• The Journal of the Petrological Society of Korea
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• v.25 no.4
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• pp.311-324
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• 2016

The characteristics of the rock cleavage in Jurassic granite from Geochang were analysed. The evaluation for three quarrying planes and three rock cleavages was performed using the parameters such as (1) reduction ratio between the value of spacing and the value of length, (2) microcrack spacing frequency(N), (3) total spacing($1mm{\geq}$), (4) exponential constant(a), (5) magnitude of exponent(${\lambda}$), (6) mean spacing($S_{mean}$), (7) difference value($S_{mean}-S_{median}$) between mean spacing and median spacing($S_{median}$) and (8) density of spacing. Especially the close dependence between the above spacing parameters and the parameters from the spacing-cumulative frequency diagrams was derived. The discrimination factors representing three quarrying planes and three rock cleavages were acquired through these mutual contrast. The analysis results of the research are summarized as follows. First, the reduction ratios of frequency(N), mean value, median value, the above difference value($S_{mean}-S_{median}$) and density for three rock cleavages are in orders of G(grain, (G1 + G2)/2) < H(hardway, (H1 + H2)/2) < R(rift, (R1 + R2)/2), H < G $\ll$ R, H < G $\ll$ R, H < G < R and H < G $\ll$ R. The values of the above five parameters for three planes show the various orders of R'(rift plane) $\ll$ H'(hardway plane) < G'(grain plane), R' $\ll$ G' < H', R' < H' < G', R' < G' < H' and R' $\ll$ H' < G', respectively. Second, the values of (I) parameters(2, 3, 4 and 5) and (II) parameters(6, 7 and 8) are in orders of (I) H < G < R and (II) R < G < H. On the contrary, the values of the above two groups(I~II) of parameters for three planes show reverse orders. Third, to review the overall characteristics of the arrangement among the six diagrams, these diagrams show an order of R2 < R1 < G2 < G1 < H2 < H1 from the related chart. In other words, above six diagrams can be summarized in order of rift(R1 + R2) < grain(G1 + G2) < hardway(H1 + H2). These results indicate a relative magnitude of rock cleavage related to microcrack spacing. Especially, two parameters for each diagram, the above difference value($S_{mean}-S_{median}$) and mean spacing, could provide advanced information for prediction the order of arrangement among the diagrams. Finally, the general chart for three planes and three rock cleavages were made. From the related chart, three exponential straight lines for three rock cleavages show an order of R(R1 + R2) < G(G1 + G2) < H(H1 + H2). On the contrary, three lines for three planes show an order of H'(R2 + G2) < G'(R1 + H2) < R'(G1 + H1). Consequently, correlation of the mutually reverse order between three planes and three rock cleavages can be drawn from the related chart.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.s02
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• pp.45-65
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• 1989

The Korean peninsular having the complexity of the photography and variability of climate is located within passing area of a lots of typhoon occurring from the southern islands of Philippines. So, there are various patterns of wind and flooding damages in paddy field occuring by the strong wind and the heavy rain concentrated during the summer season of rice growing period in Korea. The wind damages to rice plants in Korea were mainly caused by saline wind, dry wind and strong wind when typhoon occurred. The saline wind damage having symptom of white head or dried leaves occurred by 1.1 to 17.2 mg of salt per dry weight stuck on the plant which was located at 2. 5km away from seashore of southern coastal area during the period(from 27th to 29th, August, 1986) of typhoon &Vera& accompanying 62-96％ of relative humidity, more than 6 m per second of wind velocity and 22.5 to 26.4$^{\circ}C$ of air temperature without rain. Most of the typhoons accompanying 4.0 to 8. 5m per second of wind and low humidity (lesp an 60％) with high temperature in the east coastal area and southen area of Korea. were changed to dry and hot wind by the foehn phenomenon. The dry wind damages with the symptom of the white head or the discolored brownish grain occurred at the rice heading stage. The strong wind caused the severe damages such as the broken leaves, cut-leaves and dried leaves before heading stage, lodging and shattering of grain at ripening stage mechanically during typhoon. To reduce the wind damages to rice plant, cultivation of resistant varieties to wind damages such as Sangpoongbyeo and Cheongcheongbyeo and the escape of heading stage during period of typhoon by accelerating of heading within 15th, August are effective. Though the flood disasters to rice plant such as earring away of field, burying of field, submerging and lodging damage are getting low by the construction of dam for multiple purpose and river bank, they are occasionally occurred by the regional heavy rain and water filled out in bank around the river. Paddy field were submerged for 2 to 4 days when typhoon and heavy rain occurred about the end of August. At this time, the rice plants that was in younger growing stage in the late transplanting field of southern area of Korea had the severe damages. Although panicles of rice plant which was in the meiotic growing stage and heading stage were died when flooded, they had 66％ of yield compensating ability by the upper tilling panicle produced from tiller with dead panicle in ordinary transplanting paddy field. It is effective for reduction of flooding damages to cultivate the resistant variety to flooding having the resistance to bacterial leaf blight, lodging and small brown planthopper simultaneously. Especially, Tongil type rice varieties are relatively resistant to flooding, compared to Japonica rice varieties. Tongil type rice varieties had high survivals, low elongation ability of leaf sheath and blade, high recovering ability by the high root activity and photosynthesis and high yield compensating ability by the upper tillering panicle when flooded. To minimize the flooding and wind damage to rice plants in future, following research have to be carried out; 1. Data analysis by telemetering and computerization of climate, actual conditions and growing diagnosis of crops damaged by disasters. 2. Development of tolerant varieties to poor natural conditions related to flooding and wind damages. 3. Improvement of the reasonable cropping system by introduction of other crops compensating the loss of the damaged rice. 4. Increament of utilization of rice plant which was damaged.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.155-166
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• 2002

