• Journal of Periodontal and Implant Science
• /
• v.37 no.sup2
• /
• pp.427-445
• /
• 2007

To improve osseointegration at the boneto-implant interface, several studies have been carried out to modify titanium surface. Variations in surface texture or microtopography may affect the cellular response to an implant. Osteoblast-like cells attach more readily to a rougher titanium surface, and synthesis of extracellular matrix and subsequent mineralization were found to be enhanced on rough or porous coated titanium. However, regarding the effect of roughened surface by physical and mechanical methods, most studies carried out on the reactions of cells to micrometric topography, little work has been performed on the reaction of cells to nanotopography. The purpose of this study was to examme the response of osteoblast-like cell cultured on blasted surfaces and alkali treated surfaces, and to evaluate the influence of surface texture or submicro-scaled surface topography on the cell attachment, cell proliferation and the gene expression of osteoblastic phenotype using ROS 17/2.8 cell lines. In scanning electron micrographs, the blasted, alkali treated and machined surfaces demonstrated microscopic differences in the surface topography. The specimens of alkali treatment had a submicro-scaled porous sur-face with pore size about 200 nm. The blasted surfaces showed irregularities in morphology with small(<10 ${\mu}m$) depression and indentation among flatter-appearing areas of various sizes. Based on profilometry, the blasted surfaces was significantly rougher than the machined and the alkali treated surfaces (p$TiO_2$) were observed on alkali treated surfaces, whereas not observed on machined and blasted surfaces. The attachment morphology of cells according to time was observed by the scanning electron microscope. After 1 hour incubation, the cells were in the process of adhesion and spreading on the prepared surfaces. After 3 hours, the cells on all prepared surfaces were further spreaded and flattened, however on the blasted and alkali treated surfaces, the cells exhibited slightly irregular shapes and some gaps or spaces were seen. After 24 hours incubation, most cells of the all groups had a flattened and polygonal shape, but the cells were more spreaded on the machined surfaces than the blasted and alkali treated surfaces. The MTT assay indicated the increase on machined, alkali treated and blasted surfaces according to time, and the alkali treated and blasted surfaces showed significantly increased in optical density comparing with machined surfaces at 1 day (p<0.01). Gene expression study showed that mRNA expression level of ${\alpha}\;1(I)$ collagen, alkaline phosphatase and osteopontin of the osteoblast-like cells showed a tendency to be higher on blasted and alkali treated surfaces than on the machined surfaces, although no siginificant difference in the mRNA expression level of ${\alpha}\;1(I)$ collagen, alkaline phosphatase and osteopontin was observed among all groups. In conclusion, we suggest that submicroscaled surfaces on osteoblast-like cell response do not over-ride the one of the surface with micro-scaled topography produced by blasting method, although the microscaled and submicro-scaled surfaces can accelerate osteogenic cell attachment and function compared with the machined surfaces.

