• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.19-45
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• 2008

This study is to empirically examine the primary dimensions of visual merchandising (VMD) of internet shopping mall, namely store design, merchandise, and merchandising cues, to be a attractive virtual store to the shoppers. The authors reviewed the literature related to the major components of VMD from the perspective of the AIDA model, which has been mainly applied to the offline store settings. The major purposes of the study are as follows; first, tries to derive the variables related with the components of visual merchandising through reviewing the existing literatures, establish the hypotheses, and test it empirically. Second, examines the relationships between the components of VMD and the attitude toward the VMD, however, putting more emphasis on finding out the component structure of the VMD. VMD needs to be examined with the perspective that an online shopping mall is a virtual self-service or clerkless store, which could reduce the number of employees, help the shoppers search, evaluate and purchase for themselves, and to be explored in terms of the in-store persuasion processes of customers. This study reviewed the literatures related to store design, merchandise, and merchandising cues which might be relevant to the store, product, and promotion respectively. VMD is a total communication tool, and AIDA model could explain the in-store consumer behavior of online shopping. Store design has to do with triggering a consumer attention to the online mall, merchandise with a product related interest, and merchandising cues with promotions such as recommendation and links that induce the desire to pruchase. These three steps might be seen as the processes for purchase actions. The theoretical rationale for the relationship between VMD and AIDA could be found in Tyagi(2005) that the three steps of consumer-oriented merchandising are a store, a product assortment, and placement, in Omar(1999) that three types of interior display are a architectural design display, commodity display, and point-of-sales(POS) display, and in Davies and Ward(2005) that the retail store interior image is related to an atmosphere, merchandise, and in-store promotion. Lee et al(2000) suggested as the web merchandising components a merchandising cues, a shopping metaphor which is an assistant tool for search, a store design, a layout(web design), and a product assortment. The store design which includes differentiation, simplicity and navigation is supposed to be related to the attention to the virtual store. Second, the merchandise dimensions comprising product assortments, visual information and product reputation have to do with the interest in the product offerings. Finally, the merchandising cues that refer to merchandiser(MD)'s recommendation of products and providing the hyperlinks to relevant goods for the shopper is concerned with attempt to induce the desire to purchase. The questionnaire survey was carried out to collect the data about the consumers who would shop at internet shopping malls frequently. To select the subject malls, the mall ranking data announced by a mall rating agency was used to differentiate the most popular and least popular five mall each. The subjects was instructed to answer the questions after navigating the designated mall for five minutes. The 300 questionnaire was distributed to the consumers, 166 samples were used in the final analysis. The empirical testing focused on identifying and confirming the dimensionality of VMD and its subdimensions using a structural equation modeling method. The confirmatory factor analysis for the endogeneous and exogeneous variables was carried out in four parts. The second-order factor analysis was done for a store design, a merchandise, and a merchandising cues, and first-order confirmatory factor analysis for the attitude toward the VMD. The model test results shows that the chi-square value of structural equation is 144.39(d.f 49), significant at 0.01 level which means the proposed model was rejected. But, judging from the ratio of chi-square value vs. degree of freedom, the ratio was 2.94 which smaller than an acceptable level of 3.0, RMR is 0.087 which is higher than a generally acceptable level of 0.08. GFI and AGFI is turned out to be 0.90 and 0.84 respectively. Both NFI and NNFI is 0.94, and CFI 0.95. The major test results are as follows; first, the second-order factor analysis and structural equational modeling reveals that the differentiation, simplicity and ease of identifying current status of the transaction are confirmed to be subdimensions of store design and to be a significant predictors of the dependent variable. This result implies that when designing an online shopping mall, it is necessary to differentiate visually from other malls to improve the effectiveness of the communications of store design. That is, the differentiated store design raise the contrast stimulus to sensory organs to promote the memory of the store and to have a favorable attitude toward the VMD of a store. The results that navigation which means the easiness of identifying current status of shopping affects the attitude to VMD could be interpreted that the navigating processes via the hyperlinks which is characteristics of an internet shopping is a complex and cognitive process and shoppers are likely to lack the sense of overall structure of the store. Consequently, shoppers are likely to be alost amid shopping not knowing where to go. The orientation tool enhance the accessibility of information to raise the perceptive power about the store environment.(Titus & Everett 1995) Second, the primary dimension of merchandise and its subdimensions was confirmed to be unidimensional respectively, have a construct validity, and nomological validity which the VMD dimensions supposed to have a positive correlation with the dependent variable. The subdimensions of product assortment, brand fame and information provision proved to have a positive effect on the attitude toward the VMD. It could be interpreted that the more plentiful the product and brand assortment of the mall is, the more likely the shoppers to favor it. Brand fame and information provision as well affect the VMD attitude, which means that the more famous the brand, the more likely the shoppers would trust and feel familiar with the mall, and the plentifully and visually presented information could have the shopper have a favorable attitude toward the store VMD. Third, it turned out to be that merchandising cue of product recommendation and hyperlinks affect the VMD attitude. This could be interpreted that recommended products could reduce the uncertainty related with the purchase decision, and the hyperlinks to relevant products would help the shopper save the cognitive effort exerted into the information search and gathering, which could lead to a favorable attitude to the VMD. This study tried to sheds some new light on the VMD of online store by reviewing the variables mentioned to be relevant with offline VMD in the existing literatures, and tried to link the VMD components from the perspective of AIDA model. The effect size of the VMD dimensions on the attitude was in the order of the merchandise, the store design and the merchandising cues.It is said that an internet has an unlimited place for display, however, the virtual store is not unlimited since the consumer has a limited amount of cognitive ability to process the external information and internal memory. Particularly, the shoppers are likely to face some difficulties in decision making on account of too many alternative and information overloads. Therefore, the internet shopping mall manager should take into consideration the cost of information search on the part of the consumer, to establish the optimal product placements and search routes. An efficient store composition would be possible by reducing the psychological burdens and cognitive efforts exerted to information search and alternatives evaluation. The store image is in most part determined by the product category and its brand it deals in. The results of this study support this proposition that the merchandise is most important to the VMD attitude than other components, the manager is required to take a strategic approach to VMD. The internet users are getting more accustomed and more knowledgeable about the internet media and more likely to accept the internet as a shopping channel as the period of time during which they use the internet to shop become longer. The web merchandiser should be aware that the product introduction using a moving pictures and a bulletin board become more important in order to present the interactive product information visually and communicate with customers more actively, therefore leading to making the quantity and quality of product information more rich.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.4
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• pp.129-144
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• 2013

