This study investigates the influence of particulate matter concentrations on the incidence of asthma, focusing on the delayed onset of symptoms and subsequent medical consultations. Analysis incorporates a four-day lag from the initiation of fine dust exposure and compares asthma patterns before and after the World Health Organization's (WHO) classification of fine dust as a Group 1 carcinogen in November 2013. Utilizing daily PM10 data and asthma-related medical visit counts in Seoul from 2008 to 2016, the study additionally incorporates Google search frequencies and newspaper article counts on fine dust to assess public awareness. Results reveal a surge in search frequencies and article publications after WHO announcement, indicating heightened public interest. To standardize the long-term asthma occurrence trend, the daily asthma patient numbers are ratio-adjusted based on annual averages. The analysis uncovers an increase in asthma medical visits 2 to 3 days after fine dust events. Additionally, greater public awareness of fine dust hazards correlates with a significant reduction in asthma occurrence after such events, even within 'normal' fine dust concentrations. Notably, behavioral changes, like limiting outdoor activities, contribute to this decrease. This study highlights the importance of analyzing accumulated medical data over an extended period to identify general public behavioral patterns, deviating from conventional survey methods in social sciences. Future research aims to extend data collection beyond 2016, exploring recent trends and considering the potential impact of decreased fine dust awareness amid the COVID-19 pandemic.

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people’s life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing ‘heavy snow’ in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

Recently in Korea, YouTube stock channels increased rapidly due to the high social interest in the stock market during the COVID-19 period. Accordingly, the role of new media channels such as YouTube is attracting attention in the process of generating and disseminating market information. Nevertheless, prior studies on the market forecasting power of YouTube stock channels remain insignificant. In this study, the market forecasting power of the information from the YouTube stock channel was examined and compared with traditional news media. To measure information from each YouTube stock channel and news media, positive and negative opinions were extracted. As a result of the analysis, opinion in channels operated by media outlets were found to be leading indicators of KOSPI market returns among YouTube stock channels. The prediction accuracy by using logistic regression model show 74%. On the other hand, Sampro TV, a popular YouTube stock channel, and the traditional news media simply reported the market situation of the day or instead showed a tendency to lag behind the market. This study is differentiated from previous studies in that it verified the market predictive power of the information provided by the YouTube stock channel, which has recently shown a growing trend in Korea. In the future, the results of advanced analysis can be confirmed by expanding the research results for individual stocks.

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald’s (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

Recently the meaning of the road name address is expended as an information through the revision of the Road Name Address Act. As this revision, the address of things (AoT) become more important indicating the possibility for the expansion to the related business. However, recent study about AoT does not concern how the current priority system works from the first research. In this study, we analyze perception about addressable object between AoT experts and public using AHP analysis. We structured the importance of addressable objects as two categories; urgency and value creation. The necessity in emergency or daily, accessibility and welfare conform the urgency index. Meanwhile, public value creation in public domain or profitability in the business area and economics conform value creation index. We conducted survey for total of 89 of experts and public. The results of this study indicate the relative importance of AoT measured by experts and public. Generally, public tend to concern more about accessibility conforming the urgency index than experts. Moreover, the public WiFi and the sports complex scored the high priority among the remain addressable objects, in respect of the urgency and the value creation. This result could be implemented for the activation of the smart city industry base on the geospatial information including AoT.

Investors aim to maximize the return rate for their own investment, utilizing various information as possible as they can access. However those investors, especially individual investors, have limitations of interpretation of the domain-specific information or even the acquisition of the information itself. Thus, individual investors tend to make decision affectively and frequently, which may cause a loss in returns. This study aims to analyze analysts’ target price and to suggest the strategy that could maximize individual’s return rate. Most previous literature revealed that the optimistic bias exists in the analysts’ target price and it is also confirmed in this study. In this context, this study suggests the upper limit of target rate of returns and the optimal value named ‘alpha(α)’ which performs the adjustment of proposed target rate to maximize excess earning returns eventually. To achieve this goal, this study developed an optimization problem using linear programming. Specifically, when the analysts’ proposed target rate exceeds 30%, it could be adjusted to the extent of 59% of its own target rate. As apply this strategy, the investors could achieve 1.2% of excess earning rate on average. The result of this study has significance in that the individual investors could utilize analysts’ target price practically.

