• Journal of Distribution Research
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• v.17 no.2
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• pp.29-62
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• 2012

As distribution environment is changing rapidly and competition is more intensive in the channel of distribution, the importance of retailer image and retailer equity is increasing as a different competitive advantages. Also, consumers are not functionally oriented and that their behavior is significantly affected by the symbols such as retailer image which identify retailer in the market place. That is, consumers do not choose products or retailers for their material utilities but consume the symbolic meaning of those products or retailers as expressed in their self images. The concept of self-image congruence has been utilized by marketers and researchers as an aid in better understanding how consumers identify themselves with the brands they buy and the retailer they patronize. Although self-image congruity theory has been tested across many product categories, the theory has not been tested extensively in the retailing. Therefore, this study attempts to investigate the impact of self image congruence between retailer image and self image of consumer on retailer equity such as retailer awareness, retailer association, perceived retailer quality, and retailer loyalty. The purpose of this study is to find out whether retailer-self image congruence can be a new antecedent of retailer equity. In addition, this study tries to examine how four-dimensional retailer equity constructs (retailer awareness, retailer association, perceived retailer quality, and retailer loyalty) affect customers' repatronage intention. For this study, data were gathered by survey and analyzed by structural equation modeling. The sample size in the present study was 254. The reliability of the all seven dimensions was estimated with Cronbach's alpha, composite reliability values and average variance extracted values. We determined whether the measurement model supports the convergent validity and discriminant validity by Exploratory factor analysis and Confirmatory Factor Analysis. For each pair of constructs, the square root of the average variance extracted values exceeded their correlations, thus supporting the discriminant validity of the constructs. Hypotheses were tested using the AMOS 18.0. As expected, the image congruence hypotheses were supported. The greater the degree of congruence between retailer image and self-image, the more favorable were consumers' retailer evaluations. The all two retailer-self image congruence (actual self-image congruence and ideal self-image congruence) affected customer based retailer equity. This result means that retailer-self image congruence is important cue for customers to estimate retailer equity. In other words, consumers are often more likely to prefer products and retail stores that have images similar to their own self-image. Especially, it appeared that effect for the ideal self-image congruence was consistently larger than the actual self-image congruence on the retailer equity. The results mean that consumers prefer or search for stores that have images compatible with consumer's perception of ideal-self. In addition, this study revealed that customers' estimations toward customer based retailer equity affected the repatronage intention. The results showed that all four dimensions (retailer awareness, retailer association, perceived retailer quality, and retailer loyalty) had positive effect on the repatronage intention. That is, management and investment to improve image congruence between retailer and consumers' self make customers' positive evaluation of retailer equity, and then the positive customer based retailer equity can enhance the repatonage intention. And to conclude, retailer's image management is an important part of successful retailer performance management, and the retailer-self image congruence is an important antecedent of retailer equity. Therefore, it is more important to develop and improve retailer's image similar to consumers' image. Given the pressure to provide increased image congruence, it is not surprising that retailers have made significant investments in enhancing the fit between retailer image and self image of consumer. The enhancing such self-image congruence may allow marketers to target customers who may be influenced by image appeals in advertising.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 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 Distribution Research
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• v.17 no.1
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• pp.65-86
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• 2012

