• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1161-1170
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• 2014

In global competition like today, intellectual property of novel areas such as traditional knowledge, traditional creation, hereditary resource, etc. are perceived as important resources. Therefore making solid competitive power in overall knowledge resources like cultural contents, brand, design etc. in nation is a pressing question. Accordingly in this study, to prepare for intellectual property rights dispute and advantage-sharing problem that would be variously derived from the Nagoya Protocol which will come into force after 2014, this research selected 200 knowledge of middle region in Korea from 2,047 literal and 931 oral knowledge using preconditioning process. The order of priority of top 50 of them was ranked by a quantitative research method, the Delphi survey. Among them, 30 was literal traditional knowledge and 20 was oral traditional knowledge. Result of this research could be used later as basic material for qualitative researches like the focus group interviewing. Furthermore in this paper is meaningful; the selected traditional knowledge can contribute remarkably to traditional biologic knowledge resource in nation which can be acknowledged in international society, announcing validity (hold precedence for patent) later on.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.656-664
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• 2020

This study obtained basic data for developing human resources with creativity convergence competency by surveying and analyzing the level of creativity convergence competency of university students. The study was conducted from October 1, 2019 to November 10, 2019 on university students attending the departments of computer science, pharmaceutical engineering, physical therapy and dental hygiene. The data from 296 students was finally used for this study, and IBM SPSS/Win statics 23.0 programs were used to analyze the data. Students who graduated from Seoul/Gyeonggi High School or those students with high undergraduate satisfaction were found to have high creativity convergence ability, and these results were statistically significant. Further, the group of students who had experience with Campus/Suburban competition, Global Competency training/ International exchange programs or the Capstone Design/Team Based Project showed high creativity convergence competency, and these results were statistically significant. Thus, this study identified the necessity of developing and operating various extra-curricular programs at education institutes in order to enhance students' creativity convergence capability.

• Journal of Distribution Science
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• v.14 no.4
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• pp.93-101
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• 2016

Purpose - As more consumers pursue high quality products at reasonable prices, Korean retail companies are increasing investment in expanding their outlet stores. Despite the growing importance of the outlet business, there has been very little empirical research on consumers' outlet shopping behaviors. This study aimed to investigate the relationships between consumers' perceived product value (performance quality, value for money, and social value) of outlet stores and overall shopping satisfaction and the effect of shopping satisfaction on outlet store loyalty. Research design, data and methodology - The authors developed a structural model in which performance quality, value for money, and social value of products are proposed to affect overall outlet shopping satisfaction, thus increasing customer loyalty. To analyze the research model, data were collected from 88 shoppers at suburban outlets. SPSS 21.0 and AMOS 21.0 were utilized to test the hypotheses. The unidimensionality of each construct was supported from the results of the reliability test with Cronbach's α and confirmatory factor analyses. Correlation analysis was performed and the results warranted the nomological validity of the measures. The fit statistics of the overall model analysis demonstrated an acceptable fit(X²(₁₆₁)=171.651, p=.000; X²/df=1.546; GFI=.821, NFI=.879, TLI=.942, CFI=.953, RMR=.035, RMSEA=.079). Results - The findings are as follows. First, consumers' perceived value of product performance quality had a significant positive effect on overall outlet shopping satisfaction. Consumers, who evaluate performance quality of the product more positively, tend to express stronger satisfaction and happiness about outlet shopping experience. Second, consumers' perceived social value of outlet products influenced their overall satisfaction significantly. Consumers who believe that products of outlet stores enhance self-concepts are more likely to satisfy with outlet shopping experience. However, consumers' perception of outlet products on value for money was not found to significantly influence overall shopping satisfaction. Finally, overall shopping satisfaction had a significant and positive influence on loyalty. Conclusions - While outlet retailers have traditionally focused on promoting competitively priced merchandise, the results of this study suggest that customers' overall satisfaction with outlet shopping is influenced more by the non-price-related product values. In the context of an outlet shopping environment, performance quality and social value of the products were found to be more critical predictors of customer overall satisfaction. Therefore, it would not be efficient for outlet retailers to highlight economic value of their merchandise. Instead, they need to investigate the performance quality of the products regularly and try to deliver quality guaranteed goods to enhance customer satisfaction. Also, outlet retailers should differentiate their businesses by carrying more unique and prestigious brands and emphasize higher social value and symbolic meanings of their products. As competition among outlet retailers are getting fierce, retail companies need to focus on strengthening customer loyalty with a long-term perspective. With a deeper understanding of the relationship between consumers' perceived product values and shopping satisfaction, outlet retailers will be able to develop customer loyalty strategies effectively and to achieve competitive advantage.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.129-134
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• 2021

Recently, the demand for electricity is gradually increasing due to the rapid industrial development and the improvement of living standards. In the case of Korea, which is highly dependent on fossil fuels due to such a surge in electricity demand, reduction and freezing of greenhouse gas emissions due to international environmental regulations will immediately lead to a contraction in industrial activities. Accordingly, there are many difficulties in competition with advanced countries that want to link the environment with the country's industrial production activities, and the development of alternative energy as a countermeasure is of great interest around the world. Among these new power generation methods, small-scale power generation facilities with relatively small capacity include photovoltaic generation, wind power generation, and fuel cell generation. Among them, the fuel cell attracts the most attention in consideration of continuous operation, high power generation efficiency, and long-term durability, which are important factors for practical use. Therefore, in this paper, the fuel cell power generation system was researched and constructed by designing the power conversion circuit necessary to finally obtain the AC power used in our daily life by using the DC power generated from the fuel cell as an input.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.