Lately, there have been tremendous shifts in the business technology landscape. Advances in cloud technology and mobile applications have enabled businesses and IT users to interact in entirely new ways. One of the most rapidly growing technologies in this sphere is business intelligence, and associated concepts such as big data and data mining. BI is the collection of systems and products that have been implemented in various business practices, but not the information derived from the systems and products. On the other hand, big data has come to mean various things to different people. When comparing big data vs business intelligence, some people use the term big data when referring to the size of data, while others use the term in reference to specific approaches to analytics. As the volume of data grows, businesses will also ask more questions to better understand the data analytics process. As a result, the analysis team will have to keep up with the rising demands on the infrastructure that supports analytics applications brought by these additional requirements. It’s also a good way to ascertain if we have built a valuable analysis system. Thus, Business Intelligence and Big Data technology can be adapted to the business’ changing requirements, if they prove to be highly valuable to business environment.

Most demand forecasting studies for telecommunication services have focused on estimating market size at the introductory stage of new products or services, or on suggesting improvement methods of forecasting models. Although such studies forecast business growth and market sizes through demand forecasting for new technologies and overall demands in markets, they have not suggested more specific information like relative market share, customers’ preferences on technologies or service, and potential sales power. This study focuses on the telecommunication service industry and explores ways to calculate the relative market shares between competitors, considering competitive situations at the introductory stage of a new mobile telecommunication service provider. To reflect the competitive characteristics of the telecommunication markets, suggested is an extended conjoint analysis using service coverage and service switching rates as modification variables. This study is considered to be able to provide strategic implications to businesses offering existing service and ones planning to launch new services. The result of analysis shows that the new service provider has the greatest market share at the competitive situation where the new service covers the whole country, offers about 50% of existing service price, and allows all cellphones except a few while the existing service carrier maintains its price and service and has no response to the new service introduction. This means that the market share of the new service provider soars when it is highly competitive with fast network speed and low price.

This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, ‘False alarm’ and ‘Miss’ in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a ‘False alarm’ error. In addition, this study found that people’s dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users’ opinion in almost real time, which is impossible through survey or interview.

Recently, nanotechnology has grown as one of the leading science technology along with other converging technologies such as biology, information, medicine etc., bringing the continuous investment of the government in nano-related field. However, it is difficult to measure and evaluate the performance of the national research and development programs because of the multidimensional character of the expected outcomes. This study aims to measuring efficiency of the national nanotechnology research and development programs using DEA model. The decision making units are nine nano-related ministries including the Ministry of Science, ICT and Future Planning. The input variables are total expenditure, number of the programs and average expenditure per program. The output variables are science, technology and economic indicator, and the combination of these outputs are respectively measured as seven different DEA cases. The Ministry of Science, ICT and Future was the first efficient ministry in total technical efficiency. Ministry of Agriculture, Food and Rural Affairs and the Ministry of Food and Drug Safety were efficient in pure technical efficiency, when the Ministry of Commerce Industry and Energy took the first in the scale efficiency. The program efficiency was affected by organizational characteristics such as the institution’s scale, the concentration of the research paper or the patent, technology transfer or the commercialization. The result of this study could be utilized in development of the policy in the nanotechnology and the related field. Furthermore, it could be applied for the modification of expenditure management or the adjustment of the research and development programs’ input and output scale for each ministry.

Nanotechnology has been growing constantly and it is becoming the leading technology in scientific research and development. Although nanotechnology has important applications in broad variety of fields without boundary of any particular industrial area, the study of nanotechnology related to its commercialization has been conducted in a few ways. To put that figure in context, this study investigates public and expert perceptions about negative potentials of nanotechnology. Through a series of surveys with public (N = 541) and experts (N = 62), we analyzed about public willingness to pay for nano-applied products. Survey results showed that public and experts preferred nano-applied products in the order of electronics, cosmetics, and food and medicine. Experts express high payment intention to electronics rather than public intention. In addition, the survey results showed the purchasing intention of both public and expert group was affected by the attributes of nano-applied products in the order of risk fatality, risk chance, certification, and labeling. But experts put more importance in risk fatality than risk chance comparing to public. Through the case analysis of the effects of labeling and certification, we revealed either labeling or certification can induce both public and experts to buy the nano-applied products with high risk chance and low risk fatality. However, for the nano-applied product with high risk fatality and low risk chance, both labeling and certification are simultaneously required to make customers have positive purchasing intention. The result of this study could be utilized for the nanotechnology-based company to get the consumer behavior information about nano-based product and to establish their marketing strategy.

