• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.239-250
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• 2020

The purpose of this paper is to explore the influence of the "contextualization" effect of web celebrity live broadcast on the e-commerce platform on consumers' perception of product value, risk and purchase intention. Live in this paper, using Taobao shopping consumers as the research object, the survey method, questionnaire survey is adopted, the form through the questionnaire and distributed network, a live in order to further validation of web celebrity effect of contextualized actual influence on consumer purchase intention, questionnaire design the Likert scale, seven and recycling questionnaire analysis using the statistical software SPSS 23.0 and AMOS 22.0 after processing the data. After determining the reliability and validity of the questionnaire, the exploratory factor analysis was used to verify the hypothesis and calculate the actual adjustment degree of the "contextualization" effect of web celebrity live broadcasting on consumers' purchase intention. The research results of this paper are summarized as follows :(1) consumers' perceived value of products can significantly positively affect their purchase intention, while perceived risk has a significantly negative impact on their purchase intention; (2) consumers' trust and purchase intention to products are regulated by the "contextualization" of web celebrity live broadcast. Specifically, for web celebrity live broadcasting with good "contextualization" effect, the perceived value of consumer products has a positive impact on product trust, which is higher than that of web celebrity live broadcasting with poor "contextualization" effect. In terms of resolving consumers' perceived risks to products, web celebrity live broadcast with good "contextualization" effect is also significantly better than web celebrity live broadcast with poor "contextualization" effect. Based on empirical analysis, this paper concludes that web celebrity live broadcasting will become a new breakthrough for the sustainable growth of the e-commerce industry, and puts forward Suggestions on the e-commerce marketing mode and the transformation of web celebrity live broadcasting industry.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.162-171
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• 2021

The Korean government proposed the K-SDGs in 2019 to promote the UN SDGs, but the role and tasks of science and technology, an important means of implementing the SDGs, have not been materialized. Accordingly, the role of science and technology ODA for the SDGs was established through the Ministry of Science and ICT's policy research project 'Science and Technology ODA Promotion Roadmap for Spreading the New Southern Policy and Realizing the 2030 SDGs'. In addition, goals, strategies, and core tasks for the next 10 years were derived in 10 fields such as water, climate change, energy, and ICT. In this paper, we analyze 30 key tasks of the ODA promotion roadmap for science and technology for the realization of SDGs, and propose mid- and short-term tasks and implementation plans for effective roadmap promotion. Among the key tasks in each field, four common elements were derived: ICT/smartization, a global problem-solving center, cooperation/communication platform, and business model/startup support platform/living lab that can create and integrate roadmap implementation conditions. In addition, the four mid- and short-term tasks, 1) Establishment of science and technology ODA network, 2) Establishment of living lab business platform linked to start-up support business, 3) Local smartization of recipient countries, and 4) Expand and secure sustainability of global problem-solving centers, were set in relation to the implementation of the detailed roadmap. For the derived mid- and short-term tasks, detailed implementation plans based on the ICTization of global problem-solving centers were presented. The implementation of the mid- and short-term tasks presented in this paper can contribute to the more effective achievement of the science and technology ODA roadmap, and it is expected that Korea's implementation of SDGs will also achieve high performance.

• Asia Marketing Journal
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• v.13 no.4
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• pp.105-137
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• 2012

After relationship marketing emerged as a new approach in the marketing field in the 1980s, it has been widely studied in the United States, Europe and Asia. Rapid environmental changes and global competition has made it inevitable for companies to consider their relationships with the environment more closely. Under these circumstances, relationship marketing has held a position as a pivotal paradigm in the field of strategy as well as in marketing. In addition, relationship marketing has overcome the limitations of a traditional marketing research while providing richer implications in company's marketing activities. The paradigm shift to relationship marketing has brought fundamental changes in a marketing point of view. First, in philosophical aspects, unlike past research which focused solely on customer satisfaction, organizational relationship parameters which focuses on trust and commitment has become key elements of successful relationship marketing while shifts in thoughts naturally take place from adaptive marketing to strategic marketing. Second, in structural aspects, the relational mechanism of governance such as network structure with a variety of relational partners has emerged as a new marketing organization from the previous simple structure focusing on the micro-economic, marketbased trading between seller and customer. Third, in behavioral aspects, it proposed the strategic course of the action of gaining an advantage over the competition on the individual firm level by focusing on building long-term relationships and considering partnership with the components in the entire marketing system, rather than with one-time transaction-centric action between a seller and a customer. Fourth, in the aspects of marketing performance, marketing performance was sought through the long-term and cooperative relationship with various stakeholders, including customers in the marketing system, focusing on the overall competitive advantage based on relationship rather than individual performance of individual companies' marketing activities, such as market share and customer satisfaction. However, studies of relationship marketing were mostly centered in interorganizational relationships focusing on the relational structure and properties of commercial sector in the marketing system. Paradoxically, the circumstance of the consumer's side that must be considered is evolving again in relationship marketing. In structural aspects, a community, as the new relationship governance structure in the digital environment, and in behavioral aspects, the changing role of consumer participation demanding big changes in the digital environment engaged in the marketing system. The possibility of building a relationship marketing community for common value creation is presented in terms of organization of consumers with the focus on changing marketing environment and marketing system according to the new realities of the 21st century- the popularity of digital environments and the diffusion of customer participation. Therefore, future research of relationship marketing must seek for a truly integrated model including all of the existing structure and properties of the research oriented relationship from both the commercial and consumer sector.

