• Journal of Distribution Science
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• v.8 no.3
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• pp.27-35
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• 2010

As the population, buying power, and intensity of self-expression of the elderly generation increase, its importance as a market segment is also growing. Therefore, the mass marketing strategy for the elderly generation must be changed to a micro-marketing strategy based on the results of sub-segmentation that suitably captures the characteristics of this generation. Furthermore, as a customer access strategy is decided by sub-segmentation, proper segmentation is one of the key success factors for micro-marketing. Segments or sub-segments are different from sectors, because segmentation or sub-segmentation for micro-marketing is based on the homogeneity of customer needs. Theoretically, complete segmentation would reveal a single voice. However, it is impossible to achieve complete segmentation because of economic factors, factors that affect effectiveness, etc. To obtain a single voice from a segment, we sometimes need to divide it into many individual cases. In such a case, there would be a many segments to deal with. On the other hand, to maximize market access performance, fewer segments are preferred. In this paper, we use the term "sub-segmentation" instead of "segmentation," because we divide a specific segment into more detailed segments. To sub-segment the elderly generation, this paper takes their lifestyles and life stages into consideration. In order to reflect these aspects, various surveys and several rounds of expert interviews and focused group interviews (FGIs) were performed. Using the results of these qualitative surveys, we can define six sub-segments of the elderly generation. This paper uses five rules to divide the elderly generation. The five rules are (1) mutually exclusive and collectively exhaustive (MECE) sub-segmentation, (2) important life stages, (3) notable lifestyles, (4) minimum number of and easy classifiable sub-segments, and (5) significant difference in voices among the sub-segments. The most critical point for dividing the elderly market is whether children are married. The other points are source of income, gender, and occupation. In this paper, the elderly market is divided into six sub-segments. As mentioned, the number of sub-segments is a very key point for a successful marketing approach. Too many sub-segments would lead to narrow substantiality or lack of actionability. On the other hand, too few sub-segments would have no effects. Therefore, the creation of the optimum number of sub-segments is a critical problem faced by marketers. This paper presents a method of evaluating the fitness of sub-segments that was deduced from the preceding surveys. The presented method uses the degree of homogeneity (DoH) to measure the adequacy of sub-segments. This measure uses quantitative survey questions to calculate adequacy. The ratio of significantly homogeneous questions to the total numbers of survey questions indicates the DoH. A significantly homogeneous question is defined as a question in which one case is selected significantly more often than others. To show whether a case is selected significantly more often than others, we use a hypothesis test. In this case, the null hypothesis (H0) would be that there is no significant difference between the selection of one case and that of the others. Thus, the total number of significantly homogeneous questions is the total number of cases in which the null hypothesis is rejected. To calculate the DoH, we conducted a quantitative survey (total sample size was 400, 60 questions, 4~5 cases for each question). The sample size of the first sub-segment-has no unmarried offspring and earns a living independently-is 113. The sample size of the second sub-segment-has no unmarried offspring and is economically supported by its offspring-is 57. The sample size of the third sub-segment-has unmarried offspring and is employed and male-is 70. The sample size of the fourth sub-segment-has unmarried offspring and is not employed and male-is 45. The sample size of the fifth sub-segment-has unmarried offspring and is female and employed (either the female herself or her husband)-is 63. The sample size of the last sub-segment-has unmarried offspring and is female and not employed (not even the husband)-is 52. Statistically, the sample size of each sub-segment is sufficiently large. Therefore, we use the z-test for testing hypotheses. When the significance level is 0.05, the DoHs of the six sub-segments are 1.00, 0.95, 0.95, 0.87, 0.93, and 1.00, respectively. When the significance level is 0.01, the DoHs of the six sub-segments are 0.95, 0.87, 0.85, 0.80, 0.88, and 0.87, respectively. These results show that the first sub-segment is the most homogeneous category, while the fourth has more variety in terms of its needs. If the sample size is sufficiently large, more segmentation would be better in a given sub-segment. However, as the fourth sub-segment is smaller than the others, more detailed segmentation is not proceeded. A very critical point for a successful micro-marketing strategy is measuring the fit of a sub-segment. However, until now, there have been no robust rules for measuring fit. This paper presents a method of evaluating the fit of sub-segments. This method will be very helpful for deciding the adequacy of sub-segmentation. However, it has some limitations that prevent it from being robust. These limitations include the following: (1) the method is restricted to only quantitative questions; (2) the type of questions that must be involved in calculation pose difficulties; (3) DoH values depend on content formation. Despite these limitations, this paper has presented a useful method for conducting adequate sub-segmentation. We believe that the present method can be applied widely in many areas. Furthermore, the results of the sub-segmentation of the elderly generation can serve as a reference for mature marketing.