Since 1990s. there has been an increasing number of traffic accidents at intersection. which requires more urgent measures to insure safety on intersection. This study set out to analyze the road conditions, traffic conditions and traffic operation conditions on signalized intersection. to identify the elements that would impose obstructions in safety, and to develop a traffic accident prediction model to evaluate the safety of an intersection using the cop relation between the elements and an accident. In addition, the focus was made on suggesting appropriate traffic safety policies by dealing with the danger elements in advance and on enhancing the safety on the intersection in developing a traffic accident prediction model fir a signalized intersection. The data for the study was collected at an intersection located in Wonju city from January to December 2001. It consisted of the number of accidents, the road conditions, the traffic conditions, and the traffic operation conditions at the intersection. The collected data was first statistically analyzed and then the results identified the elements that had close correlations with accidents. They included the area pattern, the use of land, the bus stopping activities, the parking and stopping activities on the road, the total volume, the turning volume, the number of lanes, the width of the road, the intersection area, the cycle, the sight distance, and the turning radius. These elements were used in the second correlation analysis. The significant level was 95% or higher in all of them. There were few correlations between independent variables. The variables that affected the accident rate were the number of lanes, the turning radius, the sight distance and the cycle, which were used to develop a traffic accident prediction model formula considering their distribution. The model formula was compared with a general linear regression model in accuracy. In addition, the statistics of domestic accidents were investigated to analyze the distribution of the accidents and to classify intersections according to the risk level. Finally, the results were applied to the Spearman-rank correlation coefficient to see if the model was appropriate. As a result, the coefficient of determination was highly significant with the value of 0.985 and the ranks among the intersections according to the risk level were appropriate too. The actual number of accidents and the predicted ones were compared in terms of the risk level and they were about the same in the risk level for 80% of the intersections.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• The Korean Society of Law and Medicine
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• v.23 no.4
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• pp.29-74
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• 2022

As social disasters occur under the Disaster Management Act, which can damage the people's "life, body, and property" due to the rapid spread and spread of unexpected COVID-19 infectious diseases in 2020, information collected through inspection and reporting of infectious disease pathogens (Article 11), epidemiological investigation (Article 18), epidemiological investigation for vaccination (Article 29), artificial technology, and prevention policy Decision), (3) It was used as an important basis for decision-making in the context of an infectious disease crisis, such as promoting vaccination and understanding the current status of damage. In addition, medical policy decisions using infectious disease data contribute to quarantine policy decisions, information provision, drug development, and research technology development, and interest in the legal scope and limitations of using infectious disease data has increased worldwide. The use of infectious disease data can be classified for the purpose of spreading and blocking infectious diseases, prevention, management, and treatment of infectious diseases, and the use of information will be more widely made in the context of an infectious disease crisis. In particular, as the serious stage of the Disaster Management Act continues, the processing of personal identification information and sensitive information becomes an important issue. Information on "medical records, vaccination drugs, vaccination, underlying diseases, health rankings, long-term care recognition grades, pregnancy, etc." needs to be interpreted. In the case of "prevention, management, and treatment of infectious diseases", it is difficult to clearly define the concept of medical practicesThe types of actions are judged based on "legislative purposes, academic principles, expertise, and social norms," but the balance of legal interests should be based on the need for data use in quarantine policies and urgent judgment in public health crises. Specifically, the speed and degree of transmission of infectious diseases in a crisis, whether the purpose can be achieved without processing sensitive information, whether it unfairly violates the interests of third parties or information subjects, and the effectiveness of introducing quarantine policies through processing sensitive information can be used as major evaluation factors. On the other hand, the collection, provision, and use of infectious disease data for research purposes will be used through pseudonym processing under the Personal Information Protection Act, consent under the Bioethics Act and deliberation by the Institutional Bioethics Committee, and data provision deliberation committee. Therefore, the use of research purposes is recognized as long as procedural validity is secured as it is reviewed by the pseudonym processing and data review committee, the consent of the information subject, and the institutional bioethics review committee. However, the burden on research managers should be reduced by clarifying the pseudonymization or anonymization procedures, the introduction or consent procedures of the comprehensive consent system and the opt-out system should be clearly prepared, and the procedure for re-identifying or securing security that may arise from technological development should be clearly defined.