• Journal of Aquaculture
• /
• v.19 no.2
• /
• pp.84-89
• /
• 2006

Effect of dietary inclusion of various sources of green tea on growth, immune system and challenging test of juvenile olive flounder Paralichthys olivaceus was investigated. Five experimental diets with triplicates were prepared: control, raw leaves, dry leaves, by-product and extract. Twenty five (an initial body weight of 52.5 g) were randomly distributed into 15 of 180 L flow-through tanks. Nutrient requirements of the experimental diets satisfied growth of juvenile olive flounder. The feeding trial lasted for 7 weeks. After 7-week feeding trial, blood were sampled from three randomly chosen fish for serum analysis of Iysozyme and bactericidal activity, and ten fish were infected with Edwardsiella tarda for challenging test from each tank. Weight gain (g/fish) of fish fed the diet containing extract and control diet was significantly higher than that of fish fed the other diets. Feed efficiency ratio for fish fed the diet containing extract and control diet was significantly higher than that for fish fed the diets containing raw leaves and by-product, but not significantly different from that for fish fed the diet containing dry leaves. Serum Iysozyme activity (units/ml) of fish fed the diets containing dry leaves and extract was significantly higher than that of fish fed the diets containing raw leaves and by-product, but not significantly different from that of fish fed the control diet. Serum bactericidal activity (${\times}10^6$ bacteria/ml) of fish fed the diet containing dry leaves and extract was significantly lower than that of fish fed the diets containing raw leaves, by-product and control diet in 3 hour. However, serum bactericidal activity of fish fed the diet containing extract was significantly lower than that of fish fed the other diets in 6 hour. And serum bacterial activity was low in fish fed the diets containing dry and raw leaves, by-product, and control in 6 hour in order. Accumulative mortality (%) of fish fed the control diet was low compared to that of fish fed the diets containing raw leaves and by-product, but high compared to that of fish fed the diets containing dry leaves and extract although no significant difference was found among treatments. In considering above results, dietary inclusion of extract and dry leaves of green tea seemed to be highly effective to improve immune system and endurance against E. tarda infection of juvenile olive flounder.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.127-146
• /
• 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 Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.147-168
• /
• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of Yeungnam Medical Science
• /
• v.8 no.2
• /
• pp.185-201
• /
• 1991

A household survey was conducted to compare the patterns of morbidity and medical care utilization between medical aid beneficiaries and medical insurance beneficiaries. The study population included 285 medical aid beneficiaries that were completely surveyed and 386 medical insurance benficiaries selected by simple random sampling from a Dong(Township) in Taegu. Well-trained surveyers mainly interviewed housewives with a structured questionnaire. The morbidity rates of acute illness during the 15-day period, were 63 per 1,000 medical aid beneficiaries and 62 per 1,000 medical insurance beneficiaries. The rates for chronic illness were 123 per 1,000 medical aid beneficiaries and 73 per 1,000 medical insurance beneficiaries. The most common type of acute illness in medical aid and medical insurance beneficiaries was respiratory disease. In medical aid beneficiaries, musculoskeletal disease was most common, but in medical insurance beneficiaries, gastrointestinal disease was most common. The mean duration of acute illness of medical aid beneficiaries was 3.8 days and that of medical insurance beneficiaries was 6.8 days. During the one year period, mean duration of medical aid beneficiaries chronic illnesses was 11.5 months which was almost twice as long compared to medical insurance beneficiaries. Pharmacy was most preferrable facility among the acute illness patient in medical aid beneficiaries, but acute cases of medical insurance beneficiaries visited the clinic most commonly. Chronic cases of both groups visited the clinic most frequently. There were some findings suggesting that much unmet need existed among the medical aid beneficiaries. In acute cases, the average number of days of medical aid users utilized medical facilities was less than medical insurance users. On the other hand, the length of medical care utilization of chronic cases was reversed. Geographical accessibility was the most important factors in utilization of medical facilities. Almost half of the study population answered the questions about source of funds on medical security correctly. Most respondents considered that the objective of medical security was afford ability. The chief complaint on hospital utilization was the complicated administrative procedures. These findings suggest that there were some problems in the medical aid system, especially in the referral system.

• Information Systems Review
• /
• v.16 no.3
• /
• pp.161-177
• /
• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Neonatal Medicine
• /
• v.18 no.1
• /
• pp.82-88
• /
• 2011

