferences was employed for data processing to improve the Delphi results. For this task, a model for data processing was developed. One-dimensional Monte Carlo Analysis was applied to get a distribution of values of the weighting factor. The mean and median values of the weighting factor for the indirect effects appeared to be 2.59 and 2.08, respectively. These values are higher than the value suggested by OECD/NEA, 1.25. Some factors such as small territory and public attitude sensitive to radiation could affect the judgement of panel. Then the parameters of the model for estimating the direct costs were classified as U- and V-types, and two-dimensional Monte Carlo analysis was applied to quantify the overall economic risk. The resulting median of the overall economic risk was about 3.9% of the gross domestic products(GDP) of Korea in 2006. When the cost of electricity loss, the highest direct cost, was not taken into account, the overall economic risk was reduced to 2.2% of GDP. This assessment can be used as a reference for justifying the radiological emergency planning and preparedness.

• Information Systems Review
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• v.12 no.3
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• pp.49-73
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• 2010

The lack of customer satisfaction and trust remains a key barrier to electronic commerce. From the standpoint of online merchants, it is critical to build consumer trust by lessening sources of apprehensions and uneasiness associated with online transactions. This paper explores the relationships between customer satisfaction and intermediary's trustworthiness factors in B2C e-marketplaces. It also aims at examining the effects of consumer characteristics, including propensity to trust and Internet shopping self-efficacy, upon trust and purchase intentions. To meet the research objectives, an empirical study has been conducted by surveying 223 active e-marketplace buyers in Korea. The findings of the present research indicate that customer satisfaction positively affects all the three attributes of trustworthiness (i.e., competence, benevolence, and integrity), and more specifically it has a quite strong association with benevolence. In addition, propensity to trust has no significant influence on trust or purchasing intentions, and only affects benevolence and integrity with no direct effect on competence. Finally, Internet shopping self-efficacy was found to affect both trust and purchasing intentions, suggesting that e-marketplaces seek an online strategy designed to strengthen loyalty for customers with high self-efficacy, while they use a strategy to improve the usability and usefulness of their website to attract customers with low self-efficacy. The paper concludes with implications and directions for future research.

• The Journal of Bigdata
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• v.7 no.2
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• pp.153-172
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• 2022

Online shopping has transformed and rapidly grown the entire market at the forefront of wholesale and retail services as an effective solution to issues such as digital transformation and social distancing policy (COVID-19 pandemic). Small business owners, who form the majority at the center of the online shopping industry, are constantly collecting policy changes and market trend information to overcome these problems and use them for marketing and other sales activities in order to overcome these problems and continue to grow. Objective and refined information that is more closely related to the business is also needed. Therefore, in this paper, through the collection and analysis of big data information, which is the core technology of digital transformation, key variables are set in product classification, sales trends, consumer preferences, and review information of online shopping malls, and a method of using them for competitor comparison analysis and business sustainability evaluation has been prepared and we would like to propose it as a service. If small and medium-sized businesses can benchmark competitors or excellent businesses based on big data and identify market trends and consumer tendencies, they will clearly recognize their level and position in business and voluntarily strive to secure higher competitiveness. In addition, if the sustainable growth of the online shopping mall operator can be confirmed as an indicator, more efficient policy establishment and risk management can be expected because it has an improved measurement method.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.