• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Sleep Medicine and Psychophysiology
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• v.26 no.1
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• pp.33-43
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• 2019

Objectives: Bipolar disorder is characterized by repetitive relapses that result in psychosocial dysfunctions. The functioning of bipolar disorder patients is related to the severity of symptoms, quality of sleep, drug compliance, and social support. The purpose of this study was to investigate the association between sociodemographic and clinical factors and functional status in bipolar disorder patients. Methods: A total of 52 bipolar disorder patients participated in the study. The following scales were utilized: Korean version of personal and social performance scale (K-PSP), Korean version of Hamilton rating scale for depression (K-HDRS), Korean version of young mania rating scale (K-YMRS), Korean version of pittsburgh sleep quality index (PSQI-K), Korean version of drug attitude inventory (K-DAI), mood disorders insight scale (MDIS), and multidimensional scale of perceived social support (MSPSS). Results: The K-PSP score showed a negative relationship with K-HDRS score (r = -0.387, p = 0.005), but not with K-YMRS score (r = -0.205, p = 0.145). The K-PSP score showed a negative relationship with global PSQI-K score (r = -0.378, p = 0.005) and overall sleep quality (r = -0.353, p = 0.010). The K-PSP scores were positively associated with the KDAI score (r = 0.409, p = 0.003) and MSPSS score (r = 0.334, p = 0.015). The predictive factors for K-PSP were overall sleep quality and social support from family. Conclusion: Our study showed that depressive symptoms were related to overall function in bipolar disorder. Also, our study suggested that improving sleep quality is important in maintaining functional status. Appropriate social support and positive perception toward the drug may lead to the higher level of functioning. This study is meaningful in that the functional status of bipolar disorder patients is analyzed in a multivariate manner in relation to various variables in psychosocial aspects.

• Korean Journal of Family Social Work
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• no.58
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• pp.5-36
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• 2017

This study aims to empirically analyze whether the women's cognitive attitude toward gender role, which is formed through social norms, enforces the gender division of housework. In this study, 4,435 married women aged 18-59 years from the 5th wave dataset of Korean Longutudinal Survey of Women and Family Data were selected for analysis. Using the Structural Equation Model(SEM), we examine the direct effect of "attitude toward behavior" and "subjective norm" on the domestic working hours and whether those two independent variables, such as "attitude toward behavior" and "subjective norm," influence the mediator variable "Behavior Intention" which in turn affect the dependent variable. The study reveals that "attitude toward the gender division of housework" has a statistically significant direct effect on the domestic working hours as well as an indirect effect operating through "behavior intention." And"subjective norm "has only a statistically significant indirect effect on the domestic working hours, operating through "behavior intention." Despite the fact that many women are now aware that various work-life balance policies are avaliable to mitigate the gender inequality of domestic works, it is proven that the gender division of housework becomes worse. The reason behind this is not only because there exist some problems in implementing the institutions themselves, but also because women's deeply internalized self-perception of gender role based on the traditional patriarchal culture somehow exacerbates the gender division of housework. Hence, in order to instill a progressive change in gender division of housework, it is important for women to try to change the way they perceive the stereotypical gender roles as well as for men to treat women equally.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• The Korean Journal of Air & Space Law and Policy
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• v.33 no.2
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• pp.345-366
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• 2018

