• International Area Studies Review
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• v.15 no.3
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• pp.239-261
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• 2011

The cyclicality of productivity has been one of the essential issues in macroeconomics. Since Solow(1957) developed the dominant approach to the measurement of productivity growth, Solow's approach, which assumes the perfect competition, the constant returns to scale, and the full use of input factor has been modified particularly in Hall's(1990) and Basu's(1996) works. Their researches take account of market power, returns to scale, and variable factor utilization. This paper establishes the empirical model based on Hall's(1990) and Basu's(1996) models, estimates 4 types of Solow's reidual in manufacturing and 2 service industries over the period 1975:1-2010:4, and analyzes the cyclicality of measured productivity. The result proved the measured productivity to be procyclical in manufacturing industries and electricity and water industry, and in contrast to the Basu's, the variable factor utilization transformed the countercyclicality of measured productivity into its procyclicality in the Korean economy.

• The Korean Journal of Mycology
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• v.27 no.4 s.91
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• pp.283-288
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• 1999

The effect of water activity ($a_w,\;0.9{\sim}0.995$) and temperature ($18{\sim}30^{\circ}$C) on in vitro growth and interactions between ochratoxin-producing Aspergillus ochraceus and six other fungi (Alternaria alternata, Aspergillus candidus, A. flavus, A. niger, Eurotium amstelodami, E. rubrum) isolated from maize grain were investigated. A. ochraceus and each six other species were paired and their interactions given a numerical score to obtain an index of dominance ($I_D$) for each species. Generally A. ochraceus was very competitive and dominant against other fungi. It was, however, dominanted by Alternaria alternata and A. niger at high $a_w\;(0.995\;a_w)$, and mutually antagonistic when paired with E. amstelodami and E. rubrum at low $a_w\;(0.9\;a_w)$. The growth rates of each species were also calculated under the same range of environmental conditions. They were markedly influenced by aw and temperature. At high temperature ($30^{\circ}C$), A. ochraceus grew most rapidly under slightly drier conditions ($0.95\;a_w$), while A. alternata, A. flavus and A. niger did at high water availability level ($0.995\;a_w$). At $18^{\circ}C\;and\;25^{\circ}C$, and high $a_w$ level ($0.995\;a_w$), A. alternata grew fastest, while A. candidus, E. amstelodami and E. rubrum grew very slowly. Using Biolog plates the effect of $a_w$ and temperature on utilization patterns of carbon sources in maize was evaluated. The niche overlap index (NOI) relative to A. ochraceus was determined and compared with that of each interacting species. Under high water available condition ($0.995\;a_w$). the NOI of A. ochraceus was often >0.9, indicative of the coexistence with other interacting species. However, against E. amstelodami and E. rubrum at $18^{\circ}C$, the species had NOI <0.8, indicative of occupation of different niches. At low $a_w\;(0.95\;a_w)$, NOI for A. ochraceus was <0.8 when paired with A. alternata and A. niger also suggested the occupation of different niches.

• Journal of Energy Engineering
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• v.20 no.1
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• pp.63-76
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• 2011

This paper studies the impact of Korean introduction of the Generalized System of Preferences(GSP) to developing countries, which are continuously arguing to support research and development for reducing greenhouse gas(GHG) emission with developed countries in the Conference of the Parties(COP) of UNFCCC. This paper is focusing on the expecting effects of trade and GHG emission reduction when Korea provides GSP to Indonesia, Brazil, and Ethiopia, which are selected in the first session of Global Green Growth Institute(GGGI). This paper uses the methodology of the intra-industry trade index multiplied by Korean import-induced coefficients. To Indonesia, Korean probable GSP would benefit exports of Indonesian agriculture, forestry, fishery, and livestock farming industries, which would contribute to Indonesian reduction of GHG emission. To Brazil, the exports to Korea would increase in the GHG sensitive industries such as metal, fat, oils, food, and beverage industries. Ethiopia belongs to the least developed countries. So Korean GSP would support the exports and GHG reduction in Ethiopian agriculture, forestry, fisheries, textiles, and leather industries. Without conflicting most favored nation treatment(MFN) principle in WTO, the introduction of GSP would be a good compensation for GHG reduction to developing countries.

• Korean Journal of Ecology and Environment
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• v.39 no.1 s.115
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• pp.21-31
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• 2006

The objectives of this study were to analyze trends of temporal water quality and trophic state in Andong and Imha reservoirs using chemical dataset during 1993 ${\sim}$ 2004, obtained from the Ministry of Environment, Korea. According to long-term limnological analyses, Suspended solids (SS) in Imha Reservoir were 2 ${\sim}$ 8 fold2 greater, than those in SS of Andong Reservoir, and the high solids increased total phosphorus (TP) and biological oxygen demand ($BOD_5$) and decreased the transparency, measured as Secchi depth (SD). Chlorophyll-a (CHL-a) increased little or decreased slightly in the both reservoirs during the high solids, resulting in reduced yields of CHL-a : TP ratios. The deviation analysis of Trophic State Index (TSI) in Imha Reservoir showed that about 70% of TSI (CHL-a)-TSI (SD) and TSI (CHL-a)-TSI(TP) values were less than zero and the lowest values were-60, indicating that influence of inorganic solids (or non-volatile solids) on phytoplankton growth was evident in Imha Reservoir and the impact was greater than that of Andong Reservoir. Inorganic solids in Imha Reservoir resulted in light limitation on phytoplankton growth and thus contributed variations in the relations among three parameters of trophic state index. Especially, seasonal data analysis of nutrients in both reservoirs showed that during the postmonsoon, mean TP concentration was Imha Reservoir greater in than that in Andong Reservoir. The higher TP concentrantion was mainly attributed to increases of inorganic solids from soil erosions and nonpoint source inputs within the watershed. The high inorganic turbidity in Imha Reservoir should be reduced for the conservation of water quality for, especially a tap water supply.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Distribution Research
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• v.16 no.1
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• pp.95-115
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• 2011