• Clinical and Experimental Pediatrics
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• v.45 no.3
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• pp.346-353
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• 2002

Purpose : Penicillin- and multidrug-resistant S. pneumoniae poses a serious threat to clinicians because the rate of resistance of S. pneumoniae to penicillin in Korea has surged up to the world's highest level. This study was performed to assess the carriage rate, serogroups and antimicrobial susceptibility of S. pneumoniae isolated from oropharynx in children. Methods : From March to July 1998, 209 children under 5 years of age were recruited from five day care centers. The carriage rate for pneumococci was obtained. Antimicrobial susceptibilities were determined with the E-test and agar dilution methods. Serogrouping was performed on 48 of the pneumococcal isolates by the Quellung reaction. Results : The carriage rate of S. pneumoniae was 30.1%. Antimicrobial susceptibility profiles were available for 59 of the isolates. Sixty-six percent of isolates were not susceptible to penicillin, and multidrug-resistance was observed in 76.3% of the isolates. A high proportion of the penicillin-resistant strains showed associated resistance to trimethoprim-sulfamethoxazole, tetracycline, erythromycin, and oxacillin. The most prevalent oropharyngeal serogroups were 19, 6, 3, 23, and 29. Resistance of the pneumococcal isolates to penicillin was different according to the serogroups. All of the strains of serogroup 19, 23, and 29 was resistant to penicillin but 87.5% of serogroup 3 strains were susceptible to penicillin. Conclusion : The resistance rate of S. pneumoniae isolated from oropharynx in children was very high to penicillin and other antimicrobial agents. For the reduction of the drug-resistant rate of S. pneumoniae, clinicians should be required to be more judicious in their use of antimicrobial agents.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.171-193
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• 2018

LOD(Linked Open Data) cloud is a practical implementation of semantic web. We suggested a new method that provides identity links conveniently in LOD cloud. It also allows changes in LOD to be reflected to searching results without any omissions. LOD provides detail descriptions of entities to public in RDF triple form. RDF triple is composed of subject, predicates, and objects and presents detail description for an entity. Links in LOD cloud, named identity links, are realized by asserting entities of different RDF triples to be identical. Currently, the identity link is provided with creating a link triple explicitly in which associates its subject and object with source and target entities. Link triples are appended to LOD. With identity links, a knowledge achieves from an LOD can be expanded with different knowledge from different LODs. The goal of LOD cloud is providing opportunity of knowledge expansion to users. Appending link triples to LOD, however, has serious difficulties in discovering identity links between entities one by one notwithstanding the enormous scale of LOD. Newly added entities cannot be reflected to searching results until identity links heading for them are serialized and published to LOD cloud. Instead of creating enormous identity links, we propose LOD to prepare its own link policy. The link policy specifies a set of target LODs to link and constraints necessary to discover identity links to entities on target LODs. On searching, it becomes possible to access newly added entities and reflect them to searching results without any omissions by referencing the link policies. Link policy specifies a set of predicate pairs for discovering identity between associated entities in source and target LODs. For the link policy specification, we have suggested a set of vocabularies that conform to RDFS and OWL. Identity between entities is evaluated in accordance with a similarity of the source and the target entities' objects which have been associated with the predicates' pair in the link policy. We implemented a system "Change Acceptable In-Depth Searching System(CAIDS)". With CAIDS, user's searching request starts from depth_0 LOD, i.e. surface searching. Referencing the link policies of LODs, CAIDS proceeds in-depth searching, next LODs of next depths. To supplement identity links derived from the link policies, CAIDS uses explicit link triples as well. Following the identity links, CAIDS's in-depth searching progresses. Content of an entity obtained from depth_0 LOD expands with the contents of entities of other LODs which have been discovered to be identical to depth_0 LOD entity. Expanding content of depth_0 LOD entity without user's cognition of such other LODs is the implementation of knowledge expansion. It is the goal of LOD cloud. The more identity links in LOD cloud, the wider content expansions in LOD cloud. We have suggested a new way to create identity links abundantly and supply them to LOD cloud. Experiments on CAIDS performed against DBpedia LODs of Korea, France, Italy, Spain, and Portugal. They present that CAIDS provides appropriate expansion ratio and inclusion ratio as long as degree of similarity between source and target objects is 0.8 ~ 0.9. Expansion ratio, for each depth, depicts the ratio of the entities discovered at the depth to the entities of depth_0 LOD. For each depth, inclusion ratio illustrates the ratio of the entities discovered only with explicit links to the entities discovered only with link policies. In cases of similarity degrees with under 0.8, expansion becomes excessive and thus contents become distorted. Similarity degree of 0.8 ~ 0.9 provides appropriate amount of RDF triples searched as well. Experiments have evaluated confidence degree of contents which have been expanded in accordance with in-depth searching. Confidence degree of content is directly coupled with identity ratio of an entity, which means the degree of identity to the entity of depth_0 LOD. Identity ratio of an entity is obtained by multiplying source LOD's confidence and source entity's identity ratio. By tracing the identity links in advance, LOD's confidence is evaluated in accordance with the amount of identity links incoming to the entities in the LOD. While evaluating the identity ratio, concept of identity agreement, which means that multiple identity links head to a common entity, has been considered. With the identity agreement concept, experimental results present that identity ratio decreases as depth deepens, but rebounds as the depth deepens more. For each entity, as the number of identity links increases, identity ratio rebounds early and reaches at 1 finally. We found out that more than 8 identity links for each entity would lead users to give their confidence to the contents expanded. Link policy based in-depth searching method, we proposed, is expected to contribute to abundant identity links provisions to LOD cloud.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