The consumption of coffee has been drastically increased last two decades. Now almost all the Korean adult people enjoy the coffee and diverse cultures related coffee have been spread widely in Korea. Therefore new marketing strategies are necessary to satisfy consumers according to ages, attitudes, and other characters. It has been continuously discussed whether the coffee gives negative impacts to health. Regardless of the discussions of the effects to health, now coffee became a part of modern daily lives. In this study the motivations of coffee consumption were classified to five; wellbeing motivation, refreshment motivation, social motivation, habitual motivation, and emotional motivation. Future intention of coffee consumption were also classified to five factors: sound mental intention, addictive intention, side-effect recovery intention, economic intention, and psychological intention. The survey was conducted in Seoul City and Kyeongki Province from January 3 to February 2, 2013. Total 500 questionaries were distributed and 450 were collected and 428 samples were used for the analysis of this study. The data were analyzed by SPSS Win 18 Version. The methods used in this study were factors analysis test, reliability test, validity test, t-testy, One-Way ANOVA, and regression analysis. The hypnosis in this study were as follows. First, The motivations of coffee consumption would influence to the intention of coffee consumption. Second, there would be statistical differences to the intention of coffee consumption according to the demographic characteristics. According to the result of the study, the motivation of coffee partially affected to the intention of coffee consumption. And there were statistical differences according to age, occupations, educational levels, and monthly incomes. The implications of this study were the factors related health and emotional feeling were considered more important than tastes and characters of coffee-shop that people thought more important before.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.70-79
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• 2010

