• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.18
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• pp.54-87
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• 1975

The effects of various weather factors on yield components of rice, year variation of yield components within regions, and regional differences of yield components within year were investigated at three Crop Experiment Stations O.R.D., Suweon, Iri, Milyang, and at nine provincial Offices of Rural Development for eight years from 1966 to 1973 for the purpose of providing information required in improving cultural practices and predicting the yield level of rice. The experimental results analyzed by standard partial regression analysis are summarized as follows: 1. When rice was grown in ordinary seasonal culture the number of panicles greatly affected rice yield compared to other yield components. However, when rice was seeded in ordinary season and transplanted late, and transplanted in ordinary season in the northern area the ratio of ripening was closely related to the rice yield. 2. The number of panicles showed the greatest year variation when the Jinheung variety was grown in the northern area. The ripening ratio or 1, 000 grain weight also greatly varied due to years. However, the number of spikelets per unit area showed the greatest effects on yield of the Tongil variety. 2. Regional variation of yield components was classified into five groups; 1) Vegetation dependable type (V), 2) Partial vegetation dependable type (P), 3) Medium type (M), 4) Partial ripening dependable type (P.R), and 5) Ripening dependable type (R). In general, the number of kernel of rice in the southern area showed the greatest partial regression coefficient among yield components. However, in the mid-northern part of country the ripening ratio was one of the component!; affecting rice yield most. 4. A multivariate equation was obtained for both normal planting and late planting by log-transforming from the multiplication of each component of four yield components to additive fashion. It revealed that a more accurate yield could be estimated from the above equation in both cases of ordinary seasonal culture and late transplanting. 5. A highly positive correlation coefficient was obtained between the number of tillers from 20 days after transplanting and the number of panicles at each(tillering) stage 20 days after transplanting in normal planting and late planting methods. 6. A close relationship was found between the number of panicles and weather factors 21 to 30 days, after transplanting. 7. The average temperature 31 to 40 days after transplanting was greatly responsible for the maximum number of tillers while the number of duration of sunshine hours per day 11 to 30 days after transplantation was responsible for that character. The effect of water temperature was negligible. 8. No reasonable prediction for number of panicles was calculated from using either number of tillers or climatic factors. The number of panicles could early be estimated formulating a multiple equation using number of tillers 20 days after transplantation and maximum temperature, temperature range and duration of sunshine for the period of 20 days from 20 to 40 days after transplantation. 9. The effects of maximum temperature and day length 25 to 34 days before heading, on kernel number per panicle, were great in the mid-northern area. However, the minimum temperature and day length greatly affected the kernel number per panicle in the southern area. The maximum temperature had a negative relationship with the kernel number per panicle in the southern area. 10. The maximum temperature was highly responsible for an increased ripening ratio. On the other hand, the minimum temperature at pre-heading and early ripening stages showed an adverse effect on ripening ratio. 11. The 1, 000 grain weight was greatly affected by the maximum temperature during pre- or mid-ripening stage and was negatively associated with the minimum temperature over the entire ripening period.

• Journal of Distribution Research
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• v.15 no.4
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• pp.1-19
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• 2010

