• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.379-389
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• 2019

The study empirically investigates financial factors that may influence on corporate R&D intensity during the post-era of the global financial turmoil (from 2010 to 2015) to mitigate possible spillover effect associated with the crisis. Concerning the empirical research settings of the study, chaebol firms listed in the KOSPI stock market are used as sample data with adopting various econometric estimation methods to enhance validity of the results. In the first hypothesis test, it is found that there exist inter-industry financial differences in terms of the ratio of R&D expenditure across all the sample years, but the statistical differences may arise from only a few domestic industries beloning to the high-growth sector. Moreover, it is also interesting to identify that, for the high-tech sector, 3 explanatory variables such as R&D intensity in a prior year, firm size and change in cash holdings are proved to be financial factors to discriminate between chaebol firms and their counterparts of non-chaebol firms, whereas a proportion of tangible assets over total assets as well as the former two variables are shown to be significant factors on the R&D intensity for the low-tech sector.

• Journal of Korea Entertainment Industry Association
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• v.15 no.6
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• pp.55-70
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• 2021

This study investigates the following important financial questions using entertainment enterprises: 1) how does cash reserve affect a firm's financial value? 2) what factors influence the level of cash retention of a firm? For empirical tests, we use accounting and financial data of entertainment companies listed in the KOSPI and KOSDAQ markets for a long-term time period covering from 2000 to 2018. The main findings of this paper are as follows: First, entertainment companies maintain higher level of cash holdings compared to non-entertainment companies. Second, the cash holdings of entertainment enterprises have positive influence on firms' financial value. Third, among various firm characteristics known for affecting the cash holdings level, leverage and profitability exhibit strong relationships in entertainment enterprises. Entertainment firms with lower leverage and higher profitability tend to reserve more cash inside them. These findings suggest that entertainment companies are highly valued by stock market participants as having prospective opportunities, thus, firms with sufficient cash holdings tend to have higher firm value. In addition, these findings imply that cash in entertainment enterprises functions as a substitute for debts and the cash holdings are less likely driven by agency problems.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.219-228
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• 2022

In order to improve audit quality, it is essential to understand the occurrence of disagreement between auditors and managers, and this study aims to analyze the impact of Business Strategies on audit risk and accounting audit delay. To this end, we conducted an empirical analysis using sample 2,910 firm-year data from 2018 to 2020 of KOSPI-listed and KOSDAQ-listed companies. The results of the empirical analysis of this study are as follows. First, compared to the companies of defender type, prospectors can expand audit procedures for new products, R&D costs, and intangible assets, and increase audit delays due to disagreement between managers and auditors. Second, compared to KOSPI-listed companies, the prospectors in KOSDAQ are more likely to have lower financial reporting quality, which further increases audit delays. The results of this study analyzed whether a company's Business Strategy affects the possibility of disagreement between an auditor and a company, and verified whether there is a difference in the audit report lag by stock market. The results of this study show that auditors' strong duty of care is needed for the companies of prospector type with high audit risk, and it is meaningful to present reinforced audit systems and specific guidelines for the companies of prospector type through the definition of prospector type. It also enables the expansion of research to identify the relationship between non-financial factors and audit risks that make up the companies of prospector type.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.151-159
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• 2010

Currently, by using the Internet, We can do varius things such as Web surfing, email, on-line shopping, stock trading on your home or office. However, as being out of the concept of security from the beginning, it is the big social issues that malicious user intrudes into the system through the network, on purpose to steal personal information or to paralyze system. In addition, network intrusion by ordinary people using network attack tools is bringing about big worries, so that the need for effective and powerful intrusion detection system becomes very important issue in our Internet environment. However, it is very difficult to prevent this attack perfectly. In this paper we proposed the algorithm for the detection of DoS attacks, and developed attack detection tools. Through learning in a normal state on Step 1, we calculate thresholds, the number of packets that are coming to each port, the median and the average utilization of each port on Step 2. And we propose values to determine how to attack detection on Step 3. By programing proposed attack detection algorithm and by testing the results, we can see that the difference between the median of packet mounts for unit interval and the average utilization of each port number is effective in detecting attacks. Also, without the need to look into the network data, we can easily be implemented by only using the number of packets to detect attacks.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.28 no.4
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• pp.158-177
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• 2023

An estuary is a transitional water area that links the land and sea through rivers and streams, transporting various components from the land to the sea, which plays an important role in determining primary productivity in the coastal environment, and this coastal ecosystem captures a huge amount of carbon into biomass, known as blue carbon, which mitigates climate change as a potential carbon reservoir. This study examined the variation of mean grain size and organic carbon content of the surface sediments for 6 years and analyzed their relationship in the western and southern estuarine areas (Han River Estuary, Geum River Estuary, Yeongsan River Estuary, Seomjin River Estuary, and Nakdong River Estuary) and the East Sea upwelling area. During the sampling period (2015 to 2020), seasonal variation of both properties was not observed, because their variations might be controlled by diverse oceanographic environments and hydrographic conditions within each survey area. However, despite the synoptic problem of all samples, the positive relationship was obtained between the averages of mean grain size and organic carbon content, which clearly distinguishes each survey area. The unique positive relationship in all estuarine areas implies that the same process by sediment clay particles is important in the organic carbon accumulation. However, additional important factor may be expected in the organic carbon accumulation in the East Sea upwelling area. Further necessary data (sedimentation rate, dry bulk density etc) should be required for the estimation of carbon stock to evaluate the major estuaries in Korea as potential carbon reservoirs in the coastal environment.

