• Asia Marketing Journal
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• v.7 no.2
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• pp.47-69
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• 2005

With the explosive growth of mobile products industry, tons of newer versions of products are putting on the market. From the marketer's perspective, understanding consumers' replacement purchases, especially the replacement timing, is essential to product planning and selling. This study presents an approach to finding out factors influencing the timing of buyers' replacement purchases of cell phones, using duration analysis; a hazard function specification is applied to describe consumers' replacement timing decision. Based on the data collected from a mobile telecommunication company, five categories of factors have been inspected. These are consumer's innovative service usage, data service usage, voice service usage, participation in loyalty programs, and the demographic characteristics. The results of the study are as follows. Firstly, the positive coefficient of 'the number of related services used' suggests that the consumers who have more usage knowledge tend to replace faster. Secondly, customers participating in the membership service are positively associated with early replacement purchases. Lastly, younger customers(vs. older) and male(vs. female) customers turned out to replace cell phones earlier.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.11
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• pp.1307-1317
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• 2003

The level of consumer knowledge has been gradually increased with the growth of living standard of consumer and the development of communication technology. The purpose of this study was to investigate the structure and measure of consumer knowledge and to find out relationship between objective knowledge and subjective knowledge. The subjects of this study were female adults from theirs twenties to theirs forties who lived in Seoul, Kyunggi or Incheon areas and Quota sampling method was used age and areas. The data were obtained from 603 questionnaires. Data were statistically analyzed using SPSS 10 and major statistical methods are mean, frequency, Pearson correlation coefficient, and Cronbach's $\alpha$ coefficient. The results are as follows: 1. Objective knowledge consisted of the product knowledge, brand knowledge, purchase knowledge and usage knowledge, and subjective knowledge consisted of experience and self-evaluation knowledge. 2. Consumer knowledge was related to the subjective knowledge and objective knowledge. But, there was relatively a low relationship between subjective knowledge and objective knowledge of apparel.

• International Journal of Human Ecology
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• v.4 no.1
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• pp.1-14
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• 2003

This study examines apparel brand extensions in terms of types and leverage effects. The researchers identified types of apparel brand extensions based on recent data gathered from the Korean apparel market. Three hundred forty eight Korean female subjects in their 20's evaluated three hypothetically extended brands from a major casual brand which actually exists in the Korean market. Major findings of the study follow. First, apparel brands are extended to different product categories by adjusting mostly target profiles as well as product usage, product class, and distribution channel. Secondly, leverage effects, the extent of image transfer from the parent brand to extended brands, are different according to the extension types.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.2
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• pp.59-75
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• 2004

Recommendation is a personalized information filtering technology to help customers find which products they would like to purchase. Collaborative filtering works by matching customer preferences to other customers in making recommendations. But collaborative filtering based recommendations have two major limitations, sparsity and scalability. To overcome these problems we suggest using adjusted product hierarchy, grain. This methodology focuses on dimensionality reduction and uses a marketer's specific knowledge or experience to improve recommendation quality. The qualify of recommendations using each grain is compared with others by several experimentations. Experiments present that the usage of a grain holds the promise of allowing CF-based recommendations to scale to large data sets and at the same time produces better recommendations. In addition. our methodology is proved to save the computation time by 3∼4 times compared with collaborative filtering.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.253-260
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• 1999

The recycling cell formation problem means that disposal products me classified into recycling part families using group technology in their end of life phase. Disposal products have the uncertainties of product status by usage influences during product use phase and recycling cells are formed design, process and usage attributes. In order to treat the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem far disposal products. In this paper, a heuristic approach fuzzy ART neural network is suggested. The modified fuzzy ART neural network is shown that it has a great efficiency and give an extension for systematically generating alternative solutions in the recycling cell formation problem. We present the results of this approach applied to disposal refrigerators and the comparison of performances between other algorithms. This paper introduced a procedure which integrates economic and environmental factors into the disassembly of disposal products for recycling in recycling cells. A qualitative method of disassembly analysis is developed and its ai is to improve the efficiency of the disassembly and to generated an optimal disassembly which maximize profits and minimize environmental impact. Three criteria established to reduce the search space and facilitate recycling opportunities.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.69-77
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• 2019

