• Korean Journal of Heritage: History & Science
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• v.37
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• pp.371-405
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• 2004

International circulation of cultural assets involves numerous countries thereby making an approach based on international law essential to resolving this problem. Since the end of the $2^{nd}$ World War, as the value of cultural assets evolved from material value to moral and ethical values, with emphasis on establishing national identities, newly independent nations and former colonial states took issue with ownership of cultural assets which led to the need for international cooperation and statutory provisions for the return of cultural assets. UNESCO's 1954 "Convention for the Protection of Cultural Property in the Event of Armed Conflict" as preparatory measures for the protection of cultural assets, the 1970 "Convention on the Means of Prohibiting and Preventing the Illicit Import and Transfer of Ownership of Cultural Property" to regulate transfer of cultural assets, and the 1995 "Unidroit Convention on Stolen or Illegally Exported Cultural Objects" which required the return of illegally acquired cultural property are examples of international agreements established on illegal transfers of cultural assets. In addition, the UN agency UNESCO established the Division of Cultural Heritage to oversee cultural assets related matters, and the UN since its 1973 resolution 3187, has continued to demonstrate interest in protection of cultural assets. The resolution 3187 affirms the return of cultural assets to the country of origin, advises on preventing illegal transfers of works of art and cultural assets, advises cataloguing cultural assets within the respective countries and, conclusively, recommends becoming a member of UNESCO, composing a forum for international cooperation. Differences in defining cultural assets pose a limitation on international agreements. While the 1954 Convention states that cultural assets are not limited to movable property and includes immovable property, the 1970 Convention's objective of 'Prohibiting and preventing the illicit import, export and transfer of ownership of cultural property' effectively limits the subject to tangible movable cultural property. The 1995 Convention also has tangible movable cultural property as its subject. On this point, the two conventions demonstrate distinction from the 1954 Convention and the 1972 Convention that focuses on immovable cultural property and natural property. The disparity in defining cultural property is due to the object and purpose of the convention and does not reflect an inherent divergence. In the case of Korea, beginning with the 1866 French invasion, 36 years of Japanese colonial rule, military rule and period of economic development caused outflow of numerous cultural assets to foreign countries. Of course, it is neither possible nor necessary to have all of these cultural properties returned, but among those that have significant value in establishing cultural and historical identity or those that have been taken symbolically as a demonstration of occupational rule can cause issues in their return. In these cases, the 1954 Convention and the ratification of the first legislation must be actively considered. In the return of cultural property, if the illicit acquisition is the core issue, it is a simple matter of following the international accords, while if it rises to the level of diplomatic discussions, it will become a political issue. In that case, the country requesting the return must convince the counterpart country. Realizing a response to the earnest need for preventing illicit trading of cultural assets will require extensive national and civic societal efforts in the East Asian area to overcome its current deficiencies. The most effective way to prevent illicit trading of cultural property is rapid circulation of information between Interpol member countries, which will require development of an internet based communication system as well as more effective deployment of legislation to prevent trading of illicitly acquired cultural property, subscription to international conventions and cataloguing collections.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.197-218
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• 2019

In addition to economic growth and national income increase, China is also experiencing rapid growth in consumption of cosmetics. About 67% of the total trade volume of Chinese cosmetics is made by e-commerce and especially K-Beauty products, which are Korean cosmetics are very popular. According to previous studies, 80% of consumer goods such as cosmetics are affected by the word of mouth information, searching the product information before purchase. Mostly, consumers acquire information related to cosmetics through comments made by other consumers on SNS such as SINA Weibo and Wechat, and recently they also use information about beauty related video channels. Most of the previous online word-of-mouth researches were mainly focused on media itself such as Facebook, Twitter, and blogs. However, the informational characteristics and the expression forms are also diverse. Typical types are text, picture, and video. This study focused on these types. We analyze the unstructured data of SINA Weibo, the SNS representative