• International Commerce and Information Review
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• v.8 no.3
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• pp.71-95
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• 2006

In recent, T-commerce is widely dispersed as alternative type of commerce. It is forecasted that t-commerce system is used more than e-commerce system. Therefore more and more t-commerce-related industries are also recognizing that t-commerce is a critical business model. It is needed to understand the concept of t-commerce and develop the t-commerce marketing strategy. CEO analyses consumer's behaviors according to the data about buyers and applies the advantage of t-commerce to the communication with customers. This t-commerce system plays an important role in maximizing customer satisfaction and affecting their intention to reuse it. Therefore this paper attempts to identify T-commerce critical success factors and divide between use-intention group and unuse-intention group by taking out a discriminant function by the discriminant analysis. This lays a foundation in developing T-commerce strategy. According to the discriminant function extracted, convenience factor, amusement factor, system quality factor, product perception factor are significant in the sequence of influential degree. However, usefulness factor and speedy connection factor are not significant. In result, the target hitting rate is 77.9% in the first unuse-intention group and it is 95.2% in the second use-intention group. The total discriminant target hitting rate is computed to higher value, 86.55%. The statistic package, SPSS 12.0, is used to survey and analyse data and test the hypothesis. The validity and reliability of variables are verified by both reliability analysis and factor analysis. The discriminant analysis is used to tell the difference between use-intention group and unuse-intention group.

• Journal of Fashion Business
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• v.4 no.3
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• pp.103-114
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• 2000

The purpose of this study was to test the discriminant analysis model of Quick Response system and to examine the detailed relationship between each discriminant factor and Quick Response adoption. In this discriminant analysis model of Quick Response system, firm size, strategic type, product category, fashion trend, selling time and the Quick Response benefits were included as discriminant factors. Onehundred and two subjects were randomly selected for the survey study and discriminant analysis, descriptive analysis, t-test, and x square test were used for the data analysis. The results of this study were: 1. Wilks Lambda and F value support the discriminant analysis model that, taken together firm size, strategic type, product category, fashion trend, selling time and the Quick Response benefits significantly help to explain Quick Response adoption. 2. The importance of discriminant ability was, in order, firm size, the Quick Response benefits, women's wear, fashion trend, analyzer, selling time, reactor, defender and men's wear. 3. The discriminant function had the high hit ratio, so this can be well used for the classification of Quick Response adoption/nonadoption.

• International Commerce and Information Review
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• v.9 no.2
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• pp.19-40
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• 2007

In recent, T-commerce is widely dispersed as alternative type of commerce. It is forecasted that t-commerce system is used more than e-commerce system. Therefore more and more t-commerce-related industries are also recognizing that t-commerce is a critical business model. It is needed to understand the concept of t-commerce and develop the t-commerce marketing strategy. CEO analyses consumer's behaviors according to the data about buyers and applies the advantage of t-commerce to the communication with customers. This t-commerce system plays an important role in maximizing customer satisfaction and affecting their intention to reuse it. Therefore this paper attempts to identify T-commerce critical success factors and divide between use-intention group and unuse-intention group by taking out a discriminant function by the discriminant analysis. This lays a foundation in developing T-commerce strategy. According to the discriminant function extracted, convenience factor, amusement factor, system quality factor, product perception factor are significant in the sequence of influential degree. However, usefulness factor and speedy connection factor are not significant. In result, the target hitting rate is 77.9% in the first unuse-intention group and it is 95.2% in the second use-intention group. The total discriminant target hitting rate is computed to higher value, 86.55%. The statistic package, SPSS 12.0, is used to survey and analyse data and test the hypothesis. The validity and reliability of variables are verified by both reliability analysis and factor analysis. The discriminant analysis is used to tell the difference between use-intention group and unuse-intention group.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.1
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• pp.15-26
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• 2002