The $20^{th}$ century was the era of manufacturer brands which built higher brand equity for consumers. Consumers moved from generic products of inconsistent quality produced by local factories in the $19^{th}$ century to branded products from global manufacturers and manufacturer brands reached consumers through distributors and retailers. Retailers were relatively small compared to their largest suppliers. However, sometime in the 1970s, things began to slowly change as retailers started to develop their own national chains and began international expansion, and consolidation of the retail industry from mom-and-pop stores to global players was well under way (Kumar and Steenkamp 2007, p.2) In South Korea, since the middle of the 1990s, the bulking up of retailers that started then has changed the balance of power between manufacturers and retailers. Retailer private labels, generally referred to as own labels, store brands, distributors own private-label, home brand or own label brand have also been performing strongly in every single local market (Bushman 1993; De Wulf et al. 2005). Private labels now account for one out of every five items sold every day in U.S. supermarkets, drug chains, and mass merchandisers (Kumar and Steenkamp 2007), and the market share in Western Europe is even larger (Euromonitor 2007). In the UK, grocery market share of private labels grew from 39% of sales in 2008 to 41% in 2010 (Marian 2010). Planet Retail (2007, p.1) recently concluded that "[PLs] are set for accelerated growth, with the majority of the world's leading grocers increasing their own label penetration." Private labels have gained wide attention both in the academic literature and popular business press and there is a glowing academic research to the perspective of manufacturers and retailers. Empirical research on private labels has mainly studies the factors explaining private labels market shares across product categories and/or retail chains (Dahr and Hoch 1997; Hoch and Banerji, 1993), factors influencing the private labels proneness of consumers (Baltas and Doyle 1998; Burton et al. 1998; Richardson et al. 1996) and factors how to react brand manufacturers towards PLs (Dunne and Narasimhan 1999; Hoch 1996; Quelch and Harding 1996; Verhoef et al. 2000). Nevertheless, empirical research on factors influencing the production in terms of a manufacturer-retailer is rather anecdotal than theory-based. The objective of this paper is to bridge the gap in these two types of research and explore the factors which influence on manufacturer's private label production based on two competing theories: S-C-P (Structure - Conduct - Performance) paradigm and resource-based theory. In order to do so, the authors used in-depth interview with marketing managers, reviewed retail press and research and presents the conceptual framework that integrates the major determinants of private labels production. From a manufacturer's perspective, supplying private labels often starts on a strategic basis. When a manufacturer engages in private labels, the manufacturer does not have to spend on advertising, retailer promotions or maintain a dedicated sales force. Moreover, if a manufacturer has weak marketing capabilities, the manufacturer can make use of retailer's marketing capability to produce private labels and lessen its marketing cost and increases its profit margin. Figure 1. is the theoretical framework based on a strategic market management perspective, integrated concept of both S-C-P paradigm and resource-based theory. The model includes one mediate variable, marketing capabilities, and the other moderate variable, competitive intensity. Manufacturer's national brand reputation, firm's marketing investment, and product portfolio, which are hypothesized to positively affected manufacturer's marketing capabilities. Then, marketing capabilities has negatively effected on private label production. Moderating effects of competitive intensity are hypothesized on the relationship between marketing capabilities and private label production. To verify the proposed research model and hypotheses, data were collected from 192 manufacturers (212 responses) who are producing private labels in South Korea. Cronbach's alpha test, explanatory / comfirmatory factor analysis, and correlation analysis were employed to validate hypotheses. The following results were drawing using structural equation modeling and all hypotheses are supported. Findings indicate that manufacturer's private label production is strongly related to its marketing capabilities. Consumer marketing capabilities, in turn, is directly connected with the 3 strategic factors (e.g., marketing investment, manufacturer's national brand reputation, and product portfolio). It is moderated by competitive intensity between marketing capabilities and private label production. In conclusion, this research may be the first study to investigate the reasons manufacturers engage in private labels based on two competing theoretic views, S-C-P paradigm and resource-based theory. The private label phenomenon has received growing attention by marketing scholars. In many industries, private labels represent formidable competition to manufacturer brands and manufacturers have a dilemma with selling to as well as competing with their retailers. The current study suggests key factors when manufacturers consider engaging in private label production.

