• Journal of Technology Innovation
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• v.26 no.4
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• pp.199-232
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• 2018

There are increasing needs for understanding and fathoming of business management environment through big data analysis at industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm level analyses using publicly available company disclousre data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels. Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries' topic trend, software and hardware industries are compared in recent 20 years. Also, the changes of management subject at firm level are observed with comparison of two companies in software industry. The changes of topic trends provides lens for identifying decreasing and growing management subjects at industrial and firm level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at firm level in software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades. For suggesting methodology to develop analysis model based on public management data at industrial and corporate level, there may be contributions in terms of making ground of practical methodology to identifying changes of managements subjects. However, there are required further researches to provide microscopic analytical model with regard to relation of technology management strategy between management performance in case of related to various pattern of management topics as of frequent changes of management subject or their momentum. Also more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.48-61
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• 2022

South Korea is quite vegetation rich country which has 63% forests and 16% cropland area. Massive NOx emissions from megacities, therefore, are easily combined with BVOCs emitted from the forest and cropland area, then produce high ozone concentration. BVOCs emissions have been estimated using well-known emission models, such as BEIS (Biogenic Emission Inventory System) or MEGAN (Model of Emission of Gases and Aerosol from Nature) which were developed using non-Korean emission factors. In this study, we ran MEGAN v2.1 model to estimate BVO Cs emissions in Korea. The MO DIS Land Cover and LAI (Leaf Area Index) products over Korea were used to run the MEGAN model for June 2012. Isoprene and Monoterpenes emissions from the model were inter-compared against the enclosure chamber measurements from Taehwa research forest in Korea, during June 11 and 12, 2012. For estimating emission from the enclosed chamber measurement data. The initial results show that isoprene emissions from the MEGAN model were up to 6.4 times higher than those from the enclosure chamber measurement. Monoterpenes from enclosure chamber measurement were up to 5.6 times higher than MEGAN emission. The differences between two datasets, however, were much smaller during the time of high emissions. More inter-comparison results and the possibilities of improving the MEGAN modeling performance using local measurement data over Korea will be presented and discussed.

• Journal of Korea Entertainment Industry Association
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• v.14 no.6
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• pp.53-60
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• 2020

The study is based on the assumption that the trend of phenomena and trends in research are contextually consistent. Therefore the purpose of this study is to explore the trend of dance through the subject analysis of the Korean creative dance study by utilizing text mining. Thus, 1,291 words were analyzed in the 616 journal title, which were established on the paper search website. The collection, refining and analysis of the data were all R 3.6.0 SW. According to the study, keywords representing the times were frequently used before the 2000s, but Korean creative dance research types were also found in terms of education and physical training. Second, the frequency of keywords related to the dance troupe's performance was high after the 2000s, but it was confirmed that Choi Seung-hee was still in an important position in the study of Korean creative dance. Third, an analysis of the overall research subjects of the Korean creative dance study showed that the research on 'Art of Choi Seung-hee in the modern era' was the highest proportion. Fourth, the Hot Topics, which are rising as of 2000, appeared as 'the performance activities of the National Dance Company' and 'the choreography expression and utilization of traditional dance'. However, since the recent trend of the National Dance Company's performance is advocating 'modernization based on tradition', it has been confirmed that the trend of Korean creative dance since the 2000s has been focused on the use of traditional dance motifs. Fifth, the Cold Topic, which has been falling as of 2000, has been shown to be a study of 'dancing expressions by age'. It was judged that interest in research also decreased due to the tendency to mix various dance styles after the establishment of the genre of Korean creative dance.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.181-203
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• 2023

