• Journal of Distribution Research
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• v.16 no.4
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• pp.1-27
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• 2011

Introduction: Sales of private labels(PU have been growing m recent years. Globally, PLs have already achieved 20% share, although between 25 and 50% share in most of the European markets(AC. Nielson, 2005). These products are aimed to have comparable quality and prices as national brand(NB) products and have been continuously eroding manufacturer's national brand market share. Stores have also started introducing premium PLs that are of higher-quality and more reasonably priced compared to NBs. Worldwide, many retailers already have a multiple-tier private label architecture. Consumers as a consequence are now able to have a more diverse brand choice in store than ever before. Since premium PLs are priced higher than regular PLs and even, in some cases, above NBs, stores can expect to generate higher profits. Brand extensions and private label have been extensively studied in the marketing field. However, less attention has been paid to the private label extension. Therefore, this research focuses on private label extension using the Multi-Attribute Attitude Model(Fishbein and Ajzen, 1975). Especially there are few studies that consider the hierarchical effect of the PL's two parent brands: store brand and the original PL. We assume that the attitude toward each of the two parent brands affects the attitude towards the extended PL. The influence from each parent brand toward extended PL will vary according to the perceived fit between each parent brand and the extended PL. This research focuses on how these two parent brands act as reference points to one another in the consumers' choice consideration. Specifically we seek to understand how store image and attitude towards original PL affect consumer perceptions of extended premium PL. How consumers perceive extended premium PLs could provide strategic suggestions for retailer managers with specific suggestions on whether it is more effective: to position extended premium PL similarly or dissimilarly to original PL especially on the quality dimension and congruency with store image. There is an extensive body of research on branding and brand extensions (e.g. Aaker and Keller, 1990) and more recently on PLs(e.g. Kumar and Steenkamp, 2007). However there are no studies to date that look at the upgrading and influence of original PLs and attitude towards store on the premium PL extension. This research wishes to make a contribution to this gap using the perceived fit difference between parent brands and extended premium PL as the context. In order to meet the above objectives, we investigate which factors heighten consumers' positive attitude toward premium PL extension. Research Model and Hypotheses: When considering the attitude towards the premium PL extension, we expect four factors to have an influence: attitude towards store; attitude towards original PL; perceived congruity between the store image and the premium PL; perceived similarity between the original PL and the premium PL. We expect that all these factors have an influence on consumer attitude towards premium PL extension. Figure 1 gives the research model and hypotheses. Method: Data were collected by an intercept survey conducted on consumers at discount stores. 403 survey responses were attained (total 59.8% female, across all age ranges). Respondents were asked to respond to a series of Questions measured on 7 point likert-type scales. The survey consisted of Questions that measured: the trust towards store and the original PL; the satisfaction towards store and the original PL; the attitudes towards store, the original PL, and the extended premium PL; the perceived similarity of the original PL and the extended premium PL; the perceived congruity between the store image and the extended premium PL. Product images with specific explanations of the features of premium PL, regular PL and NB we reused as the stimuli for the Question response. We developed scales to measure the research constructs. Cronbach's alphaw as measured each construct with the reliability for all constructs exceeding the .70 standard(Nunnally, 1978). Results: To test the hypotheses, path analysis was conducted using LISREL 8.30. The path analysis for verification of the model produced satisfactory results. The validity index shows acceptable results(${\chi}^2=427.00$(P=0.00), GFI= .90, AGFI= .87, NFI= .91, RMSEA= .062, RMR= .047). With the increasing retailer use of premium PLBs, the intention of this research was to examine how consumers use original PL and store image as reference points as to the attitude towards premium PL extension. Results(see table 1 & 2) show that the attitude of each parent brand (attitudes toward store and original pL) influences the attitude towards extended PL and their perceived fit moderates these influences. Attitude toward the extended PL was influenced by the relative level of perceived fit. Discussion of results and future direction: These results suggest that the future strategy for the PL extension needs to consider that positive parent brand attitude is more strongly associated with the attitude toward PL extensions. Specifically, to improve attitude towards PL extension, building and maintaining positive attitude towards original PL is necessary. Positioning premium PL congruently to store image is also important for positive attitude. In order to improve this research, the following alternatives should also be considered. To improve the research model's predictive power, more diverse products should be included in study. Other attributes of product should also be included such as design, brand name since we only considered trust and satisfaction as factors to build consumer attitudes.

