• Title/Summary/Keyword: Obstacle Factors

Search Result 184, Processing Time 0.022 seconds

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

  • Koh, In-Kon;Lee, Sang-Seok;Kim, Dae-Ho
    • 한국벤처창업학회:학술대회논문집
    • /
    • 2006.04a
    • /
    • pp.45-75
    • /
    • 2006
  • How much influence does a business plan have on a corporate performance? Whilst previous studies and literatures all assert a strong correlation between the two, very few have actually conducted practical analyses to support that. This study takes an empirical approach in its analysis of Korea' s small and medium-sized enterprises (SME) with the view to finding an answer to the question. A business plan' s components, which have to date been suggested only in theory and in concept, have been selected through the study of literatures and preliminary examination. The selected components were then narrowed down into five factors of productivity, implementation, operational direction, product/service and customer accessibility by applying factor analysis. With which items to measure corporate performance is also an important question as results differ depending on which measurement items were used. For the purpose of this study, corporate performance was classified into effectiveness, adaptability and efficiency to measure how greatly each is influenced by the components of a business plan. Results show that effectiveness and adaptability have a positive (+) influence on corporate performance. The regression model seems to explain effectiveness particularly well. However, different directions of influences were showed in explain power of the research model were not high. And it can be interpreted that implementation of the plan is as important as the establishment of it. Thus a good corporate performance is to be had only under an excellent plan and following an excellent implementation. In most of the companies surveyed, business plans were established regularly led by the intense involvement of the CEO. Such plans were then used in internal operations, such as guiding operational direction and measuring corporate performance. Unlike general expectations, relatively few companies used them in financing from external sources such as banks or venture capitals. These findings are different from previous studies conducted in this field. Also, as market uncertainty was pointed out as the biggest obstacle to business planning. a manager must pay more attention to acquiring external information and knowledge so as to minimize it.

  • PDF

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

  • Yoon, Neul-Bom;Lee, Sung-Woo;Park, Su-Min;Jeong, Il-Hwan;Park, So-Young;Han, Song-Yee;Lee, Yu-Rim;Jung, Jin-Kyu;Kim, Joon-Mo;Kim, Su-Young;Um, Soo-Jung;Lee, Soo-Keol;Son, Choon-Hee;Hong, Young-Hee;Lee, Ki-Nam;Roh, Mee-Sook;Kim, Kyeong-Hee
    • Tuberculosis and Respiratory Diseases
    • /
    • v.71 no.2
    • /
    • pp.120-125
    • /
    • 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.

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

  • Cho, Yujung;Sohn, Kwonsang;Kwon, Ohbyung
    • Journal of Intelligence and Information Systems
    • /
    • v.27 no.1
    • /
    • pp.103-128
    • /
    • 2021
  • Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

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

  • Park, Hyun-Jung;Rho, Sang-Kyu
    • Asia pacific journal of information systems
    • /
    • v.21 no.2
    • /
    • pp.89-116
    • /
    • 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.