YouTube exhibits a hybrid personality, incorporating traits of both over-the-top (OTT) and personal broadcasting platforms. However, limited research has investigated these hybrid characteristics, particularly in the context of paid YouTube channel memberships. Therefore, building upon consumption value theory and prior literature, this study examines the influence of consumption value factors associated with paid YouTube channel memberships on usage satisfaction and continuance intention. Specifically, the study identifies four perceived consumption value factors (functional, social, emotional, and epistemic values) within the paid YouTube channel membership context and assesses their impact on usage satisfaction and continuance intention. Additionally, the study explores the moderating role of conditional value (the experience of watching live streams on paid YouTube channels) in these relationships. Data was collected via an online survey from Korean adults who subscribed to multiple paid YouTube channel memberships, resulting in 274 responses. The proposed hypotheses were tested using structural equation modeling (SEM). The SEM results indicate that all four consumption value factors significantly influence usage satisfaction, with usage satisfaction in turn positively affecting continuance intention. Furthermore, the study reveals that conditional value moderates the relationships between functional/emotional values and usage satisfaction, as well as between usage satisfaction and continuance intention. This study is the first to focus on YouTube channel paid memberships, which encompass characteristics from both OTT and personal broadcasting platforms. It is anticipated that this research will offer insights to personal broadcasters and stakeholders regarding the motivational factors that impact user satisfaction and encourage subscriptions to channel memberships.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.28 no.3
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• pp.72-84
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• 2010

This study was attempted in order to offer basic data for implementing and applying Samjonseokjo(三尊石造), which is one of traditional stone construction method, by confirming how the constructive principle is expressed such as proportional beauty, which is contained in the modeling of Triad of Buddha Carved on Rock that was formed in the period of the Three States, centering on Triad of Buddha Carved on Rock in Susan. The summarized findings are as follows. 1. As a result of analyzing size and proportion of totally 17 of Triad of Buddha Carved on Rock, the average total height in Bonjonbul(本尊佛) was 2.96m. Right Hyeopsi(右挾侍) was 2.19m. Left Hyeopsi(左挾侍) was 2.16m. The height ratio according to this was 100:75:75, thereby having shown the relationship of left-right symmetrical balance. The area ratio in left-right Hyeopsi was 13.4:13.7, thereby the two area having been evenly matched. 2. The Triad of Buddha Carved on Rock in Seosan is carved on Inam(印岩) rock after crossing over Sambulgyo bridge of the Yonghyeon valley. Left direction was measured with $S47^{\circ}E$ in an angle of direction. This is judged to target an image change and an aesthetic sense in a Buddhist statue according to direction of sunlight while blocking worshipers' dazzling. 3. As for iconic characteristics of Buddha Carved on Rock in Seosan, there is even Hyeopsi in Bangasang(半跏像) and Bongjiboju(捧持寶珠) type Bosangipsang. In the face of Samjon composition in left-right asymmetry, the unification is indicated while the same line and shape are repeated. Thus, the stably visual balance is being shown. 4. In case of Triad of Buddha Carved on Rock in Seosan, total height in Bonjonbul, left Hyeopsi, and right Hyeopsi was 2.80m, 1.66m, and 1.70m, respectively. Height ratio in left-right Hyeopsibul was 0.60:0.62, thereby having been almost equal. On the other hand, the area ratio was 28.8:25.2, thereby having shown bigger difference. The area ratio on a plane was grasped to come closer to Samjae aesthetic proportion. 5. The axial angle of centering on Gwangbae was 84:46:50, thereby having been close to right angle. On the other hand, the axial angle ratio of centering on Yeonhwajwa(蓮華坐: lotus position) was measured to be 135:25:20, thereby having shown the form of inequilateral triangle close to obtuse angle. Accordingly, the upper part and the lower part of Triad of Buddha Carved on Rock in Susan are taking the stably proportional sense in the middle of maintaining the corresponding relationship through angular proportion of inequilateral triangle in right angle and obtuse angle. 6. The distance ratio in the upper half was 0.51:0.36:0.38. On the other hand, the distance ratio in the lower half was 0.53 : 0.33 : 0.27. Thus, the up-down and left-right symmetrical balance is being formed while showing the image closer to inequilateral triangle. 7. As a result of examining relationship of Samjae-mi(三才美) targeting Triad of Buddha Carved on Rock in Susan, the angular ratio was shown to be more notable that forms the area ratio or triangular form rather than length ratio. The inequilateral triangle, which is formed centering on Gwangbae(光背) in the upper part and Yeonhwajwa(lotus position) in the lower part, is becoming very importantly internal motive of doubling the constructive beauty among Samjae, no less than the mutually height and area ratio in Samjonbul.

