• Journal of Information Technology Services
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• v.23 no.2
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• pp.63-81
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• 2024

With the advancement of Information and Communication Technologies (ICT) and Artificial Intelligence (AI), the metaverse has emerged as a transformative model across various sectors, offering a three-dimensional virtual world where activities mirroring the real world occur. This study delves into the significant factors influencing fashion brand companies' investments in the metaverse, an evolved concept from Virtual Reality (VR) that extends beyond gaming to include real-life activities through avatars. This study highlights the surge in virtual fashion engagements, as evidenced by increased avatar updates and purchases of digital fashion items on platforms like Roblox. Luxury brands are steadily entering the metaverse indicating a new revenue stream within the fashion industry. This study employs a mixed-methods approach, integrating text mining and interviews to identify key factors for fashion companies considering metaverse investments. By proposing strategies based on these findings, this study not only enriches academic discourse in fashion, e-commerce, and information systems but also serves as a guideline for fashion companies aiming to navigate the burgeoning digital market, contributing to the generation of new revenue streams in the fashion sector.

• Economic and Environmental Geology
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• v.54 no.5
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• pp.573-580
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• 2021

This study analyzed the induced effects of the aggregate and stone sectors using the industry association table. First, the added value of the aggregate and stone sectors was summarized, and then the intermediate input structure and induced effect were analyzed. In terms of value-added structure, aggregate and stone showed a higher employee remuneration rate compared to the manufacturing industry, and a higher rate of operating surplus compared to other mining industries. The intermediate input structure summarizes the sector using aggregate and stone products as intermediate inputs and their input ratio. The proportion of the intermediate element input structure was confirmed. In addition, the main input sectors of ready-mixed concrete, the largest consumer of aggregate and stone, are also summarized. The production-inducing effect of aggregate and stone showed a higher influence coefficient than the sensitivity coefficient, confirming that they had a relatively large rear chain effect. The production inducement effect was reviewed by reconstructing the industry association table, and it was found to show a relative superiority in the influence coefficient, similar to the results derived according to the provisional classification of the Bank of Korea.

• Resources Recycling
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• v.32 no.2
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• pp.12-18
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• 2023

In 2018, the demand for silver (referred to as Ag) in the electrical and electronics sector was 249 million tons. The demand stood at 81 million tons in the solar module production sector. Currently, due to the rapid increase in solar module installation, the demand for silver is increasing drastically in Korea. However, Korea's natural metal resources and reserves are insufficient in comparison to their consumption, and the domestic silver ore self-sufficiency rate was as low as 2.2% as of 2021. This implies that a recycling technology is necessary to recover valuable metal resources contained in the waste plating solution generated in the metal industry. Therefore, this study compared and analyzed, the results of the impact evaluation through life cycle assessment according to an improvement in the process of recovery of valuable metals in the waste plating solution. The process improvement resulted in reducing GWP (Global Warming Potential) and ADP(Abiotic Depletion Potential) by 50% and 67%, respectively. The GWP of electricity and industrial water was reduced by 98% and 93%, respectively, which significantly contributed to the minimization of energy and water consumption. Thus, the improvement in recycling technology has a high potential to reduce chemical and energy use and improve resource productivity in the urban mining industry.

• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.81-98
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• 2020

In this study, forest-related research papers published in North Korean journals were analyzed to understand the research trends in North Korean forest science. The Korea Science and Technology Information Institute (KISTI) North Korea Science and Technology Network (NKtech) is constructing a database related to science and technology in North Korea. From this, a total of 1,389 articles published from 1962 to 2016 were collected with forest science key words based on the South Korean National Science and Technology Standard Classification System. The topics were divided into four categories: afforestation, forest protection, forest use, and forest management. In the field of afforestation, research activities on nursery and agroforestry were active, and the survival rate was emphasized. In the forest protection field, there was a significant research effort into forest pests, and efforts were being made to reduce soil erosion through agroforestry. In the field of forest use, research activities on pulp/paper and mushrooms were active. In the forest management field, activities related to "ecological information" were conspicuous, and efforts were being made to reduce carbon. These results suggest that the perspective of North Korean forest research has changed from nature reorganization to nature protection. Thus, a comparative study on forest science and technology in each sub-sector of the forest research field, along with analysis of the relationship between policy direction and research direction of North Korea over time, would be worthwhile future investigations. To overcome the problem of technical terminology, a compilation/dictionary of inter-Korean forestry terminology would be useful for effective communication between the two Koreas.

• Economic and Environmental Geology
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• v.48 no.5
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• pp.391-400
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• 2015

Korea is a global steel producer and a major consumer while iron ore producing is very low compared to the demand. On the other hand, North Korea holds tremendous amount of iron reserves and, however, its producing rate is limited. Moreover, the data regarding mineral resources of North Korea is very limited and uncertain because of political isolation. This study estimated the amount of iron ore resource and production amount for the Musan Iron mine, the world-known open-pit mine of North Korea, using satellite imagery(Landsat MSS, ASTER) and digital maps between 1976 to 2007. As a result, the mining area of Musan mine was increased by $6.1km^2$ during the 30 years and the mining sector was estimated as $4.9km^2$. We estimated the iron resources and production amount of 0.7 and 0.2 billion metric tons, respectively based on 3D modeling and average iron ore density of Anshan formation in China. This amount indicates 8.1 million tons of annual average production and it coincides well with previous reports. We expect this study would be utilized significantly on inter-Korean exchange programs by providing trustable preliminary data.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.33-49
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• 2018