• 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 KOR-BRONCHOESO Conference
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• 1972.03a
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• pp.3.4-5
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• 1972

생활수준이 낮은 단계에 있어서는 우선 식량에 대한 수요가 강하다. 인간의 욕구가 만족스럽게 먹는다는 것에 대하여 제일 강하게 발동하는 것이다 그러나 점차 과학기술과 산업과 경제가 발전하여 성장과정에 오르게 되고 소득수준도 향상하게 되면 시장기구를 통해서 구입 할 수 있는 개인의 물적 소비재에 대해서는 점차 충족하게 되며 식량이외에도 의복, 전기기구 및 일용생활용품, 자동차 등에 이르기까지 더욱 고차원의 소비재가 보급하게 되는 것이다. 이렇게 되며는 사람의 욕구는 사적 재물이나 물적 수요에서 점진적으로 공공재나 또는 질적 수요(주택, 생활환경 등)의 방향으로 움직이게 되는 것으로써 여기에 환경오염 또는 공해문제에 대하여 의식하게 된다. 그러나 여기에서 더욱이 문제점이 되는 것은 소득 수준의 향상 과정이란 그 자체가 환경오염의 커다란 요인이라는 점이며 자동차의 급격한 보급과 생활의 편의성을 구하여 집중되는 도시인구의 집적, 높은 소득을 보장하기 위한 생산성 높은 중화학공업의 발전 등등은 그 자체가 환경권이란 사람이 요구하는 고차원의 권리를 침해하는 직접적인 요인이 된다는 것이다. 이와 같은 환경오염이나 공해문제에 대한 세계적인 논의는 이미 시작된 지 오래이지만 현재는 우리의 건강보호를 위해서나 생활환경의 보전을 위해서라는 점에서는 그치는 것이 아니고, 더욱 넓혀서 자연의 보호, 자원의 보호라는 견지로 확대되고 있다. 이와 같은 세계적인 확대된 이해와 이에 대한 대책강구의 제안은 1968년 국제연합의 경제사회이사회에서 스웨덴 정부대표에 의하여 제시되었으며 1969년의 우- 탄트 사무총장의 인간환경에 관한 보고서, 1970년 Nixon 미대통령의 연두일반교서 그리고 1972년 5월 6일 스웨덴의 스톡홀롬에서 개최되는 인간환경회의의 주제 등을 통해서 알 수 있고, 종래의 공해나 생활환경의 오염문제라는 좁은 개념에서가 아니고 인간환경전체의 문제로 다루고 있는 것이다. 즉 환경개발(도시, 산업, 지역개발에 수반된 문제), 환경오염(인위적 행위에 의하여 환경의 대인간조건이 악화하는 문제) 자연ㆍ자원의 보호관리(지하, 해양자원, 동식물, 풍경경치의 문제)란 3개 측면에서 다루고 있는 것이다. 환경오염이란 문제를 중 심하여 보면 환경을 구성하는 기본적인 요소로서 대기, 물, 토지 또는 지각. 그리고 공간의 사대요소로 집약하여 생각할 수 있음으로 이 4요소의 오염이 문제가 되는 것이다. 대기의 오염은 환경의 오염중 가장 널리 알려진, 또 가장 오랜 역사를 가진 오염의 문제로써 이에 속하는 오염인자는 분진, 매연, 유해가스(유황산화물, 불화수소, 염화수소, 질소산화물, 일산 화염소 등) 등 대기의 1차 오염과 1차 존재한 물질이 자외선의 작용으로 변화발생 하는 오존, PAN등 광화학물질이 형성되는 2차적인 오염을 들 수 있다. 기외 카도미움, 연등 유해중금속이나 방사선물질이 대기로부터 토지를 오염시켜서 토지에 서식하는 생물의 오염을 야기케 한다는 점등이 명백하여지고 있으며 대기의 오염은 이런 오염물질이 대기중에서 이동하여 강우에 의한 침강물질의 변화를 일으키게 되며 소위 광역오염문제를 발생케하며 동시에 토지의 토질저하등을 가져오게 한다. 물의 오염은 크게 내육수의 오염과 해양의 오염의 양면으로 나누어 볼 수 있다. 하천의 오염을 방지하고 하천을 보호하기 위한 움직임 역시 환경오염의 역사상 오래된 문제이며 시초에는 인분뇨와의 연결에서 오는 세균에 의한 오염이나 양수 기타 일반하수와의 연결에서 오는 오염에 대비하는 것부터 시작하였지만 근래에는 산업공장폐수에 의한 각종 화학적유해물질과 염료 그리고 석유화학의 발달에 의한 폐유등으로 인한 수질오탁문제가 점차 크게 대두되고 있다. 이것은 측 오염이란 시초에 우리에게 주는 불쾌감이 크므로 이것을 피하자는 것부터 시작하여 인간의 건강을 지키고 각종 사용수를 보존하자는 용수보존으로 그리고 이제는 건강과 용수보존뿐만 아니라 이것이 농림 수산물에 대한 큰 피해를 주게됨으로써 오는 자연환경의 생태계보전의 문제로 확대전환하고 있는 것이다. ？간 특히 해양오염에 대한 문제는 국지적인 것에만 끝이는 것이 아니고 전세계의 해양에 곧 연결되는 것이므로 세계각국의 공통관심사로 등장케 되었으며 이것은 특히 폐유가 유류수송 도중에 해양에 투기되는 유류에 의한 해양의 유막성형에서 오는 기상의 변화와 물피해등이 막심함으로 심각화 되고 있다. 각국이 자국의 해안과 해양을 보호하기 위하여 조치를 서두르고 있는 현시점에서 볼 때에는 이는 국제문제화하고 있으며 세계적인 국제적 협력과 협조의 필요성이 강조되는 좋은 예라 하겠다. 토양의 오염에 있어서는 대기나 수질의 오염이 구국적으로 토양과 관련되고 토양으로 환원되는 것이지만 근래에 많이 보급사용되는 농약과 화학비료의 문제는 토양자체의 오염에만 그치는 것이 아니고 농작물을 식품으로 하여 섭취함으로써 발생되는 인체나 기타생물체의 피해를 고려할 때 더욱 중요한 것이며, 또 토질의 저하를 가져오게 하여 농림생산에 미치는 영향이 적지 않을 것이다. 지반강하는 지각 에 주는 인공적 영향의 대표적인 것으로써 지하수나 지하 천연가스를 채취이용하기 위하여 파들어 감으로써 지반이 침하 하는 것이며 건축물에 대한 영향 특히 풍수해시의 재해를 크게 할 우려가 있는 것이다. 공간에 있어서의 환경오염에는 소음, 진동, 광선, 악취 등이 있다. 