• Journal of Information Technology Applications and Management
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• 제14권1호
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• pp.117-135
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• 2007

Globalization changes in market structures and consumer needs, as well as technology innovations force organizations to adopt new structures and collaborative networks to cope with rapidly changing environments. These Collaborative Networks are based on the Idea of virtual enterprise. A virtual enterprise(VE) is a temporary alliance of globally distributed independent enterprises that share core competencies and computer networks. This paper presents a proposal for a methodology to measure a key factor of success and risk First of all, we chose thirty experts' defines on virtual enterprise, fifteen are academic specialist and other fifteen are from industry. For this study we define twenty two factors determining VE's success and seventeen factors determining VE's risk using by Delphi method. And we built the influence model on virtual enterprise. A research model was established according to preceeding research and consensus on experts then the revised model of key factors on virtual enterprise. This survey was based on the Analytical Hierarchy Process (AHP). AHP is an approach to decision making that involves structuring multiple choice criteria into a hierarchy, the assessing the relative important of these criteria, comparing alternative for each criteria, and determining an overall ranking of the alternatives. A model was constructed as 3 level hierarchy. The hierarches are organizational, strategic, technical criteria. for success model on VE has 22 factors and 17 factors for risk model. They are selected by all 30 experts. 14 copies among 30 copies distributed to carry out on the Analytical Hierarchy Process (AHP). Consistency ratio confirm high validity and reliability of instrument and support theoretical model. The results of this study are summarized as follows. (1) This study presented success on VE influenced strategic criteria, and essential factor is Creating a value. Risk on VE influenced strategic criteria, and essential factor is Outcome/Distribution. (2) Its enable that ranking the criteria influence on VE. These are supported VE management and using guideline of VE.

• Asian Pacific Journal of Cancer Prevention
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• 제15권21호
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• pp.9535-9541
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• 2014

Background: Identifying risk factors of breast cancer is a key point for preventive strategies to reduce the incidence. The aim of current study was to determine most important risk factors for breast cancer in the Eastern Mediterranean Region (EMR) using a systematic review. Materials and Methods: PubMed, Scopus, Web of Science till August 24, 2012 and the reference lists of all included studies were searched. Analytic studies which had reported odds ratios (OR), relative risk (RR) or required data to calculate them were included. A total of 343 studies were critically appraised and finally 30 studies were meta-analyzed. Heterogeneity between the studies was assessed by $I^2$ and Cochran's Q. Egger's test was used to assess publication bias. Results: Twenty five casecontrol studies, one nested case-control and four cohort studies were included. The largest ORs were obtained for history of no live birth (2.25; 95%CI: 1.58-3.18), body mass index (BMI) more than 30 (2.21; 95%CI: 1.71-2.36), age at first pregnancy more than 30 years old (1.52; 95%CI: 1.30-1.77) and meat consumption more than three times per week (1.39; 95%CI: 1.03-1.87). The other important predictors were higher education and smoking as risk factors, physical activity and ovulatory stimulating medication as protective factors. Conclusions: The most important predictors of breast cancer in EMR were history of no live birth, BMI more than 30, age at first pregnancy more than 30 years old, physical inactivity and smoking. Almost all these risk factors are consistent with known risk factors for this cancer in other parts of the world.

• 한국IT서비스학회지
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• 제7권1호
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• pp.151-166
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• 2008

The Outsourcing Industry has grown at a swift pace and evolved over time, and the Global IT Outsourcing market has shown signs of a steady growth. In the Korean financial industry, however, IT Outsourcing is not active as that of the advanced countries and there is lack of literature to understand the characteristics of IT Outsourcing in the financial industry. This paper, therefore, analyze the outcomes surveying 40 financial companies in Korea to investigate how IT Outsourcing risk factors affects IT Outsourcing intention. Based on our literature reviews based on number of key articles, journals, and the focus group interviews, IT Outsourcing risk factors are proposed into four different domains: Transaction, Client, Vendor and Environmental perspective. It found that two risk factors (Client and Environmental perspective) are closely related to the IT Outsourcing intention of the Korean financial industry. Finally, this paper concludes that concrete SLAs (Service Level Agreements) of the clients and support of government agencies are important to mitigate the IT Outsourcing risks.

• 산업경영시스템학회지
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• 제23권56호
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• pp.65-85
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• 2000

Industrial accidents have been consistently increased in terms of medical costs, lost work days, and incidence rates every year in Korea. Since the infrastructure of the industry changed shifts rapidly from 1980s in the developing countries such as South Korea, the nature and magnitude of the industrial accidents have also undergone a major shift. The situation is especially severe in small-to-medium sized industry(SMI). This article reports the development of a systematic evaluation system of risk factors specifically for the SMIs. The new approach introduced by this article is geared to the systematic identification and evaluation of the injuries from power press machines using the Analytic Hierarchy Process with the key evaluation data generated and evaluated by the employees on site. A total of 21 companies was studied and surveyed using the hierarchical structures of the cause-effect relationship of the mechanical injuries and their countermeasures. For the relative weighting of each risk factor, separate questionnaire survey was conducted for the selected workers from each company who had worked for more than 10 years in press work. Most participants (48 out of 62) replied that human attributes were the most significant factors for mechanical injuries fellowed by administration, machine, and work environment factors. The result also showed that the self-motivated risk assessment and safety enhancement activities would be an effective and efficient way of managing the risk factors in the SMIs.

