• Journal of Global Scholars of Marketing Science
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• v.9
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• pp.175-188
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• 2002

The objectives of this study are to develop and test a research model specifying the relationship between brand loyalty and sales of rice brands and to provide insight on establishing a marketing strategy for rice brands in South Korea. Results indicate that the information source a consumer relies upon is related to brand loyalty in the rice category. Second, consumers who are highly involved with the product category tend to be more brand loyal. Third, demographic of purchasing behavior are positively related to rice brand loyalty. Fourth, demographic characteristics can partially explain differences in rice brand loyalty. Finally, rice brand loyalty was positively related to consumer satisfaction.

• Journal of Fashion Business
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• v.14 no.5
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• pp.49-65
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• 2010

The purpose of this paper is to derive the categories of negative e-WOM (electronic word of mouth) via consumer review. Disclosing the details of negative e-WOM based consumer reviews has never been done before. For this reason, a content analysis was adopted to provide knowledge and understanding of the phenomenon. This paper analyzes the content of 630 consumer reviews posted on the open market internet site, www.auction.co.kr. The analysis was conducted from October 20th, 2008 to March 10th, 2009. The results indicated that the negative e-WOM based consumer reviews can be divided into two categories: the cognitive evaluation and the expression of consumer's emotion. The category of cognitive evaluation is consisted of negative e-WOM of product, negative e-WOM of service, and warning about the risk of purchasing products. The category of expressing consumers' emotion are composed of venting customers' dissatisfaction and passive response of dissatisfaction. Investigating the details of negative e-WOM has a number of implications. Most importantly, the results revealed multidimensional structure of negative e-WOM. This understanding of negative e-WOM communication allows marketers to improve products and services that better meet customers' current and future needs.

• The Research Journal of the Costume Culture
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• v.27 no.2
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• pp.98-108
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• 2019

With the expansion of various distribution channels in online and offline stores, TV, and mobile, consumers now have more information search and retail selection channels to choose from than ever before. Major retailers now use multi- and omni-channel strategies. This study focused on cross-channel consumption, which involves the use of different information search and purchase channels. Using cross-channel consumption, consumers can search for information online and then make purchases offline and vice versa. The purpose of this study was to examine the relationship between channel strategies and other consumer variables, and the study also assessed the effect of product type. To conduct this empirical study, the researchers developed a consumer questionnaire concerning three consumer channel strategies-on-on, cross, and off-off-and four product categories-clothing, cosmetics, books, and electronics. The results indicated that gender and marital status did not influence consumer channel strategies, but that age did have a significant influence. The analysis showed that consumers in their 40s preferred the cross channel strategy, perceiving it to be effective, satisfactory, and rewarding. Compared to other products, clothing products showed higher levels of cross channel strategies. Consumers indicated that they prefer searching for information online and then purchasing clothing offline. Overall, clothing products generated higher levels of channel satisfaction and channel switch intentions. Cross-channel clothing shoppers reported effective information retrieval times but longer delivery times.

• Journal of the Korean Society of Costume
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• v.62 no.2
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• pp.103-121
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• 2012

The purpose of this study is to provide basic information to improve competitiveness of Korean fashion brands in the Chinese fashion market by taking photos of street styles in Seoul that is the hub of Korean fashion and in Dalian, the northeastern district where Korean fashion brands are launched as a test market China to compare and analyze the clothing colors preferred mostly by young women in their twenties and thirties who have the highest purchasing power. The study methods used literature review and empirical study simultaneously. Dalian and Seoul are two fashion cities in northeast of China and Korea where street fashion was photographed. Clothing colors that were mainly worn by young women in their twenties and thirties were qualitatively analyzed using the photos taken. Color analysis was based on the Munsell Color Order System to grasp general preferences of colors on the basis of previous researches, and color tones were based on the ISCC-NBS System. In order to grasp the basic materials on Seoul, the Korean fashion city and Dalian, the northeastern fashion city in China, street fashion styles of 2010/11 F/W season were compared and analyzed. As a result, black and deep tone PB color appeared most frequently. The vivid and strong tone of R, YR, Y color showed high frequency of clothing colors. For the top wear, women in both areas preferred similar tones in the YR color category and Dalian women preferred vivider and brighter S tones in the R color category. For the bottom wear, women in both areas highly preferred achromatic colors and colors in the PB color category. For bags, women in both areas preferred black and colors in the YR color category. Finally, for the shoes, while women in both areas preferred black in the achromatic color category, they showed different color preferences in the chromatic color category. R color categories were preferred by the women in Seoul and the YR color categories were preferred by the women in Dalian. Conclusively, women in both cities highly preferred achromatic colors especially black for the top and bottom wear, bags, and shoes. It may reflect their desire to look refined and slender through colors. Moreover, continuous and steady research on fashion trends in Seoul and Dalian may have positive effects on Korean fashion businesses that plan to be launched in China, the area of competition for global brands.

