• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.39-54
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• 2018

This study aims to expand K-Wave business. For this, it firstly investigated previous studies and pointed out limitations of the current scope of the K-Wave business. Therefore, as a theoretical background, it attempts to construct an analysis framework based on four types of creativity type model and to redefine the concept of K-Wave business, which refers to a series of business activities that create, utilize the asset, and reuse the originality of intellectual property assets. This study analyzes the business activities of K-Wave's asset creation, utilization, and talent linkage during 2013~2017. The scope of the asset creation covers the highest ranked movies, dramas, and K-pops, while the utilization of those is analyzed in cosmetics, food, and fashion industries. The personal talent is the source of new K-Wave value creation and Webtoon IP is analyzed. As a result, in the case of movies and dramas, the representative market is China, which is the result of the efforts to avoid the continuation of China's regulation and the development of local OTTs. It is confirmed that the product development for Chinese consumers is active as activities of K-Wave utilization in cosmetics, food and fashion. Interesting is that new K-Wave content is circulated in the beauty sector. Finally, it is confirmed that Webtoon IP, which has been structured with a solid story in individual talent, is the origin of new K-Wave asset creation such as movies and dramas.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.7 no.2
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• pp.271-286
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• 2000

This study is designed to investigate what elders think about the meaning of aging. We have used Q-methods to identify meaning of aging from elders, and developed self-referenced surveys to analyze characteristics In this study, we used a non-structured method to select Q sampling. From 183 Q populations, we selected 36 Q sampling. A total of 32 persons sixty-years or older were randomly selected for P samples, When the Q-sorting was complete, we interviewed the participants at both end of the extremes(agree or disagree), and documented their responses. We used PC QUANL to process the data and used principal component analysis for Q factor analysis. There were five subjective types for the meaning of aging by elders. Of the 32 P-samples of elders, 11 were identified as Type 1, 7 as Type 2, 2 as Type 3, 8 as Type 4, and 4 as Type 5. Type 1 : 'Matured elders' Elders wished the well being of their children, thought older persons should maintain good health, worried about becoming senile, and dependent God believing in life after death. Type 2 : 'Assertive-Rights' Elders categorized as Assertive-Rights insisted on their rights to life as a person. Type 2 elders characterized themselves as people who should keep themselves healthy, become weak and lack sexual desires, act selfish like a child, need to be protected, and be financially independent. Type 3 : 'Passive-Dependents' Elders characterize themselves as those who pray for their children's well being, worry about the children even after their death. and becoming senile. Type 4 : 'Hopeless' The 'Hopeless' type of elders characterized aging as a time to pray for their children, insignificant beings, thoughts were selfish and child-like, poor, worried about going senile, regret their life overall, and preferred to die than to live as an old person. Type 5 : 'Attached-Present' The 'Attached-Present' type of elders thought elderly characterized themselves as acting selfish and child-like, wiser, anxious, regret their life, stand aloof of greed and worldly things, being a model for the society, and deserving to be treated with filial respect. Thus far, Korean elders seemed to have a positive and negative meaning of aging due to the current changes in the society, value system, and family structures. The above five subjective meanings of aging confirm that we need to approach and nurse the elderly differently. Years of aging are a part of and a natural process of life with various physical, psychological, and sociological changes. Nurses need to assist elderly to find the positive meaning of their life by providing appropriate physical, psychological, and social support at an earlier stage in nursing. Based on this study, we could derive the following two implication from the perspectives of science of nursing to care for elders. 1) Based on the studies investigating the type of meaning of aging, we could develop tools to assist in nursing intervention programs for elderly. 2) Based on research on the meaning of aging for different developmental stages of life, we could develop a model for roles for different family members in nursing and caring for the elders.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Korean Journal of Occupational Health Nursing
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• v.7 no.1
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• pp.5-19
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• 1998

