• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.8
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• pp.1128-1140
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• 2002

As consumers' alternatives in fashion store selection increase, they select different store types to purchase different product types. Therefore, the probability of patronage mix behavior according to product type increases. Understanding consumers patronage mix behavior, finding out the determinant attributes of fashion stores for each product type are important to retailers and marketers for building a successful merchandising and marketing strategies. Most of the previous researches in store selection and patronage assume consumer behavior as single store patronage, and thus relatively little is known about patronage mix behavior. Also, little attention has been given to product characteristics as an important variable in store patronage behavior. The purposes of this study were to determine the effects of product characteristics in consumers patronage mix behavior. In this study, a theoretical model including product characteristics as an intervening variable between store attributes and patronage behavior was developed. A Questionnaire was developed and data were collected form 464 adult women living in Seoul area in Korea. Factor analysis, paired t-test, ANOVA, Duncan test and discriminant analysis were employed to analyze the data. As a result, the suggested model was partially verified. First, the product characteristic was found to have an effect on perceived importance of store attributes and on determinant attributes of stores. Consumers were found to use different evaluative criteria for store attributes when they purchase different product types. Second, the perceived importance of store attributes was determined by consumer characteristics as well as product characteristics. Third, the determinant attributes in store selection were affected not only by the perceived importance of store attributes but also by the type of product to be purchased.

• Journal of Fashion Business
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• v.20 no.3
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• pp.89-103
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• 2016

This study examines female new seniors' clothing consumption behaviors, such as their information sources, clothing store selection criteria, and perceived importance of sales associates' attributes, based on their level of clothing involvement. A face-to-face survey was conducted through a market research firm. The subjects of this study were female adults in their 50s and 60s residing in the Seoul metro area (N=197). Significant differences were found in the clothing consuming behaviors of new seniors depending on their clothing involvement(CI). High-CI new seniors tended to place more importance on both personal and non-personal information sources than low-CI new seniors. High-CI new seniors place importance both "merchandise assortment and store atmosphere" and "sales associates and service" when they select stores for clothing than low-CI new seniors. In terms of sales associates' attributes, high-CI new seniors tend to place more importance on customer-orientation than medium- and low-CI groups. Medium- and high-CI new seniors tend to place more importance on appearance than low-CI groups. However, there were no significant statistical differences for professional expertise among the three different CI groups.

• Journal of Distribution Science
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• v.14 no.6
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• pp.17-26
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• 2016

Purpose - The purpose of this study is to measure the effect on the attributes for store selection and Korean government regulations for large retailers on consumption emotion. Research Design, Data, and Methodology - Using SPSS ver.22, factor analysis & Cronbach's alpha, correlation, and regression test were performed. The total of 287 questionnaires were used for the analysis Result - Familiarity, perceived price, and perceived image have a positive effect on consumption emotion, but perceived quality & service did not. The Consumption emotion had a positive effect on traditional market revisit intentions. Retail regulation preference has a mediate effect between consumption emotion and familiarity & perceived price. Retail regulation preference also has a mediate effect between consumption emotion and traditional market revisit intension. Conclusion - Relationship marketing with sincerity is very important to keep familiarity, perceived price and image. People's consumption trend was changed due to dual career couple and heavy traffic jam in big cities. Therefore, a retail regulation cannot be a right solution for revitalizing traditional markets. So we need to find out an actual situation and design a win-win strategy between large retailers and traditional market.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.9
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• pp.1354-1365
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• 2002

With the appearance of many new types of fashion stores in Korea, competitions among fashion store types art fiercer than ever before. As consumers alternatives in fashion store selection increase, they select different store types to purchase different product types. Therefore, the probability of patronage mix behavior according to product type increases. Understanding consumers patronage mix behavior, finding out the determinant attributes of fashion stores for each product type, and analyzing competitive structures among fashion stores are important to retailers and marketers for building a successful merchandising and marketing strategies. An empirical study was conducted to analyze the competitive structure among the store types by consumers' patronage mix behavior. A questionnaire was developed and data were collected from 464 adult women living in Seoul area in Korea. Factor analysis, paired t-test, ANOVL Duncan test, and discriminant analysis were employed to analyze the data. Data regarding patronage mix behavior by product type proved that certain store types had ‘natural dominance’ in a particular product type as Hirschman(l978) pointed out. Also, a new analytic method of the competitive structures among fashion store types was suggested in the study, by which a specific store type retailer can analyze his/her own customers' patronage mix behavior by product type. The analysis will enable retailers to distinguish which of their competitors are substitutive, selling same product types, and which are complementary, selling different product types. Retailers have to concentrate on the strategies for the substitutive competitors rather than complementary competitors because their marketing abilities and resources are limited.

• Journal of the Korean Society of Clothing and Textiles
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• v.18 no.5
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• pp.749-764
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• 1994

It was assumed that a consumer forms images of all the factors related to clothing merchandise such as trademarks, stores, products and prices that he/she perceives in the market and those images deforming his/her preference for a particular piece of clothes and intention of purchase. Under this assumption, this research was designed to find out important factors of clothes that help a consumer form an image of the clothes, among other attributes of the merchandise, and how this image is related to the existing extraneous variables such as the trademark image, the consumer's self image, the price, etc. in leading the consumer to purchase the particular clothes in the end. In the empirical research, a preliminary survey was conducted to select brands of womenswear that were suitable for this study and as the result, 16 brands were chosen. Then one trademark, one store and two products from each of the 16 brands were selected. With these materials, 32 stimulus sheets were prepared, and each of them was composed of 3 photos of a brand's catalog and logo, the interior of a selected store that carried products of the brand, and formal suits selected from each brand's spring/summer 1993 collections. Subjects were 460 women in the age groups of 20-50s who live in Seoul Metropolitan area. Each of the subjects was provided with a survey questionnaire and 16 stimulus sheets. The main findings of this research prove that consumers follow a certain selection process model when they purchase clothes: consumers, first, from images of merchandise based on the trademarks, stares, and products and those formed images work il.: medium variables and in the end they help the consumers decide whether to buy the products.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.