• Journal of Distribution Research
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• v.17 no.5
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• pp.1-23
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• 2012

The conflict between major corporations and small and medium-sized businesses is being aggravated, the trickle down effect is not working properly, and, as the controversy surrounding the effectiveness of the business limiting system continues to swirl, the plan proposed to protect the business domain of small and medium-sized businesses, resolve polarization between these businesses and large corporations, and protect small family run stores is the suitable business type designation system for small and medium-sized businesses. The current status of carrying out this system of selecting suitable business types among small and medium-sized businesses involves receiving applications for 234 items among the suitable business types and items from small and medium-sized businesses in manufacturing, and then selecting the items of the consultative group by analyzing and investigating the actual conditions. Suitable business type designation in the service industry will involve designation with priority on business types that are experiencing social conflict. Three major classifications of the service industry, related to the livelihood of small and medium-sized businesses, will be first designated, and subsequently this will be expanded sequentially. However, there is the concern that when designated as a suitable business type or item, this will hinder the growth motive for small to medium-sized businesses, and designation all cause decrease in consumer welfare. Also it is highly likely that it will operate as a prior regulation, cause side-effects by limiting competition systematically, and also be in violation against the main regulations of the FTA system. Moreover, it is pointed out that the system does not sufficiently reflect reverse discrimination factor against large corporations. Because conflict between small to medium sized businesses and large corporations results from the expansion of corporations to the service industry, which is unrelated to their key industry, it is necessary to introduce an advanced contract method like a master franchise or local franchise system and to develop local small to medium sized businesses through a franchise system to protect these businesses and dealers. However, this method may have an effect that contributes to stronger competitiveness of small to medium sized franchise businesses by advancing their competitiveness and operational methods a step further, but also has many negative aspects. First, as revealed by the Ministry of Knowledge Economy, the franchise industry is contributing to the strengthening of competitiveness through the economy of scale by organizing existing individual proprietors and increasing the success rate of new businesses. It is also revealed to be a response measure by the government to stabilize the economy of ordinary people and is emphasized as a 'useful way' to revitalize the service industry and improve the competitiveness of individual proprietors, and has been involved in contributions to creating jobs and expanding the domestic market by providing various services to consumers. From this viewpoint, franchises fit the purpose of the suitable business type system and is not something that is against it. Second, designation as a suitable business type may decrease investment for overseas expansion, R&D, and food safety, as well negatively affect the expansion of overseas corporations that have entered the domestic market, due to the contraction and low morale of large domestic franchise corporations that have competitiveness internationally. Also because domestic franchise businesses are hard pressed to secure competitiveness with multinational overseas franchise corporations that are operating in Korea, the system may cause difficulty for domestic franchise businesses in securing international competitiveness and also may result in reverse discrimination against these overseas franchise corporations. Third, the designation of suitable business type and item can limit the opportunity of selection for consumers who have up to now used those products and can cause a negative effect that reduces consumer welfare. Also, because there is the possibility that the range of consumer selection may be reduced when a few small to medium size businesses monopolize the market, by causing reverse discrimination between these businesses, the role of determining the utility of products must be left ot the consumer not the government. Lastly, it is desirable that this is carried out with the supplementation of deficient parts in the future, because fair trade is already secured with the enforcement of the franchise trade law and the best trade standard of the Fair Trade Commission. Overlapping regulations by the suitable business type designation is an excessive restriction in the franchise industry. Now, it is necessary to establish in the domestic franchise industry an environment where a global franchise corporation, which spreads Korean culture around the world, is capable of growing, and the active support by the government is needed. Therefore, systems that do not consider the process or background of the growth of franchise businesses and harm these businesses for the sole reason of them being large corporations must be removed. The inhibition of growth to franchise enterprises may decrease the sales of franchise stores, in some cases even bankrupt them, as well as cause other problems. Therefore the suitable business type system should not hinder large corporations, and as both small dealers and small to medium size businesses both aim at improving competitiveness and combined growth, large corporations, small dealers and small to medium sized businesses, based on their mutual cooperation, should not include franchise corporations that continue business relations with them in this system.