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

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

• Radiation Oncology Journal
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• v.19 no.1
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• pp.45-52
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• 2001

Purpose : Granulocyle-colony stimulating factor (G-CSF) has been widely used to treat neutropenia caused by chemotherapy or radiotherapy. The efficacy of recombinant human hematopoietic growth factors in improving oral mucositis after chemotherapy or radiotherapy has been recently demonstrated in some clinical studies. This study was designed to determine whether G-CSF can modify the radiation injury of the intestinal mucosa in mice. Materials and Methods : One hundred and five BALB/c mice weighing 20 grams were divided into nine subgroups including G-CSF alone group $(I:10\;{\mu}g/kg\;or\;II:100\;{\mu}g/kg)$, radiation alone group (7.5 or 12 Gy on the whole body), combination group with G-CSF and radiation (G-CSF I or II plus 7.5 Gy, G-CSF I or II plus 12 Gy), and control group. Radiation was administered with a 6 MV linear accelerator (Mevatron Siemens) with a dose rate of 3 Gy/min on day 0. G-CSF was injected subcutaneously for 3 days, once a day, from day -2 to day 0. Each group was sacrificed on the day 1, day 3, and day 7. The mucosal changes of jejunum were evaluated microscopically by crypt count per circumference, villi length, and histologic damage grading. Results : In both G-CSF I and II groups, crypt counts, villi length, and histologic damage scores were not significantly different from those of the control one (p>0.05). The 7.5 Gy and 12 Gy radiation alone groups showed significantly lower crypt counts and higher histologic damage scores compared with those of control one (p<0.05). The groups exposed to 7.5 Gy radiation plus G-CSF I or II showed significantly higher crypt counts and lower histologic damage scores on the day 3, and lower histologic damage scores on the day 7 compared with those of the 7.5 Gy radiation alone one (p<0.05). The 12 Gy radiation plus G-CSF I or II group did not show significant difference in crypt counts and histologic damage scores compared with those of the 12 Gy radiation alone one (p>0,05). Most of the mice in 12 Gy radiation with or without G-CSF group showed intestinal death within 5 days. Conclusion : These results suggest that G-CSF may protect the jejunal mucosa from the acute radiation damage following within the tolerable ranges of whole body irradiation in mice.