There has been some research conducted that addressed immoral consumer behaviors in Korea; however, most of this research focused on purchasing counterfeits or shoplifting. High return rates of apparel and used apparel returns have been acknowledged as problem areas within the fashion industry. However, very few researchers have addressed this issue. Therefore, the goal of this research was to explore consumer's retail borrowing experience using a mixed methods approach. In study 1 Korean consumer's retail borrowing experiences was explored through focus group interviews. Findings informed study 2 an examination of apparel consumers' attitudes toward retail borrowing behavior via an online survey. Findings assist both researchers' and practitioners' understanding of retail borrowing behaviors and provide insight into retail borrowing issues in the apparel retail industry. For study 1, five focus-group interviews were conducted with seven panels of individuals that had retail borrowing experience within the past year. Thirty-five Korean consumers who lived in a metropolitan area participated in the focus group interviews. Most of consumers were in their 20's (n=21) and were women (n=24). Most participants purchased apparel items from a retail store and returned the worn items for either a full refund or exchanged the worn item for another item. Motives underlying retail borrowing behavior included social needs, job-related needs, fashion needs, and "smart shopping." Similar to existing research findings from other countries, social needs were the most frequently mentioned cause of retail borrowing in fashion stores. Consumers' moral values, attitude toward large corporations, and prior retail borrowing experience were mentioned as possible factors affecting consumers' retail borrowing behavior. For study 2, the questionnaire used to gather the data was developed based on the findings of part I and existing research. Questions concerning consumers' moral beliefs, sensation seeking tendencies, self-worth, past retail job experience, retail borrowing experience, and some demographic characteristics were included in the questionnaire. The data were collected via an online survey using an online panel provided by a commercial online research company located in Seoul, Korea. In order to obtain various consumers, a quota sample was (male: female=1:1, 20's:30's:40's=1:1:1, retail experience: no retail experience=1:3) obtained from the company. A total of 401 consumers who had shopped for apparel items during the prior 6 months participated in the online survey. The results indicated that 19.7% of the respondents reported they had experience borrowing fashion merchandise. Among these individuals, male borrowers (57%) outnumbered female borrowers. In terms of age distribution, x2 revealed that there was a statistical difference between respondents with and without retail borrowing experiences: 41.8% of the respondents with retail borrowing experience were in their 40's, while respondents without retail borrowing experience were evenly distributed between their 20's to 40's. There was also a significant difference between respondents with and without retail borrowing experience in terms of income: respondents with retail borrowing experience tended to have higher incomes than those without retail borrowing experience. T-tests were performed to compare respondents' fashion shopping behavior, moral beliefs, sensation-seeking tendencies, and attitudes toward retail borrowing behavior between participants with and without retail borrowing experience. As compared to those with no borrowing experience, respondents with experience tended to shop for fashion items more frequently and spent more on shopping for fashion items. Consumers with experience borrowing tended to have higher sensation-seeking tendencies than consumers without retail borrowing experience. A regression analysis revealed that attitudes toward fashion retail borrowing were negatively related to consumers' moral beliefs, but positively related to monthly fashion shopping frequency, sensation-seeking tendencies, and past fashion retail borrowing experience. Among these variables, past retail borrowing experience was the most significant predictor, followed by moral beliefs. This research serves as an initial attempt to address the motives that underlie retail borrowing behaviors and the factors affecting those behaviors. The findings of this study may facilitate an understanding of the consumer's retail borrowing, which will provide a basis for approaches that may help decrease retail borrowing and inappropriate returns at fashion retail stores. The findings may also provide materials for consumer education over the long term. In order to better understand fashion retail borrowing behavior, more research is needed in the future.

• Korean Journal of Poultry Science
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• v.43 no.4
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• pp.235-242
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• 2016

This study was conducted to investigate the microbiological sanitation status of raw chicken meat distributed in Korea, and potential changes in chicken breast quality during storage. The microbiological sanitation status analysis of raw chicken involved studying the results of microbiological monitoring for a 5-year period (2010~2014) by the Korean Food and Drug Administration. Furthermore, the microbiological status of raw chicken meat in meat packing centers and shops in Seoul/Gyeonggi, Kangwon, and Chungcheong Provinces was investigated from July to August 2015. The total bacterial counts of chicken meat in the packaging centers and meat shop of these Provinces were below the level specified in the Korean Meat Microbiological Guideline ($1{\times}10^7$ colony forming units [CFU]/g) and showed a similar microbiological sanitation status with results of the microbiological monitoring for the analyzed 5-year period. To evaluate the relationship between quality change and microbiological level of the meat distributed in Korea, the pH and microbiological and sensory quality characteristics of the chicken breast samples during storage at $4{\pm}2^{\circ}C$were determined. On day 4, the total bacterial count of the chicken breast was 6.76 log CFU/g, which was close to the official $1{\times}10^7CFU/g$ standard, the pH was 5.96, and the overall acceptability was reduced significantly (p<0.05). In particular, the aroma score was <5, indicating that the consumer panel expressed a negative perception even though the chicken contained a lower microbial level than that specified in the Korean microbiological guideline. These results suggest that the current Korean microbiological guideline for raw chicken meat may require a stricter level of up to $1{\times}10^6CFU/g$ to satisfy both meat safety standards and organoleptic quality for consumers.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used