• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.7
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• pp.872-881
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• 1998

The purposed of this study was to investigate the dimensions of clothing purchasing motives and to examine differences of these dimensions according to the demographic variables, and to examine the relationship between the clothing attitudes and clothing purchasing motives. The subjects selected for the final analysis were 595 adults rainging from 20's to 40's in age. As for the analytic method. Cronbach's $\alpha$, factor analysis, one-way ANOVA, Tukey's multiple range test, frequency and percentage were applied. The conclusions of this study were as follows: 1. The clothing purchasing motive was classified into five dimensions: advertising and promotion, finding of clothing meeting one's own taste, extra income, practical purchasing needs and impulse purchase. And the dimensions of clothing purchasing motive were significantly different according to monthly expenditure on clothing, sex and educational level. 2. The relationship between the clothing attitudes dimensions and clothing purchasing motive dimensions were significant.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.3_4 s.141
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• pp.547-560
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• 2005

The purpose of this study was to segment Chinese career women by experience in purchasing Korean clothing and to analyze and compare clothing purchasing behavior between the 2 groups and to provide useful information to Korean manufacturers for establishment of marketing strategies for China. The subjects were 602 career women of middle and high class In their 20's and 30's. A total of 602 questionnaires were analyzed by using frequency, mean, Cronbach's $\alpha$, factor analysis, t-test and $X^2$-analysis. The results were as follows: 1. The demographic variables such as an age, residential city, marriage, and total monthly income were proven to be significantly different between the 2 groups except an academic background. 2. The clothing purchase behavior variables such as purchasing motive, using informants, clothing selection standards, store selection standards, purchasing place, satisfaction after purchasing clothes, shopping time, average monthly expenditure on clothing, purchasing frequency of casual wear, purchasing price, and paying method were proven to be significantly different between the 2 groups. However, the clothing purchase behavior variables such as purchasing frequency of formal wear and purchasing price of fur coat and sweater were proven not to be significantly different between the 2 groups.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.2 no.2
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• pp.11-18
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• 1979

The purpose of this study is to establish a purchasing cost reduction program for the management practical use. The research method of this study is a bibliographical search of the materials available in the library at Keimyung University and the writer's private study, and the case analysis. The body of this study is divided into three sections. The first section deals with the purchasing management systems in terms of the nature and characters of purchasing management and purchasing cost in total management system. The second section includes purchasing cost reduction program through value analysis technique and the third section covers summary and the following conclusions : 1) Top management should be aware of the important position of purchasing activities from the cost point of view, and they should be aware that a good purchasing cost reduction program could be established through value analysis technique. 2) For such a purchasing cost reduction program to be carried out. effectively the management should support and back up the program. 3) There should be a training program to educate and develop the personnel who can manage and operate the purchasing cost reduction program through valuean analysis technique. 4) And cooperative system should be established in management and operative organization system from the total management systems, view for the effective use of the program.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2354-2358
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• 2009

Demand response (DR) can be used to improve the efficiency of electricity markets and increase the reliability of power systems. As more utilities attempt to reduce the purchasing costs by implementing DR programs strategically, there is an increasing need for studies of how to allocate the reduced purchasing costs among DR program participants. The rebates or incentives can be given to DR program participants in proportion to the participants' contributions to the reduced purchasing costs. This paper presents Shapley Value-based method to determine the DR program participants' contributions to the reduced purchasing costs. A numerical example is presented to validate the effectiveness of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.259-270
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• 2005

The primary research objective of this study is to evaluate a weight of purchase decision making factors according to the purchasing style. In this study, the purchasing style is classified into two categories-online shopping and offline shopping group. This study adopts the AHP method to calculate a weight of factors. For this purpose, 22 purchasing factors which affect on consumer purchasing decision making are classified into four factors - a product related factor, a convenience related factor, a purchasing risk related factor and a shopping enjoyment related factor. In this study, the weights of purchasing factors are evaluated according to 1)the purchasing style-online and offline Purchasing group, 2)the frequency of online shopping-high and low group, 3)the media used for online shopping-the TV home shopping and Internet home shopping group. The result shows that there are difference the weight of factors according to the purchasing style.

• Fashion & Textile Research Journal
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• v.20 no.4
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• pp.389-399
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• 2018

The purpose of this study was to investigate the differences of sociocultural attitudes toward appearance, clothing and cosmetics purchasing behaviors according to male consumer's age group. The research method was survey and subjects were 656 male consumers. The results were as follows. First, three factors (appearance importance awareness, appearance internalization, and slimness importance awareness) were emerged on sociocultural attitudes toward appearance. Young age group showed higher level of appearance importance awareness and internalization than middle age group. Second, there were many differences on clothing purchasing behaviors by age variable. Young age group more importantly considered psycho-social purchasing motives, aesthetic selection criteria, and the internet as information source and purchasing place than middle age group. Whereas middle age group more importantly considered practical purchasing motives, practical selection criteria, and store display & salesman as information sources, and fashion outlet as purchasing place than young age group. Third, there were also many differences on cosmetics purchasing behaviors by age variable. Young age group used more and various cosmetics, and they more importantly considered skin improvement as purchasing motive, skin suitability and price as selection criteria, the internet as information source and purchasing place than middle age group. On the other hand, middle age group generally used fundamental cosmetics, and they more importantly considered skin protection as purchasing motive, quality as selection criterion, TV and store display & salesman as information sources, and discount store and cosmetics speciality store as purchasing places than young age group.

• Journal of Fashion Business
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• v.12 no.6
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• pp.124-137
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• 2008

The present study was implemented to understand high school girls' buying patterns of fashion products at online and offline shopping malls; and to obtain the information needed for the development of online and offline mall marketing strategies that are differentiated from each other. The study was conducted through a descriptive survey method using questionnaires. The sample consisted of 242 girls from four high schools located in Seoul. Descriptive statistics, cross-tabulation and a paired t-test were used for the analysis of the data. Results are as follows: First, most of the high school girls (82.2%) had experience of purchasing fashion products at online shopping malls. And, those who have purchasing experience at online shopping malls, compared to those who do not have such purchasing experience, showed a higher purchasing intension at online shopping malls. Second, both the degree of pre-purchase searching and ongoing searching was higher in online shopping than in offline shopping. Third, the quality of material, place produced, brand name, and store atmosphere/type were considered more in offline shopping than in online shopping. Shopping convenience and information service about the products were considered more in online shopping than in offline shopping. Fourth, the purchasing frequency of underwear and hair accessaries was higher at offline stores than online shopping malls, but the purchasing frequency of bags was higher in online stores than offline stores. When the differences between the purchasing patterns at online shopping malls and offline stores are considered carefully, marketing activities would be more effective.

• Journal of Fashion Business
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• v.6 no.5
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• Korean Journal of Human Ecology
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• v.11 no.2
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• pp.197-209
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• 2002

The purpose of this research was to develop of measured scales for purchasing motivation of inbound tourists about fashion goods. A self-administrated questionnaire survey method was conducted among foreign tourists visited in Dongdaemun market. Data from 615 of inbound tourists were analysed by using factor analysis. As the results of the survey, 6 factors related purchasing motivation were derived; satisfaction, practicality, fashion, memory of tourism, eonomy and impromptu. The adequate indexes were shown from final models, such as RMR=.047, SMSEA =.043, CFI=.935, GFI=.956, AGFI=.937 and NFI=.888. It means that these values are appropriate to the measured scales for purchasing motivation of inbound tourists for fashion goods.