• Journal of Global Scholars of Marketing Science
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• v.18 no.4
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• pp.119-155
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• 2008

Customer Relationship Management(CRM) has been a sustainable competitive edge of many companies. CRM analyzes customer data for designing and executing targeted marketing analysing customer behavior in order to make decisions relating to products and services including management information system. It is critical for companies to get and maintain profitable customers. How to manage relationships with customers effectively has become an important issue for both academicians and practitioners in recent years. However, the existing academic literature and the practical applications of customer relationship management(CRM) strategies have been focused on the technical process and organizational structure about the implementation of CRM. These limited focus on CRM lead to the result of numerous reports of failed implementations of various types of CRM projects. Many of these failures are also related to the absence of marketing approach. Identifying successful factors and outcomes focused on marketing concept before introducing a CRM project are a pre-implementation requirements. Many researchers have attempted to find the factors that contribute to the success of CRM. However, these research have some limitations in terms of marketing approach without explaining how the marketing based factors contribute to the CRM success. An understanding of how to manage relationship with crucial customers effectively based marketing approach has become an important topic for both academicians and practitioners. However, the existing papers did not provide a clear antecedent and outcomes factors focused on marketing approach. This paper attempt to validate whether or not such various marketing factors would impact on relational quality and CRM performance in terms of marketing oriented perceptivity. More specifically, marketing oriented factors involving market orientation, customer orientation, customer information orientation, and core customer orientation can influence relationship quality(satisfaction and trust) and CRM outcome(customer retention and customer share). Another major goals of this research are to identify the effect of relationship quality on CRM outcomes consisted of customer retention and share to show the relationship strength between two factors. Based on meta analysis for conventional studies, I can construct the following research model. An empirical study was undertaken to test the hypotheses with data from various companies. Multiple regression analysis and t-test were employed to test the hypotheses. The reliability and validity of our measurements were tested by using Cronbach's alpha coefficient and principal factor analysis respectively, and seven hypotheses were tested through performing correlation test and multiple regression analysis. The first key outcome is a theoretically and empirically sound CRM factors(marketing orientation, customer orientation, customer information orientation, and core customer orientation.) in the perceptive of marketing. The intensification of ${\beta}$coefficient among antecedents factors in terms of marketing was not same. In particular, The effects on customer trust of marketing based CRM antecedents were significantly confirmed excluding core customer orientation. It was notable that the direct effects of core customer orientation on customer trust were not exist. This means that customer trust which is firmly formed by long term tasks will not be directly linked to the core customer orientation. the enduring management concerned with this interactions is probably more important for the successful implementation of CRM. The second key result is that the implementation and operation of successful CRM process in terms of marketing approach have a strong positive association with both relationship quality(customer trust/customer satisfaction) and CRM performance(customer retention and customer possession). The final key fact that relationship quality has a strong positive effect on customer retention and customer share confirms that improvements in customer satisfaction and trust improve accessibility to customers, provide more consistent service and ensure value-for-money within the front office which result in growth of customer retention and customer share. Particularly, customer satisfaction and trust which is main components of relationship quality are found to be positively related to the customer retention and customer share. Interactive managements of these main variables play key roles in connecting the successful antecedent of CRM with final outcome involving customer retention and share. Based on research results, This paper suggest managerial implications concerned with constructions and executions of CRM focusing on the marketing perceptivity. I can conclude in general the CRM can be achieved by the recognition of antecedents and outcomes based on marketing concept. The implementation of marketing concept oriented CRM will be connected with finding out about customers' purchasing habits, opinions and preferences profiling individuals and groups to market more effectively and increase sales changing the way you operate to improve customer service and marketing. Benefiting from CRM is not just a question of investing the right software, but adapt CRM users to the concept of marketing including marketing orientation, customer orientation, and customer information orientation. No one deny that CRM is a process or methodology used to develop stronger relationships being composed of many technological components, but thinking about CRM in primarily technological terms is a big mistake. We can infer from this paper that the more useful way to think and implement about CRM is as a process that will help bring together lots of pieces of marketing concept about customers, marketing effectiveness, and market trends. Finally, a real situation we conducted our research may enable academics and practitioners to understand the antecedents and outcomes in the perceptive of marketing more clearly.