The importance of convergence activities among business is increasing due to the necessity of designing and developing new products to satisfy various customers' needs. In particular, decision makers such as CEOs are required to participate in networks between entrepreneurs for being connected with valuable convergence partners. Moreover, it is important for entrepreneurs not only to make a large number of network connections, but also to understand the networking relationship with entrepreneurs with similar topic information. However, there is a difficult limit in collecting the topic information that can show the lack of current status of business and the technology and characteristics of entrepreneur in industry sector. In this paper, we solve these problems through the topic extraction method and analyze the business network in three aspects. Specifically, there are C, S, T-Layer models, and each model analyzes amount of entrepreneurs relationship, network centrality, and topic similarity. As a result of experiments using real data, entrepreneur need to activate network by connecting high centrality entrepreneur when the corporate relationship is low. In addition, we confirmed through experiments that there is a need to activate the topic-based network when topic similarity is low between entrepreneurs.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.146-172
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• 2018

In the Small and Medium Enterprises(SMEs) sector, research has shown an increasing trend due to changes in industrial society and policy. Therefore, the interrelationship between academic research and policy is relatively high. In this study, we analyzed the trends of academic research related to SMEs innovation policy. Moreover, we examined the interrelationships. By using text mining techniques, we have identified key themes and changes in domestic policy papers published since the announcement of the "Five-Year Plan for Innovation of SMEs". Also, we compared them with "Five-Year Plan for Innovation of SMEs" of each period. The result shows that the gap between academic research and policy has been closing over time. This study shows that there is an increasing number of research studies that verify policies at the relevant time from an academic point of view, and that policy issues are in turn influencing academic research due to government-driven policies. Also, it was confirmed that there was a time gap between academic research and policy. Academic research tended to increase compared to the previous year's level, when the policy had been implemented. The results of this study are expected to contribute to the establishment of the "2019~2023 five-year plan for Small and Medium Enterprises" which will be announced in the future, and this study will demonstrate the possibility of devising evidence-based policy.

• Information Systems Review
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• v.25 no.3
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• pp.179-197
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• 2023

As a sufficient workforce supports the industry's growth, workforce training has also been carried out as part of the industry promotion policy. However, the market still has a shortage of skilled mid-level workers. The information security disclosure requires organizations to secure personnel responsible for information security work. Still, the division between information technology work and job areas is unclear, and the pay is not high for responsibility. This paper compares job keywords in advertisements for the information security workforce for 2014, 2019, and 2022. There is no difference in the keywords describing the job duties of information security personnel in the three years, such as implementation, operation, technical support, network, and security solution. To identify the actual needs of companies, we also analyzed and compared the contents of job advertisements posted on online recruitment sites with information security sector knowledge and skills defined by the National Competence Standards used for comprehensive vocational training. It was found that technical skills such as technology development, network, and operating system are preferred in the actual workplace. In contrast, managerial skills such as the legal system and certification systems are prioritized in vocational training.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.75-94
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• 2021

The low birth rate and shortened military service period are causing concerns about selecting excellent military officers. The Republic of Korea entered a low birth rate society in 1984 and an aged society in 2018 respectively, and is expected to be in a super-aged society in 2025. In addition, the troop-oriented military is changed as a state-of-the-art weapons-oriented military, and the reduction of the military service period was implemented in 2018 to ease the burden of military service for young people and play a role in the society early. Some observe that the application rate for military officers is falling due to a decrease of manpower resources and a preference for shortened mandatory military service over military officers. This requires further consideration of the policy of securing excellent military officers. Most of the related studies have used social scientists' methodologies, but this study applies the methodology of text mining suitable for large-scale documents analysis. This study extracts words of discriminative characteristics from the Republic of Korea Air Force Non-Commissioned Officer Applicant cover letters and analyzes the polarity of pass and fail. It consists of three steps in total. First, the application is divided into general and technical fields, and the words characterized in the cover letter are ordered according to the difference in the frequency ratio of each field. The greater the difference in the proportion of each application field, the field character is defined as 'more discriminative'. Based on this, we extract the top 50 words representing discriminative characteristics in general fields and the top 50 words representing discriminative characteristics in technology fields. Second, the number of appropriate topics in the overall cover letter is calculated through the LDA. It uses perplexity score and coherence score. Based on the appropriate number of topics, we then use LDA to generate topic and probability, and estimate which topic words of discriminative characteristic belong to. Subsequently, the keyword indicators of questions used to set the labeling candidate index, and the most appropriate index indicator is set as the label for the topic when considering the topic-specific word distribution. Third, using L-LDA, which sets the cover letter and label as pass and fail, we generate topics and probabilities for each field of pass and fail labels. Furthermore, we extract only words of discriminative characteristics that give labeled topics among generated topics and probabilities by pass and fail labels. Next, we extract the difference between the probability on the pass label and the probability on the fail label by word of the labeled discriminative characteristic. A positive figure can be seen as having the polarity of pass, and a negative figure can be seen as having the polarity of fail. This study is the first research to reflect the characteristics of cover letters of Republic of Korea Air Force non-commissioned officer applicants, not in the private sector. Moreover, these methodologies can apply text mining techniques for multiple documents, rather survey or interview methods, to reduce analysis time and increase reliability for the entire population. For this reason, the methodology proposed in the study is also applicable to other forms of multiple documents in the field of military personnel. This study shows that L-LDA is more suitable than LDA to extract discriminative characteristics of Republic of Korea Air Force Noncommissioned cover letters. Furthermore, this study proposes a methodology that uses a combination of LDA and L-LDA. Therefore, through the analysis of the results of the acquisition of non-commissioned Republic of Korea Air Force officers, we would like to provide information available for acquisition and promotional policies and propose a methodology available for research in the field of military manpower acquisition.