• 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 Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.275-285
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• 2021

COVID-19 is threatening the safety of ships and seafarers by delaying seafarer rotation. Shipping companies and governments have a blindspot in case of the onboard environment of seafarers. An effective, alternative plan should be devised to eliminate the possibility of human accidents in an emergency that threatens the safety of seafarers. According to the survey of former and current seafarers, the most important factor in boarding life was safety, and the most necessary thing during emergencies was to secure smooth seafarer rotation rather than improve wages and welfare. By analyzing the major routes of national shipping companies by continent, ports with a large number of calls and a high Air Connectivity Index were selected as the base port. In addition, the route was designed for effective, domestic seafarer rotation during international shipping. Other countries must be consulted to establish a travel route linking ships, ports, and airports for the safe return of sailors to their home countries during an emergency. In addition, it is necessary to work together for the seafarers who are in trouble of seafarer rotation through cooperation with the International Maritime Organization(IMO). Starting with this, the government should have a monitoring system for the return and non-return routes as well as the number of seafarers on board. If such a system is established, it will be able to determine the response direction of our country's policy in case of an emergency. Along with the shipping company's ef orts to improve the treatment of seafarers, national and social attention will be needed to review domestic laws and improve awareness about seafarers.

• Knowledge Management Research
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• v.23 no.4
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• pp.315-338
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• 2022

In 1986, Korea established legal systems to support small and medium-sized start-ups, which becomes the main pillars of national development. The legal systems have stimulated start-up ecosystems to have more than 1 million new start-up companies founded every year during the past 30 years. To analyze the trend of Korea's start-up ecosystem, in this study, we collected 1.18 million news articles from 1991 to 2020. Then, we extracted news articles that have the keywords "start-up", "venture", and "start-up". We employed network analysis and topic modeling to analyze collected news articles. Our analysis can contribute to analyzing the government policy direction shown in the history of start-up support policy. Specifically, our analysis identifies the dynamic characteristics of government influenced by external environmental factors (e.g., society, economy, and culture). The results of our analysis suggest that the start-up ecosystems in Korea have changed and developed mainly by the government policies for corporation governance, industrial development planning, deregulation, and economic prosperity plan. Our frequency keyword analysis contributes to understanding entrepreneurial productivity attributed to activities among the networked components in industrial ecosystems. Our analyses and results provide practitioners and researchers with practical and academic implications that can help to establish dedicated support policies through forecast tasks of the economic environment surrounding the start-ups. Korean entrepreneurial productivity has been empowered by growing numbers of large companies in the mobile phone industry. The spectrum of large companies incorporates content startups, platform providers, online shopping malls, and youth-oriented start-ups. In addition, economic situational factors contribute to the growth of Korean entrepreneurial productivity the economic, which are related to the global expansions of the mobile industry, and government efforts to foster start-ups. Our research is methodologically implicative. We employ natural language processes for 30 years of media articles, which enables more rigorous analysis compared to the existing studies which only observe changes in government and policy based on a qualitative manner.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.27-40
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• 2020

The marathon has long been established as a representative lifestyle for all ages. With the recent expansion of the Work and Life Balance trend across the society, marathon with a relatively low barrier to entry is gaining popularity among young people in their 20s and 30s. By analyzing the issues and related words of the marathon event, we will analyze the spottainment elements of the marathon event that is popular among young people through keywords, and suggest a development plan for the differentiated event. In order to analyze keywords and related words, blogs, cafes and news provided by Naver and Daum were selected as analysis channels, and 'JTBC Marathon' and 'Culture' were extracted as key words for data search. The data analysis period was limited to a three-month period from August 13, 2019 to November 13, 2019, when the application for participation in the 2019 JTBC Marathon was started. For data collection and analysis, frequency and matrix data were extracted through social matrix program Textom. In addition, the degree of the relationship was quantified by analyzing the connection structure and the centrality of the degree of connection between the words. Although the marathon is a personal movement, young people share a common denominator of "running" and form a new cultural group called "running crew" with other young people. Through this, it was found that a marathon competition culture was formed as a festival venue where people could train together, participate together, and escape from the image of a marathon run alone and fight with themselves.

• Journal of Legislation Research
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• no.44
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• pp.33-62
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• 2013

In this paper, the consolidation of ICT basic legislation and ICT special legislation concerning "Ministry of Science, ICT and Future Planning" and "Korea Communications Commission" which came on the back of governmental reorganization in recent years is discussed in the theoretical and practical aspect. Development of "data communication technology" innovatively changed the method of livelihood of mankind, the emergence of network under global dimension provided financial social benefit and posed a challenge and a threat at the same time. Form digital revolution human kind can expect to receive many important blessings. Nevertheless, there are many advantages of development of technology by digital revolution, cyberspace like online media, internet etc. has realistically many problems that must be solved. To maximum positive aspects like the expansion of freedom of expression and creating plan of economy by the advance of transmission technology is needed. And to minimize side effects of informatization is required more. The First, Special Act on ICT has an adaptation in normative standardization to be fit in media convergence beyond convergence of broadcasting and telecommunications. Henceforth, there must be established a legal basis for the achievement of protection of economic evolution and freedom of speech in digital media, information, communication technology and content development. The second, the government action is to accomplish economic development and freedom of information in structural aspect of norm. Therefore minimizing normative problem by reorganization of organization remains clearly unresolved in politics. The third, Special Act on ICT must be basic law covering info-communications field, pay telecommunication and media contents field. The forth, from a technical point of view, net neutrality, conflict of interest for digital content and so on can be fixed easily. Special Act on ICT must not only pursuit of development of industry. Special Act on ICT and pursuit of enhancing quality of life of people and preparing program to promote democratization. From now on, we need to make powerful nation of information& communications technology and in information human rights protection field got to be one step ahead of others with reference to appear all the various aspects must be brought together in the discussion of legislation process of Special Act on ICT.

• 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.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.