• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.177-191
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• 2021

Due to the development of mobile devices and streaming technology, many changes in consumption patterns have appeared. In addition, social impact is becoming an era of non-face-to-face consumption due to the panthermic environment of COVID-19. Accordingly, in line with the non-face-to-face consumption trend, we focused on the importance of live commerce, which is emerging as a new distribution channel, and tried to investigate the causal relationship that the characteristics of live commerce and show hosts have on purchase intention. The respondents of this study were 235 general adults of live commerce users. Interaction, economics, entertainment as the characteristics of live commerce and attractiveness, professionality, and awareness as the characteristics of show hosts were set as independent variables. Purchase intention was set as the dependent variable, and content flow was set as the mediating variable. As a result of the study, it was found that the characteristics of live commerce such as Interaction, economics, entertainment, and the characteristics of show hosts such as attractiveness, professionality, and awareness all had a positive (+) significant effect on purchase intention. The impact was shown in the following order: entertainment of live commerce, awareness, attractiveness, professionality of show hosts, economics, interaction of live commerce. In addition, the results of the mediating effect of content flow on purchase intention are as follows. Content flow was found to play a mediating role between interaction, entertainment, attractiveness, professionality, awareness and purchase intention. On the other hand, economics was analyzed to have no mediating effect. The implications of this study are as follows. Companies and show hosts that sell products in live commerce should sell products that can inspire consumers rather than simply sell products. In addition, it is considered that content that provides entertainment and attractions gives pleasure to consumers. If not only a well-recognized show host, but also people with high recognition in various fields such as influencers and creators, become show hosts, consumers' content flow and purchase intentions will increase. And vendors must offer interesting content development and reasonable prices. Show hosts need to focus on active communication with consumers.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.105-117
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• 2022

This study analyzes the effects between stock returns and interest rate spread, difference between long-term and short-term interest rate through the polynomial linear regression analysis. The existing research concentrated on the business forecast through the interest rate spread focusing on the US market. The previous studies verified the interest rate spread based on the leading indicators of business forecast by moderating the period of long-term/short-term interest rates and analyzing the degree of leading. After the 7th reform of composite indices of business indicators in Korea of 2006, the interest rate spread was included in the items of composing the business leading indicators, which is utilized till today. Nevertheless, there are a few research on stock returns of each industry and interest rate spread in domestic stock market. Therefore, this study analyzed the stock returns of each industry and interest rate spread targeting Korean stock market. This study selected the long-term/short-term interest rates with high causality through the regression analysis, and then understood the correlations with each leading period and industry. To overcome the limitation of the simple linear regression analysis, polynomial linear regression analysis is used, which raised explanatory power. As a result, the high causality was verified when using differences between returns of corporate bond(AA-) without guarantee for three years by leading six months and call rate returns as interest rate spread. In addition, analyzing the stock returns of each industry, the relation between the relevant interest rate spread and returns of the automobile industry was the closest. This study is significant in the aspect of verifying the causality of interest rate spread, business forecast, and stock returns in Korea. Even though it could be limited to forecast the stock price by using only the interest rate spread, it would be working as a strong factor when it is properly utilized with other various factors.

• Land and Housing Review
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• v.13 no.2
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• pp.49-64
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• 2022

To effectively lead "Innovative City Season 2", it is important to investigate whether the creation of an innovative city has a positive effect on the revitalization of the hinterland town economy. This study explores the effects (external effects) of increases in the number of workers at public institutions in the innovative city of Jeollabuk-do, located closest to the hinterland town (Jeonju-si). In the results, we show that increases in the number of workers positively affected the commercial real estate market, which is one of the barometers that show the revitalization level. On the other hand, the effect was found to be limited to the new downtown commercial real estate market with good accessibility and modernized facilities and services. This suggests that the innovative city adjacent to the hinterland town meets the purpose of the innovative city to some extent by generating a positive external effect, especially in accessible and modernized areas. To further expand the positive spillover effects of an innovative city on the hinterland town, it is necessary to expand public institutions for relocation, improve settlement conditions, and establish a practical cooperation system between specialized public institutions and hinterland-related industries.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.99-112
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• 2021

This study was performed to predict spatial change of future forestland area in South Korea at regional level for supporting forest-related plans established by local governments. In the study, land use was classified to three types which are forestland, agricultural land, and urban and other lands. A logistic regression model was developed using transitional interaction between each land use type and topographical factors, land use restriction factors, socioeconomic indices, and development infrastructures. In this model, change probability from a target land use type to other land use types was estimated using raster dataset(30m×30m) for each variable. With priority order map based on the probability of land use change, the total annual amount of land use change was allocated to the cells in the order of the highest transition potential for the spatial analysis. In results, it was found that slope degree and slope standard value by the local government were the main factors affecting the probability of change from forestland to urban and other land. Also, forestland was more likely to change to urban and other land in the conditions of a more gentle slope, lower slope criterion allowed to developed, and higher land price and population density. Consequently, it was predicted that forestland area would decrease by 2027 due to the change from forestland to urban and others, especially in metropolitan and major cities, and that forestland area would increase between 2028 and 2050 in the most local provincial cities except Seoul, Gyeonggi-do, and Jeju Island due to locality extinction with decline in population. Thus, local government is required to set an adequate forestland use criterion for balanced development, reasonable use and conservation, and to establish the regional forest strategies and policies considering the future land use change trends.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Korean Journal of Food Science and Technology
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• v.40 no.4
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• pp.430-435
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• 2008