The purpose of this study was to classier the somatotype of late middle-aged women and to analyze the characteristics of each somatotype. The subjects were 337 late middle-aged women and their age range os from 45 to 59 fears old. Data were collected through anthropometry and photometry and analyzed by factor analysis, cluster analysis and discriminant analysis. The results were as follows; 1. The result of factor analysis indicated that 9 factors were extracted through factor analysis and those factors comprised 83.56 percent of total valiance. 2. Using factor scores, cluster analysis was carried out and the subject were classified into 4 cluster. Each cluster was classified as their body front and side view contour. Type 1 is tall, slim, and lower balk is flat on the side. Type 2 is standard and lean-back type on the side. Type 3 is standard height and weight, H type in front, and belly-protruded on the side. Type 4 is short, fat, and the side is hip-protruded. 3. According to the stepwise discriminant analysis, the 9 important items in classifying the somatotype of the late middle-aged women are as follows ; lower back tilt angle, hip depth(back) -back waist depth(back), bust depth(fore) - anterior waist depth(fore), jugular fossa point(fore), upper back tilt angle, burst breadth -waist breadth, right shoulder tilt, height of shoulder - height of anterior waist, abdomen breath. The correct classification rate for these items is as exact as 84.62%.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.3
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• pp.345-352
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• 2000

The purpose of this study was to classify lower body types of elementary school girls. The subjects for anthropometric study were 368 girls aged from 10 to 11. Factor analysis, cluster analysis, discriminant analysis, and analysis of variance were performed for statistical analysis of the data. Four lower body construction factors were extracted by the factor analysis of antropometric measurements. The factors extracted were lower body fatness factor, lower body height factor, lower body length from the waist to the crotch factor, and lower body configuration factor. On the basis of the cluster analysis, three different lower body types were categorized. Type 1 was short and small sized type and 42.4% of subjects belonged under this type. Type 2 was tall and fat type and 22.3% of subjects belonged under this type. Type 3 was the most similar to the average type having the largest waist-hip drop value and 35.3% of subjects belonged under this type. Discriminant analysis showed 7 discriminant factors that can classify the children's lower body type were Rohrer's index, height, fibulae length, waist girth, ilio cristale girth, trochanter girth, and weight.

• Fashion & Textile Research Journal
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• v.8 no.1
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• pp.107-112
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• 2006

This study is done to classify the upper body shapes for late elementary schoolgirls. The sampling was done for 11~12 years-old-girls resident in Busan and Kyungnam. Based on the somatometric charateristics of them, 33 anthropometic and 7 photogrphic measurment data were acquired from every girl. These data are statistically analyzed with the following methods; Factor Analysis, Cluster Analysis, and Discriminant Analysis. Resulting from the factor analysis, it is shown that 79.95% of the whole variances can be explained with 8 factors. Through the cluster analysis, 3 types of upper body shapes can be categorized as follows: Type I has average horizontal size, big vertical size and lots of protruded chest ; Type III has big horizontal size, the mean vertical size, and big upper angle of the back ; Type II has small horizontal and vertical size and long surface length of the upper body. Through the discriminant analysis, the high discriminative items in discriminant function are follows: Upper chest circumference, arm length and waist front length of discriminant function I and waist depth, front length, back breadth, nipple to nipple breadth and upper chest circumference of discriminant function II have large coefficient values.

• Journal of the Korean Statistical Society
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• v.29 no.3
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• pp.337-350
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• 2000

This paper suggests a new diagnostic measure and a stopping rule for detecting influential observations in multiple discriminant analysis (MDA). It is developed from a Bayesian point of view using a default Bayes factor obtained from the fractional Bayes factor methodology. The Bayes factor is taken as a discriminatory information in MDA. It is shown that the effect of an observation over the discriminatory information is fully explained by the diagnostic measure. Based on the measure, we suggest a stopping rule for detecting influential observations in a given training sample. As a tool for interpreting the measure a graphical method is sued. Performance of the method is used. Performance of the method is examined through two illustrative examples.