• 디지털콘텐츠학회 논문지
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• 제10권2호
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• pp.341-348
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• 2009

본 연구는 국내기업들의 IT아웃소싱 의사결정과정에서 수행의사에 영향을 미치는 위험요인을 고찰하여, IT아웃소싱의 제약요소로 작용하고 있는 위험요인을 실증적으로 제시하기 위해 수행되었다. 문헌 연구 및 전문가 인터뷰 등을 통해서 IT아웃소싱에 대한 위험요인은 거래관련 위험, 고객 위험 및 공급업체 위험으로 분류되었으며. 총 79개사로부터 수집된 총 141개 설문을 통해 실증분석한 결과 고객 위험과 공급업체 위험요인이 IT아웃소싱 수행의사에 영향을 주는 것으로 밝혀졌다. 기업에서는 고유 업무의 연속성 확보 등 고객 자체의 위험요인들과 공급업체의 기술력 확보와 인력의 전문성 등 공급업체 위험 요인들에 대한 대응을 통해서 IT아웃소싱의 적용 및 확대가 가능한 것으로 분석되었다.

• 대한토목학회논문집
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• 제42권2호
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• pp.239-248
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• 2022

최근 건설환경의 변화에 따른 건설프로젝트에서 리스크는 현저하게 증가하고 있다. 리스크를 기회로 인식하는 적극적인 리스크관리가 요구된다. 본 연구의 목적은 Kano 모델, Timko CSC (Customer Satisfaction Coefficient), ASC (Average Satisfaction Coefficient)를 활용한 비교 분석을 통해 중요도 결정 방법의 리스크관리 모델을 제안하는 것이다. 선행연구를 바탕으로 델파이기법을 활용하여 실무자 면담을 통해 Kano 모델을 수정한 설문지를 통해 리스크관리 요인 결정의 타당성을 검토한다. 이를 통해 국내 건설프로젝트 실무자가 인식하는 핵심 리스크관리 요인을 선정하여 적합한 리스크관리 모델을 제시한다. 연구 결과, 건설프로젝트 리스크관리 검증을 위해 개발된 Kano 모델은 실무자의 리스크관리를 검증하는데 유효한 것으로 평가되었다. 본 연구에서 제시한 Kano 모델이 건설프로젝트 리스크관리의 중요도를 검증하는 데에 적극적으로 활용되기를 기대한다.

• 지식경영연구
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• 제9권1호
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• pp.1-21
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• 2008

The present study investigatesthe reliability and validity of the innovation leadership scale. Originally the seven factors with 25 items were developed through literature review. With a sample of 177 employees in a large company, the results of factor analyses showed that the five-factor model with 14 items had a better fit to the data than the seven-factor model. These five factors were innovativeness pursue, problem solving, vision presentation, risk-taking, and showing initiative. All of these factors were significantly related to various criteria such as identification with the group, attachment to the group, organizational commitment, and supervisor satisfaction, confirming criterion-related validity of the scale. Results of multiple regression analyses showedthat risk taking and showing initiative were more important predictors in explaining criteria. Finally, implications and limitations were discussed. The findings suggest that the key factors of innovation leadership were initiative and risk-taking.

• Annals of Pediatric Endocrinology and Metabolism
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• 제23권4호
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• pp.182-195
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• 2018

Increasing prevalence of childhood obesity poses threats to the global health burden. Because this rising prevalence cannot be fully explained by traditional risk factors such as unhealthy diet and physical inactivity, early-life exposure to endocrine disrupting chemicals (EDCs) is recognized as emerging novel risk factors for childhood obesity. EDCs can disrupt the hormone-mediated metabolic pathways, affect children's growth and mediate the development of childhood obesity. Many organic pollutants are recently classified to be EDCs. In this review, we summarized the epidemiological and laboratory evidence related to EDCs and childhood obesity, and discussed the possible mechanisms underpinning childhood obesity and early-life exposure to non-persistent organic pollutants (phthalates, bisphenol A, triclosan) and persistent organic pollutants (dichlorodip henyltrichloroethane, polychlorinated biphenyls, polybrominated diphenyl ethers, per- and polyfluoroalkyl substances). Understanding the relationship between EDCs and childhood obesity helps to raise public awareness and formulate public health policy to protect the youth from exposure to the harmful effects of EDCs.

• Journal of Microbiology and Biotechnology
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• 제27권3호
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• pp.417-428
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• 2017

Diseases caused by foodborne or waterborne pathogens are emerging. Many pathogens can enter into the viable but nonculturable (VBNC) state, which is a survival strategy when exposed to harsh environmental stresses. Pathogens in the VBNC state have the ability to evade conventional microbiological detection methods, posing a significant and potential health risk. Therefore, controlling VBNC bacteria in food processing and the environment is of great importance. As the typical one of the gram-negatives, Escherichia coli (E. coli) is a widespread foodborne and waterborne pathogenic bacterium and is able to enter into a VBNC state in extreme conditions (similar to the other gram-negative bacteria), including inducing factors and resuscitation stimulus. VBNC E. coli has the ability to recover both culturability and pathogenicity, which may bring potential health risk. This review describes the concrete factors (nonthermal treatment, chemical agents, and environmental factors) that induce E. coli into the VBNC state, the condition or stimulus required for resuscitation of VBNC E. coli, and the methods for detecting VBNC E. coli. Furthermore, the mechanism of genes and proteins involved in the VBNC E. coli is also discussed in this review.

• 대한통합의학회지
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• 제12권2호
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.