이들은 특수한 작업환경의 경우를 제외하고는 건강에 직접적인 큰 피해를 준다고 생각할 수 없으나 소음, 진동, 관선, 악취 등은 일반 일상시민생활에 불쾌나 불안을 줌으로써 안정된 생활을 방해하는 요인이 되는 것이다. 공간의 오염물로써 새로운 주목을 끌게된 것은 도시산업폐기물로써 이들은 대기나 물 또는 토지를 오염시킬 뿐만 아니라 공간을 점령함으로써 도시의 미관이나 기능을 손상케 하는 것이다. 즉 노배폐차의 잔해, 냉장고등고형폐기물등의 재생불가능한 것이나 비니루등 합성물질로 된 용기나 포장 등으로 연소분해 되지 않은 내구소비재가 이에 해당하는 것으로 이는 maker의 양식에 호소하여 그 책임 하에 해결되어야 할 문제로 본다. 이렇듯 환경오염은 각양각색으로 그 오염물질의 주요 발생원인 산업장이나 기타 기관에서의 발생요인을 살펴보며는 다음과 같은 것으로 요약할 수 있다. A. 제도적 요인 1. 관리체재의 미비 2. 관리법규의 미비 3. 책임소재의 불명확 B. 자재적 요인 1. 사용자재의 선택부적 2. 개량대책급 연구의 미흡 C. 기술적 요인 1. 시설의 설계불량, 공정의 결함 2. 시설의 점검, 보전의 불충분 3. 도출물의 취급에 대한 검사부족 4. 발생방지 시설의 미설치, 결함 D. 교육적 요인 1. 오염물질 방제지식의 결여 2. 법규의 오해, 미숙지 E. 경제적 요인 1. 자금부족 2. 융자상의 문제 3. 경제성의 문제 F. 정신적 요인 1. 사회적 도의심의 결여(이기주의) 2. 태만 3. 무지, 무관심 등이다. 따라서 환경오염의 방지란 상기한 문제의 해결에 기대하지 않을 수 없으나 이를 해결하기 위하여는 국내적 국제적 상호협조에 의한 사회각층의 총력적 대책이 시급한 것이다. 이와 같은 환경오염이 단속된다 하며는 미구에 인류의 건강은 물론 그 존립마저 기대하기 어려울 것이며, 현재는 점진적으로 급성피해에 대하여는 그 흥미가 집중되어 그 대비책도 많이 논의되고 있지만 미량의 단속접촉에 의한 만성축적에 관한 문제나 이와 같은 환경오염이 앞으로 태어날 신생률에 대한 영향이나 유전정보에 관한 연구는 장차에 대비하는 문제로써 중요한 것이라 생각된다. 기외에 우려되는 점은 오염방지책을 적극 추진함으로써 올 수 있는 파생적인 문제이다. 즉 오염을 방지하기 위하여 생산기업체가 투자를 하게 되며는 그만큼 생산원가가 상승할 것이며 소비가격도 오를 것이다. 반면 이런 시책에 뒤떨어진 후진국의 값싼 생산국은 자연 수입이 억제 당할 것이며, 이렇게되면 후진국은 무역경쟁에서 큰 상처를 입게될 것이고 뿐만 아니라 선진국에 필요한 오염물질의 발생이 높은 생산기기를 자연후진국에 양도하게 될 것임으로 후진국의 환경오염은 배가할 우려가 있는 것이다. 또 해양오염을 방지할 목적에서와 같이 자국의 해안보호를 위하여 마련된 법의 규제는 타국의 선박운항에 많은 제약을 가하게 될 것이며 이것 역시 시설이 미약한 약소후진국의 선박에 크게 영향을 미치게 될 것임으로 교통, 해운, 무역등을 통한 약소후진국의 경제성장에 제동을 거는 것이 될 것이다. 이렇듯 환경오염의 문제는 환경자체에 대해서만 아니라 부산물적으로 특히 후진국에는 의외 문제를 던져주게 되는 것임으로 환경오염에 대해서는 물론, 전술한 바와 같이 인간환경전체의 문제로써 Nixon 대통령이 말한 결의와 창의와 그리고 자금을 가지고 과감하게 대처해 나가야 할 것이다.

• KDI Journal of Economic Policy
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• v.34 no.3
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• pp.95-131
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• 2012

This study examines whether the executive turnover and the executive resignation during the term of office affect the performance of Korean state-owned enterprises. The executive turnover in the paper means the comprehensive change of the executives which includes the change after the term of office, the change after consecutive terms and the change during the term of office. The 'resignation' was named for the executive change during the term of office to distinguish from the executive turnover. The study scope of the paper is restrained to the comprehensive executive change itself irrespective of the term of office and the resignation during the term of office. Therefore the natural change of the executive after the term of office or the change after consecutive terms is not included in the study. Spontaneous resignation and forced resignation are not distinguished in the paper as the distinction between the two is not easy. The paper uses both the margin of return