Purpose: This study aims to describe the clinical characteristics of adrenal insufficiency-associated hypotension in preterm infants and the effects of hydrocortisone therapy on their cardiovascular system and serum electrolytes. Methods: Twelve preterm infants less than 32 gestational weeks admitted to neonatal intensive care unit (NICU) of the Seoul National University Bundang Hospital from January 2007 to August 2009 with clinical and laboratory findings suggestive of adrenal insufficiency were analyzed retrospectively. Results: Gestational age was 27.8${\pm}$2.5 weeks and birth weight was 1,110${\pm}$307 g. Postnatal age, postmenstrual age, weight at the onset of adrenal insufficiency-associated hypotension were 19${\pm}$7 day, 30.6${\pm}$2.4 weeks, 1,285${\pm}$365 g. In preterm infants who showed vasopressor resistance, intravenous hydrocortisone was started with a stress dose of 4 mg/kg/day, maintained for 2.2${\pm}$0.7 days, and then tapered. Serum cortisol concentration before hydrocortisone administration was 11.6${\pm}$4.1 mg/dL. Mean blood pressure increased from 25.0${\pm}$5.4 mmHg to 35.0${\pm}$5.3 mmHg, 38.3${\pm}$8.0 mmHg and 41.9${\pm}$6.5 mmHg at time of hydrocortisone administration and 2, 4 and 6 hours after hydrocortisone administration. Urine output increased from 0.9${\pm}$0.6 mL/kg/hr to 4.1${\pm}$3.4 mL/kg/hr. Twelve hours after the administration of hydrocortisone, dopamine requirement decreased from 11.0${\pm}$2.9 $\mu$g/kg/min to 8.0${\pm}$2.3 $\mu$g/kg/min, and to 5.5${\pm}$3.4 ${\mu}g$/kg/min after 24 hours. Serum sodium concentration was increased from 130${\pm}$4 mEq/L to 136${\pm}$4 mEq/L, serum potassium concentration was decreased from 6.1${\pm}$1.1 mEq/L to 4.6${\pm}$0.6 mEq/L before and 12 hours after hydrocortisone administration. Conclusion: In preterm infants with adrenal insufficiency-associated hypotension, hydrocortisone administration improved blood pressure and urine output, decreased vasopressor requirement, and normalized serum electrolyte abnormalities.

• Journal of Distribution Research
• /
• v.17 no.1
• /
• pp.37-63
• /
• 2012

In franchise business, exclusive sales territory (sometimes EST in table) protection is a very important issue from an economic, social and political point of view. It affects the growth and survival of both franchisor and franchisee and often raises issues of social and political conflicts. When franchisee is not familiar with related laws and regulations, franchisor has high chance to utilize it. Exclusive sales territory protection by the manufacturer and distributors (wholesalers or retailers) means sales area restriction by which only certain distributors have right to sell products or services. The distributor, who has been granted exclusive sales territories, can protect its own territory, whereas he may be prohibited from entering in other regions. Even though exclusive sales territory is a quite critical problem in franchise business, there is not much rigorous research about the reason, results, evaluation, and future direction based on empirical data. This paper tries to address this problem not only from logical and nomological validity, but from empirical validation. While we purse an empirical analysis, we take into account the difficulties of real data collection and statistical analysis techniques. We use a set of disclosure document data collected by Korea Fair Trade Commission, instead of conventional survey method which is usually criticized for its measurement error. Existing theories about exclusive sales territory can be summarized into two groups as shown in the table below. The first one is about the effectiveness of exclusive sales territory from both franchisor and franchisee point of view. In fact, output of exclusive sales territory can be positive for franchisors but negative for franchisees. Also, it can be positive in terms of sales but negative in terms of profit. Therefore, variables and viewpoints should be set properly. The other one is about the motive or reason why exclusive sales territory is protected. The reasons can be classified into four groups - industry characteristics, franchise systems characteristics, capability to maintain exclusive sales territory, and strategic decision. Within four groups of reasons, there are more specific variables and theories as below. Based on these theories, we develop nine hypotheses which are briefly shown in the last table below with the results. In order to validate the hypothesis, data is collected from government (FTC) homepage which is open source. The sample consists of 1,896 franchisors and it contains about three year operation data, from 2006 to 2008. Within the samples, 627 have exclusive sales territory protection policy and the one with exclusive sales territory policy is not evenly distributed over 19 representative industries. Additional data are also collected from another government agency homepage, like Statistics Korea. Also, we combine data from various secondary sources to create meaningful variables as shown in the table below. All variables are dichotomized by mean or median split if they are not inherently dichotomized by its definition, since each hypothesis is composed by multiple variables and there is no solid statistical technique to incorporate all these conditions to test the hypotheses. This paper uses a simple chi-square test because hypotheses and theories are built upon quite specific conditions such as industry type, economic condition, company history and various strategic purposes. It is almost impossible to find all those samples to satisfy them and it can't be manipulated in experimental settings. However, more advanced statistical techniques are very good on clean data without exogenous variables, but not good with real complex data. The chi-square test is applied in a way that samples are grouped into four with two criteria, whether they use exclusive sales territory protection or not, and whether they satisfy conditions of each hypothesis. So the proportion of sample franchisors which satisfy conditions and protect exclusive sales territory, does significantly exceed the proportion of samples that satisfy condition and do not protect. In fact, chi-square test is equivalent with the Poisson regression which allows more flexible application. As results, only three hypotheses are accepted. When attitude toward the risk is high so loyalty fee is determined according to sales performance, EST protection makes poor results as expected. And when franchisor protects EST in order to recruit franchisee easily, EST protection makes better results. Also, when EST protection is to improve the efficiency of franchise system as a whole, it shows better performances. High efficiency is achieved as EST prohibits the free riding of franchisee who exploits other's marketing efforts, and it encourages proper investments and distributes franchisee into multiple regions evenly. Other hypotheses are not supported in the results of significance testing. Exclusive sales territory should be protected from proper motives and administered for mutual benefits. Legal restrictions driven by the government agency like FTC could be misused and cause mis-understandings. So there need more careful monitoring on real practices and more rigorous studies by both academicians and practitioners.