• Asia Marketing Journal
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• v.14 no.2
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• pp.39-63
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• 2012

Previous studies of reward programs have generally focused on designing the best programs for consumers and suggested that consumers' perception of the value of reward programs can vary according to the type of reward program (e.g., hedonic vs. utilitarian and direct vs. indirect) and its timing (e.g., immediate vs. delayed). These studies have typically assumed that consumers' preference for reward programs has a positive effect on brand loyalty. However, Dowling and Uncles (1997) pointed out that this preference does not necessarily foster brand loyalty. In this regard, the present study verifies this assumption by examining the effects of consumers' perception of the value of reward programs on their brand loyalty. Although reward programs are widely used by online shopping malls, most studies have examined the conditions under which consumers are most likely to value loyalty programs in the context of offline shopping. In the context of online shopping, however, consumers' preferences may have little effect on their brand loyalty because they have more opportunities for comparing diverse reward programs offered by many online shopping malls. That is, in online shopping, finding attractive reward programs may require little effort on the part of consumers, who are likely to switch to other online shopping malls. Accordingly, this study empirically examines whether consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. Meanwhile, consumers seek utilitarian and/or hedonic value from their online shopping activity(Jones et al., 2006; Barbin et al., 1994). They visit online shopping malls to buy something necessary (utilitarian value) and/or enjoy the process of shopping itself (hedonic value). In this sense, reward programs may reinforce utilitarian as well as hedonic value, and their effect may vary according to the type of reward (utilitarian vs. hedonic). According to Chaudhuri and Holbrook (2001), consumers' perception of the value of a brand can influence their brand loyalty through brand trust and affect. Utilitarian value influences brand loyalty through brand trust, whereas hedonic value influences it through brand affect. This indicates that the effect of this perception on brand trust or affect may be moderated by the type of reward program. Specifically, this perception may have a greater effect on brand trust for utilitarian reward programs than for hedonic ones, whereas the opposite may be true for brand affect. Given the above discussion, the present study is conducted with three objectives in order to provide practical implications for online shopping malls to strategically use reward program for establishing profitable relationship with customers. First, the present study examines whether reward programs can be an effective marketing tool for increasing brand loyalty in the context of online shopping. Second, it investigates the paths through which consumers' perception of the value of reward programs influences their brand loyalty. Third, it analyzes the effects of this perception on brand trust and affect by considering the type of reward program as a moderator. This study suggests and empirically analyzes a new research model for examining how consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. The model postulates the following 10 hypotheses about the structural relationships between five constructs: (H1) Consumers' perception of the value of reward programs has a positive effect on their program loyalty; (H2) Program loyalty has a positive effect on brand loyalty; (H3) Consumers' perception of the value of reward programs has a positive effect on their brand trust; (H4) Consumers' perception of the value of reward programs has a positive effect on their brand affect; (H5) Brand trust has a positive effect on program loyalty; (H6) Brand affect has a positive effect on program loyalty; (H7) Brand trust has a positive effect on brand loyalty; (H8) Brand affect has a positive effect on brand loyalty; (H9) Consumers' perception of the value of reward programs is more likely to influence their brand trust for utilitarian reward programs than for hedonic ones; and (H10) Consumers' perception of the value of reward programs is more likely to influence their brand affect for hedonic reward programs than for utilitarian ones. To test the hypotheses, we considered a sample of 220 undergraduate students in Korea (male:113). We randomly assigned these participants to one of two groups based on the type of reward program (utilitarian: transportation card, hedonic: movie ticket). We instructed the participants to imagine that they were offered these reward programs while visiting an online shopping mall. We then asked them to answer some questions about their perception of the value of the reward programs, program loyalty, brand loyalty, brand trust, and brand affect, in that order. We also asked some questions about their demographic backgrounds and then debriefed them. We employed the structural equation modeling (SEM) method with AMOS 18.0. The results provide support for some hypotheses (H1, H3, H4, H7, H8, and H9) while providing no support for others (H2, H5, H6, H10) (see Figure 1). Noteworthy is that the path proposed by previous studies, "value perception → program loyalty → brand loyalty," was not significant in the context of online shopping, whereas this study's proposed path, "value perception → brand trust/brand affect → brand loyalty," was significant. In addition, the results indicate that the type of reward program moderated the relationship between consumers' value perception and brand trust but not the relationship between their value perception and brand affect. These results have some important implications. First, this study is one of the first to examine how consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. In particular, the results indicate that the proposed path, "value perception → brand trust/brand affect → brand loyalty," can better explain the effects of reward programs on brand loyalty than existing paths. Furthermore, these results suggest that online shopping malls should place greater emphasis on the type of reward program when devising reward programs. To foster brand loyalty, they should reinforce the type of shopping value that consumers emphasize by providing them with appropriate reward programs. If consumers prefer utilitarian value to hedonic value, then online shopping malls should offer utilitarian reward programs and vice versa.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.