As having the movement of developing private brand (PB) goods, domestic big retailers are facing up with new problems. Thus, it is required studies of PB products, and how consumers recognize PB products as a consideration commodity set. Also, it is worthy in order that it gives us the important meaning on the marketing strategy with focusing on evaluating the differences between customers buying PB grocery goods with respect to demographic characteristics and purchasing behaviors. PB has some advantages for customers and retailers. However, according to AC Nielson's report (2005), Asian and emerging market has 1/5 sales relatively to Western countries. But we can assume that the emerging market has the most potential growth through this result. As a result from several other studies, it becomes necessary to not only increase the rate of selling composition of PB product temporarily, but also analyze the characteristics of customers using big retailers and segmenting customer groups to make PB product as a consideration commodity set for them. In addition, it is needed to have a variety of acts of marketing. From studies related to PB, there is a prejudice - cheap products have low quality - but, evaluation by customers who have used those products shows neutral stand, and there is a study representing that it is the most important to accumulate the belief between the retailers selling PB products and consumers using those for the accurate evaluation and intention on purchasing. Also, by the result from analyzing the characteristics of customers buying PB products, we could assume that higher income and higher education level, more preference on PB products. Especially, according to TNS's research, the primary targets of PB product are 30's who seeks value for money and planned spending habits, and 40's who have teenager children, and are interested in encouraging themselves. This paper used Probit model to analyze the characteristics of consumers. This model helps us to analyze with the variables representing the demographic characteristics of consumers (gender, age, educational level, occupation, income level, living area), and variables related to purchasing behavior (visiting frequency on big retailers, the average amount that they pay for goods in there, and check-up which brand made those goods). The method we used in this study is by man to man interview and survey on-line with the rate of 89% and 11% in Seoul and Gyunggi Province, respectively, for about one month from the beginning of February, 2008. As a result of this, under the assumption that people buy PB products more as long as they go shopping more, it was not meaningful for target groups which we pointed out as frequently visiting customers to be. Although, we have expected women buy more PB products than men do, gender doesn't mean anything for the result. And, it has inferred that married people buy more PB goods than singles do. It was also meaningless with variables related to occupation. Because housewives are often exposed to any kind of supermarket than workers are, we could not get any relatives. Moreover, we couldn't proof that younger generation prefer big retailers more than older people who 50~60's. Education levels doesn't affect on the purchase of PB product as well. Related to living area, the result is statistically not similar as we expected whether living in Seoul or not. It shows there is no relationship with the preference on retail brands and PB products, and it is similar with the study researched by TNS(2008) that customers tend to buy PB product impulsively no matter which brand it is and where they are even though their shopping place is the big market where customers are often using. Variables on which we had meaningful results are income level and living place. That is, customers who have 3,000,000~6,000,000 WON every month on average are more willing to buy PB products than other customers whose income is over 6,000,000 WON, and residents not living in Seoul prefer PB goods than those who are living in Seoul. To explain more about what we got, if there is only one condition about customer's visiting frequency on big retails, we could come up with this result that more exposed to PB products, more purchasing frequency. Consequently, it brings the important insight that large retailers have to prepare something to make customers visit them often to increase selling rate of PB products. To demonstrate the result of analyzing more, what is more efficient variables are demographically including marital status, income level, and residential area to buy items that affect the PB products and could include the frequency of visiting large markets by the purchase habits. Specifically, then, married couples rather than singles, middle-income customers than high-income customers, and local residents not living in Seoul than customers in Seoul are more likely to purchase PB goods. In addition, as long as a customer visits two times more, then the purchasing rate of PB products is to increase over 5.3%. Therefore, it seems that retailers are better to make a shopping place as fun and comfortable places. With overwhelming the idea that PB products are just cheap, one-time purchase goods, it is needed to increase the loyalty on those goods like NB products, try to make PB products as a consideration products set, and occur to sustainable sales. Especially, as suggested by this paper, it seems like it strongly needs to identify the characteristics of customers who prefer PB, to segment those customers, and to select the main target, and to do positioning with well-planned marketing strategies. Then, it is able to give us a meaningful point on marketing strategy by developing the field of PB study, identifying the difference of life style and shopping habits of customers.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.spc1
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• pp.152-160
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• 2005

This study was carried out to investigate selectable criteria in evaluating waxy corn $F_1$ hybrids for developing good eating quality waxy corn variety. The physicochemical property analysis of 6 waxy corn $F_1$ hybrids - Chalok1, Chalok2, Heugjeomchal, Yeonnongl, Chalok4, and Suwon45- showed a range of $11.2\~13.1\%$ for crude protein, $5.1\~6.0\%$ for crude fat, $91.8\~92.6\%$ for amylopectin, and $4.5\~6.6\%$ for free sugar content. The pericarp thickness which is one of the most important characteristics related to tenderness in waxy corn was ranged $34\~47{\mu}m$ in 4 waxy corn hybrids - Yeonnongl, Chalok4, Suwon45, and Heugjeomchal. On the other hand, it was ranged $64\~81{\mu}m$ in Chalok1 and Chalok2. The amylogram analysis by rapid visco analyzer showed that in fresh waxy corn hybrid (DAP25), all amylogram properties except setback were higher in Yeonnongl, Chalok4, and Suwon45 compared to those of Chalokl, Chnlok2, and Heugjeomchal. However, in matured waxy corn hybyids (DAP45), the result was the opposite - the amylogram properties were higher in Chalokl, Chalok2, and Heugjeomchal than those of Yeonnongl, Chalok4, and Suwon45. The texture analysis showed that gumminess, chewiness, and hardness increased dramatically with the time after the cooking in Chalokl and Beugjeomchal. On the other hand, these above pyoperties did not change as rapidly with the time in Yeonnongl, Chalok4, and Suwon45. Gumminess, chewiness, and hardness did not increase much within 6 hours after steamingr but increased significantly 32 hours after steaming. Therefore, we have reached a conclusion that texture analysis of cooked waxy corn should be carried out 6 hours after steaming. In the sensory evaluation, Yeonnongl, Chalok4, and Suwon45 revealed higher palatability -6.8, 7.1, and 6.9 respectively - than. that of Chnlokl, Chalok2, and Heugjeomchal. The palatability analysis of 6 waxy corn hybrids showed palatability positively correlating with free sugar content,100-kernel weight, kernel length, kernet width, and consistency, but negatively correlating with pericarp thickness, hardness, gumminess, and chewiness.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.77-97
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• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.38 no.4
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• pp.58-67
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• 2020