The sensory characteristics of imported (two dry, two sweet, and one medium dry wines) and domestic (one sweet wine) red wine were evaluated by 250 panels. The preferences of aroma, color, sweetness, tartness, astringency, and overall acceptability were determined by 5-point just-about-right scale. Among six wines, B sample obtained the highest mean overall acceptability score of 3.67 and its chemical and sensory characteristics were as follow: cherry or strawberry aroma, 9.4 brix, 3.7% of sugar content, pH 3.5, 10% of ethanol, 0.14% of tannin, 5.74 mg/mL of total organic acids, and color of $L(12.04{\pm}0.01)$, $a(33.90{\pm}0.19)$, and $b(8.22{\pm}0.00)$. These results revealed that Korean consumers generally prefer sweet taste to dry one, flat taste to bitter one, and fruity aroma to others. Panels preferred a red wine containing high sugar content of $5{\sim}10%$. On the other hand, panels showed low preference to the wine containing tannin above 0.16%. However, these general trends were slightly varied depending on their ages and consuming frequencies.

• Journal of Ginseng Culture
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• v.2
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• pp.71-83
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• 2020

College students are the potential income classes preparing for income activities and are the main consumers of the future that are very important. In order to understand the current state of ginseng-related consumption culture of young consumers, this study wanted to examine university students' attitudes toward ginseng-related products and services in the future, and to find out their purchasing status, satisfaction, and intent to purchase them. In doing so, we looked at the relative influence of the relevant variables that affect the intent of the purchase. As a result, the variables that affect college student consumers' intention to purchase ginseng-related products were shown in the order of their reliability, economy, purchasing experience, subjective interest, monthly income, monthly allowance, pride in ginseng, and age. Of these, most of the variables had positive effects, but the average monthly income, monthly allowance, and age had negative effects. In other words, the higher the reliability, economy, and self-esteem of ginseng, the higher the willingness to buy ginseng, the higher the subjective interest in ginseng, the higher the age, the lower the monthly allowance, and the lower the income, the lower the willingness to buy ginseng. To promote college students' consumption of ginseng products, it is necessary to cut prices for the younger generation, enhance the quality of the products for the younger generation, improve the taste to overcome the negative aspects of rejecting the bitter and bitter taste, and, above all, induce consumers' attention. It is also necessary to expand accessibility through the development of convenient and easy-to-eat products for young people and the expansion of sales outlets. Recently, young consumers are interested in new products they have never experienced before, products that are good to certify because of their unique design or packaging, and retro products that stimulate nostalgia in the past, so they need to promote and provide information related to consumption of ginseng products in this regard. Considering the practicality and convenience of consumers, we propose consideration of personal consumers' taste curation services, which reflect their preference for products that are convenient to carry with them in line with various living environments, and can have synergy with other products.

• Food Science of Animal Resources
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• v.29 no.4
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• pp.494-499
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• 2009

To investigate the relationship between the carcass defects considered by a meat grader and quality grade A, a total of 59,700 broiler carcasses, which were reared from September 1, 2007 to August 31, 2008 and randomly selected from 597 broiler farms with 100 broilers in each farm, were graded by a meat grader using the Korean meat grading system. The average slaughter weight of the broilers increased at the 31- to 33-d age but decreased after the 37-d age. The average daily gain to grade A ratio of the broilers was highest in autumn, and the bruises, flesh blemishes, and abnormalities of the carcasses were found to have been most frequent in winter (p<0.05). The number of economical defects (EDs) increased with the increase in the slaughter age, and the correlation coefficients of the EDs and grade A were -0.88, -0.48, and -0.12 in terms of the conformation, exposed flesh/cuts/tears, and skin discolorations of the carcasses, respectively. The correlation coefficients of the non-economical defects (NEDs) and the grade A of the broiler carcasses were -0.16, -0.22, and -0.32 in terms of the feathers, alien substances, and carcass treatment, respectively. The total number of defects (EDs + NEDs) and the grade A were highly correlated (-0.93), and in particular, the correlation between the grade A and the EDs was -0.91. It can be deemed that the grade A from a meat grader is highly correlated with the EDs. Therefore, the results suggest that broiler carcass grade evaluation using a meat grading system can provide direct information on profits in the market. The resulting carcass grade of broilers can also provide the information needed to decide on a proper incentive for a farm that has attained a good quality grade.

• Journal of the Korean earth science society
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• v.34 no.6
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• pp.515-523
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• 2013

The eastern coast of South Korea is expected to be damaged by volcanic ash when Mt. Baekdusan volcano erupts. Even if the amount of volcanic ash is small, it can be fatal on the agricultural sector withering many plants and causing soil acidification. Thus, in this paper, we aim to estimate agricultural losses caused by the volcanic ash and to visualize them with Google map. To estimate the volcanic ash losses, a damage assessment model is needed. As the volcanic ash hazard depends on the kind of a crops and the ash thickness, the fragility function of damage assessment model should represent the relation between ash thickness and damage rate of crops. Thus, we model the fragility function using the damage rate for each crop of RiskScape. The volcanic ash losses can be calculated with the agricultural output and the price of each crop using the fragility function. This paper also represents the estimated result of the losses in Gangwon province, which is most likely to get damaged by volcanic ashes in Korea. According to the result with gross agricultural output of Gangwon province in 2010, the amount of volcanic ash losses runs nearly 635,124 million wons in Korean currency if volcanic ash is accumulated over four millimeters. This amount represents about 50% of the gross agricultural output of Gangwon province. We consider the damage only for the crops in this paper. However, a volcanic ash fall has the potential to damage the assets for a farm, including the soil fertility and installations. Thus, to estimate the total amount of volcanic ash damage for the whole agricultural sectors, these collateral damages should also be considered.