• Tuberculosis and Respiratory Diseases
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• v.47 no.3
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• pp.356-364
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• 1999

Backgrounds: Previous reports have revealed a high morbidity and mortality in fatal asthma patients, especially those treated in the medical intensive care unit(MICU). But it has not been well known about the predictable factors for the mortality of fatal asthma(F A) with acute respiratory failure. In order to define the predictable factors for the mortality of FA at the admission to MICU, we analyzed the relationship between the clinical parameters and the prognosis of FA patients. Methods: A retrospective analysis of all medical records of 59 patients who had admitted for FA to MICU at a tertiary care MICU from January 1992 to March 1997 was performed. Results: Over all mortality rate was 32.2% and 43 patients were mechanically ventilated. In uni-variate analysis, the death group had significantly older age ($66.2{\pm}10.5$ vs. $51.0{\pm}18.8$ year), lower FVC($59.2{\pm}21.1$ vs. $77.6{\pm}23.3%$) and lower $FEV_1$($41.4{\pm}18.8$ vs. $61.l{\pm}23.30%$), and longer total ventilation time ($255.0{\pm}236.3$ vs. $98.1{\pm}120.4$ hour) (p<0.05) compared with the survival group (PFT: best value of recent 1 year). At MICU admission, there were no significant differences in vital signs, $PaCO_2$, $PaO_2/FiO_2$, and $AaDO_2$, in both groups. However, on the second day of MICU, the death group had significantly more rapid pulse rate ($121.6{\pm}22.3$ vs. $105.2{\pm}19.4$ rate/min), elevated $PaCO_2$ ($50.1{\pm}16.5$ vs. $41.8{\pm}12.2 mm Hg$), lower $PaO_2/FiO_2$, ($160.8{\pm}59.8$ vs. $256.6{\pm}78.3 mm Hg$), higher $AaDO_2$ ($181.5{\pm}79.7$ vs. $98.6{\pm}47.9 mm Hg$), and higher APACHE III score ($57.6{\pm}21.1$ vs. $20.3{\pm}13.2$) than survival group (p<0.05). The death group had more frequently associated with pneumonia and anoxic brain damage at admission, and had more frequently developed sepsis during disease progression than the survival group (p<0.05). Multi-variate analysis using APACHE III score and $PaO_2/FiO_2$, ratio on first and second day, age, sex, and pneumonia combined at admission revealed that APACHE III score (40) and $PaO_2/FiO_2$ ratio (<200) on second day were regarded as predictive factors for the mortality of fatal asthma (p<0.05). Conclusions: APACHE III score ($\geq$40) and $PaO_2/FiO_2$ ratio (<200) on the second day of MICU, which might reflect the response of treatment, rather than initially presented clinical parameters would be more important predictable factors of mortality in patients with FA.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• 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.

• Tuberculosis and Respiratory Diseases
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• v.46 no.2
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• pp.204-214
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• 1999

Background: Resting pulmonary function tests(PFTs) are routinely used in the evaluation of pulmonary impairment/disability. But the significance of the cardiopulmonary exercise test(CPX) in the evaluation of pulmonary impairment is controvertible. Many experts believe that dyspnea, though a necessary part of the assessment, is not a reliable predictor of impairment. Nevertheless, oxygen requirements of an organism at rest are different from at activity or exercising, and a clear relationship between resting PFTs and exercise tolerance has not been established in patients with chronic pulmonary disease. As well, the relationship between resting PFTs and dyspnea is complex. To investigate the relationship of dyspnea, resting PFTs, and CPX, we evaluated the patients of stabilized chronic pulmonary disease with clinical dyspnea rating(baseline dyspnea index, BDI), resting PFTs, and CPX. Method: The 50 patients were divided into two groups: non-severe and severe group on basis of results of resting PFTs(by criteria of ATS), CPX(by criteria of ATS or Ortega), and dyspnea rating(by focal score of BDI). Groups were compared with respect to pulmonary function, indices of CPX, and dyspnea rating. Results: 1. According to the criteria of pulmonary impairment with resting PFTs, $VO_2$max, and focal score of BDI were significantly low in the severe group(p<0.01). According to the criteria of $VO_2$max(ml/kg/min) and $VO_2$max(%), the parameters of resting PFTs, except $FEV_1$ were not significantly different between non-severe and severe(p>0.05). According to focal score($FEV_1$(%), FVC(%), MW(%), $FEV_1/FVC$, and $VO_2$max were significantly lower in the severe group(p<0.01). However, in the more severe dyspneic group(focal score<5), only $VO_2$max(ml/kg/min) and $VO_2$max(%) were low(p<0.01). $FEV_1$(%) was correlated with $VO_2$max(%)(r=0.52;p<0.01), but not predictive of exercise performance. The focal score had the correlation with max WR(%) (r=0.55;p<0.01). Sensitivity and specificity analysis were utilized to compare the different criteria used to evaluate the severity of pulmonary impairment, revealed that the classification would be different according to the criteria used. And focal score for dyspnea showed similar sensitivity and specificity. Conclusion : According to these result, resting PFTs were not superior to rating of dyspnea in prediction of exercise performance in patients with chronic pulmonary diseases and less correlative with focal score for dyspnea than $VO_2$max and max WR. Therefore, if not contraindicated, CPX would be considered to evaluate the severity of pulmonary impairment in patients with chronic pulmonary diseases, including with severe resting PFTs. Current criteria used to evaluate the severity of impairment were insufficient in considering the degree of dyspnea, so new criteria, including the severity of dyspnea, may be necessary.