The present study was conducted as part of basic research for selecting species of street trees with historical value in Seoul. It also made up a list of traditional landscape trees for a variety of alternatives. The following results are shown below. As to the history of street trees in Korea, records on to-be-estimated street trees are found in historical documents written in King Yangwon during the second year of Goguryeo Dynasty (546) and King Myeongjong during 27 year of Goryeo (1197). However, it is assumed that lack of clarity is found in historical records. During the 23 year of King Sejong in the early Joseon Dynasty (1441), the record showed that the state planted street trees as guideposts on the postal road. The records revealed that Ulmus spp. and Salix spp. were planted as guidance trees. The street tree system was performed in the early Joseon Dynasty as recorded in the first year of King Danjong document. Pinus densiflora, Pinus koraiensis, Pyrus pyrifolia var. culta, Castanea crenata, Styphnolobium japonicum and Salix spp. were planted along the avenue at both left and right sides. Morus alba were planted on streets during the five year of King Sejo (1459). As illustrated in pieces Apgujeong by painter Jeongseon and Jinheonmajeongsaekdo in the reign of King Yeongjo, street trees were planted. This arrangement is associated with a number of elements such as king procession, major entrance roads in Seoul, place for horse markets, prevention of roads from flood and indication. In the reign of King Jeongjo, there are many cases related to planting Pinus densiflora, Abies holophylla and Salix spp. for king procession. Turning king roads and related areas into sanctuaries is considered as technique for planting street trees. During the 32 year of King Gojong after opening ports (1985), the state promoted planting trees along both sides of roads. At the time, many Populus davidiana called white poplars were planted as rapidly growing street trees. There are 17 taxa in the Era of Three Kingdoms records, 31 taxa in Goryeo Dynasty records and 55 taxa in Joseon Dynasty records, respectively, described in historical documents to be available for being planted as street trees in Seoul. 16 taxa are recorded in three periods, which are Era of Three Kingdoms, Goryeo Dynasty and Joseon Dynasty. These taxa can be seen as relatively excellent ones in terms of historical value. The introduction of alien plants and legal improvement in the Japanese colonial period resulted in modernization of street tree planting system. Under the six-year street tree planting plan (1934-1940) implemented as part of expanding metropolitan areas outside the capital launched in 1936, four major street trees of top 10 taxa were a Populus deltoides, Populus nigra var. italica, Populus davidiana, Populus alba. The remaining six trees were Salix babylonica, Robinia pseudoacacia, platanus orientalis, Platanus occidentalis, Ginkgo biloba, and Acer negundo. Beginning in the mid- and late 1930s, platanus orientalis, Platanus occidentalis were introduced into Korea as new taxa of street trees and planted in many regions. Beginning on 1942, Ailanthus altissima was recommended as street trees for the purpose of producing silks. In 1957 after liberation, major street tree taxa included Platanus occidentalis, Ginkgo biloba, Populus nigra var. italica, Ailanthus altissima, Populus deltoides and Salix babylonica. The rank of major street tree species planted in the Japanese colonial period had changed. Tree planting trend around that period primarily representing Platanus occidentalis and Ginkgo biloba still holds true until now.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• The Journal of Korean Academic Society of Nursing Education
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• v.4 no.2
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• pp.249-263
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• 1998