platform of China, and Meipai, the video platform, and analyze the impact of K-Beauty brand sales by dividing online word-of-mouth information with quantity and direction information. We analyzed about 330,000 data from Meipai, and 110,000 data from SINA Weibo and analyzed the basic properties of cosmetics. As a result of analysis, the amount of online word-of-mouth information has a positive effect on the sales of cosmetics irrespective of the type of media. However, the online videos showed higher impacts than the pictures and texts. Therefore, it is more effective for companies to carry out advertising and promotional activities in parallel with the existing SNS as well as video related information. It is understood that it is important to generate the frequency of exposure irrespective of media type. The positiveness of the video media was significant but the positiveness of the picture and text media was not significant. Due to the nature of information types, the amount of information in video media is more than that in text-oriented media, and video-related channels are emerging all over the world. In particular, China has made a number of video platforms in recent years and has enjoyed popularity among teenagers and thirties. As a result, existing SNS users are being dispersed to video media. We also analyzed the effect of online type of information on the online cosmetics sales by dividing the product type of cosmetics into basic cosmetics and color cosmetics. As a result, basic cosmetics had a positive effect on the sales according to the number of online videos and it was affected by the negative information of the videos. In the case of basic cosmetics, effects or characteristics do not appear immediately like color cosmetics, so information such as changes after use is often transmitted over a period of time. Therefore, it is important for companies to move more quickly to issues generated from video media. Color cosmetics are largely influenced by negative oral statements and sensitive to picture and text-oriented media. Information such as picture and text has the advantage and disadvantage that the process of making it can be made easier than video. Therefore, complaints and opinions are generally expressed in SNS quickly and immediately. Finally, we analyzed how product diversity affects sales according to online word of mouth information type. As a result of the analysis, it can be confirmed that when a variety of products are introduced in a video channel, they have a positive effect on online cosmetics sales. The significance of this study in the theoretical aspect is that, as in the previous studies, online sales have basically proved that K-Beauty cosmetics are also influenced by word-of-mouth. However this study focused on media types and both media have a positive impact on sales, as in previous studies, but it has been proven that video is more informative and influencing than text, depending on media abundance. In addition, according to the existing research on information direction, it is said that the negative influence has more influence, but in the basic study, the correlation is not significant, but the effect of negation in the case of color cosmetics is large. In the case of temporal fashion products such as color cosmetics, fast oral effect is influenced. In practical terms, it is expected that it will be helpful to use advertising strategies on the sales and advertising strategy of K-Beauty cosmetics in China by distinguishing basic and color cosmetics. In addition, it can be said that it recognized the importance of a video advertising strategy such as YouTube and one-person media. The results of this study can be used as basic data for analyzing the big data in understanding the Chinese cosmetics market and establishing appropriate strategies and marketing utilization of related companies.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.33-56
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• 2017

As Internet usage is becoming more common worldwide and smartphone become necessity in daily life, technologies and applications related to mobile Internet are developing rapidly. The results of the Internet usage patterns of consumers around the world imply that there are many potential new business opportunities for mobile Internet technologies and applications. The location-based service (LBS) is a service based on the location information of the mobile device. LBS has recently gotten much attention among many mobile applications and various LBSs are rapidly developing in numerous categories. However, even with the development of LBS related technologies and services, there is still a lack of empirical research on the intention to use LBS. The application of previous researches is limited because they focused on the effect of one particular factor and had not shown the direct relationship on the intention to use LBS. Therefore, this study presents a research model of factors that affect the intention to use and actual use of LBS whose market is expected to grow rapidly, and tested it by conducting a questionnaire survey of 330 users. The results of data analysis showed that service customization, service quality, and personal innovativeness have a positive effect on the intention to use LBS and the intention to use LBS has a positive effect on the actual use of LBS. These results implies that LBS providers can enhance the user's intention