This descriptive-correlational study was conducted to identify the major factors affecting health promoting behaviors. 344 workers who employed in four different manutacturing plants in Taegu and Kyungbuk area were selected by convenience sampling method. Data were collected from April let to April 18th, 1998 by ready structured questionaires. The purpose of this study was to offer the basic data for health promotion theory development and health promotion strategy planning. This study was based on Pender's Health Promotion Model and examined three variables health promoting behavior, self-concept and perceived importance of health. The Life Style and Health Habit Assessment scale(LHHA) developed by Pender(1982).The Self-concept scale developed by Choi(1972) and the Health Value scale developed by Wallston, Maides and Wallston(1980) were used for this study. Data was analyzed by percentage, mean. t-test. ANOVA, Pearson Correlation Coefficient, and Stepwise Multiple Regression. The major findings of this study are as follows ; 1. The average level of health promoting behavior practice was 63.2% and possible range was from 62 to 248 point. The mean score of respondent's positive self-concept was 75.8. 81.4% of respondents put a high priority on the importance of health. 2. There was a significant difference between the practice level in the category of general self care and less amount of working hours per day(P=0.000), less amount of working hours per week(P=0.000). There was a significant difference between the practice level in the category of nutrition and age(0.002), marital status(0.000), working hour per day(0.008), working hours per week(0.001), There was a significant difference between the practice level in the category of nutriton and sex(0.000), age(0.000), marital status(0.025), education level(0.000), working hours per day(0.002), working hours per week(0.006). There was a significant difference between the practice level in the category of sleep and rest and age(0.003), marital status(0.002), working hours per day(0.001), working hours per week(0.001). There was a significant difference between the practice level in the category of stress management and working hours per day(0.001), working hours per week(0.002). There was a significant difference between the practice level in the category of self-actualization and working hours per day(0.050). 3. General characteristics influencing the respodent's self-concept were level(P=0.009) and worksite(P=0.001). 4. The results of the hypothesis tests are as follows The first hypothesis, that "The respondent who have more positive self-concept will have higher scores in the practice of health promoting behavior." was supported(r=0.2973, P=0.0001). The second hypothesis that "The respondent who have higher perception level on importance of health will have higher scores in the practice health promoting behavior." was rejected(r=- 0665, P=0.2225). 5. The most important factor that affects health promoting behavior practice was working hours per week(6.0%). The combination of working hours per week, age, education level accounted for 10.0% of the variance in health promoting behavior. In conclusion, the results of this study on industrial workers supported Pender's health promotion model in partial and showed the relatedness between self concept and the practice of health promoting behavior. Further research is required to find factors influencing health promoting behaviors of industrial workers.

• 한국사회정책
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• v.24 no.1
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• pp.33-66
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• 2017

Based on the premise that the systematic career process of workers in the general labor market was one of core elements of successful achievements and their establishment both at the individual and organizational level, this study set out to conduct empirical analysis of factors influencing the subjective career success of disabled workers in competitive employment at the multi-dimensional levels of individuals and organizations(corporations) and thus provide practical implications for the career management directionality of their successful vocational life with data based on practical and statistical accuracy. For those purposes, the investigator administered a structured questionnaire to 126 disabled workers at 48 companies in Seoul, Gyeonggi, Chungcheong, and Gangwon and collected data about the individual and organizational characteristics. Then the influential factors were analyzed with the multilevel analysis technique by taking into consideration the organizational effects. The analysis results show that organizational characteristics explained 32.1% of total variance of subjective career success, which confirms practical implications for the importance of organizational variables and the legitimacy of applying the multilevel model. The significant influential factors include the degree of disability, desire for growth, self-initiating career attitude and value-oriented career attitude at the individual level and the provision of disability-related convenience, career support, personnel support, and interpersonal support at the organizational level. The latter turned out to have significant moderating effects on the influences of subjective career success on the characteristic variables at the individual level. Those findings call for plans to increase subjective career success through the activation of individual factors based on organizational effects. The study thus proposed and discussed integrated individual-corporate practice strategies including setting up a convenience support system by reflecting the disability characteristics, applying a worker support program, establishing a frontier career development support system, and providing assistance for a human network.

• (The) Research of the performance art and culture
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• no.36
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• pp.531-551
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• 2018

The educational trait that the arts and cultural education and dance strive for at a time when the ethical tasks of life is the experience for insight of life. The awareness of time entrusted with the intensity [depth] of artistic and aesthetic experience is to contain its implication with policy and system. In the policy territory, broad perception and strategy are combined and practiced to produce new implication. Therefore, on the basis of characteristics and spectrum persuaded at a time when the arts and cultural education and dance education are broadly expanded, the result of this study after taking a look at the role of dance education within the arts and cultural education is shown as follows. The value striving for by the culture and arts education and dance education is to structure the life form with the artistic experience through the art as the ultimate life description. This is attributable to the fact that the artistic trait structured with self-understanding and self-expression contains the directivity of life that is recorded and depicted in the process of life. The dance education in the culture and arts education has the trait to view the world with the dance structure as the comprehensive study as in other textbook or art genre under the awareness of time and education system category within the school system and it has diverse social issues combined as related to the frame of social growth and advancement outside of school. When taking a look at the practical characteristics (method) of dance based on the arts and cultural education business, it facilitates the practice strategy through dance, in dance, about dance, between dance with the artist for art [dance]. At this time, the approachability of dance is deployed in a program based on diverse artistry for technology, expression, understanding, symbolism and others and it has the participation of enjoyment and preference. In the policy project of the culture and arts education, the dance education works as the function of education project as an alternative model on the education system and it also sometimes works as the function for social improvement and development to promote the community awareness and cultural transformation through the involvement and intervention of social issues.