on asset and the margin of return on asset adjusted by industry as proxies of the performance of state-owned enterprises. The business nature of state-owned enterprise is considered in the study, the public nature not in it. The paper uses the five year (2004 to 2008) samples of 24 firms designated as public enterprises by Korean government. The analysis results are as follows. First, 45.1% of CEOs were changed a year during the sample period on the average. The average tenure period of CEOs was 2 years and 3 months and 49.9% among the changed CEOs resigned during the term of office. 41.6% of internal auditors were changed a year on the average. The average tenure period of internal auditors was 2 years and 2 months and 51.0% among the changed internal auditors resigned during the term of office. In case of outside directors, on average, 38.2% were changed a year. The average tenure period was 2 years and 7 months and 25.4% among the changed internal directors resigned during the term of office. These statistics show that numerous CEOs resigned before the finish of the three year term in office. Also, considering the tenure of an internal auditor and an outside director which diminished from 3 years to 2 years by an Act on the Management of Public Institutions (applied to the executives appointed since April 2007), it seems most internal auditors resigned during the term of office but most outside directors resigned after the end of the term. Secondly, There was no evidence that the executives were changed during the term of office because of the bad performance of prior year. On the other hand, contrary to the normal expectation, the performance of prior year of the state-owned enterprise where an outside director resigned during the term of office was significantly higher than that of other state-owned enterprises. It means that the clauses in related laws on the executive dismissal on grounds of bad performance did not work normally. Instead it can be said that the executive change was made by non-economic reasons such as a political motivation. Thirdly, the results from a fixed effect model show there were evidences that performance turned negatively when CEOs or outside directors resigned during the term of office. CEO's resignation during the term of office gave a significantly negative effect on the margin of return on asset. Outside director's resignation during the term of office lowered significantly the margin of return on asset adjusted by industry. These results suggest that the executive's change in Korean state-owned enterprises was not made by objective or economic standards such as management performance assessment and the negative effect on performance of the enterprises was had by the unfaithful obeyance of the legal executive term.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.