• Journal of radiological science and technology
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• v.33 no.1
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• pp.37-44
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• 2010

We sought to confirm an independent factor about in-stent restenosis (ISR) in the patients who underwent drug-eluting stent (DES) and know a possibility as a predictor of measured coronary artery calcium score by MDCT. A total of 178 patients (159 men, $61.7{\pm}10.0$ years of age) with 190 coronary artery lesions were included in this study out of 1,131 patients who underwent percutaneous coronary intervention (PCI) with DES implantation for significant stenosis on MDCT at Chonnam National University Hospital between May 2006 and May 2009. All lesions were divided into two groups with the presence of ISR : group I (re ISR, N = 57) and group II (no ISR, N = 133). Compared to group II, group I was more likely to be older ($65.8{\pm}9.0$ vs. $60.2{\pm}9.9$ years, p = 0.0001), diabetic (21.8% vs. 52.6%, p = 0.0001), have old myocardial infarction (8.8% vs. 2.3%, p = 0.040), left main stem disease (5.3% vs. 0.8%, p = 0.047), and smaller stent size ($3.1{\pm}0.3\;mm$ vs. $3.3{\pm}0.4\;mm$, p = 0.004). Group II was more likely to be smokers (19.3% vs. 42.1%, p = 0.003), have dyslipidemia (8.8% vs. 23.3%, p = 0.019). Left ventricular ejection fraction, lesion complexity, and stent length were not different between the two groups. Total CAC score was $389.3{\pm}458.3$ in group I and $371.2{\pm}500.8$ in group II (p = 0.185). No statistical difference was observed between the groups in CAC score in the culprit vessel, left main stem, left anterior descending artery, left circumflex artery, and right coronary artery. On multivariate logistic regression analysis, left main stem disease (OR = 168.0, 95% CI = 7.83-3,604.3, p = 0.001), male sex (OR = 36.5, 95% CI = 5.89-2,226.9, p = 0.0001), and the presence of diabetes (OR = 2.62, 95% CI = 1.071-6.450, p = 0.035) were independent predictors of ISR after DES implantation. In patients who underwent DES implantation for significant coronary stenosis on MDCT, ISR was associated with left main stem disease, male sex, and the presence of diabetes. However, CAC score by MDCT was not a predictor of ISR in this study population.

• Tuberculosis and Respiratory Diseases
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• v.51 no.6
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• pp.559-569
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• 2001

Background : Pleural effusion is one of the most common clinical manifestations associated with a variety of pulmonary diseases such as malignancy, tuberculosis, and pneumonia. However, there are no useful laboratory tests to determine the specific cause of pleural effusion. Therefore, an attempt was made to analyze the various types of pleural effusion and search for useful laboratory tests for pleural effusion in order to differentiate between the diseases, especially between a malignant pleural effusion and a non-malignant pleural effusion. Methods : 93 patients with a pleural effusion, who visited the Severance hospital from January 1998 to August 1999, were enrolled in this study. Ultrasound-guided thoracentesis was done and a confirmational diagnosis was made by a gram stain, bacterial culture, Ziehl-Neelsen stain, a mycobacterial culture, a pleural biopsy and cytology. Results : The male to female ratio was 56 : 37 and the average age was $47.1{\pm}21.8$ years. There were 16 cases with a malignant effusion, 12 cases with a para-malignant effusion, 36 cases with tuberculosis, 22 cases with a para-pneumonic effusion, and 7 cases with transudate. The LDH2 fraction was significantly higher in the para-malignant effusion group compared to the para-pneumonic effusion group [$30.6{\pm}6.4%$ and $20.2{\pm}7.5%$, respectively (p<0.05)] and both the LDH1 and LDH2 fraction was significantly in the para-malignant effusion group compared to those with tuberculosis [$16.4{\pm}7.2%$ vs. $7.6{\pm}4.7%$, and $30.6{\pm}6.4%$ vs.$17.6{\pm}6.3%$, respectively (p<0.05)]. The pleural effusion/serum LDH4 fraction ratio was significantly lower in the malignant effusion group compared to those with tuberculosis [$1.5{\pm}0.8$ vs. $2.1{\pm}0.6$, respectively (p<0.05)]. The LDH4 fraction and the pleural effusion/serum LDH4 fraction ratio was significantly lower in the para-malignant effusion group compared to those with tuberculosis [$17.0{\pm}5.8%$ vs. $23.5{\pm}4.6%$ and $1.3{\pm}0.4$ vs. $2.1{\pm}0.6$, respectively (p<0.05)]. Conclusion : These results suggest that the LDH isoenzyme was the only useful biochemical test for a differential diagnosis of the various diseases. In particular, the most useful test was the pleural effusion/serum LDH4 fraction ratio to distinguish between a para-malignant effusion and a tuberculous effusion.