Article 37 of the International Convention on Civil Aviation requires that rules should be adopted to keep in compliance with international standards and recommended practices established by ICAO. As SARPs are revised annually, each ICAO Member State needs to reflect the new content in its national aviation Acts in a timely manner. In recent years, data-driven international standards have been developed because of the important roles of aviation safety data and information-based legislation in accident prevention based on human factors. The Safety Management System and crew Fatigue Risk Management Systems were reviewed as examples of the result of data-driven rulemaking. The safety management system was adopted in 2013 with the introduction of Annex 19 and Chapter 5 of the relevant manual describes safety data collection and analysis systems. Through analysis of safety data and information, decision makers can make informed data-driven decisions. The Republic of Korea introduced Safety Management System in accordance with Article 58 of the Aviation Safety Act for all airlines, maintenance companies, and airport corporations. To support the SMS, both mandatory reporting and voluntary safety reporting systems need to be in place. Up until now, the standard of administrative penal dispensation for violations of the safety management system has been very weak. Various regulations have been developed and implemented in the United States and Europe for the proper legislation of the safety management system. In the wake of the crash of the Colgan aircraft, the US Aviation Safety Committee recommended the US Federal Aviation Administration to establish a system that can identify and manage pilot fatigue hazards. In 2010, a notice of proposed rulemaking was issued by the Federal Aviation Administration and in 2011, the final rule was passed. The legislation was applied to help differentiate risk based on flight according to factors such as the pilot's duty starting time, the availability of the auxiliary crew, and the class of the rest facility. Numerous amounts data and information were analyzed during the rulemaking process, and reflected in the resultant regulations. A cost-benefit analysis, based on the data of the previous 10 year period, was conducted before the final legislation was reached and it was concluded that the cost benefits are positive. The Republic of Korea also currently has a clause on aviation safety legislation related to crew fatigue risk, where an airline can choose either to conform to the traditional flight time limitation standard or fatigue risk management system. In the United States, specifically for the purpose of data-driven rulemaking, the Airline Rulemaking Committee was formed, and operates in this capacity. Considering the advantageous results of the ARC in the US, and the D4S in Europe, this is a system that should definitely be introduced in Korea as well. A cost-benefit analysis is necessary, and can serve to strengthen the resulting legislation. In order to improve the effectiveness of data-based legislation, it is necessary to have reinforcement of experts and through them prepare a more detailed checklist of relevant variables.

• The Journal of Bigdata
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• v.3 no.2
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• pp.87-99
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• 2018

This study analyze the factors affecting the number of bidders in public contracts by collecting contract data such as purchase of goods, service and facility construction through KONEPS among various forms of public contracts. The reason why the number of bidders is important in public contracts is that it can be a minimum criterion for judging whether to enter into a rational contract through fair competition and is closely related to the budget reduction of the ordering organization or the profitability of the bidders. The purpose of this study is to analyze the factors that determine the participation of bidders in public contracts and to present the problems and policy implications of bidders' participation in public contracts. This research distinguishes the existing sampling based research by analyzing and analyzing many contracts such as purchasing, service and facility construction of 4.35 million items in which 50,000 public institutions have been placed as national markets and 300,000 individual companies and corporations participated. As a research model, the number of announcement days, budget amount, contract method and winning bid is used as independent variables and the number of bidders is used as a dependent variable. Big data and multidimensional analysis techniques are used for survey analysis. The conclusions are as follows: First, the larger the budget amount of public works projects, the smaller the number of participants. Second, in the contract method, restricted competition has more participants than general competition. Third, the duration of bidding notice did not significantly affect the number of bidders. Fourth, in the winning bid method, the qualification examination bidding system has more bidders than the lowest bidding system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.51-78
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• 2021

Followed by 'start-up', the theme of 'scale-up' has been considered as an important agenda in both corporate and policy spheres. In particular, although it is a term commonly used in industry and policy fields, even a conceptual definition has not been achieved from the academic perspective. "Corporate Growth" in the academic aspect and "Business Growth" in the practical management field have different understandings (Achtenhagen et al., 2010). Previous research on corporate growth has not departed from Penrose(1959)'s "Firm as a bundle of resources" and "the role of managers". Based on the theory and background of economics, existing research has mainly examined factors that contribute to firms' growth and their growth patterns. Comparatively, we lack knowledge on the firms' growth with a focus on 'annual revenue growth rate'. In the early stage of the firms, they tend to exhibit a high growth rate as it started with a lower level of annual revenue. However, when the firms reach annual revenue of more than 100 billion KRW, a threshold to be classified as a 'middle-standing enterprise' by Korean standards, they are unlikely to reach a high level of revenue growth rate. In our study, we used our sample of 333 companies (6.7% out of 5,000 'fastest-growing' companies) which reached 15% of the compound annual growth rate in the last three years with more than USD 100 million. It shows that sustaining 'high-growth' above a certain firm size is difficult. The study focuses on firms with annual revenue of more than $100 billion (approximately 120 billion KRW) from the 'Inc. 2020 fast-growing companies 5,000' list. The companies have been categorized into 1) Fast-growing companies (revenue CAGR 15%~40% between 2016 and 2019), 2) Hyper-growing companies (40%~99.9%), and 3) Super-growing (100% or more) with in-depth analysis of each group's characteristics. Also, the relationship between the revenue growth rate, individual company's strategy choice (market orientation, generic strategy, growth strategy, pioneer strategy), industry/market environment, and firm age is investigated with a quantitative approach. Through conducting the study, it aims to provide a reference to the 'Hyper-Growing Model' that combines the paths and factors of growth strategies. For policymakers, our study intends to provide a reference to which factors or environmental variables should be considered for 'optimal effective combinations' to promote firms' growth.

