• 제목/요약/키워드: Obstacle Factors

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사업계획서의 구성요소와 기업성과와의 관계에 관한 연구 (A Study on the Relationship between Business Plan Components and Corporate Performance)

  • 고인곤;이상석;김대호
    • 한국벤처창업학회:학술대회논문집
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    • 한국벤처창업학회 2006년 춘계학술발표회
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    • pp.45-75
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    • 2006
  • 지금까지 이론적, 개념적으로만 제시되었던 사업계획서의 구성요소들을 문헌 연구와 예비조사를 통하여 선정하였다. 본 연구에서는 요인분석을 통하여 이들 구성요소들을 생산관련 요인, 실행관련 요인, 운영방향 관련 요인, 제품/서비스 관련 요인, 관련 요인의 5개 요인으로 축소하였다. 한편 기업 성과를 어떤 항목으로 측정할 것인가의 문제도 중요한 이슈가 된다. 평가 항목에 따라서 기업의 활동이 달라지기 때문이다. 본 연구에서는 기업성과를 효과성, 적응성 및 효율성으로 구분하여 각각에 미치는 사업계획서 구성요소들의 영향을 살펴보았다. 연구 결과 사업계획서 구성요소들은 효과성, 적응성 기업 성과에 정(+)의 영향을 미치는 것을 발견하였다. 특히 효과성에 있어서는 다른 성과지표에 비해서 상대적으로 회귀모형의 설명력이 양호하게 나타났다. 그러나 효율성 기업 성과에는 영향력의 방향이 일치하지 않았다. 전반적인 모형의 설명력을 나타내는 $R^2$$0.132^{\sim}0.213$으로서 회귀모형의 설명력이 낮게 나타나고 있는 것은 계획수립뿐만 아니라 실행에 있어서 그 중요성을 보여주는 것으로 해석할 수 있다. 즉, 기업성과는 훌륭한 계획 하에서 훌륭한 실행이 뒷받침되어야 양호하게 산출될 수 있는 것이다. 본 연구는 그동안 개념적인 수준에 머무르고 있던 사업계획서의 구성요소와 기업성과와의 관계에 대한 실증적 연구라는 점에서 의의가 있다.

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일개 부산지역 3차 병원에서 관찰한 다제내성 결핵의 실태, 2005~2009 (The Current Status of Multidrug-Resistant Tuberculosis in One Tertiary Hospital in Busan, 2005~2009)

  • 윤늘봄;이성우;박수민;정일환;박소영;한송이;이유림;정진규;김준모;김수영;엄수정;이수걸;손춘희;홍영희;이기남;노미숙;김경희
    • Tuberculosis and Respiratory Diseases
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    • 제71권2호
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    • pp.120-125
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    • 2011
  • Background: Although the prevalence of pulmonary tuberculosis has progressively decreased all over the world, drug-resistant tuberculosis is major obstacle in treating tuberculosis. This study was performed to examine the current prevalence and risk factors of drug resistant tuberculosis in a single tertiary hospital in Busan, Korea. Methods: We enrolled 367 patients with active pulmonary tuberculosis on a retrospective basis who had undergone mycobacterium culture and drug sensitivity tests between January 2005 and December 2009. We analyzed all clinical and radiographic parameters to find predictors related to drug resistant tuberculosis. Results: At least one incident of drug resistance was found in 75 (20.4%) patients. Isoniazid (18.8%) was the most frequent resistant drug, followed by rifampin (10.9%), ethambutol (7.1%), streptomycin (4.9%), and fluoroquinolone (2.7%). Resistance to second-line drugs was found in 37 (10.1%) patients. Multidrug resistance and extensively drug resistance was evident in 39 (10.6%) and 4 (1.1%) patients, respectively. Using multiple logistic regression analysis, history of previous treatment including relapse (odd ratio [OR], 11.3; 95% confidence interval [CI], 4.92~26.08; p<0.01), treatment failure (OR, 24.1; 95% CI, 5.65~102.79; p<0.01) and an age of below 46 years-old (OR, 3.8; 95% CI, 1.62~8.65; p<0.01) were found to be independent predictors of multidrug resistant tuberculosis. Conclusion: We found that the prevalence of drug resistant tuberculosis was considerably high. A careful consideration for possible drug resistant tuberculosis is warranted in patients with a history of previous treatment or for younger patients.