Food service is the major part of franchise business in Korea, accounting for 69.9% of the brands in the market. As the food service industry becomes mature, many franchisees have struggled to survive in the market. In general, consumers have higher levels of expectation toward service quality of franchised outlets compared that of (non-franchised) independent ones. They also tend to believe that franchisees deliver standardized service at the uniform food price, regardless of their locations. Such beliefs seem to be important reasons that consumers prefer franchised outlets to independent ones. Nevertheless, few studies examined the impact of qualify features of franchisees on customer satisfaction so far. To this end, this study examined the characteristics of various quality features of franchisees in the food service industry, regarding their relationship with customer satisfaction and dissatisfaction. The quality perception of heavy-users was also compared with that of light-users in order to find insights for developing differentiated marketing strategy for the two segments. Customer satisfaction has been understood as a one-dimensional construct while there are recent studies that insist two-dimensional nature of the construct. In this regard, Kano et al. (1984) suggested to categorize quality features of a product or service into five types, based on their relation to customer satisfaction and dissatisfaction: Must-be quality, Attractive quality, One-dimensional quality, Indifferent quality, and Reverse quality. According to the Kano model, customers are more dissatisfied when Must-be quality(M) are not fulfilled, but their satisfaction does not arise above neutral no matter how fully the quality fulfilled. In comparison, customers are more satisfied with a full provision of Attactive quality(A) but manage to accept its dysfunction. One-dimensional quality(O) results in satisfaction when fulfilled and dissatisfaction when not fulfilled. For Indifferent quality(I), its presence or absence influences neither customer satisfaction nor dissatisfaction. Lastly, Reverse quality(R) refers to the features whose high degree of achievement results in customer dissatisfaction rather than satisfaction. Meanwhile, the basic guidelines of the Kano model have a limitation in that the quality type of each feature is simply determined by calculating the mode statistics. In order to overcome such limitation, the relative importance of each feature on customer satisfaction (Better value; b) and dissatisfaction (Worse value; w) were calculated following the formulas below (Timko, 1993). The Better value indicates how much customer satisfaction is increased by providing the quality feature in question. In contrast, the Worse value indicates how much customer dissatisfaction is decreased by providing the quality feature. Better = (A + O)/(A+O+M+I) Worse = (O+M)/(A+O+M+I)(-1) An on-line survey was performed in order to understand the nature of quality features of franchisees in the food service industry by applying the Kano Model. A total of twenty quality features (refer to the Table 2) were identified as the result of literature review in franchise business and a pre-test with fifty college students in Seoul. The potential respondents of our main survey was limited to the customers who have visited more than two restaurants/stores of the same franchise brand. Survey invitation e-mails were sent out to the panels of a market research company and a total of 257 responses were used for analysis. Following the guidelines of Kano model, each of the twenty quality features was classified into one of the five types based on customers' responses to a set of questions: "(1) how do you feel if the following quality feature is fulfilled in the franchise restaurant that you visit," and "(2) how do you feel if the following quality feature is not fulfilled in the franchise restaurant that you visit." The analyses revealed that customers' dissatisfaction with franchisees is commonly associated with the poor level of cleanliness of the store (w=-0.872), kindness of the staffs(w=-0.890), conveniences such as parking lot and restroom(w=-0.669), and expertise of the staffs(w=-0.492). Such quality features were categorized as Must-be quality in this study. While standardization or uniformity across franchisees has been emphasized in franchise business, this study found that consumers are interested only in uniformity of price across franchisees(w=-0.608), but not interested in standardizations of menu items, interior designs, customer service procedures, and food tastes. Customers appeared to be more satisfied when the franchise brand has promotional events such as giveaways(b=0.767), good accessibility(b=0.699), customer loyalty programs(b=0.659), award winning history(b=0.641), and outlets in the overseas market(b=0.506). The results are summarized in a matrix form in Table 1. Better(b) and Worse(w) index indicate relative importance of each quality feature on customer satisfaction and dissatisfaction, respectively. Meanwhile, there were differences in perceiving the quality features between light users and heavy users of any specific franchise brand in the food service industry. Expertise of the staffs was labeled as Must-be quality for heavy users but Indifferent quality for light users. Light users seemed indifferent to overseas expansion of the brand and offering new menu items on a regular basis, while heavy users appeared to perceive them as Attractive quality. Such difference may come from their different levels of involvement when they eat out. The results are shown in Table 2. The findings of this study help practitioners understand the quality features they need to focus on to strengthen the competitive power in the food service market. Above all, removing the factors that cause customer dissatisfaction seems to be the most critical for franchisees. To retain loyal customers of the franchise brand, it is also recommended for franchisor to invest resources in the development of new menu items as well as training programs for the staffs. Lastly, if resources allow, promotional events, loyalty programs, overseas expansion, award-winning history can be considered as tools for attracting more customers to the business.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.355-380
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• 2004

I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in