• Journal of Nutrition and Health
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• v.54 no.1
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• pp.39-53
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• 2021

Purpose: This study examined the dietary behavior, health status and nutrient intake by considering the level of household income of elderly people using data obtained from the Korea National Health and Nutrition Survey (KNHANES VII) 2018. Methods: The study subjects were 1,355 elderly people over 65 years old (558 men, 797 women). Based on their household income, participants were classified into three groups: low-income, middle-income, and high-income. The variables consisted of general characteristics, dietary behavior, health status, health related behavior, and dietary intakes. Dietary data were estimated by the 24-hour dietary recall. Results: In men, the low-income group encompassed older, less educated, less employed, and living with family of first generation. However, in women, there were no differences in employment by the level of income, and women living alone had lower income than subjects living with family. Elderly men in the high-income group had a significantly higher level of nutrient intake (energy, protein, fat, phosphorous, riboflavin, niacin and vitamin C). Men in the low-income group consumed a significantly lower intake of fruits, seaweeds and eggs, including total food. Women in the low-income group had significantly less intake of protein, fat, calcium, phosphorous, iron, vitamin A, riboflavin, and niacin whereas women in the high-income group had significantly higher intake of sugar & sweet, eggs, and beverages, including total food. Conclusion: These results suggest that the level of household income is an important factor that influences food and nutrient intake in the Korean elderly. The socioeconomic status needs to be considered differently among elderly men and women when implementing food assistant programs and designing nutrition education programs.

• (The)Study of the Eastern Classic
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• no.49
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• pp.393-430
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• 2012

In this paper, Bowen's family therapy from the perspective of the theory, narrative analysis of Korean novels, three Regis - tration Subsection.Bowen's description of the individual's behavior is causing problems within the family of anxiety and self-differentiation using two variables. The home if problems or conflicts expressed in these works, the figures showed that the undifferentiated ego at the center of the problem. Undifferentiated ego character felt extreme anxiety when their alienation from the relationship of the family-oriented jeokjangja Undifferentiated ego to relieve anxiety and to consolidate their position in the family relationship, so people were strongly united with each other. Sohyunseongnok, Chossisamdaerok series structures and patterns of a series of domestic problems occur, "mother and son, self-differentiation self undifferentiated undifferentiated ego and self-differentiation mother son, mother and self-ego undifferentiated undifferentiatedcan be subdivided into the son '.Established a symbiotic relationship between them and the U.S. established the presence of a pattern, healer, depending on the deployment method depends narrative. And is divided accordingly, self-determination and to the Son, a son, a son to be born again through repentance of the execution. Depending on the presence or absence healer than what was described on the deployment structure differs. Undifferentiated ego and self undifferentiated mother son family therapist within the family, the problem is solved. Son, a son to repent and be born again, and that caused the problem. Ego Undifferentiated mother and son self-differentiation, undifferentiated ego and self-differentiation mother son home my healer in the absence son committed suicide and executions each tragedy occurred. Personal level, but occurred at home conflicts or problems about this when analyzing the Three-generation stories of the Joseon Dynasty, by applying the theory of Bowen's Family Therapy view dimension in the relationship between family were. Toughness or desire of any one individual, but serious conflicts and problems within the family, the institution of the family itself is the root cause was. And was able to reveal aspects of narrative flow, depending on the presence or absence of family therapists vary significantly depending on his role in the rest of the family comfort and peace determines whether the Three-generation stories of the Joseon Dynasty, received an important narrative of men and axis formation. In a gauze-like situation of this problem in the Three-generation stories of the Joseon Dynasty, a personal desire or toughness in confined without the dimension of the entire family. And extrinsic psychological approach against the background of the wall in the main narrative of the sufferings of women of Korean novels, approached significance.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.151-176
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• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.