to use LBS by offering service customization through the provision of various LBSs based on users' needs, improving information service qualities such as accuracy, timeliness, sensitivity, and reliability, and encouraging personal innovativeness. However, privacy concerns in the context of LBS are not significantly affected by service customization and personal innovativeness and privacy concerns do not significantly affect the intention to use LBS. In fact, the information related to users' location collected by LBS is less sensitive when compared with the information that is used to perform financial transactions. Therefore, such outcomes on privacy concern are revealed. In addition, the advantages of using LBS are more important than the sensitivity of privacy protection to the users who use LBS than to the users who use information systems such as electronic commerce that involves financial transactions. Therefore, LBS are recommended to be treated differently from other information systems. This study is significant in the theoretical point of contribution that it proposed factors affecting the intention to use LBS in a multi-faceted perspective, proved the proposed research model empirically, brought new insights on LBS, and broadens understanding of the intention to use and actual use of LBS. Also, the empirical results of the customization of LBS affecting the user's intention to use the LBS suggest that the provision of customized LBS services based on the usage data analysis through utilizing technologies such as artificial intelligence can enhance the user's intention to use. In a practical point of view, the results of this study are expected to help LBS providers to develop a competitive strategy for responding to LBS users effectively and lead to the LBS market grows. We expect that there will be differences in using LBSs depending on some factors such as types of LBS, whether it is free of charge or not, privacy policies related to LBS, the levels of reliability related application and technology, the frequency of use, etc. Therefore, if we can make comparative studies with those factors, it will contribute to the development of the research areas of LBS. We hope this study can inspire many researchers and initiate many great researches in LBS fields.

• Journal of National Security and Military Science
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• s.13
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• pp.687-738
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• 2016

To prepare for the complicated international relationship regarding Korean Peninsula after reunification, this thesis started off with the awareness that Unified Korea should build its international posture and national security at an early stage by determining its appropriate military strength for independent defense and military strategies that Unified Korea should aim. The main theme of this thesis is 'The research on appropriate military strength of the Unified Korean military'. To derive appropriate military strength of Unified Korea, this research focuses on conflict scenario and relative balance strategy based on potential threats posed by neighboring countries, and this is the part that differentiates this research from other researches. First of all, the main objective of the research is to decide appropriate military strength for Unified Korea to secure defense sufficiency. For this, this research will decide efficient military strategy that Unified Korea should aim. Than by presuming the most possible military conflict scenario, this research will judge the most appropriate military strength for Unified Korea to overcome the dispute. Second, after deciding appropriate military strength, this research will suggest how to operate presumed military strength in each armed force. The result of this thesis is as in the following. First, Unified Korea should aim 'relative balance strategy'. 'Relative balance strategy' is a military strategy which Unified Korea can independently secure defense sufficiency by maintaining relative balance when conflicts occur between neighboring countries. This strategy deters conflicts in advance by relative balance of power in certain time and place. Even if conflict occurs inevitably, this strategy secures initiative. Second, when analyzing neighboring countries interest and strategic environment after unification, the possibility of all-out war will be low in the Korean Peninsula because no other nation wants the Korean Peninsula to be subordinated to one single country. Therefore appropriate military strength of the Unified Korean military would be enough when Unified Korea can achieve relative balance in regional war or limited war. Third, Northeast Asia is a region where economic power and military strength is concentrated. Despite increasing mutual cooperation in the region, conflicts and competition to expand each countries influence is inherent. Japan is constantly enhancing their military strength as they aim for normal statehood. China is modernizing their military strength as they aspire to become global central nation. Russia is also enhancing their military strength in order to hold on to their past glory of Soviet Union as a world power. As a result, both in quality and quantity, the gap between military strength of Unified Korea and each neighboring countries is enlarged at an alarming rate. Especially in the field of air-sea