• Tuberculosis and Respiratory Diseases
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• v.50 no.4
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• pp.450-461
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• 2001

Background : The underlying pathogenesis of radiation-induced lung fibrosis (RTLF) has not been very well defined. However, the role of TGF-$\beta$ in the generation of RTLF has been a major focus because there is an increase in the expression of both the TGF-${\beta}m$-RNA and its protein preceding RTLF lesions. The down stream signal after a TGF-$\beta$ stimulated lung fibrosis includes the activation of many mediators such as Smad and c-Jun N-terminal kinase (JNK) through TAK1. It is we hypothesized that JNK activation may play a pivotal role in RTLF pathogenesis through increased transcription of the fibrogenic cytokines. The present study evaluates JNK activity in alveolar macrophages after irradiation and the relationship between JNK activity and the amount of collagen in the lung tissues. Methods : C57BL/6 mice(20-25 gr, males) received chlorotetracycline(2g/L) in their drinking water 1 week prior to irradiation and continuously there after. The mice were irradiated once with 1400 cGy of $60CO{\gamma}$-ray over the whole chest. The cellular composition of the whole lung bronchoalveoalr lavage fluids(BALF), elastin expression in the lung tissues, the level of hydroxyproline in lung tissues, and an in vitro JNK assay was measured before irradiation and one, four, and eight weeks after irradiation (RT). Results : The volumes of BALF retrieved from instilled 4 mL of saline with 2% heparin were 3.7-3.8 mL for each group. The cell numbers were similar before($4.1{\times}10^4{\pm}0.5{\times}10^4/mL$) and 1 week($3.1{\times}10^4{\pm}0.5{\times}10^4/mL$) after RT. At four and eight weeks after RT, the cell number reached to $14.0{\times}10^4{\pm}1.5{\times}10^4mL$ and $10.0{\times}10^4{\pm}1.3{\times}10^4/mL$, respectively. There we no changes in the lymphocytes and neutrophils population observed in the BALF after RT. The H-E stain of the lung tissues did not show any structural and fibrotic change in the lung tissues at 4 and 8 weeks after RT. In addition, the amount of elastin and collagen were not different on Verhoeff staining of the lung tissues before RT to eight weeks after RT. The hydroxyproine content was measured with the left lung dissected from the left main bronchus. The lung were homogenized and hydrolyzed with 6 N Hel for 12 hours at $110^{\circ}C$ then measured as previously described. The content of hydroxyproline, standardized with a lung protein concentration, reached a peak 4 weeks after RT, and thereafter showed a plateau. AnIn vitro JNK assay using c-$Jun_{1-79}$-GST sepharose beads were performed with the alveolar macrophages obtained from the BAL. JNK activity was not detected prior to RT, However, the JNK activity increased from one week after RT and reached a peak four weeks after RT. Conclusion : JNK may be involved in the pathogenesis because the JNK activity showed similar pattern observed with the hydroxyproine content. However, it is necessary to clarify that the JNK increases the transcription of fibrogenic cyiokines through the transcription factor.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.141-156
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• 2013

