• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.1-12
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• 2017

Item-based collaborative filtering (IBCF) is an important technology that is widely used in recommender system of online shopping malls. It uses historical information to compute item-item similarity and make predictions. However, in offline shopping each customer's purchasing pattern can be occurred continuously and repeatedly due to time and space constraints contrast to online shopping. Those facts can make IBCF to have limitations from being applied to offline shopping malls directly. In order to improve the quality of recommendations made by IBCF in offline shopping mall, we propose an ensemble approach that considers both item-item similarity of IBCF and each customer's purchasing patterns which are modeled by item networks. Our experimental results show that this approach produces recommendation results superior to those of existing works such as pure IBCF or bestseller approaches.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.456-463
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• 2001

Understanding and adapting to changes of customer behavior in internet shopping mall is an important aspect to survive in continuously changing environment. This paper develops a methodology based on decision tree algorithms to detect changes of customer behavior automatically from customer profiles and sales data at different time snapshots. We first define three types of changes as emerging pattern, unexpected change and the added/perished rule. Then, it is developed similarity and difference measures for rule matching to detect all types of change. Finally, the degree of change is developed to evaluate the amount of change. A Korean internet shopping mall case is evaluated to represent the performance of our methodology. And practical business implications for this methodology are also provided.

• Proceedings of the CALSEC Conference
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• 2005.11a
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• pp.147-153
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• 2005

When a customer wants to buy an item at the Internet shopping mall, one of the difficulties is to decide when to buy the item because its price changes over time. If the shopping mall can be able to recommend appropriate buying points, it will be greatly helpful for the customer. Therefore, in this presentation, we propose a method to recommend buying points based on the time series analysis using a database that contains past prices data of items. The procedure to provide buying points for an item is as follows. First, we search past time series patterns from the database using normalized similarity, which are similar to the current time series pattern of the item. Second, we analyze the retrieved past patterns and predict the future price pattern of the item. Third, using the future price pattern, we recommend when to buy the item.

• Journal of Distribution Science
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• v.14 no.1
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• pp.51-58
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• 2016

Purpose - This study is to identify how the retail regulations influence customer shopping behaviours and furthermore, whether the Store Closing Act really protects independent retailers as well as traditional markets in Korea. Research design, data, and methodology - By adopting frequency analysis and factor analysis method, the research achieved research objectives. Before a field survey, the authors pre-tested the questionnaire developed, based on similar previous articles, and finalized. Amongst the 353 questionnaires distributed, 332 were returned. It means the response rate is 94.5%. Furthermore, available questionnaires are 330. Results - Rather than stimulating customers to more frequently visit public markets, the regulation has provoked new customer shopping behaviours. In other words, some consumers tend to shop in big box retailers before or after a store closing day, whereas others are likely to stop shopping. What is important is that customers do not patronise small retailers and conventional markets, thanks to the Store Closing Act. Conclusions - In order to keep retailers and public markets independent, the researchers suggest that the government should introduce new techniques without impeding the growth of a retailing sector.

• Journal of Korea Trade
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• v.27 no.1
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• pp.119-138
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• 2023

Purpose - This research is to empirically explore the differences in apparel consumption among male and female teenagers and college students in Korea and China. By conducting a survey to understand customers' needs and behaviors, fashion businesses will be able to improve their customer satisfaction and avoid redundancy, inventory, and the waste of resources, effort and money. Design/methodology - The research design considers the consumption patterns of male and female high school and college students in Korea and China. To analyze the data, the study employs decision trees, a type of machine learning algorithm. A decision tree model was developed to examine the relationship between the explanatory and response variables, which can be either quantitative or qualitative in nature. Findings - The main findings of this study indicate that there are differences in shopping behavior among different customer segments. The results show that men have a simpler shopping behavior compared to women. Additionally, cultural factors and the difference in fashion needs between students and non-students have a significant impact on the shopping choices of Chinese and Korean individuals. Originality/value - Existing studies often assume that the shopping behavior of high school and university students is similar and that there are no significant differences in clothing purchases between men and women across countries. The results provide valuable insights into the unique shopping behavior of different customer segments, and can inform fashion businesses in their efforts to meet the needs of their customers.

• Journal of Korean Society for Quality Management
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• v.33 no.4
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• pp.21-41
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• 2005

This study analyzes the Internet utilization pattern of customer by comprehensively investigating the previous studies on the behavior pattern of customer in terms of Internet business. Based on the analysis, this study develops research framework that supports strategic decision-making for resource allocation in Internet business. Such research framework would be helpful for providing the typology of Internet business model that can be specialized by each industry. As a result of the simulation analysis, it was found that the optimal resource allocation portfolio providing maximum profits to the Internet bookstore involves large-scale investment on delivery service and customer support service which are the key factors for post-purchase customer satisfaction, regardless of the growth pattern or size of Internet bookstore market. Consequently, from the above analysis, the investment ratio of resources for the profit maximization of Internet bookstore was drawn. Conclusively, based on the comprehensive examination of the results, this study provided a framework for dynamic resource allocation decision-making, and proposed a management strategy which allows consumers to shop under more favorable environment, and simultaneously enables the Internet bookstore to accomplish management objectives such as continuous growth and profit maximization.

• Proceedings of the Safety Management and Science Conference
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• 2002.11a
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• pp.239-246
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• 2002

In this report, we provide the focus on suggesting a method of estimating & measurement of CBM(Customer Behavior Model). Through the use of internet, a new trend of business for e-CRM on B2C Web Site which is now known as EC has emerged. The Purpose of this study is to identify the relationship between the customers of a shopping mall and CBM characteristics. Result showed that there is a significant relationship between the some customers pattern of shopping mall and CBM, CVM(Customer Visit Model).

• Proceedings of the Safety Management and Science Conference
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• 2006.11a
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• pp.299-307
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• 2006

In this report, we provide the focus on suggesting a method of estimating and measurement of CBM(Customer Behavior Model). Through the use of internet, a new trend of business for e-CRM on B2C Web Site known as EC has emerged. The purpose of this study is to identify the relationship between the customers of a shopping mall and CBM characteristics. It can be used to gain a better understanding of customers. From this we can determine trends, and so refine business toward customer's needs and target new products to particular customer groups. Result shows that there is a significant relationship between the customers pattern of shopping mall and CBM, CVM(Customer Visit Model).

• Asia pacific journal of information systems
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• v.12 no.1
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• pp.1-16
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• 2002

Most of searching systems used by shopping-mall provide too much information for user requirements or fail to provide appropriate items reflecting customer's preference. This paper aims to design and implement the product searching systems based on customer preference which will enable efficient product selection in the internet shopping-mall. The proposed system consists of user/provider interface, searching and model agent, data management system, and model management system. Especially, we construct the searching pattern database to support fast search using association mining method. And this system includes the customer-oriented decision model which shows the highly preferred products. Input weight value per attribute and preference level should be needed to compute priority grade of preference.

• Journal of the Korea Safety Management & Science
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• v.4 no.4
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• pp.119-128
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• 2002

In this report, we provide the focus on suggesting a method of estimating and measurement of CBM(Customer Behavior Model). Through the use of internet, a new trend of business for e-CRM on B2C Web Site known as EC has emerged. The purpose of this study is to identify the relationship between the customers of a shopping mall and CBM characteristics. It can be used to gain a better understanding of customers. from this we can determine trends, and so refine business toward customer's needs and target new products to particular customer groups. Result shows that there is a significant relationship between the customers pattern of shopping mall and CBM, CVM(Customer Visit Model).