• Nutrition Research and Practice
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• v.18 no.1
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• pp.149-164
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• 2024

BACKGROUND/OBJECTIVES: The economic recession caused by the coronavirus disease 2019 pandemic disproportionately affected poor and vulnerable populations globally. Better uunderstanding of vulnerability to shocks in food supply and demand in the Asia Pacific region is needed. SUBJECTS/METHODS: Using secondary data from rapid assessment surveys during the pandemic response (n = 10,420 in mid-2020; n = 6,004 in mid-2021) in India, Indonesia, Myanmar, and Vietnam, this study examined the risk factors for reported income reduction or job loss in mid-2021 and the temporal trend in food security status (household food availability, and market availability and affordability of essential items) from mid-2020 to mid-2021. RESULTS: The proportion of job loss/reduced household income was highest in India (60.4%) and lowest in Indonesia (39.0%). Urban residence (odds ratio [OR] range, 2.20-4.11; countries with significant results only), female respondents (OR range, 1.40-1.69), engagement in daily waged labor (OR range, 1.54-1.68), and running a small trade/business (OR range, 1.66-2.71) were significantly associated with income reduction or job loss in three out of 4 countries (all P < 0.05). Food stock availability increased significantly in 2021 compared to 2020 in all four countries (OR range, 1.91-4.45) (all P < 0.05). Availability of all essential items at markets increased in India (OR range, 1.45-3.99) but decreased for basic foods, hygiene items, and medicine in Vietnam (OR range, 0.81-0.86) in 2021 compared to 2020 (all P < 0.05). In 2021, the affordability of all essential items significantly improved in India (OR range, 1.18-3.49) while the affordability of rent, health care, and loans deteriorated in Indonesia (OR range, 0.23-0.71) when compared to 2020 (all P < 0.05). CONCLUSIONS: Long-term social protection programs need to be carefully designed and implemented to address food insecurity among vulnerable groups, considering each country's market conditions, consumer food purchasing behaviors, and financial support capacity.

• Journal of Digital Convergence
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• v.21 no.3
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• pp.1-14
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• 2023

This raises the question of how competitive advantage can be created, prompting firms to enhance their capacity for change. In this context, the role of knowledge creation becomes increasingly vital. This research aims to explore the role of intellectual capital and how to improve knowledge cration ability through absorptive capacity framework. It examines the links among knowledge acquisition, learning of new knowledge, knowledge creation, intellectual capital, and competitive advantage, drawing from both internal and external sources. The study focuses on small and medium-sized supplier firms in Korea, with data collected from 15 industries, totaling 106 responses. The research model employs structural equation modeling (SEM) and utilizes AMOS 22 for analysis. As anticipated, all hypotheses were supported. The study provides robust evidence that absorptive capacity is a pivotal factor in cultivating suppliers' competitive advantage. Furthermore, it posits that intellectual capital should be viewed as a criucial component of suppliers' knowledge stock, significantly enhancing the impact of absorptive capacity on their competitive edge. Future studies should aim to validate the research model in different international settings or across multinational corporations to enhance its generalizabulity.

• Information Systems Review
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• v.26 no.2
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• pp.45-57
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• 2024

This study develops a deep learning-based methodology for predicting Crude Palm Oil (CPO) prices. Palm oil is an essential resource across various industries due to its yield and economic efficiency, leading to increased industrial interest in its price volatility. While numerous studies have been conducted on palm oil price prediction, most rely on time series forecasting, which has inherent accuracy limitations. To address the main limitation of traditional methods-the absence of stationarity-this research introduces a novel model that uses the ratio of future prices to current prices as the dependent variable. This approach, inspired by return modeling in stock price predictions, demonstrates superior performance over simple price prediction. Additionally, the methodology incorporates the consideration of lag values of independent variables, a critical factor in multivariate time series forecasting, to eliminate unnecessary noise and enhance the stability of the prediction model. This research not only significantly improves the accuracy of palm oil price prediction but also offers an applicable approach for other economic forecasting issues where time series data is crucial, providing substantial value to the industry.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.124-138
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• 2018

This research was conducted to examine the availability of spatial data for assessing absorption rates of $CO_2$ in the forest of Ansan-si and evaluate the validity of methods that analyze $CO_2$ absorption. To statistically assess the $CO_2$ absorption rates per year, the 1:5,000 Digital Forest-Map (Lim5000) and Standard Carbon Removal of Major Forest Species (SCRMF) methods were employed. Furthermore, Land Cover Map (LCM) was also used to verify $CO_2$ absorption rate availability per year. Great variations in $CO_2$ absorption rates occurred before and after the year 2010. This was due to improvement in precision and accuracy of the Forest Basic Statistics (FBS) in 2010, which resulted in rapid increase in growing stock. Thus, calibration of data prior to 2010 is necessary, based on recent FBS standards. Previous studies that employed Lim5000 and FBS (2015, 2010) did not take into account the $CO_2$ absorption rates of different tree species, and the combination of SCRMF and Lim5000 resulted in $CO_2$ absorption of 42,369 ton. In contrast to the combination of SCRMF and Lim5000, LCM and SCRMF resulted in $CO_2$ absorption of 40,696 ton. Homoscedasticity tests for Lim5000 and LCM resulted in p-value <0.01, with a difference in $CO_2$ absorption of 1,673 ton. Given that $CO_2$ absorption in forests is an important factor that reduces greenhouse gas emissions, the findings of this study should provide fundamental information for supporting a wide range of decision-making processes for land use and management.