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

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.28 no.3
• /
• pp.279-293
• /
• 1995

The most obvious and easily recognizable sources of potential water pollution are point sources such as domestic and industrial wastes. But recently, the potential effects of nonpoint sources on water quality have been increased apparently. In order to evaluate the characteristics and the effects of nonpoint sources on water quality, this study was performed in Suyeong Bay from May, 1992 to July, 1992. The depth-averaged 2-dimensional numerical model, which consists of the hydrodynamic model and the diffusion model was applied to simulate the water quality in Suyeong Bay. When flowrate was $65.736m^3/s,$ the concentration of pollutants (COD, TSS and VSS) at Oncheon stream (Sebeong bridge) during second flush were very high as much as 121.4mg/l of COD, 1148.0mg/l of TSS and 262.0mg/1 of VSS. When flowrate was 4.686m^3/s, the concentration of pollutants $(TIN,\;NH_4\;^+-\;N,\;NO_2\;^--N\;and\;PO_4\;^{3-}-P)$ during the first flush were very high as much as 20.306mg/1 of TIN, 14.154mg/1 of $NH_4\;^+-N$, 9.571mg/l of $NO_2\;^--N$ and l.785mg/l of $PO_2\;^{3-}-P$ As results of the hydrodynamic model simulation, the computed maximum velocity of tidal currents in Suyeong Bay was 0.3m/s and their direction was clockwise flow for ebb tide and counter clockwise flow for Hood tide. Four different methods were applied for the diffusion simulation in Suyeong Bay. There were the effects for the water quality due to point loads, annual nonpoint loads and nonpoint loads during the wet weather and the investigation period, respectively. The efforts of annual nonpoint loads and nonpoint loads during the wet weather seem to be slightly deteriorated in comparison with the effects of point loads. However, the bay was significantly polluted by the nonpoint loads during the investigation period. In this case, COD and SS concentrations ranged 2.0-30.0mg/l, 7.0- 200.0mg/l in ebb tide, respectively. From these results, it can be emphasized that the large amount of pollutants caused by nonpoint sources during the wet weather were discharged into the bay, and affected significantly to both the water quality and the marine ecosystem. Therefore, it is necessary to consider the loadings of nonpoint pollutants to plan wastewater treatment plant.