The competence of newly graduated nurses is based on various clinical expriences gained when they were students. Therefore, instructors of nursing students, professors in nursing schools or directors in hospitals must play a critical role in assisting them to obtain various knowledge and experienced nursing skills. The purpose of this study was to investigate nursing care activities and nursing care hours practiced by nursing students in a general hospital. The subject students were total 214 nursing students, 2nd graders(sophomores) and 3rd graders(juniors) from 5 Junior Nursing Colleges in Seoul and they practiced at S general hospital to gain clinical experience. The data were collected for 4 days. The tools for this study were the direct nursing care activity list consisted of 15 nursing areas and the indirect nursing care activity list consisted of 9 nursing areas. The subject students were supposed to record their own score. The results of this study are ; 1. The nursing care hours per nursing student 1) The average total nursing care hours a day per each nursing student are 362.65 mins(6.04hr), the direct nursing care hours per each nursing stuent are 202.09 mins(direct nursing care rate 56.0%) and it is higher than the indirect nursing care hours, 159.75mins(indirect nursing care rate 44.0%). The direct nursing care rate of each student by a team approach in the evening shift(56.3%) is higher than that in the day shift(55.8%). 2) The hours of checking vital signs are the longest(47.35mins) among the direct nursing care activites and next is in order of counseling 8l emotional support, nurse rounds, and accompaning a patient during examination. The hours of reporting are the longest(32.39mins) among the indirect nursing care activites, and next is the activities related to education such as reviewing chart, looking up references, etc. 3) The freqency of checking vital signs practiced by nursing student is the highest(the average of 55.7 times) among the direct nursing care activities and next is in order of nurse rounds, assistance of feeding, and counseling & emotional support. The required time for nursing students to accompany their patient during examination is the longest(20.7mins) and next are in order of restriction on patient' activity, orientated by a head nurse, skin care, sitz bath, bathing & hair shampoo, and assisting with patients' exercise. 2. The nursing care hours per grader 1) The average hours of total nursing care per a nursing student are 369.2mins(6.2hrs) to 2nd graders, 355.9mins(5.9 hrs) to third graders. The direct nursing care rate per each nursing student to 3rd graders(59.3%) was higher than that to End graders(52.8%). 2) For 2nd graders, the highly marked nursing activities are teaching associated with direct nursing care activities such as drawing up papers, looking up references, reviewing charts, and being orientated by staff nurses. For 3rd graders, measurments, observations, and nurse rounds in indirect nursing care activities are taken highly 3) The most frequent practice of the nursing care activites is checking vital signs : 65 times to 3rd graders and 46.5 times to 2nd graders. Our suggestions based on the results of this study are : 1. It is recommanded to repeat the same designed study in a variety of clinical fields for further study. 2. It is recommanded to collect data not by self-record method but by observated method. 3. It is needed for nursing instructors in Nursing Schools and in hospitals to develop the guidelines and check-list of clinical practice courses.

• The Korean Journal of Air & Space Law and Policy
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• v.24 no.2
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• pp.69-104
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• 2009

This study is to know the Enhancement of Aviation Safety for Low Cost Carrier in Korea through the long and mid term air safety policy. Especially, the aviation safety authorities of the developed countries in aviation establish action plans under the system plan of central government. Then the countries implement those plans systematically to the related aviation business so that they promote efficient air safety policy implementation. At this time, the Korean government should present the vision about an air safety and systematic strategic plan to cope with the future aviation industry change. Also, it is needed to establish a specific aviation safety action plan. Namely, an air safety master plan and long-term road map must be established. This paper deduces some implications through the abroad cases of aviation safety plan, and then tries to find the applying method of the implications to Korea in the rapidly changing aviation market in the 21st century. It is expected that this paper will help the Korean aviation industry to play a major role in the future. In oder to get suggestions aviation policies of advanced countries with regard to aviation safety, we have looked at the aviation policies of the U.S., the U.K., Australia and Japan, and also LCC's states overseas, LCC's safety policies in Korea, and aviation safety status. Since existing LCCs and new LCCs based in Korea have become the new concept, this new market for LCC has been booming recently. Around Southeast Asia, while there are some LCCs including Air Asia which is supported by the government of Malaysia with emphasis on safety, there are other LCCs, which have failed to achieve confidence in safety and have led to aircraft accidents and financial mismanagement, so we need to verify the safety of overseas LCCs, try to improve domestic LCCs in order to fly international routes and aid international aviation safety. LCCs have been increasing lately thanks to open skies policy and a wide variety of flights.lines. Air Busan, Jin Air, Jeju air, Eastar Air are in service. so the risk of new potential hazards may increase. Therefore it is necessary to take the initiative in aviation markets inside and outside of Korea and the safety management of new LCCs should be taken more seriously than ever before. Among overseas aviation safety policies, we need to implement the FAA's Filght Plan which has a specific Business Plan. I hope this thesis will help improve aviation safety locally and internationally.