The traffic accidents in Korea have been increasing every year due to various reasons and simultaneously causing socioeconomic cost at the national level. This study has analyzed the correlation between meteorological factors and the traffic accidents in Seoul during 2013. Especially, we have selected season, rain and temperature among the meteorological factors to identify their significance with the traffic accidents. In addition, analysis of variance, t-test and a multiple regression technique is applied. Major findings from the analyses are discussed at the district point of view, including the different effect of weather condition and the interaction effect of rain and temperature in winter. The results of this study would be useful for developing management strategies to reduce car crashes and injury severity in Seoul.

This paper deals with the economic value analysis of meteorological forecasts for a hypothetical inventory decision-making situation in the pharmaceutical industry. The value of Asian dust (AD) forecasts is assessed in terms of the expected value of profits by using a decision tree, which is transformed from the specific payoff structure. The forecast user is assumed to determine the inventory level by considering base profit, inventory cost, and lost sales cost. We estimate the information value of AD forecasts by comparing the two cases of decision-making with or without the AD forecast. The proposed method is verified for the real data of AD forecasts and events in Seoul during the period 2004~2008. The results indicate that AD forecasts can provide the forecast users with benefits, which have various ranges of values according to the relative rate of inventory and lost sales cost.

Recently, the number of smartphone users has skyrocketed because of a plenty of useful services that smartphone applications provide. In the Information Systems (IS) literature, however, there has been little understanding about the effects of the service quality of smatphone applications on user satisfaction and loyalty. Thereby, this study examined 1) the effects of the service quality of smartphone applications on users beliefs, perceived usefulness and easiness and 2) the effects of the user beliefs as mediating variables on users' attitude, satisfaction, and loyalty. A survey was conducted and SEM was employed to analyze the data. The results showed that the service quality of smartphone applications affect users' attitude, satisfaction, and loyalty through the mediating variables.

황사(Asian dust storm, ADS)란 중국이나 몽골 등 중앙아시아 지역의 사막 지대의 작은 모래나 황토 또는 먼지가 하늘에 떠다니다가 상층풍을 타고 멀리까지 날아가 떨어지는 현상을 말하며, 주로 봄철에 우리나라를 비롯한 동아시아 지역에 영향을 준다. 이와 같은 황사에 영향을 받는 지역에서는 거주민들의 건강에 부정적인 영향을 미치는것으로 알려져 있다. 본 연구는 2005년도에서 2008년도까지 4년간 서울지역 거주민들 사이에서 황사현상이 천식질

Information is known to be a key element for the successful operation of a supply chain, which is required of the efficient ordering strategies and accurate predictions of demands. This study proposes a method to effectively utilize the meteorological for

Information is known to be a key element for the successful operation of a supply chain, which is required of the efficient ordering strategies and accurate predictions of demands. This study proposes a method to effectively utilize the meteorological forecast information in order to make decisions about ordering and prediction of demands by using the Taguchi experimental design. It is supposed that each echelon in a supply chain determines the order quantity with the prediction of precipitation in the next day based on probability forecast information. The precipitation event is predicted when the probability of the precipitation exceeds a chosen threshold. Accordingly, the choice of the threshold affect the performances of a supply chain. The Taguchi method is adopted to deduce a set of thresholds for echelons which is least sensitive to changes in environmental conditions, such as variability of demand distributions and production periods. A simulation of the beer distribution game was conducted to show that the set of thresholds found by the Taguchi method can reduce the cumulative chain cost, which consists of inventory and backlog costs.

The introduction and application of IT(Information Technology) to a company has become to be viewed as a key factor for the business competitiveness and improved productivity. This study presents an approach to evaluation of a company's informatization le

  In this paper the economic value of weather forecasts is valuated for profit-oriented enterprise decision-making situations. Value is estimated in terms of monetary profits (or benefits) resulted from the forecast user’s decision under the specific payo

In this study, instead of economic estimation of forecast value, we evaluated the value score(VS) of the user satisfaction using the concept of satisfaction/dissatisfaction. We compared the collective Value Scores (cVS) based on outputs of probabilistic forecasts of precipitation seasonally in Seoul and Busan during the period of 2004 to 2013 and ÿnally found the optimum threshold that can improve cVS of both cities. When using 30% threshold, the users can expect a higher cVS compared with those using other thresholds. When using the seasonal optimum threshold in Seoul, the cVS is additionally higher by 9%. These results show the level of satisfaction of the forecast that can be improved when the meteorological communities inform the users the correct threshold of the rainfall probabilistic forecast.