• Journal of the Korean Home Economics Association
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• v.42 no.11
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• pp.85-107
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• 2004

This study classified figure types of adult males into several kinds of shape to provide fundamental data for their clothing sizing system. The subjects were 1496 men aged between 20 and 60 years old. Data were analyzed by factor analysis, cluster analysis and discriminant analysis. The results were as follows 1. For the result of the interview, the data were grouped into three age brackets: 20-35,31-45 and 41-60 years. 2. Factor analysis using values, which were measurements divided by either weight or height, was carried out to extract factors which characterize the various figures. fve factors to determine the figure types were extracted. 3. Cluster analysis using factor scores was carried out to categorize the figure types within the age groups. Figure types, describing shoulder angie and body shape, were categorized into 3 per age group. 4. Stepwise discriminant analysis w3s used to ensure that these clusters could be utilized with appropriate hit ratio. The hit ratio for each age group was around 80%.

• Journal of Preventive Medicine and Public Health
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• v.23 no.4 s.32
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• pp.436-450
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• 1990

This study was designed to investigate the mar factors affecting selection of delivery facilities by pregnant women. Five hundred women hospitalized at 23 Seoul-area delivery facilities, such as university hospitals, general hospitals, hospitals, and clinics were selected and given questionnaires from April 24 to May 7, 1990. A total of 350 questionnaires were collected and analysed for the study. The results are as follows ; 1. In general, variables which significantly affected the choice of delivery facilities included the age of women, their educational level, the educational level of their husbands, monthly average incomes and residential areas. 2. In analyzing the obstetrical characteristics of the women, those variables significantly affecting the choice of delivery facilities were the gestational period, the facilities for prenatal care, the frequency of prenatal care, the type of delivery, the frequency of miscarriage, previous delivery experiences and the awareness on prenatal care. 3. In comparing the motivation factors for selecting the delivery facilities, all the factors except convenience and need for hospitalization differed significantly among delivery facilities. 4. The factor analysis was assessed for twenty possible factors motivating the choice of delivery facilities. Six factors including personal service, scale of the facility, reputation, urgency, convenience, and experience were noted explaining by 57.7%. 5. In the discriminant analysis used to clarify the major factors affecting the selection of delivery facilities, the 16 significant variables were regarded as independent variables, and the type of delivery facilities was considered a dependent variable. The stepwise method was applied to the analysis. Detected discriminant variables were the facilities for prenatal care, scale factor, personal service factor, urgency factor, convenience factor, reputation factor, experience factor, gestational period, types of delivery, frequency of miscarriage, age and income. These 12 discriminant variables were tested, with reference to discriminant prediction, on their importance in the choice of the delivery facility, by the discriminant functional formula. The test showed a hit-rate of 67.7%. The results suggest that general characteristics, obstetrical characteristics, and motivations for selecting the delivery facilities differ significantly according to the types of the delivery facilities. This study implies that all types of delivery facilities should attempt to acommodate characteristics and motivations of pregnant women. The facilities should be prepared to increase their patients satisfaction with required medical conditions by improving service and responding to the pregnant women's preferences.

• Journal of Korea Technology Innovation Society
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• v.10 no.2
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• pp.183-205
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• 2007

An investigation was undertaken of the optimal discriminant model for predicting the likelihood of insolvency in advance for medium-sized firms based on the technology evaluation. The explanatory variables included in the discriminant model were selected by both factor analysis and discriminant analysis using stepwise selection method. Five explanatory variables were selected in factor analysis in terms of explanatory ratio and communality. Six explanatory variables were selected in stepwise discriminant analysis. The effectiveness of linear discriminant model and logistic discriminant model were assessed by the criteria of the critical probability and correct classification rate. Result showed that both model had similar correct classification rate and the linear discriminant model was preferred to the logistic discriminant model in terms of criteria of the critical probability In case of the linear discriminant model with critical probability of 0.5, the total-group correct classification rate was 70.4% and correct classification rates of insolvent and solvent groups were 73.4% and 69.5% respectively. Correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify the present sample. However, the actual correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify a future observation. Unfortunately, the correct classification rate underestimates the actual correct classification rate because the data set used to estimate the discriminant function is also used to evaluate them. The cross-validation method were used to estimate the bias of the correct classification rate. According to the results the estimated bias were 2.9% and the predicted actual correct classification rate was 67.5%. And a threshold value is set to establish an in-doubt category. Results of linear discriminant model can be applied for the technology financing banks to evaluate the possibility of insolvency and give the ranking of the firms applied.