As information technology advances, the penetration of smart devices connected to the Internet, such as smart phone and tablet PC, has rapidly expanded, and as sensor prices have fallen the Internet of Things has begun to be introduced in the industry. Today's industry is rapidly changing and evolving, requiring companies to respond to the new paradigm of business. In this situation, companies need to actively manage and maintain customer relationships in order to acquire loyal customers who bring them a high return. The purpose of this study is to suggest a method to manage customer relationship using real time IoT data including IoT product usage data, customer characteristics and transaction data. This study proposes a method of segmenting customers through RFM analysis and transition index analysis. In addition, a real-time monitoring through control charts is used to identify abnormalities in product use and suggest ways of differentiating marketing for each group. In the study, 44 samples were classified as 9 churn customers, 10 potential customers, and 25 active customers. This study suggested ways to induce active customers by providing after-sales benefit for product reuse to a group of churn customers and to promote the advantages or necessity of using the product by setting the goal of increasing the frequency of use to a group of potential customers. Finally, since the active customer group is a loyal customer, this study proposed an one-on-one marketing to improve product satisfaction.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.646-653
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• 2016

Outbound market is a rapidly growing global industry, and has evolved into a 11 trillion won trade. A lot of recommender systems, which are based on collaborative and content filtering, target the existing purchase log or rely on studies based on similarity of products. These researches are not highly efficient as data was not obtained in advance, and acquiring the overwhelming amount of data has been relatively slow. The characteristics of an outbound product are that it should be purchased at least twice in a year, and its pricing should be in the higher category. Since the repetitive purchase of a product is rare for the outbound market, the old recommender system which profiles the existing customers is lacking, and has some limitations. Therefore, due to the scarcity of data, we suggest an improved customer-profiling method using web usage mining, algorithm of association rule, and rule-based algorithm, for faster recommender system of outbound product.

• Korean Journal of Computational Design and Engineering
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• v.17 no.3
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• pp.216-223
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• 2012

Bill of material (BOM), which structurally represents the relation among parts constructing a product, is usually created when enterprises start to develop a new product. And it is shown as various types of BOM according to business needs and usage such as eBOM (engineering BOM), gBOM (green BOM), mBOM (manufacturing BOM), pBOM (process BOM), etc. eBOM, generally called BOM and created in the design stage of the new product, includes the drawing information of parts in the view of product function. eBOM is extended to gBOM adding the material information of parts to cope with international regulations for environment. eBOM is transformed to mBOM, which includes manufacturing sequence of parts and adds some parts required to fabricate parts and to assemble the product. mBOM is also extended to pBOM adding the process information of each part and additional assembly processes. This study introduces the concept of multi-BOM covering eBOM, gBOM, mBOM and pBOM, and proposes an advanced way to manage product data using multi-BOM system. The multi-BOM system proposed is to manage their relations using transformation function of BOM and master information of all BOMs.

• The Journal of Society for e-Business Studies
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• v.4 no.2
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• pp.209-220
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• 1999

Quick response in the textiles and clothing industry is widely recognized at a strategic concept implemented by information technology to transfer changes in customer needs to the final product as soon as possible. This paper addresses which factors should affect the adoption of quick response systems in Korea. We surveyed 108 companies in the textile and clothing industry by using a questionnaire. We asked whether they adopted QR or not, and measured the usage level of QR-related ITs and the level of both internal and external factors in Likert's scale. We conducted multiple linear regression to analyze the data collected by using SAS. The findings are as follows: The adoption of QR, measured by the usage level of QR-related ITs, is positively affected by internal factors such as the recognition of QR concept and the capability for IT applications and by external factors such as the overall informating level of the industry, the needs for cooperation between organizations, and the leadership as an industry coordinator.

• Journal of the Korean Society of Clothing and Textiles
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• v.23 no.2
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• pp.185-195
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• 1999

The purpose of this study is to identify store image and clothing satisfaction of various clothing distribution type and is to compare the difference due to demographic variables. The data were obtained 407 housewives using questionnaire. The results were as follows. First Store image of clothing distribution types was found significant differences in product service atmosphere. Second Clothing satisfaction of clothing distribution types was founded significant differences in price brand name fashion design material sewing size. Third The new distribution types in general were not visited upon despite their high degree of recognition and using experience of new distribution types was founded significant differences in Store image of new distribution types. Fourth Among demographic variables significant difference in usage of the distributions was founded. Fifth Among demographic variables significant difference in usage of the distributions was founded. Fifth Among demographic variables significant difference in clothing satisfaction of the distributions was founded.