power, arms race is occurring between each nation. Therefore Unified Korea should be equipped with appropriate military strength in order to achieve relative balance with each threats posed by neighboring countries. Fourth, the most possible conflicts between Unified Korea and neighboring countries could be summarized into four, which are Dokdo territorial dispute with Japan, Leodo jurisdictional dispute with China, territorial dispute concerning northern part of the Korea Peninsula with China and disputes regarding marine resources and sea routes with Russia. Based on those conflict scenarios, appropriate military strength for Unified Korea is as in the following. When conflict occurs with Japan regarding Dokdo, Japan is expected to put JMSDF Escort Flotilla 3, one out of four of its Japan Maritime Self-Defense Force Escort Fleet, which is based in Maizuru and JMSDF Maizuru District. To counterbalance this military strength, Unified Korea needs one task fleet, comprised with three task flotilla. In case of jurisdictional conflict with China concerning Leodo, China is expected to dispatch its North Sea fleet, one out of three of its naval fleet, which is in charge of the Yellow Sea. To response to this military action, Unified Korea needs one task fleet, comprised with three task flotilla. In case of territorial dispute concerning northern part of the Korean Peninsula with China, it is estimated that out of seven Military Region troops, China will dispatch two Military Region troops, including three Army Groups from Shenyang Military Region, where it faces boarder with the Korean Peninsula. To handle with this military strength, Unified Korea needs six corps size ground force strength, including three corps of ground forces, two operational reserve corps(maneuver corps), and one strategic reserve corps(maneuver corps). When conflict occurs with Russia regarding marine resources and sea routes, Russia is expected to send a warfare group of a size that includes two destroyers, which is part of the Pacific Fleet. In order to balance this strength, Unified Korea naval power requires one warfare group including two destroyers. Fifth, management direction for the Unified Korean military is as in the following. Regarding the ground force management, it would be most efficient to deploy troops in the border area with china for regional and counter-amphibious defense. For the defense except the border line with china, the most efficient form of force management would be maintaining strategic reserve corps. The naval force should achieve relative balance with neighboring countries when there is maritime dispute and build 'task fleet' which can independently handle long-range maritime mission. Of the three 'task fleet', one task fleet should be deployed at Jeju base to prepare for Dokdo territorial dispute and Leodo jurisdictional dispute. Also in case of regional conflict with china, one task fleet should be positioned at Yellow Sea and for regional conflict with Japan and Russia, one task fleet should be deployed at East Sea. Realistically, Unified Korea cannot possess an air force equal to neither Japan nor China in quantity. Therefore, although Unified Korea's air force might be inferior in quantity, they should possess the systematic level which Japan or China has. For this Unified Korea should build air base in island areas like Jeju Island or Ullenong Island to increase combat radius. Also to block off infiltration of enemy attack plane, air force needs to build and manage air bases near coastal areas. For landing operation forces, Marine Corps should be managed in the size of two divisions. For island defense force, which is in charge of Jeju Island, Ulleung Island, Dokdo Island and five northwestern boarder island defenses, it should be in the size of one brigade. Also for standing international peace keeping operation, it requires one brigade. Therefore Marine Corps should be organized into three divisions. The result of the research yields a few policy implications when building appropriate military strength for Unified Korea. First, Unified Korea requires lower number of ground troops compared to that of current ROK(Republic of Korea) force. Second, air-sea forces should be drastically reinforced. Third, appropriate military strength of the Unified Korean military should be based on current ROK military system. Forth, building appropriate military strength for Unified Korea should start from today, not after reunification. Because of this, South Korea should build a military power that can simultaneously prepare for current North Korea's provocations and future threats from neighboring countries after reunification. The core of this research is to decide appropriate military strength for Unified Korea to realize relative balance that will ensure defense sufficiency from neighboring countries threats. In other words, this research should precisely be aware of threats posed by neighboring countries and decide minimum level of military strength that could realize relative balance in conflict situation. Moreover this research will show the path for building appropriate military strength in each armed force.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• 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.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.53-77
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• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.