인공지능의 사회적 수용도에 따른 키워드 검색량 기반 주가예측모형 비교연구 (Comparison of Models for Stock Price Prediction Based on Keyword Search Volume According to the Social Acceptance of Artificial Intelligence)

  • 조유정;손권상;권오병
    • 지능정보연구
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    • 제27권1호
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    • pp.103-128
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    • 2021
  • 최근 주식의 수익률과 거래량을 설명하는 주요 요인으로서 투자자의 관심도와 주식 관련 정보 전파의 영향력이 부각되고 있다. 또한 인공지능과 같은 혁신 신기술을 개발보급하거나 활용하려는 기업의 경우 거시환경 및 시장 불확실성 때문에 기업의 미래 주식 수익률과 주식 변동성을 예측하기 어렵다는 문제를 가지고 있다. 이는 인공지능 활성화의 장애요인으로 인식되고 있다. 따라서 본 연구의 목적은 인공지능 관련 기술 키워드의 인터넷 검색량을 투자자의 관심 척도로 사용하여, 기업의 주가 변동성을 예측하는 기계학습 모형을 제안하는 것이다. 이를 위해 심층신경망 LSTM(Long Short-Term Memory)과 벡터자기회귀(Vector Autoregression)를 통해 주식시장을 예측하고, 기술의 사회적 수용 단계에 따라 키워드 검색량을 활용한 주가예측 성능 비교를 통해 기업의 투자수익 예측이나 투자자들의 투자전략 의사결정을 지원하는 주가 예측 모형을 구축하였다. 또한 인공지능 기술의 세부 하위 기술에 대한 분석도 실시하여 기술 수용 단계에 따른 세부 기술 키워드 검색량의 변화를 살펴보고 세부기술에 대한 관심도가 주식시장 예측에 미치는 영향을 살펴보았다. 이를 위해 본 연구에서는 인공지능, 딥러닝, 머신러닝 키워드를 선정하여, 2015년 1월 1일부터 2019년 12월 31일까지 5년간의 인터넷 주별 검색량 데이터와 코스닥 상장 기업의 주가 및 거래량 데이터를 수집하여 분석에 활용하였다. 분석 결과 인공지능 기술에 대한 키워드 검색량은 사회적 수용 단계가 진행될수록 증가하는 것으로 나타났고, 기술 키워드를 기반으로 주가예측을 하였을 경우 인식(Awareness)단계에서 가장 높은 정확도를 보였으며, 키워드별로 가장 좋은 예측 성능을 보이는 수용 단계가 다르게 나타남을 확인하였다. 따라서 기술 키워드를 활용한 주가 예측 모델 구축을 위해서는 해당 기술의 하위 기술 분류를 고려할 필요가 있다. 본 연구의 결과는 혁신기술을 기반으로 기업의 투자수익률을 예측하기 위해서는 기술에 대한 대중의 관심이 급증하는 인식 단계를 포착하는 것이 중요하다는 점을 시사한다. 또한 최근 금융권에서 선보이고 있는 빅데이터 기반 로보어드바이저(Robo-advisor) 등 투자 의사 결정 지원 시스템 개발 시 기술의 사회적 수용도를 세분화하여 키워드 검색량 변화를 통해 예측 모델의 정확도를 개선할 수 있다는 점을 시사하고 있다.

폭소노미 사이트를 위한 랭킹 프레임워크 설계: 시맨틱 그래프기반 접근 (A Folksonomy Ranking Framework: A Semantic Graph-based Approach)

  • 박현정;노상규
    • Asia pacific journal of information systems
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    • 제21권2호
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    • pp.89-116
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    • 2011
  • In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.