• Economic and Environmental Geology
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• v.56 no.5
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• pp.603-618
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• 2023

This study focused on evaluating the suitability of the WRK (waste repository Korea) bentonite as a buffer material in the SNF (spent nuclear fuel) repository. The U (uranium) adsorption/desorption characteristics and the adsorption mechanisms of the WRK bentonite were presented through various analyses, adsorption/desorption experiments, and kinetic adsorption modeling at various pH conditions. Mineralogical and structural analyses supported that the major mineral of the WRK bentonite is the Ca-montmorillonite having the great possibility for the U adsorption. From results of the U adsorption/desorption experiments (intial U concentration: 1 mg/L) for the WRK bentonite, despite the low ratio of the WRK bentonite/U (2 g/L), high U adsorption efficiency (>74%) and low U desorption rate (<14%) were acquired at pH 5, 6, 10, and 11 in solution, supporting that the WRK bentonite can be used as the buffer material preventing the U migration in the SNF repository. Relatively low U adsorption efficiency (<45%) for the WRK bentonite was acquired at pH 3 and 7 because the U exists as various species in solution depending on pH and thus its U adsorption mechanisms are different due to the U speciation. Based on experimental results and previous studies, the main U adsorption mechanisms of the WRK bentonite were understood in viewpoint of the chemical adsorption. At the acid conditions (＜pH 3), the U is apt to adsorb as forms of UO₂²⁺, mainly due to the ionic bond with Si-O or Al-O(OH) present on the WRK bentonite rather than the ion exchange with Ca²⁺ among layers of the WRK bentonite, showing the relatively low U adsorption efficiency. At the alkaline conditions (>pH 7), the U could be adsorbed in the form of anionic U-hydroxy complexes (UO₂(OH)₃^-, UO₂(OH)₄^2-, (UO₂)₃(OH)₇^-, etc.), mainly by bonding with oxygen (O^-) from Si-O or Al-O(OH) on the WRK bentonite or by co-precipitation in the form of hydroxide, showing the high U adsorption. At pH 7, the relatively low U adsorption efficiency (42%) was acquired in this study and it was due to the existence of the U-carbonates in solution, having relatively high solubility than other U species. The U adsorption efficiency of the WRK bentonite can be increased by maintaining a neutral or highly alkaline condition because of the formation of U-hydroxyl complexes rather than the uranyl ion (UO₂²⁺) in solution,and by restraining the formation of U-carbonate complexes in solution.

• Journal of the East Asian Society of Dietary Life
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• v.21 no.6
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• pp.934-947
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• 2011

The purpose of this study is to analyze products for good taste and convenience, which become an engine to constantly create customers. In addition, this study is aimed at investigating the relationship between the selective attributes of Home Meal Replacement, the perceived utilitarian value, and the repurchase intention, and drawing new suggestions on the Home Meal Replacement market from a new marketing perspective. Based on a total of 215 samples, this study reviewed the reliability and fitness of the research model and verified a total of 5 hypothesized using the Amos program. The result of study modeling was GFI=0.905, AGFI=0.849, NFI=0.889, CFI=0.945, and RMR=0.0.092 at the level of $x^2$=230.22 (df=126, p<0.001). First, the food quality (${\beta}$=0.221), convenience (${\beta}$=0.334), packing (${\beta}$=0.278), and employee service (${\beta}$=0.204) of home meal replacement consideration attributes had a positive (+) influence on perceived utilitarian value. Second, perceived utilitarian value (${\beta}$=0.584) had a positive (+) influence on repurchase intention. The factors to differentiate one company from other competitors in terms of the utilitarian value are the quality of food, convenience, wrapping, and services by employees. This study has illustrated the need to focus on the development of a premium menu to compete with other companies and to continue to research and develop nutritious foods that are easy to cook. Moreover, the key factors to have a distinct and constant competitive edge over other companies are the alleviation of consumer anxiety over wrapping container materials, the development of more designs, and the accumulation of service know-how. Therefore, it is necessary for a company to strongly develop the key factors based on its resources as a core capability.