• 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 the Korean Home Economics Association
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• v.42 no.1
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• pp.153-166
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• 2004

The purpose of this study was to examine the internet shoppers' new purchase behavior, to examine the general purchase behavior(i.e., purchase pattern, preference), and to examine the related factors to promotion strategies(i.e., e-mail, event) of internet shopping mall. Focus group interviews were done with 40 internet shopping-mall users on May, 2003 for the data collection. Data were analyzed by content analysis and descriptive statistics(i.e., frequency, percent). The results of this study were as following. First, competitive price, accurate product and service information and convenience were considered as important factors in the new purchase behavior among internet shoppers. Second, the more frequent purchasing time through the internet shopping mall were on weekdays rather than weeekends and the most preferred information search engine were category type, item type, and price type in order. Third, e-mails from internet shopping mall were most likely opened by internet shoppers, that is to say, e-mail can be the efficient communication tool as well as the possible promotion strategies. Specifically, the title of email was considered as an important factor to approach the target consumers.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.1-17
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• 2012

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.775-782
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• 2002

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of agents can monitor customer's purchasing behaviors, clutter them in similar categories, and induce customer's preference from each category. In order to implement our adaptive e-commerce agent system, we focus on following 3 components-the monitor agent which can monitor customer's browsing/purchasing data and abstract them, the conceptual cluster agent which cluster customer's abstract data, and the customer profile agent which generate profile from cluster, In order to infer more accurate customer's preference, we propose a 2 layered structure consisting of conceptual cluster and inductive profile generator. Many systems have been suffered from errors in deriving user profiles by using a single structure. However, our proposed 2 layered structure enables us to improve the qualify of user profile by clustering user purchasing behavior in advance. This approach enables us to build more user adaptive e-commerce system according to user purchasing behavior.

• Informatization Policy
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• v.21 no.4
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• pp.81-97
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• 2014

The purpose of this research is to analyze consumers'choice behavior with regard to information and communication technology(ICT) devices and related services. This research focuses on the relationships not only within each category but also among different categories by considering multiple choice situations in a variety of categories simultaneously. The multivariate probit model with demographic variables and the alternative specific constant model with variance-covariance matrix are estimated using survey data; moreover, the multi-dimensional scaling method is utilized for the presentation of the relationship map. It is evident from the results that some devices and services have a complementary or substitute relationship each other. This study can provide useful information for the development of new products and services by understanding and predicting consumer's behavior.

• Korean journal of food and cookery science
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• v.30 no.3
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• pp.291-306
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• 2014

The purpose of this study was to examine the use of social commerce restaurant products by college students according to demographic characteristics and eating out behavior. The questionnaire for the survey was distributed to 450 college students, who have experiences of purchasing a restaurant product on social commerce, with 286 responses used for analysis. From the result, college students frequently use smart phones and SNS for making such purchases. While the awareness of social commerce was high, they sometimes visited the websites and purchased products. The awareness and purchase experience of Coupang and Ticket Monster turned out to be the highest. The most frequently purchased product was restaurant discount coupons, followed by fashion/accessories, movie or concert tickets, food products, and beauty shop discount coupons. The discount rate was mostly 30 to 40% on average. The most significantly considered matter in purchasing products and services was product quality, followed by discount rate and consumer review. The respondents ate out at least 3 to 5 times a week, spent $100,000{\leq}200,000$ won, and were generally satisfied with the restaurant products from social commerce sites. The main satisfaction reason was price, whereas the dissatisfaction reason was false and puffy advertising. Service quality improvement and variety of category were the most necessary factors for improvement. Among the demographic characteristics, there was a difference in purchase expenditure of social commerce restaurant products, as well as purpose, companion, time used and word-of-mouth experience according to gender. According to grade, there was a difference in purchase expenditure, companion, area of use and impulsive purchase. Among the eating out behavior, there was a difference in purpose, companion and word-of-mouth experience according to the eating out frequency. Meanwhile, there was a difference in purchase expenditure, companion, time used, word-of-mouth experience and tool according to the eating out cost.