Researchers in welfare services of local government in Korea have rather been on isolated issues as disables, childcare, aging phenomenon, etc. (Kang, 2004; Jung et al., 2009). Lately, local officials, yet, realize that they need more comprehensive welfare services for all residents, not just for above-mentioned focused groups. Still cases dealt with focused group approach have been a main research stream due to various reason(Jung et al., 2009; Lee, 2009; Jang, 2011). Social Security Information System is an information system that comprehensively manages 292 welfare benefits provided by 17 ministries and 40 thousand welfare services provided by 230 local authorities in Korea. The purpose of the system is to improve efficiency of social welfare delivery process. The study of local government expenditure has been on the rise over the last few decades after the restarting the local autonomy, but these studies have limitations on data collection. Measurement of a local government's welfare efforts(spending) has been primarily on expenditures or budget for an individual, set aside for welfare. This practice of using monetary value for an individual as a "proxy value" for welfare effort(spending) is based on the assumption that expenditure is directly linked to welfare efforts(Lee et al., 2007). This expenditure/budget approach commonly uses total welfare amount or percentage figure as dependent variables (Wildavsky, 1985; Lee et al., 2007; Kang, 2000). However, current practice of using actual amount being used or percentage figure as a dependent variable may have some limitation; since budget or expenditure is greatly influenced by the total budget of a local government, relying on such monetary value may create inflate or deflate the true "welfare effort" (Jang, 2012). In addition, government budget usually contain a large amount of administrative cost, i.e., salary, for local officials, which is highly unrelated to the actual welfare expenditure (Jang, 2011). This paper used local government welfare service data from the detailed data sets linked to the Social Security Information System. The purpose of this paper is to analyze the factors that affect social welfare spending of 230 local authorities in 2012. The paper applied multiple regression based model to analyze the pooled financial data from the system. Based on the regression analysis, the following factors affecting self-funded welfare spending were identified. In our research model, we use the welfare budget/total budget(%) of a local government as a true measurement for a local government's welfare effort(spending). Doing so, we exclude central government subsidies or support being used for local welfare service. It is because central government welfare support does not truly reflect the welfare efforts(spending) of a local. The dependent variable of this paper is the volume of the welfare spending and the independent variables of the model are comprised of three categories, in terms of socio-demographic perspectives, the local economy and the financial capacity of local government. This paper categorized local authorities into 3 groups, districts, and cities and suburb areas. The model used a dummy variable as the control variable (local political factor). This paper demonstrated that the volume of the welfare spending for the welfare services is commonly influenced by the ratio of welfare budget to total local budget, the population of infants, self-reliance ratio and the level of unemployment factor. Interestingly, the influential factors are different by the size of local government. Analysis of determinants of local government self-welfare spending, we found a significant effect of local Gov. Finance characteristic in degree of the local government's financial independence, financial independence rate, rate of social welfare budget, and regional economic in opening-to-application ratio, and sociology of population in rate of infants. The result means that local authorities should have differentiated welfare strategies according to their conditions and circumstances. There is a meaning that this paper has successfully proven the significant factors influencing welfare spending of local government in Korea.

• Journal of Distribution Science
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• v.9 no.3
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• pp.37-46
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• 2011

Since the Korean retail industry was made accessible to the big conglomerates and foreign retail companies, local traditional markets have faced serious problems. To sustain the local traditional markets' survival, the Korean government established various remedial policies for addressing, and many scholars published articles to suggest how to find solutions to, the problem. Unfortunately, the results have not been satisfactory. The purpose of this study is to find another way to help the Korean traditional retail market, from the view point of the Green Growth Policy, an initiative designed to address environmentally balanced economic growth in Korea. In order to survive and to maintain sustainable growth, it is incumbent upon retailers in the traditional market to understand the concept of the Green Growth Policy. A survey was conducted as a means of testing the degree of awareness of the Green Growth Policy, as well as determining the relationship between the degree of awareness and the degree of organizational commitment by the retailers in the local traditional markets. Interestingly, we were able to detect some of the features (e.g., they were distinguished by the elderly and the young, as well as low level of education and high level of education) in the traditional market retailers' demographic characteristics. We utilized the analysis of variance (ANOVA) statistical method to simultaneously compare the differences in retailers' demographic characteristics; the results were as follows: Overall, the results showed that the awareness of the Green Growth Policy, the degree of trust in the government's policy, levels of self-efficacy, and levels of organizational commitment were higher with the older traditional market retailers than the younger traditional market retailers. Specifically, the degree of trust in government policies (F=9.964,p < .05), levels of self-efficacy (F=5.532,p < .05), and levels of organizational commitment (F=5.697,p < .05) were statistically significant. Moreover, in the portion of the study that addressed the difference between education levels, all the variables were averaged in the higher education category of the traditional market retailers. Specifically, awareness levels of the Green Growth Policy (F=8.564,p < .005) and levels of self-efficacy (F=6.754,p < .005) were statistically significant. These results revealed that the traditional market retailers' demographic characteristics should be considered important factors in order to realize their policy. The results of the study showed the following: 1) The degree of awareness of the government's Green Growth Policy was statistically significant as it related to traditional market retailers' organizational commitment. 2) The degree of trust of the government's policy was significantly moderated between the awareness of the government's Green Growth Policy and the traditional market retailers' organizational commitment. This result demonstrates that the traditional market retailers' awareness of the government's Green Growth Policy will show more organizational commitment with higher levels of trust of the government's policy. 3) It also revealed that traditional market retailers' self-efficacy was fully mediated between the awareness of the Green Growth Policy of the government and traditional market retailers' organizational commitment. The results suggest that the government should show an interest in showing traditional market retailers how to enhance their traditional markets. Implications and future research directions are also discussed.