• Asia pacific journal of information systems
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• v.16 no.3
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• pp.67-93
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• 2006

Some consumers prefer online and others prefer offline. What makes them prefer online or offline? There has been a lack of theoretical development to adequately explain consumers' channel switching behavior between traditional physical stores and new virtual stores. Through consumers' purchase decision processes, this study examined the reasons why consumers changed channels depending on purchase process stages. Consumer's purchase decision process could be divided into three stages: pre-purchase stage, purchase stage, and post-purchase stage. We used the intention of channel selection as a surrogate dependent variable of channel selection. And some constructs, that is, channel function, channel benefits, customer relationship benefits, and perceived behavioral control, were selected as independent variables. In buying look-and-feel products, it was identified that consumers preferred virtual stores to physical stores at pre-purchase stage. To put it concretely, all constructs except channel benefits were more influenced to consumers at virtual stores. This result implied that information searching function, which is a main function at pre-purchase stage, was better supported by virtual stores than physical stores. In purchase stage, consumers preferred physical stores to virtual stores. Specially, all constructs influenced much more to consumers at physical stores. This result implied that although escrow service and trusted third parties were introduced, consumers felt that financial risk, performance risk, social risk, etc. still remained highly online. Finally, consumers did not prefer any channel at post-purchase stage. But three independent variables, i.e. channel function, channel benefits, and customer relationship benefits, were significantly preferred at physical stores rather than virtual stores at post-purchase stage. So we concluded that physical stores were a little more preferred to virtual stores at post-purchase stage. Through this study, it was identified that most consumers might switch channels according to purchase process stages. So, first of all, sales representatives should decide that what benefits should be given them through virtual stores at the pre-purchase stage and through physical stores at the purchase and post-purchase stages, and then devise collaborative channel strategies.

• Journal of the Korean Institute of Plant Engineering
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• v.23 no.4
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• pp.57-64
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• 2018

User-based collaborative filtering is a method of recommending an item to a user based on the preference of the neighbor users who have similar purchasing history to the target user. User-based collaborative filtering is based on the fact that users are strongly influenced by the opinions of other users with similar interests. Item-based collaborative filtering is a method of recommending an item by comparing the similarity of the user's previously preferred items. In this study, we create a recommendation model using user-based collaborative filtering and item-based collaborative filtering with consumer's consumption data. Collaborative filtering is performed by using RFM (recency, frequency, and monetary) technique with purchasing data to recommend items with high purchase potential. We compared the performance of the recommendation system with the purchase amount and the performance when applying the RFM method. The performance of recommendation system using RFM technique is better.

• The Research Journal of the Costume Culture
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• v.19 no.3
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• pp.531-541
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• 2011

This study suggests basic data for the standardization of school uniform purchase by examining the statistics of purchasing practice school uniforms from information sources, purchasing methods, and consumer' perception about collaborative purchases. A survey was conducted with first grade male and female middle-school students, and their parents. A total of 344 questionnaires were returned and analyzed. The results were as follows: first, when making purchases, information sources were explained by parents, friends, senior students, or workers at uniform shops. The purchasing methods were popular brand uniforms or specialized uniform shops. Second, four factors were extracted from purchasing data for factor analysis. The factors were comfort, appearance, service, other external factors, and promotions. Third, the perception analysis and need of collaborative purchases were indicated by 90% of the students' parents, who were aware of collaborative purchase. Additionally, 71.2% answered collaborative purchase was necessary. Fourth, for future uniform purchases, 75.6% of the students answered to buy more popular brands, or products from specialized school uniform shops, while 54.4% of the parents answered positively to collaborative purchases. The results of the examination of consumer school uniform purchasing behavior will provide useful strategies for the standardization system for purchasing school uniforms.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.131-149
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• 2004

Collaborative filtering is one of popular techniques for personalized recommendation in e-commerce sites. An advantage of collaborative filtering is that the technique can work with sparse evaluation data to predict preference scores of new alternative contents or advertisements. There is, however, no in-depth study about the sparsity effect of customer's evaluation data to the performance of recommendation. In this study, we investigate the sparsity effect and hybrid usages of customers' evaluation data and purchase data using an experiment result. The result of the analysis shows that the performance of recommendation decreases monotonically as the sparsity increases, and also the hybrid usage of two different types of data; customers' evaluation data and purchase data helps to increase the performance of recommendation in sparsity situation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.331-339
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• 2004

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this paper, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.69-80
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• 2007

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques with better performance.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.2
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• pp.232-249
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• 2022

As the postsecondary school-age population continues to decline, the competition among universities to attract potential students has intensified. As an alternative, we propose to introduce a collaborative marketing strategy to universities to gain the attention of precollege students and parents. This study examines perceived fit, the prestige of university and fashion brands, consumption values, and the category of fashion brands in the context of collaboration between university identity and fashion brands. Utilizing an online survey, we collected 391 responses. The results indicate that perceived fit between universities and fashion brands has a significant impact on the purchase intention of collaborative university merchandise. In addition, the prestige of fashion brands plays a key role, while the prestige of universities has no direct effect on purchase intention. However, the indirect effect of university prestige on purchase intention mediated by perceived fit is significant. Also, this study confirms that social value and emotional value have significant impacts on purchase intention. These findings present a guideline for selecting a collaborative partner, which is the most important task in a collaboration strategy. Finally, merchandising strategies reflected consumption values based on precollege students and their parents' needs are proposed.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Korean Management Science Review
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• v.23 no.2
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• pp.1-16
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• 2006

With the diffusion of the Internet, firms try to electronically collaborate with their partners in order to cut costs and gain profits. This, electronic Partnership, called 'Collaborative IT' is quite popular between large purchase enterprises and small-to-medium sized sub-contractors. This study investigates such relations. This study proposes three groups of research variables-interorganizational relationship, collaborative IT activity, and firm performance. the interorganizational relationship consists of trust, commitment, and asymmetry of commitment. Collaborative IT activity is composed of information sharing and workflow integration. The ultimate dependent variable is firm performance. It is hypothesized that the relationship factors influence the level of collaborative IT activity, while the latter in turn affects the firm performance. The relationship factors nay also directly affect the dependent variable. In addition, collaborative IT motive, as a moderating variable, may influence the causal relationship. By means of survey, ore hundred and eighty-two responses were obtained. Most sample companies are small-sized, in the manufacturing sector. The analysis of data reveals that both trust and commitment positively affects the level of collaborative IT activity, while asymmetry of commitment has negative effects. The workflow integration is significantly related with firm performance. Information sharing, however, has no signific3nt effects. Furthermore, asymmetry of commitment shows reverse relationship with firm performance. Collaborative IT motive works as a moderating variable between information sharing and firm performance. Finally, workflow integration is believed to mediate between relationship factors and firm performance.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.19-45
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• 2005

Collaborative filtering is one of the most common methods that e-commerce sites and Internet information services use to personalize recommendations. Collaborative filtering has the advantage of being able to use even sparse evaluation data to predict preference scores for new products. To date, however, no in-depth investigation has been conducted on how the data sparsity effect in customers' evaluation data affects collaborative filtering-based recommendation performance. In this study, we analyzed the sparsity effect and used a hybrid method based on customers' evaluations and purchases collected from an online bookstore. Results indicated that recommendation performance decreased monotonically as sparsity increased, and that performance was more sensitive to sparsity in evaluation data rather than in purchase data. Results also indicated that the hybrid use of two different types of data (customers' evaluations and purchases) helped to improve the recommendation performance when evaluation data were highly sparse.