• Information Systems Review
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• v.21 no.4
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• pp.99-123
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• 2019

The software industry is a high value-added industry in the knowledge information age, and its importance is growing as it not only plays a key role in knowledge creation and utilization, but also secures global competitiveness. Among various SW available in today's business environment, Open Source Software(OSS) is rapidly expanding its activity area by not only leading software development, but also integrating with new information technology. Therefore, the purpose of this research is to empirically examine and analyze the effect of factors on the switching behavior to OSS. To accomplish the study's purpose, we suggest the research model based on "Push-Pull-Mooring" framework. This study empirically examines the two categories of antecedents for switching behavior toward OSS. The survey was conducted to employees at various firms that already switched OSS. A total of 268 responses were collected and analyzed by using the structural equational modeling. The results of this study are as follows; first, continuous maintenance cost, vender dependency, functional indifference, and SW resource inefficiency are significantly related to switch to OSS. Second, network-oriented support, testability and strategic flexibility are significantly related to switch to OSS. Finally, the results show that willingness to secures SW competitiveness has a moderating effect on the relationships between push factors and pull factor with exception of improved knowledge, and switch to OSS. The results of this study will contribute to fields related to OSS both theoretically and practically.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.149-169
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• 2018

Korea is currently accumulating a large amount of data in public institutions based on the public data open policy and the "Government 3.0". Especially, a lot of data is accumulated in the tourism field. However, the academic discussions utilizing the tourism data are still limited. Moreover, the openness of the data of restaurants, hotels, and online tourism information, and how to use SNS Big Data in tourism are still limited. Therefore, utilization through tourism big data analysis is still low. In this paper, we tried to analyze influencing factors on foreign tourists' satisfaction in Korea through numerical data using data mining technique and R programming technique. In this study, we tried to find ways to revitalize the tourism industry by analyzing about 36,000 big data of the "Survey on the actual situation of foreign tourists from 2013 to 2015" surveyed by the Korea Culture & Tourism Research Institute. To do this, we analyzed the factors that have high influence on the 'Satisfaction', 'Revisit intention', and 'Recommendation' variables of foreign tourists. Furthermore, we analyzed the practical influences of the variables that are mentioned above. As a procedure of this study, we first integrated survey data of foreign tourists conducted by Korea Culture & Tourism Research Institute, which is stored in the tourist information system from 2013 to 2015, and eliminate unnecessary variables that are inconsistent with the research purpose among the integrated data. Some variables were modified to improve the accuracy of the analysis. And we analyzed the factors affecting the dependent variables by using data-mining methods: decision tree(C5.0, CART, CHAID, QUEST), artificial neural network, and logistic regression analysis of SPSS IBM Modeler 16.0. The seven variables that have the greatest effect on each dependent variable were derived. As a result of data analysis, it was found that seven major variables influencing 'overall satisfaction' were sightseeing spot attraction, food satisfaction, accommodation satisfaction, traffic satisfaction, guide service satisfaction, number of visiting places, and country. Variables that had a great influence appeared food satisfaction and sightseeing spot attraction. The seven variables that had the greatest influence on 'revisit intention' were the country, travel motivation, activity, food satisfaction, best activity, guide service satisfaction and sightseeing spot attraction. The most influential variables were food satisfaction and travel motivation for Korean style. Lastly, the seven variables that have the greatest influence on the 'recommendation intention' were the country, sightseeing spot attraction, number of visiting places, food satisfaction, activity, tour guide service satisfaction and cost. And then the variables that had the greatest influence were the country, sightseeing spot attraction, and food satisfaction. In addition, in order to grasp the influence of each independent variables more deeply, we used R programming to identify the influence of independent variables. As a result, it was found that the food satisfaction and sightseeing spot attraction were higher than other variables in overall satisfaction and had a greater effect than other influential variables. Revisit intention had a higher ${\beta}$ value in the travel motive as the purpose of Korean Wave than other variables. It will be necessary to have a policy that will lead to a substantial revisit of tourists by enhancing tourist attractions for the purpose of Korean Wave. Lastly, the recommendation had the same result of satisfaction as the sightseeing spot attraction and food satisfaction have higher ${\beta}$ value than other variables. From this analysis, we found that 'food satisfaction' and 'sightseeing spot attraction' variables were the common factors to influence three dependent variables that are mentioned above('Overall satisfaction', 'Revisit intention' and 'Recommendation'), and that those factors affected the satisfaction of travel in Korea significantly. The purpose of this study is to examine how to activate foreign tourists in Korea through big data analysis. It is expected to be used as basic data for analyzing tourism data and establishing effective tourism policy. It is expected to be used as a material to establish an activation plan that can contribute to tourism development in Korea in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• The Korean Society of Law and Medicine
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• v.17 no.2
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• pp.281-313
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• 2016

Our Constitution obliges the state to protect the health of the people, and the Medical Law, which embodied Constitution, sets out in detail the matters related to open the medical institution and one of them is to prohibit the operation of multiple medical institutions In the past, there was a provision stipulating the same purpose. But because the Supreme Court interpreted that several medical institutions could be opened if the medical treatment was not made at the additional medical instition which was opened in the another doctor,s license, multiple medical institutions could be opened and operated. However, some health care providers opened the several medical institutions to another doctor's license just by the excuse of the business management and then did illegal medical cares like the unfair luring of patients, overtreatment, and commition treatment for more profits. So, the health rights of the people came to be infringed on. Accordingly, lawmakers amended the Medical Law for medical personnel not to open and to operate more than one medical institution. As the amended medical law prohibited a medical personnel to open multiple medical institution, some medical personnels insisted that the amended medical law is unconstitutional under which they could not be able to open and operate medical institutions on based on free investment and bring out the benefits of network hospitals. But the regulation to prohibit multiple institutions does not apply only to a medical personnel. Many other experts like lawyer and pharmacist can open only one office under such a restriction. If the regulation goes out of force, the procedure that multiple medical institutions should be opened and operated in the capacity as a medical corporation or a non-profit corporation does not have to be followed. And we should keep in mind that the permission for medical personels to open multiple medical institutions could lead virtually to commercial hospital. If in the nation with a very low rate of public medical service, If only a few medical personnels with capital own many medical institutions and operate commercially them, this could cause a falling-off in quality of medical service, ultimately infringe on the health rights and the life right of the people.

• Cartoon and Animation Studies
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• s.42
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• pp.151-175
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• 2016

The game industry has evolved from mobile games to PC online games after the smart-phone industry was opened up. In this environment, the game industry has rather been negatively developing its commercials means than the sufficient fundamental entertainment to the users. Especially, many games were released with better graphic qualities yet poor originality, continuing to be popular without enhancing the market itself. Moreover, the user's recognition level has improved. The users share their online gaming experience easily with the development of network environment. They receive the feedbacks on the quality of the game through the online channels and media by sharing them together. The high margin of the game industry will lead to the negative feedbacks of the users, effecting them to critique the content although the market looks good for now. The game industry's evolution has to be reviewed in the perspective of users, to look back at the successful cases of the past before the mobile era by analyzing and indicating the quality of the games and content's direction. This research is focused on the success factors of from the user's point of view, which has been widely claimed as a popular game franchise publicly before the mobile games had risen. WOW has been the most successful MMORPG game with its user record of 1.2 million till now. For these reasons, this study analyzes 's success factors from the user's point of view by configuring five expert groups, sequentially applying expert group survey, interview, Jobs-to-be-done and Fishbein Model as UX methodologies based on the business model to see through its long term rein in the industry. Consequently, The success factors from the user side of MMORPG provides an opportunity for the users to interact deeply with the game by (1) using well designed 'world view' over 10 years, (2) providing 'national policy' that is based on the locations of the users' culture and language, (3) providing 'expansions' with changes in time to give the digging elements to the users.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.4
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• pp.113-132
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• 2019

The degree of females' participation in corporate activity has been recently increased over the world and females' participation in economic activity may be new dynamic fuel for the Korean economy that falls into the vicious cycle of low growth. Start-up, therefore, has increasingly taken attention as an opportunity for females whose careers were interrupted to re-enter the labor market. The need for studies that examine factors influencing the decision of start-up is also increased along with the increase of the ratio of females' start-up. This study aims to verify effects of the women's characteristics(women discrimination, women's role conflict) and the human networks of females whose careers were interrupted, with the intention for entrepreneurial intention, which are mediated by personal attitudes and subjective norm suggested by Ajzen's Theory of Reasoned Action, based on an empirical research. The findings show that the human networks of females have an effect on attitudes toward start-up activity and subjective norm and the woman discrimination influence the personal attitudes. In contrast, the women's role conflict have no effect on both personal attitude toward start-up activity and subjective norm. This can be supposed as an outcome resulted from the subjects' low level of conflict caused by their sex roles, on their age distribution. The relation between subjective norm and entrepreneurial Intention seemed to be moderated by their perceived strong entrepreneurial supporting policy. Their attitudes toward start-up activity were found to have a mediating effect on the relation between the women discrimination, human networks and entrepreneurial Intention, while the subjective norm only mediated the relation between human networks and entrepreneurial Intention. Based on such results, this study attempts to suggest theoretical suggestions and the direction of various entrepreneurial supporting policy for the increase and the growth of start-up of females whose careers were interrupted, in Korea.